WO2024227034A1

WO2024227034A1 - T-cell receptor signatures indicative of early stages of cancer

Info

Publication number: WO2024227034A1
Application number: PCT/US2024/026587
Authority: WO
Inventors: Roman YELENSKY; Michelle NAHAS; Yilong Li; Dennis Stephens
Original assignee: Serum Detect, Inc.
Priority date: 2023-04-27
Filing date: 2024-04-26
Publication date: 2024-10-31

Abstract

Predictive models are deployed to generate cancer predictions (e.g., presence, absence, or likelihood of early stage of cancer) for subjects of interest. Predictive models analyze feature counts of TCR RFUs and can identify, with high sensitivity and specificity, subjects with a presence of early stage of cancer.

Description

Attorney Docket No: SRU-004WO T-CELL RECEPTOR SIGNATURES INDICATIVE OF EARLY STAGES OF CANCER CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims priority to U.S. Provisional Application No.63/498,704, filed April 27, 2023, U.S. Provisional Application No.63/513,737, filed July 14, 2023, U.S. Provisional Application No.63/560,114, filed March 1, 2024, and U.S. Provisional Application No.63/575,228, filed April 5, 2024, each of which is hereby incorporated by reference in its entirety. BACKGROUND [0002] Cancer remains a difficult disease to treat, due to the fact that by the time symptoms present in an individual, the cancer has often progressed to an incurable stage. Yet, identifying individuals at an early enough stage for curative treatment is still elusive. Thus, there is a need for practical methods that can rapidly and affordably identify individuals that are likely to have a presence of early stages of cancer. SUMMARY [0003] Disclosed herein are methods, systems, non-transitory computer readable media, and kits for generating cancer predictions (e.g., predicting presence, absence, or likelihood of cancer, such as early stages of cancer) for subjects of interest. [0004] In various embodiments, a method for predicting presence, absence, or likelihood of cancer in a subject is provided, wherein the method comprises: obtaining or having obtained a dataset comprising identities of a plurality of T-cell receptors (TCRs) from the subject; generating a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs are encoded by at least TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20- 1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; and generating a prediction of presence, absence, or IPTS/128553107.1 Attorney Docket No: SRU-004WO likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. [0005] In various embodiments, a method for predicting presence, absence, or likelihood of cancer in a subject, comprises: obtaining or having obtained a dataset comprising identities of a plurality of T-cell receptors (TCRs) from the subject; generating a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs comprise at least one CDR3 amino acid sequence having at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identity to any one of CDR3 amino acid sequences as provided in Table 1; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. [0006] In various embodiments, a non-transitory computer-readable storage medium is provided, wherein the computer-readable storage medium comprises instructions that when executed by a processor, cause the processor to: obtain or having obtained a dataset comprising identities of a plurality of T-cell receptors (TCRs) from the subject; generate a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs are encoded by at least: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; and generate a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. [0007] In various embodiments, a system is provided, wherein the system comprises: a set of reagents used for determining identities of a plurality of T-cell receptors (TCRs) from a test sample from the subject; an apparatus configured to receive a mixture of one or more IPTS/128553107.1 Attorney Docket No: SRU-004WO reagents in the set and the test sample and to measure the identities of a plurality of T-cell receptors (TCRs) from the test sample; and a computer system communicatively coupled to the apparatus to: obtain a dataset comprising identities of a plurality of T-cell receptors (TCRs) from the test sample; generate a subject feature count across a plurality of cancer- associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer- associated TCR RFUs are encoded by at least: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; and generate a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. [0008] In various embodiments, a kit for predicting presence, absence, or likelihood of cancer in a subject is provided, wherein the kit comprises: a set of reagents for determining identities of a plurality of T-cell receptors (TCRs) from a test sample from the subject; and instructions for using the set of reagents to: generate a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the sample from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs are encoded by at least: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1- 3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2- 6, and TRBJ2-7; and generate a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. [0009] In various embodiments, a method for developing cancer-associated TCR repertoire functional units (RFUs) is provided, wherein the method comprises: obtaining or having IPTS/128553107.1 Attorney Docket No: SRU-004WO obtained TCR sequencing data of a plurality of TCRs from a plurality of training samples; sorting the plurality of TCRs into candidate RFUs by: clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc); further processing candidate RFUs by performing one or more of: filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a first threshold number of training samples; and/or filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, or 32; and analyzing, through a generalized linear model, the candidate RFUs to identify cancer-associated RFUs. [0010] In various embodiments, a method for developing a predictive model for predicting presence, absence, or likelihood of cancer is provided, wherein the model comprises: obtaining or having obtained feature counts across a plurality of cancer-associated TCR repertoire functional units (RFUs), wherein a plurality of variable regions of the cancer- associated TCR RFUs are encoded by at least: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; and analyzing, through a ML implemented method, the feature counts across the plurality of cancer-associated TCR RFUs to train the predictive model useful for predicting presence, absence, or likelihood of a cancer. BRIEF DESCRIPTION OF THE DRAWINGS [0011] These and other features, aspects, and advantages of the present disclosure will become better understood with regard to the following description and accompanying drawings. [0012] Figure (FIG.) 1A depicts an overview of an environment for generating a cancer prediction in a subject via a cancer prediction system, in accordance with an embodiment. [0013] FIG.1B is an example block diagram of the cancer prediction system, in accordance with an embodiment. [0014] FIG.2 depicts a flow diagram for predicting cancer in a subject, in accordance with an embodiment. IPTS/128553107.1 Attorney Docket No: SRU-004WO [0015] FIG.3 depicts a flow diagram for methods of identifying T-cell receptor (TCR) repertoire functional unites (RFUs), in accordance with an embodiment. [0016] FIG.4 depicts a flow diagram for methods of training the predictive model, in accordance with an embodiment. [0017] FIG.5 illustrates an example computer for implementing the entities shown in FIGS. 1A, 1B, and 2. [0018] FIG.6 depicts the overall strategy for identifying cancer TCR RFUs and using these for cancer prediction. [0019] FIG.7 summarizes the demographic variables and TCR sequencing metrics for the RFU discovery case/control cohort. [0020] FIG.8 summarizes the distribution of cancer stage and histology in the RFU discovery case/control cohort. [0021] FIGs.9A-9C give the summary statistics of the discovered RFUs. [0022] FIG.10 provides example detail of an RFU for a known flu epitope. [0023] FIG.11 provides the volcano plot showed effect sizes and FDRs of discovered RFUs. [0024] FIG.12 provides an example age associated cancer RFU. [0025] FIG.13 provides an example non-age associated cancer RFU. [0026] FIG.14 provides the ROC curve of the cancer predictive model trained on the 32 cancer RFUs. [0027] FIG.15 provides the Stage I sensitivities corresponding to the ROC curve in FIG.14. [0028] FIG.16 provides the ROC curve of a cancer predictive model trained on 17 cancer associated protein biomarkers. [0029] FIG.17 shows the number of called driver mutations in the ctDNA/gDNA cohort. [0030] FIG.18 shows the sensitivity for cancer detection of ctDNA/gDNA mutation calls. [0031] FIG.19 shows the TCR RFU cancer prediction model score grouped by cancer stage or non-cancer disease status. [0032] FIG.20 compares the detection sensitivity of the cancer TCR RFU model to mutation and protein-based detection in Stage I lung cancer. [0033] FIG.21 compares the detection sensitivity of the cancer TCR RFU model to mutation and protein-based detection in Stage II-IV lung cancer. [0034] FIG.22 is example detail of an RFU for a known CMV epitope. [0035] FIG.23 exemplifies the RFU filtering analysis for significant RFU identification. [0036] FIG.24 exemplifies RFU value adjustment for clinical covariates. IPTS/128553107.1 Attorney Docket No: SRU-004WO [0037] FIG.25 shows stability of the cancer TCR RFU score by gender. [0038] FIG.26 shows stability of the cancer TCR RFU score by age. [0039] FIG.27 shows stability of the cancer TCR RFU score by race. [0040] FIG.28 shows stability of the cancer TCR RFU score by smoking status. [0041] FIG.29 shows stability of the cancer TCR RFU score by sample source. [0042] FIG.30 shows stability of the cancer TCR RFU score by TCR sequencing depth. [0043] FIG.31 summarizes the demographic variables and TCR sequencing metrics for the RFU further discovery case/control cohort. [0044] FIG.32 summarizes the distribution of cancer stage and histology in the RFU further discovery case/control cohort. [0045] FIG.33 provides the volcano plot showed effect sizes and FDRs of discovered RFUs. [0046] FIG.34 provides an example non-age associated cancer RFU. [0047] FIG.35 provides an example age associated cancer RFU. [0048] FIG.36 provides the Stage 0-I (left panel) and II-IV (right panel) sensitivities. [0049] FIG.37 provides the ROC curve of the cancer predictive model trained on the 90 cancer RFUs. [0050] FIG.38 compares the detection sensitivity of the cancer TCR RFU model to mutation and protein-based detection in Stage I lung cancer. [0051] FIG.39 compares the detection sensitivity of the cancer TCR RFU model to mutation and protein-based detection in Stage II-IV lung cancer. [0052] FIG.40 summarizes the demographic variables and TCR sequencing metrics for the refined RFU discovery case/control cohort. [0053] FIG.41 summarizes the distribution of cancer stage and histology in the refined RFU discovery case/control cohort. [0054] FIG.42 provides the volcano plot showed effect sizes and FDRs of discovered RFUs. [0055] FIG.43 shows a pattern of decreasing TCR count with increasing age in all individuals, and higher TCR counts in cancer patients relative to age-matched controls. [0056] FIG.44 provides the ROC curves of the cancer predictive model trained on the positively associated cancer RFUs. [0057] FIG.45 provides the Stage 0-I sensitivities. [0058] FIG.46 shows cancer prediction scores from samples of varying source. [0059] FIG.47 shows cancer prediction scores generated by varying TCR repertoire depth. IPTS/128553107.1 Attorney Docket No: SRU-004WO [0060] FIG.48 compares the detection sensitivity of the cancer TCR RFU model to mutation and protein-based detection in Stage I lung cancer. [0061] FIG.49 compares the detection sensitivity of the cancer TCR RFU model to mutation and protein-based detection in Stage II-IV lung cancer. [0062] FIG.50 shows the TCR RFU cancer prediction model score grouped by cancer stage or benign nodule status. [0063] FIGs.51A-51C give the summary statistics of discovered RFUs. [0064] FIG.52 summarizes the demographic variables and TCR sequencing metrics for the refined RFU discovery case/control cohort. [0065] FIG.53 summarizes the distribution of cancer stage and histology in the refined RFU discovery case/control cohort. [0066] FIG.54 provides the volcano plot showing effect sizes and FDRs of discovered RFUs. [0067] FIG.55 provides a box plot with an example effect size and FDR of a discovered RFU. [0068] FIG.56 provides a box plot with an example effect size and FDR of a discovered RFU. [0069] FIG.57 provides the ROC curves of the cancer predictive model trained on the cancer associated RFUs. [0070] FIG.58 provides the Stage 0-I sensitivities. [0071] FIG.59 shows lung cancer prediction scores across lung cancer histologies. [0072] FIG.60 shows lung cancer prediction scores against other conditions. [0073] FIG.61 shows cancer prediction scores from samples of varying source. [0074] FIG.62 shows cancer prediction scores by varying TCR repertoire depth. [0075] FIG.63 compares the detection sensitivity of the cancer TCR RFU model to mutation and protein-based detection in Stage I lung cancer. [0076] FIG.64 compares the detection sensitivity of the cancer TCR RFU model to mutation and protein-based detection in Stage II-IV lung cancer and additionally gives the multi-analyte specificity and analysis sample counts. [0077] FIG.65 illustrates primer constructs. [0078] FIG.66 illustrates an extension reaction. [0079] FIG.67 illustrates a PCR1 reaction. [0080] FIG.68 illustrates a PCR2 reaction. IPTS/128553107.1 Attorney Docket No: SRU-004WO DETAILED DESCRIPTION I. Definitions [0081] Terms used in the claims and specification are defined as set forth below unless otherwise specified. [0082] The term “subject” encompasses a cell, tissue, or organism, human or non-human, whether in vivo, ex vivo, or in vitro, male or female. [0083] The term “mammal” encompasses both humans and non-humans and includes but is not limited to humans, non-human primates, canines, felines, murines, bovines, equines, and porcines. [0084] The term “sample” can include a single cell or multiple cells or fragments of cells or an aliquot of body fluid, such as a blood sample, taken from a subject, by means including venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage sample, scraping, surgical incision, or intervention or other means known in the art. Examples of an aliquot of body fluid include amniotic fluid, aqueous humor, bile, lymph, breast milk, interstitial fluid, blood, blood plasma, cerumen (earwax), Cowper’s fluid (pre-ejaculatory fluid), chyle, chyme, female ejaculate, menses, mucus, saliva, urine, vomit, tears, vaginal lubrication, sweat, serum, semen, sebum, pus, pleural fluid, cerebrospinal fluid, synovial fluid, intracellular fluid, and vitreous humour. [0085] The terms “marker,” “markers,” “biomarker,” and “biomarkers” encompass, without limitation, lipids, lipoproteins, proteins, cytokines, chemokines, growth factors, peptides, nucleic acids, genes, and oligonucleotides, together with their related complexes, metabolites, mutations, variants, polymorphisms, modifications, fragments, subunits, degradation products, elements, and other analytes or sample-derived measures. A marker can also include mutated proteins, mutated nucleic acids, variations in copy numbers, and/or transcript variants, in circumstances in which such mutations, variations in copy number and/or transcript variants are useful for generating a predictive model, or are useful in predictive models developed using related markers (e.g., non-mutated versions of the proteins or nucleic acids, alternative transcripts, etc.). [0086] The term "antibody" is used in the broadest sense and specifically covers monoclonal antibodies (including full length monoclonal antibodies), polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies), and antibody fragments that are antigen-binding so long as they exhibit the desired biological activity, e.g., an antibody or an antigen-binding fragment thereof. IPTS/128553107.1 Attorney Docket No: SRU-004WO [0087] "Antibody fragment", and all grammatical variants thereof, as used herein are defined as a portion of an intact antibody comprising the antigen binding site or variable region of the intact antibody, wherein the portion is free of the constant heavy chain domains (i.e. CH2, CH3, and CH4, depending on antibody isotype) of the Fc region of the intact antibody. Examples of antibody fragments include Fab, Fab', Fab'-SH, F(ab')2, and Fv fragments; diabodies; any antibody fragment that is a polypeptide having a primary structure consisting of one uninterrupted sequence of contiguous amino acid residues (referred to herein as a "single-chain antibody fragment" or "single chain polypeptide"). [0088] The term “biomarker panel” refers to a set biomarkers that are informative for generating a cancer prediction. For example, expression levels of the set of biomarkers in the biomarker panel can be informative for generating a cancer prediction. In various embodiments, a biomarker panel can include two, three, four, five, six, seven, eight, nine, ten eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty one, twenty two, twenty three, twenty four, or twenty five biomarkers. [0089] The term “feature count,” as used herein, refers to the number of counts for a T-cell receptor (TCR) repertoire functional unit (RFU). In various embodiments, the feature count is determined for a subject (e.g., a “subject feature count”), which represents the number of counts for a TCR RFU for the subject. In various embodiments, the feature count can be determined by comparing the sequenced plurality of TCRs that are present in a subject to the TCR RFUs. Therefore, a number of counts for a TCR RFU can reflect the TCRs present in the subject that fall within the TCR RFU. [0090] The term "identity," refers to the molecular characteristics and/or features that define and distinguish individual TCRs. These characteristics encompass a variable region of the TCR, the variable region including one or more of variable (V), diversity (D), and joining (J) gene segments, as well as the complementarity-determining region 3 (CDR3) sequence. Together, they contribute to the antigen specificity and recognition properties of TCRs. The identity of TCRs based on circulating DNA or RNA is ascertained through the examination of transcripts or exons encoding portions of TCRs (e.g., TCR alpha (α) and beta (β) chains or TCR gamma (γ) and delta (δ) chains, respectively). [0091] The term “obtaining a dataset associated with a sample” encompasses obtaining a set of data determined from at least one sample. Obtaining a dataset encompasses obtaining a sample and processing the sample to experimentally determine the data. The phrase also encompasses receiving a set of data, e.g., from a third party that has processed the sample to experimentally determine the dataset. Additionally, the phrase encompasses mining data from IPTS/128553107.1 Attorney Docket No: SRU-004WO at least one database or at least one publication or a combination of databases and publications. A dataset can be obtained by one of skill in the art via a variety of known ways including stored on a storage memory. [0092] It must be noted that, as used in the specification, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. II. Overview [0093] Predictive models, as disclosed herein, are useful for distinguishing subjects having a presence, absence, or likelihood of cancer, such as early stage cancer or non-early stage cancer. Example early stage cancer includes stage I and/or stage II cancer. In comparison, non-early stage cancer (e.g., late stage cancer) includes stage III and/or stage IV cancer. In particular embodiments, the early stage cancer is an early stage lung cancer. In particular embodiments, for a subject of interest, predictive models analyze a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing identities of the plurality of TCRs from a subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs) to generate a cancer prediction (e.g., a prediction of a presence, absence, or likelihood of early stage cancer or non-early stage cancer in the subject of interest). [0094] Figure (FIG.) 1A depicts an overview of a system environment 100 for generating a cancer prediction in a subject via a cancer prediction system 130, in accordance with an embodiment. The system environment 100 provides context in order to introduce a TCR quantification assay 120, a feature count 130, and a cancer prediction system 130. [0095] In various embodiments, a test sample is obtained from the subject 110. The sample can be obtained by the individual or by a third party, e.g., a medical professional. Examples of medical professionals include physicians, emergency medical technicians, nurses, first responders, psychologists, phlebotomist, medical physics personnel, nurse practitioners, surgeons, dentists, and any other obvious medical professional as would be known to one skilled in the art. [0096] In various embodiments, the subject 110 is a healthy subject, or a subject suspected of having an early stage cancer or non-early stage cancer. For example, the subject 110 may have exhibited symptoms of early stage cancer or non-early stage cancer. In various embodiments, the subject is not suspected of having an early stage cancer or non-early stage cancer. For example, the subject 110 may be undergoing a standard examination and a test sample is obtained from the subject 110 during the standard examination. IPTS/128553107.1 Attorney Docket No: SRU-004WO [0097] The test sample is tested to determine identities of a plurality of TCRs by performing a quantification assay 120. The quantification assay 120 determines identity values of one or more TCRs from the test sample. The quantification assay 120 may be an amplification- based assay, or a sequencing-based assay, examples of which are described in further detail below. The quantified identity values of the TCRs are provided to the feature count 130. [0098] The quantified identity values of the TCRs are compared to the variable regions of the cancer-associated TCRs clustered into repertoire functional units (RFUs) by performing a feature count 130. The resultant subject feature counts is provided to the cancer prediction system 140. [0099] Generally, the cancer prediction system 140 includes one or more computers, embodied as a computer system 300 as discussed below with respect to FIG.3. Therefore, in various embodiments, the steps described in reference to the cancer prediction system 140 are performed in silico. The cancer prediction system 140 analyzes the received subject feature from the feature count 130 across the plurality of cancer-associated TCR RFUs to generate a cancer prediction 150 (e.g., a presence, absence, or likelihood of cancer) for the subject 110. [00100] In various embodiments, the TCR quantification assay 120, the feature count 130, and the cancer prediction system 140 can be employed by different parties. For example, a first party performs the marker quantification assay 120, which then provides the results to a second party, which performs the feature count 130, which then provides the results to a third party, which deploys the cancer prediction system 140. For example, the first party may be a clinical laboratory that obtains test samples from subjects 110 and performs the assay 120 on the test samples. The second party receives the identity values of TCRs resulting from the performed assay 120 and analyzes the identity values of TCRs against the cancer-associated TCR RFUs. The third party then receives the feature counts of TCRs resulting from the performed feature count 130 and analyzed the feature counts using the cancer prediction system 140. [00101] FIG.1B is an example block diagram of the cancer prediction system 140, in accordance with an embodiment. Specifically, the cancer prediction system 140 may include a model training module 160, a model deployment module 170, and a training data store 180. [00102] The components of the cancer prediction system 140 are hereafter described in reference to two phases: 1) a training phase and 2) a deployment phase. More specifically, the training phase refers to the building and training of one or more predictive models based on training data that includes feature counts of TCRs obtained from individuals that are known to have a presence, absence, or likelihood of cancer. Therefore, during the IPTS/128553107.1 Attorney Docket No: SRU-004WO deployment phase, the predictive model is applied to feature counts from a test sample obtained from a subject of interest to generate a cancer prediction for the subject of interest. [00103] In some embodiments, the components of the cancer prediction system 140 are applied during one of the training phase and the deployment phase. For example, the model training module 160 and training data store 180 (indicated by the dotted lines in FIG.1B) are applied during the training phase whereas the model deployment module 170 is applied during the deployment phase. In various embodiments, the components of the cancer prediction system 140 can be performed by different parties depending on whether the components are applied during the training phase or the deployment phase. In such scenarios, the training and deployment of the predictive model are performed by different parties. For example, the model training module 160 and training data store 180 applied during the training phase can be employed by a first party (e.g., to train a predictive model) and the model deployment module 170 applied during the deployment phase can be performed by a second party (e.g., to deploy the predictive model). [00104] In various embodiments, the system environment 100 further comprises obtaining or having obtained TCR sequencing data for a plurality of TCRs from a plurality of training samples, assigning TCRs of the plurality of TCRs into candidate RFUs by grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric, combining variable gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores, and clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc). [00105] In various embodiments, the dissimilarity index (dc) corresponds to the maximum distance at which TCRs are linked to the same RFU. In various embodiments, the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch. In various embodiments, the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion. In various embodiments, the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. In various embodiments, the dissimilarity index is established to cluster TCRs with any conservative amino acid mismatch for CDR3 length greater than or equal to 15 amino acid residues. In various embodiments, the dissimilarity index is established to cluster TCRs with any amino acid mismatch for CDR3 length greater than or equal to 15 amino acid residues. In various embodiments, the dissimilarity index is established to cluster TCRs with any amino acid IPTS/128553107.1 Attorney Docket No: SRU-004WO mismatch for CDR3 length greater than or equal to 11 amino acid residues, and any two amino acid mismatches for CDR3 length greater than or equal to 16. [00106] In some embodiments, the system environment 100 further comprises filtering candidate RFUs to retain candidate RFUs that are observed in at least a first threshold number of training samples, filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a second threshold number of training samples, and/or filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than a threshold value of independent observations (observations of same amino acid sequence in different individuals or observations of same amino acid sequence arising from different nucleotide sequences in same individual). [00107] In various embodiments, the minimum amino acid-level recurrence is greater than 0, 1, 2, 3, 4, or 5. In various embodiments, the minimum amino acid-level recurrence is greater than 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, or 32. In various embodiments, the minimum amino acid-level recurrence is greater than 0. In various embodiments, the minimum amino acid-level recurrence is greater than 1. In various embodiments, the minimum amino acid-level recurrence is greater than 2. In various embodiments, the minimum amino acid-level recurrence is greater than 3. In various embodiments, the minimum amino acid-level recurrence is greater than 4. In various embodiments, the minimum amino acid-level recurrence is greater than 5. In various embodiments, the minimum amino acid-level recurrence is greater than 6. In various embodiments, the minimum amino acid-level recurrence is greater than 7. In various embodiments, the minimum amino acid-level recurrence is greater than 8. In various embodiments, the minimum amino acid-level recurrence is greater than 9. In various embodiments, the minimum amino acid-level recurrence is greater than 10. In various embodiments, the minimum amino acid-level recurrence is greater than 11. In various embodiments, the minimum amino acid-level recurrence is greater than 12. In various embodiments, the minimum amino acid-level recurrence is greater than 13. In various embodiments, the minimum amino acid-level recurrence is greater than 14. In various embodiments, the minimum amino acid-level recurrence is greater than 15. In various embodiments, the minimum amino acid-level recurrence is greater than 16. In various embodiments, the minimum amino acid-level recurrence is greater than 17. In various embodiments, the minimum amino acid-level recurrence is greater than 18. In various embodiments, the minimum amino acid-level recurrence is greater than 19. In various embodiments, the minimum amino acid-level recurrence is greater than 20. In various IPTS/128553107.1 Attorney Docket No: SRU-004WO embodiments, the minimum amino acid-level recurrence is greater than 21. In various embodiments, the minimum amino acid-level recurrence is greater than 22. In various embodiments, the minimum amino acid-level recurrence is greater than 23. In various embodiments, the minimum amino acid-level recurrence is greater than 24. In various embodiments, the minimum amino acid-level recurrence is greater than 25. In various embodiments, the minimum amino acid-level recurrence is greater than 26. In various embodiments, the minimum amino acid-level recurrence is greater than 27. In various embodiments, the minimum amino acid-level recurrence is greater than 28. In various embodiments, the minimum amino acid-level recurrence is greater than 29. In various embodiments, the minimum amino acid-level recurrence is greater than 30. In various embodiments, the minimum amino acid-level recurrence is greater than 31. In various embodiments, the minimum amino acid-level recurrence is greater than 32. [00108] In various embodiments, the minimum amino acid-level recurrence is 1. In various embodiments, the minimum amino acid-level recurrence is 2. In various embodiments, the minimum amino acid-level recurrence is 3. In various embodiments, the minimum amino acid-level recurrence is 4. In various embodiments, the minimum amino acid-level recurrence is 5. In various embodiments, the minimum amino acid-level recurrence is 6. In various embodiments, the minimum amino acid-level recurrence is 7. In various embodiments, the minimum amino acid-level recurrence is 8. In various embodiments, the minimum amino acid-level recurrence is 9. In various embodiments, the minimum amino acid-level recurrence is 10. In various embodiments, the minimum amino acid-level recurrence is 11. In various embodiments, the minimum amino acid-level recurrence is 12. In various embodiments, the minimum amino acid-level recurrence is 13. In various embodiments, the minimum amino acid-level recurrence is 14. In various embodiments, the minimum amino acid-level recurrence is 15. In various embodiments, the minimum amino acid-level recurrence is 16. In various embodiments, the minimum amino acid-level recurrence is 17. In various embodiments, the minimum amino acid-level recurrence is 18. In various embodiments, the minimum amino acid-level recurrence is 19. In various embodiments, the minimum amino acid-level recurrence is 20. In various embodiments, the minimum amino acid-level recurrence is 21. In various embodiments, the minimum amino acid-level recurrence is 22. In various embodiments, the minimum amino acid-level recurrence is 23. In various embodiments, the minimum amino acid-level recurrence is 24. In various embodiments, the minimum amino acid-level recurrence is 25. In various embodiments, the minimum amino acid-level recurrence is 26. In various embodiments, the minimum amino acid-level IPTS/128553107.1 Attorney Docket No: SRU-004WO recurrence is 27. In various embodiments, the minimum amino acid-level recurrence is 28. In various embodiments, the minimum amino acid-level recurrence is 29. In various embodiments, the minimum amino acid-level recurrence is 30. In various embodiments, the minimum amino acid-level recurrence is 31. In various embodiments, the minimum amino acid-level recurrence is 32. [00109] In various embodiments, the first threshold number of training samples is at least 200, at least 210, at least 220, at least 230, at least 240, at least 250, at least 260, at least 270, at least 280, at least 290, or at least 300. In various embodiments, the first threshold number of training samples is at least 200. In various embodiments, the first threshold number of training samples is at least 210. In various embodiments, the first threshold number of training samples is at least 220. In various embodiments, the first threshold number of training samples is at least 230. In various embodiments, the first threshold number of training samples is at least 240. In various embodiments, the first threshold number of training samples is at least 250. In various embodiments, the first threshold number of training samples is at least 260. In various embodiments, the first threshold number of training samples is at least 270. In various embodiments, the first threshold number of training samples is at least 280. In various embodiments, the first threshold number of training samples is at least 290. In various embodiments, the first threshold number of training samples is at least 300. [00110] In various embodiments, the first threshold number of training samples is at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50. In various embodiments, the first threshold number of training samples is at least 1. In various embodiments, the first threshold number of training samples is at least 2. In various embodiments, the first threshold number of training samples is at least 3. In various embodiments, the first threshold number of training samples is at least 4. In various embodiments, the first threshold number of training samples is at least 5. In various embodiments, the first threshold number of training samples is at least 6. In various embodiments, the first threshold number of training samples is at least 7. In various embodiments, the first threshold number of training samples is at least 8. In various embodiments, the first threshold number of training samples is at least 9. In various IPTS/128553107.1 Attorney Docket No: SRU-004WO embodiments, the first threshold number of training samples is at least 10. In various embodiments, the first threshold number of training samples is at least 11. In various embodiments, the first threshold number of training samples is at least 12. In various embodiments, the first threshold number of training samples is at least 13. In various embodiments, the first threshold number of training samples is at least 14. In various embodiments, the first threshold number of training samples is at least 15. In various embodiments, the first threshold number of training samples is at least 16. In various embodiments, the first threshold number of training samples is at least 17. In various embodiments, the first threshold number of training samples is at least 18. In various embodiments, the first threshold number of training samples is at least 19. In various embodiments, the first threshold number of training samples is at least 20. In various embodiments, the first threshold number of training samples is at least 21. In various embodiments, the first threshold number of training samples is at least 22. In various embodiments, the first threshold number of training samples is at least 23. In various embodiments, the first threshold number of training samples is at least 24. In various embodiments, the first threshold number of training samples is at least 25. In various embodiments, the first threshold number of training samples is at least 26. In various embodiments, the first threshold number of training samples is at least 27. In various embodiments, the first threshold number of training samples is at least 28. In various embodiments, the first threshold number of training samples is at least 29. In various embodiments, the first threshold number of training samples is at least 30. In various embodiments, the first threshold number of training samples is at least 31. In various embodiments, the first threshold number of training samples is at least 32. In various embodiments, the first threshold number of training samples is at least 33. In various embodiments, the first threshold number of training samples is at least 34. In various embodiments, the first threshold number of training samples is at least 35. In various embodiments, the first threshold number of training samples is at least 36. In various embodiments, the first threshold number of training samples is at least 37. In various embodiments, the first threshold number of training samples is at least 38. In various embodiments, the first threshold number of training samples is at least 39. In various embodiments, the first threshold number of training samples is at least 40. In various embodiments, the first threshold number of training samples is at least 41. In various embodiments, the first threshold number of training samples is at least 42. In various embodiments, the first threshold number of training samples is at least 43. In various IPTS/128553107.1 Attorney Docket No: SRU-004WO embodiments, the first threshold number of training samples is at least 44. In various embodiments, the first threshold number of training samples is at least 45. In various embodiments, the first threshold number of training samples is at least 46. In various embodiments, the first threshold number of training samples is at least 47. In various embodiments, the first threshold number of training samples is at least 48. In various embodiments, the first threshold number of training samples is at least 49. In various embodiments, the first threshold number of training samples is at least 50. [00111] In various embodiments, the first threshold number of training samples is 1. In various embodiments, the first threshold number of training samples is 2. In various embodiments, the first threshold number of training samples is 3. In various embodiments, the first threshold number of training samples is 4. In various embodiments, the first threshold number of training samples is 5. In various embodiments, the first threshold number of training samples is 6. In various embodiments, the first threshold number of training samples is 7. In various embodiments, the first threshold number of training samples is 8. In various embodiments, the first threshold number of training samples is 9. In various embodiments, the first threshold number of training samples is 10. In various embodiments, the first threshold number of training samples is 11. In various embodiments, the first threshold number of training samples is 12. In various embodiments, the first threshold number of training samples is 13. In various embodiments, the first threshold number of training samples is 14. In various embodiments, the first threshold number of training samples is 15. In various embodiments, the first threshold number of training samples is 16. In various embodiments, the first threshold number of training samples is 17. In various embodiments, the first threshold number of training samples is 18. In various embodiments, the first threshold number of training samples is 19. In various embodiments, the first threshold number of training samples is 20. In various embodiments, the first threshold number of training samples is 21. In various embodiments, the first threshold number of training samples is 22. In various embodiments, the first threshold number of training samples is 23. In various embodiments, the first threshold number of training samples is 24. In various embodiments, the first threshold number of training samples is 25. In various embodiments, the first threshold number of training samples is 26. In various embodiments, the first threshold number of training samples is 27. In various embodiments, the first threshold number of training samples is 28. In various embodiments, the first threshold number of training samples is 29. In various embodiments, the first threshold number of training samples is 30. In various embodiments, the first threshold number of training IPTS/128553107.1 Attorney Docket No: SRU-004WO samples is 31. In various embodiments, the first threshold number of training samples is 32. In various embodiments, the first threshold number of training samples is 33. In various embodiments, the first threshold number of training samples is 34. In various embodiments, the first threshold number of training samples is 35. In various embodiments, the first threshold number of training samples is 36. In various embodiments, the first threshold number of training samples is 37. In various embodiments, the first threshold number of training samples is 38. In various embodiments, the first threshold number of training samples is 39. In various embodiments, the first threshold number of training samples is 40. In various embodiments, the first threshold number of training samples is 41. In various embodiments, the first threshold number of training samples is 42. In various embodiments, the first threshold number of training samples is 43. In various embodiments, the first threshold number of training samples is 44. In various embodiments, the first threshold number of training samples is 45. In various embodiments, the first threshold number of training samples is 46. In various embodiments, the first threshold number of training samples is 47. In various embodiments, the first threshold number of training samples is 48. In various embodiments, the first threshold number of training samples is 49. In various embodiments, the first threshold number of training samples is 50. [00112] In some embodiments, the system environment 100 further comprises applying a generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. In various embodiments, the generalized linear model is a gamma-Poisson generalized linear model. In various embodiments, applying the gamma-Poisson generalized linear model further comprises incorporating demographic covariates. In various embodiments, the demographic covariates comprise age, sex, race, or any combination thereof. In various embodiments, the demographic covariates comprise age. In various embodiments, the demographic covariates comprise sex. In various embodiments, the demographic covariates comprise race. III. Predictive model III.A. Feature Counts [00113] Disclosed herein are predictive models that are trained and/or deployed to analyze feature counts of TCR RFUs, such as cancer-associated TCR-RFUs. These feature counts, such as feature count 130 described in relation to FIG.1A, can be determined from the output of a TCR quantification assay 120. In various embodiments, a feature count is determined IPTS/128553107.1 Attorney Docket No: SRU-004WO for a subject (referred to herein as a “subject feature count”), which represents the number of counts for a TCR RFU for the subject. For example, the feature count of a TCR RFU can be determined by comparing identities of plurality of TCRs that are present in a subject to the TCR RFUs. Therefore, a feature count for a TCR RFU can reflect the TCRs present in the subject that fall within the TCR RFU. [00114] In various embodiments, determining a feature count of a TCR RFU involves obtaining sequence reads generated from the TCR quantification assay (e.g., a TCR-seq assay). The sequence reads may be generated from a sample (e.g., blood sample) obtained from a subject. In various embodiments, the sequence reads may include variable region sequences of TCRs, such as TCRs present in the subject. Such variable region sequences of TCRs can include one or more of a V gene segment, a D gene segment, a J gene segment, a CDR1 sequence, a CDR2 sequence, and CDR3 sequence. In particular embodiments, variable region sequences of TCRs can include a V gene segment, a J gene segment, and a CDR3 sequence. [00115] In various embodiments, determining the feature count of a TCR RFU includes comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR RFUs. For example, the identities of the plurality of TCRs can include the sequence reads of the plurality of TCRs, or data derived from the sequence reads of the plurality of TCRs, present in the subject. Comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR RFUs can involve comparing the variable region sequences of the sequence reads to variable regions of the TCR RFUs. [00116] In various embodiments, comparing the variable region sequences of the sequence reads to variable regions of the TCR RFUs involves comparing a V gene encoding for a V gene segment of the variable region sequences of the sequence reads to a V gene encoding for a V gene segment of the TCR RFU. In various embodiments, comparing the variable region sequences of the sequence reads to variable regions of the TCR RFUs involves comparing a J gene encoding for a J gene segment of the variable region sequences of the sequence reads to a J gene encoding for a J gene segment of the TCR RFU. In various embodiments, comparing the variable region sequences of the sequence reads to variable regions of the TCR RFUs involves comparing a CDR1 sequence of the variable region sequences of the sequence reads to a CDR1 sequence of the TCR RFU. In various embodiments, comparing the variable region sequences of the sequence reads to variable regions of the TCR RFUs involves comparing a CDR2 sequence of the variable region sequences of the sequence reads to a IPTS/128553107.1 Attorney Docket No: SRU-004WO CDR2 sequence of the TCR RFU. In various embodiments, comparing the variable region sequences of the sequence reads to variable regions of the TCR RFUs involves comparing a CDR3 sequence of the variable region sequences of the sequence reads to a CDR3 sequence of the TCR RFU. In particular embodiments, comparing the variable region sequences of the sequence reads to variable regions of the TCR RFUs involves comparing: 1) comparing a V gene encoding for a V gene segment of the variable region sequences of the sequence reads to a V gene encoding for a V gene segment of the TCR RFU, 2) comparing a J gene encoding for a J gene segment of the variable region sequences of the sequence reads to a J gene encoding for a J gene segment of the TCR RFU, and 3) comparing a CDR3 sequence of the variable region sequences of the sequence reads to a CDR3 sequence of the TCR RFU. [00117] In various embodiments, a TCR RFU includes a plurality of TCRs that make up the TCR RFU. For example, the plurality of TCRs may be clustered together and therefore, the cluster of TCRs form the TCR RFU. Methods for identifying and clustering TCRs to identify TCR RFUs are further described herein. In various embodiments, a variable region of the TCR RFU is defined according to a centroid sequence representing a centroid value of a plurality of TCRs that make up the TCR RFU. Example centroid sequences of TCR RFUs are shown in Table 1 and Table 2. In some embodiments, a centroid sequence of a TCR RFU has an amino acid sequence of any one of SEQ ID NO: 1-4129. In such embodiments, comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR RFUs can involve comparing a variable region sequence of a sequence read to the centroid sequence of the TCR RFU. [00118] In various embodiments, comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR RFUs involves comparing the identities of the plurality of TCRs from the subject to variable regions of one or more of the plurality of TCRs that make up the TCR RFU. In particular embodiments, comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR RFUs involves comparing the identities of the plurality of TCRs from the subject to variable regions of each of the plurality of TCRs that make up the TCR RFU. For example, assuming that N different TCRs define a TCR RFU, then comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR RFUs can involve comparing a variable region sequence of a sequence read to each of the variable regions of the N different TCRs that define a TCR RFU. Example TCRs within exemplary 197 TCR RFUs are shown in Table 1. In particular, as shown in Table 1, a TCR RFU is represented as a centroid identifier (e.g., IPTS/128553107.1 Attorney Docket No: SRU-004WO “46433470” or “50622746”). A TCR in a TCR RFU is identified according to a V gene (column titled “v_gene”), a J gene (column titled “j_gene”) and a CDR3 sequence (column titled “cdr3”). Each row of Table 1 includes two TCRs, where columns 1-6 describe a first TCR, and columns 7-12 describe a second TCR. Additionally, Table 2 below documents the number of TCRs in each of the 197 TCR RFUs shown in Table 1. [00119] As shown in Table 2 (and further referred to herein), a “RFU centroid CDR3” is a CDR3 sequence representing the centroid or geometric center of the CDR3 sequences in the RFU. The RFU centroid CDR3 sequences are identified using clustering methods like those described in Example 2 and Example 7. Table 2 TCR d c Centroid RFU centroid CDR3 Number of TCRs t t t t t t t t t t

IPTS/128553107.1 Attorney Docket No: SRU-004WO R^FU11 12 30192241 CASSLGGNTGELFF About, at least, or at most (SEQ ID NO: 2847) 34 TCRs t t t t t t t t t t t t t t t

IPTS/128553107.1 Attorney Docket No: SRU-004WO R^FU27 12 15963481 CASSDSGGSYNEQFF About, at least, or at most (SEQ ID NO: 2710) 12 TCRs t t t t t t t t t t t t t t t

IPTS/128553107.1 Attorney Docket No: SRU-004WO R^FU43 1.1 57261475 CASSLGQGANTGELFF About, at least, or at most (SEQ ID NO: 270) 29 TCRs t t t t t t t t t t t t t t t

IPTS/128553107.1 Attorney Docket No: SRU-004WO R^FU59 22 53895951 CASSLSGANVLTF About, at least, or at most (SEQ ID NO: 3964) 12 TCRs t t t t t t t t t t t t t t t

IPTS/128553107.1 Attorney Docket No: SRU-004WO R^FU75 11 16620295 CANSVGGNTEAFF About, at least, or at most (SEQ ID NO: 2188) 18 TCRs t t t t t t t t t t t t t t t

IPTS/128553107.1 Attorney Docket No: SRU-004WO R^FU91 2.2 26126879 CAGSAPPCSYEQYV About, at least, or at most (SEQ ID NO: 1680) 13 TCRs t t t t t t t t t t t t t t t

IPTS/128553107.1 Attorney Docket No: SRU-004WO R^FU107 22 47773979 CASSLSGNYGYTF About, at least, or at most (SEQ ID NO: 3857) 21 TCRs t t t t t t t t t t t t t t t

IPTS/128553107.1 Attorney Docket No: SRU-004WO R^FU123 12 66235104 CASSDSGTGELFF About, at least, or at most (SEQ ID NO: 3255) 14 TCRs t t t t t t t t t t t t t t t

IPTS/128553107.1 Attorney Docket No: SRU-004WO R^FU139 12 22933283 CASSLGGNTEAFF About, at least, or at most (SEQ ID NO: 2747) 17 TCRs 7 t t t t t t t t t t t t t t

IPTS/128553107.1 Attorney Docket No: SRU-004WO R^FU155 1.2 1711543 CASSLGGANTGELFF About, at least, or at most (SEQ ID NO: 323) 16 TCRs t t t t t t t t 7 t t t t t t

IPTS/128553107.1 Attorney Docket No: SRU-004WO R^FU171 2.2 42914861 CVSSVGGANTGELFF About, at least, or at most (SEQ ID NO: 1869) 42 TCRs t t t t t t t t t t t t t t t

IPTS/128553107.1 Attorney Docket No: SRU-004WO R^FU187 12 13309737 CATSLGGSGNTIYF About, at least, or at most (SEQ ID NO: 2626) 13 TCRs t t t t t t t t t t

[00120] In various embodiments, comparing a variable region sequence of a sequence read to a variable region of a TCR involves one or more of: 1) comparing a V gene encoding for a V gene segment of the variable region sequence of the sequence read to a V gene encoding for a V gene segment of the TCR, 2) comparing a J gene encoding for a J gene segment of the variable region sequence of the sequence read to a J gene encoding for a J gene segment of the TCR, and 3) comparing a CDR3 sequence of the variable region sequences of the sequence reads to a CDR3 sequence of the TCR. Table 1 shows the V gene, J gene, and CDR3 sequence of example TCRs that have been categorized in TCR RFUs. [00121] In particular embodiments, comparing a variable region sequence of a sequence read to a variable region of a TCR involves performing each of (1), (2), and (3). Returning again IPTS/128553107.1 Attorney Docket No: SRU-004WO to the example where a TCR RFU is defined by N different TCRs, then the variable region sequence of a sequence read is compared to the N variable regions of the N different TCRs by repeating each of steps (1), (2), and (3) across the N different TCRs. [00122] By comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR RFUs, the feature counts for TCR RFUs are determined. In various embodiments, the feature counts for a TCR RFU are a total number of positive comparisons for the TCR RFU. As one example, assume that the comparison involves comparing a V gene encoding for a V gene segment of the variable region sequences of the sequence reads to a V gene encoding for a V gene segment of the TCR RFU. A positive comparison can be a match between the V gene that encodes for the V gene segment of the variable region of a sequence read and the V gene that encodes for a V gene segment of the TCR RFU. In contrast, a non-positive comparison can be a lack of a match between the V gene that encodes for the V gene segment of the variable region of a sequence read and the V gene that encodes for a V gene segment of the TCR RFU. [00123] As another example, assume the comparison includes comparing a variable region sequence of a sequence read to a variable region of a TCR by: 1) comparing a V gene encoding for a V gene segment of the variable region sequence of the sequence read to a V gene encoding for a V gene segment of the TCR, 2) comparing a J gene encoding for a J gene segment of the variable region sequence of the sequence read to a J gene encoding for a J gene segment of the TCR, and 3) comparing a CDR3 sequence of the variable region sequences of the sequence reads to a CDR3 sequence of the TCR. In some embodiments, a positive comparison can be a match of one or more of (1) the V gene, (2) the J gene, and (3) the CDR3 sequence. In some embodiments, a positive comparison can be a match of each of (1) the V gene, (2) the J gene, and (3) the CDR3 sequence. In such embodiments, a non- positive comparison can be a lack of a match between any of (1) the V gene, (2) the J gene, and (3) the CDR3 sequence. [00124] In various embodiments, a match between CDR3 sequences refers to 100% sequence identity between the two CDR3 sequences (e.g., the CDR3 sequence of the variable region sequence of a sequence read and a CDR3 sequence of the TCR). In some embodiments, a match between CDR3 sequences refers to 1 or fewer nucleotide mismatches between the CDR3 sequences. In particular embodiments, a match between CDR3 sequences refers to 2 or fewer nucleotide mismatches between the CDR3 sequences. In various embodiments, a match between CDR3 sequences refers to 3 or fewer, 4 or fewer, or 5 or fewer nucleotide mismatches between the CDR3 sequences. IPTS/128553107.1 Attorney Docket No: SRU-004WO [00125] In various embodiments, a match between CDR3 sequences refers to a CDR3 sequence metric (e.g., a CDR3 distance metric) between the two CDR3 sequences being below a threshold value (e.g., 1 amino acid mismatch of any kind). The threshold value can be set to ensure that the two CDR3 sequence are sufficiently similar. Examples of CDR3 sequence metrics can include TCRdist (described in Dash, P. et al., “Quantifiable predictive features define epitope-specific T cell receptor repertoires,” Nature, 547 (7661) (2017), pp. 89-93, and in Mayer-Blackwell, K. et al., TCR meta-clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA-restricted clusters of SARS-CoV-2 TCRs eLife, 2021, 10:e68605, each of which is hereby incorporated by reference in its entirety), CDRdist (described in further detail in Thakkar, N., et al., “Balancing sensitivity and specificity in distinguishing TCR groups by CDR sequence similarity” BMC Bioinf, 20 (1) (2019), pp.1-14, which is hereby incorporated by reference in its entirety), Tcr Repertoire Utilities for Solid Tissue (TRUST) (described in Zhang, H., et al., “Investigation of Antigen- Specific T-Cell Receptor Clusters in Human Cancers” Clin Cancer Res, 26(6):1359-1371 (2020), which is hereby incorporated by reference in its entirety), DeepTCR (described in Sidhom, JW., et al., “DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires” Nat Comm, 12, 1605 (2021), which is hereby incorporated by reference in its entirety), or GLIPH2 (described in Huang, H., et al., “Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening” Nat Biotech, 38, 1194 (2020), which is hereby incorporated by reference in its entirety). [00126] In various embodiments, the number of positive comparisons for a TCR RFU represents the feature counts. As one example, given the feature counts across TCR RFUs, they can be used as features to train a predictive model to predict presence, absence, or likelihood of cancer, as is described in further detail herein. As another example, given the features counts across TCR RFUs, they can be as features to be inputted into a trained predictive model to predict presence, absence, or likelihood of cancer for a subject. [00127] FIG.3 depicts a flow diagram for identifying T-cell receptor (TCR) repertoire functional units (RFUs), in accordance with an embodiment. [00128] Step 310 involves obtaining or having obtained TCR sequencing data of a plurality of TCRs from a plurality of training samples. In some embodiments, the TCRs are such as those provided in Table 1. IPTS/128553107.1 Attorney Docket No: SRU-004WO [00129] Step 320 involves sorting the plurality of TCRs into candidate RFUs, by clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc) as in step 330. In various embodiments, step 330 represents a substep of step 320. [00130] Step 340 involves further processing candidate RFUs by: 1) filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a first threshold number of training samples as in step 350; and/or 2) filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, or 32, as in step 360. In various embodiments, steps 350 and 360 represent substeps of step 340. [00131] Step 370 involves analyzing, through a generalized linear model, the candidate RFUs to identify cancer-associated RFUs. [00132] In various embodiments, steps 310-370 further comprise grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores. [00133] In various embodiments, steps 310-370 further comprise filtering candidate RFUs to retain candidate RFUs that are observed in at least a second threshold number of training samples. [00134] In various embodiments, steps 310-370 further comprise grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores; and filtering candidate RFUs to retain candidate RFUs that are observed in at least a second threshold number of training samples. [00135] In various embodiments, analyzing, through the generalized linear model further comprises incorporating demographic covariates. In various embodiments, the generalized linear model is a gamma-Poisson generalized linear model. III.B. Training a Predictive model [00136] During the training phase, the model training module 160 trains one or more predictive models using training data comprising feature counts of TCR RFUs (e.g., cancer- associated TCR RFUs) and/or expression values of biomarkers (e.g., protein biomarkers and/or mutations). In particular embodiments, the model training module 160 trains one or more predictive models using training data comprising feature counts of TCR RFUs (e.g., IPTS/128553107.1 Attorney Docket No: SRU-004WO cancer-associated TCR RFUs), wherein the training data does not further include expression values of biomarkers (e.g., protein biomarkers and/or mutations). [00137] In various embodiments, the model training module 160 generates the training data comprising feature counts of TCR RFUs by analyzing identity values of TCRs in test samples from individuals known to have a presence, absence, or likelihood of cancer. In various embodiments, the model training module 160 obtains the training data comprising feature counts of TCR RFUs from a third party. The third party may have analyzed test samples to determine the feature counts of TCR RFUs. [00138] In various embodiments, the training data further comprises reference ground truth values that indicate a cancer status (e.g., presence, absence, or likelihood of cancer) in an individual from whom the feature counts of TCR RFUs were obtained. Example reference ground truth values can be a binary value (e.g., “0” indicating absence of cancer and “1” indicating presence of cancer) or continuous values. Thus, over training iterations, the predictive model is trained (e.g., the parameters are tuned) to minimize a prediction error between a cancer prediction (e.g., presence, absence, or likelihood of cancer) and the reference ground truth values. In various embodiments, the prediction error is calculated based on a loss function, examples of which include a L1 regularization (Lasso Regression) loss function, a L2 regularization (Ridge Regression) loss function, or a combination of L1 and L2 regularization (ElasticNet). [00139] In some embodiments, the model training module 160 retrieves the training data from the training data store 180 and randomly partitions the training data into a training set and a test set. As an example, 80% of the training data may be partitioned into the training set and the other 20% can be partitioned into the test set. Other proportions of training set and test set may be implemented. As such, the training set is used to train predictive models whereas the test set is used to validate the predictive models. [00140] In various embodiments, the predictive model is any one of a regression model (e.g., linear regression, logistic regression, or polynomial regression), decision tree, decision tree ensemble, random forest, support vector machine, Naïve Bayes model, k-means cluster, or neural network (e.g., feed-forward networks, convolutional neural networks (CNN), deep neural networks (DNN), autoencoder neural networks, generative adversarial networks, or recurrent networks (e.g., long short-term memory networks (LSTM), bi-directional recurrent networks, deep bi-directional recurrent networks), or any combination thereof. [00141] The predictive model can be trained using a machine learning implemented method, such as any one of a linear regression algorithm, logistic regression algorithm, decision tree IPTS/128553107.1 Attorney Docket No: SRU-004WO algorithm, support vector machine classification, Naïve Bayes classification, K-Nearest Neighbor classification, random forest algorithm, deep learning algorithm, gradient boosting algorithm, extreme gradient boosting algorithm, and dimensionality reduction techniques such as manifold learning, principal component analysis, factor analysis, autoencoder regularization, and independent component analysis, or combinations thereof. In various embodiments, the predictive model is trained using supervised learning algorithms, unsupervised learning algorithms, semi-supervised learning algorithms (e.g., partial supervision), weak supervision, transfer, multi-task learning, or any combination thereof. [00142] In various embodiments, the predictive model has one or more parameters, such as hyperparameters or model parameters. Hyperparameters are generally established prior to training. Examples of hyperparameters include the learning rate, depth or leaves of a decision tree, number of hidden layers in a deep neural network, number of clusters in a k- means cluster, penalty in a regression model, and a regularization parameter associated with a cost function. Model parameters are generally adjusted during training. Examples of model parameters include weights associated with nodes in layers of neural network, support vectors in a support vector machine, and coefficients in a regression model. The model parameters of the predictive model are trained (e.g., adjusted) using the training data to improve the predictive capacity of the predictive model. [00143] In various embodiment, the model training module 160 performs a feature selection process to identify the set of feature counts of TCR RFUs or biomarkers to be included in the panel. For example, the model training module 160 performs a sequential forward feature selection based on the feature counts of TCR RFUs or biomarkers and their importance in predicting the particular output (e.g., presence, absence, or likelihood of cancer). For example, feature counts of TCR RFUs or biomarkers that are determined to be highly correlated with a presence, absence, or likelihood of cancer would be deemed highly important are therefore likely to be included in the feature counts of TCR RFUs or biomarkers in comparison to other feature counts of TCR RFUs or biomarkers that are not highly correlated with a presence, absence, or likelihood of cancer. In various embodiments, the model training module 160 performs separate feature selection processes for TCR RFUs and biomarkers, such that the top TCR RFUs and the top biomarkers that are predictive of cancer are identified through separate workflows. [00144] In some embodiments, the importance of each TCR RFU or biomarker is determined by using a method including one of random forest (RF), gradient boosting (GBM), extreme gradient boosting (XGB), or LASSO algorithms. For example, if using random forest IPTS/128553107.1 Attorney Docket No: SRU-004WO algorithms, the random forest algorithm may provide, for each TCR RFU and/or biomarker, 1) a mean decrease in model accuracy and/or 2) a mean decrease in a Gini coefficient which is a measure of how much each TCR RFU or biomarker contributes to the homogeneity of nodes and leaves in the random forest. In one scenario, the importance of each TCR RFU or biomarker is dependent on one or both of the mean decrease in model accuracy and mean decrease in Gini coefficient. [00145] In various embodiments, the model training module 160 trains a predictive model to achieve certain performance metrics. Performance metrics include, but are not limited to, area under a receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, true positive rate, true negative rate, false positive rate, false negative rate, negative predictive value, or false discovery rate. As used herein, accuracy refers to the ratio of the sum of true positives and true negatives divided by the sum of all positives and negatives. Sensitivity is used herein as the ratio of true positives divided by the sum of true positives and false negatives. Specificity is used herein as the ratio of true negatives divided by the sum of true negatives and false positives. Positive predictive value is used herein as the ratio of true positives divided by the sum of true positives and false positives. Negative predictive value is used herein as the ratio of true negatives divided by the sum of true negatives and false negatives. True positive rate, as used herein, refers to the rate of correct classification by the model of the cancer status in a subject as positive. True negative rate, as used herein, refers to the rate of correct classification by the model of the cancer status in a subject as negative. False positive rate, as used herein, refers to the rate of incorrect classification by the model of the cancer status in a subject as positive. False negative rate, as used herein, refers to the rate of incorrect classification by the model of the cancer status in a subject as negative. False discovery rate, as used herein, refers to the expected proportion of false discoveries among all discoveries. [00146] In various embodiments, the model training module 160 trains a predictive model which achieves a particular AUC performance metric. In various embodiments, the predictive model achieves an AUC of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, at least 0.80, at least 0.81, at least 0.82, at least 0.83, at least 0.84, at least 0.85, at least 0.86, at least 0.87, at least 0.88, at least 0.89, at least 0.90, at least 0.91, at least 0.92, at least 0.93, at least 0.94, at least 0.95, at least 0.96, at least 0.97, at least 0.98, or at least 0.99. In various embodiments, the predictive model achieves an AUC of at least 0.60. IPTS/128553107.1 Attorney Docket No: SRU-004WO In various embodiments, the predictive model achieves an AUC of at least 0.61. In various embodiments, the predictive model achieves an AUC of at least 0.62. In various embodiments, the predictive model achieves an AUC of at least 0.63. In various embodiments, the predictive model achieves an AUC of at least 0.64. In various embodiments, the predictive model achieves an AUC of at least 0.65. In various embodiments, the predictive model achieves an AUC of at least 0.66. In various embodiments, the predictive model achieves an AUC of at least 0.67. In various embodiments, the predictive model achieves an AUC of at least 0.68. In various embodiments, the predictive model achieves an AUC of at least 0.69. In various embodiments, the predictive model achieves an AUC of at least 0.70. In various embodiments, the predictive model achieves an AUC of at least 0.71. In various embodiments, the predictive model achieves an AUC of at least 0.72. In various embodiments, the predictive model achieves an AUC of at least 0.73. In various embodiments, the predictive model achieves an AUC of at least 0.74. In various embodiments, the predictive model achieves an AUC of at least 0.75. In various embodiments, the predictive model achieves an AUC of at least 0.76. In various embodiments, the predictive model achieves an AUC of at least 0.77. In various embodiments, the predictive model achieves an AUC of at least 0.78. In various embodiments, the predictive model achieves an AUC of at least 0.79. In various embodiments, the predictive model achieves an AUC of at least 0.80. In various embodiments, the predictive model achieves an AUC of at least 0.81. In various embodiments, the predictive model achieves an AUC of at least 0.82. In various embodiments, the predictive model achieves an AUC of at least 0.83. In various embodiments, the predictive model achieves an AUC of at least 0.84. In various embodiments, the predictive model achieves an AUC of at least 0.85. In various embodiments, the predictive model achieves an AUC of at least 0.86. In various embodiments, the predictive model achieves an AUC of at least 0.87. In various embodiments, the predictive model achieves an AUC of at least 0.88. In various embodiments, the predictive model achieves an AUC of at least 0.89. In various embodiments, the predictive model achieves an AUC of at least 0.90. In various embodiments, the predictive model achieves an AUC of at least 0.91. In various embodiments, the predictive model achieves an AUC of at least 0.92. In various embodiments, the predictive model achieves an AUC of at least 0.93. In various embodiments, the predictive model achieves an AUC of at least 0.94. In various IPTS/128553107.1 Attorney Docket No: SRU-004WO embodiments, the predictive model achieves an AUC of at least 0.95. In various embodiments, the predictive model achieves an AUC of at least 0.96. In various embodiments, the predictive model achieves an AUC of at least 0.97. In various embodiments, the predictive model achieves an AUC of at least 0.98. In various embodiments, the predictive module achieves an AUC of at least 0.99. [00147] In various embodiments, the model training module 160 trains a predictive model which achieves a particular accuracy performance metric. In various embodiments, the predictive model achieves an accuracy of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, at least 0.80, at least 0.81, at least 0.82, at least 0.83, at least 0.84, at least 0.85, at least 0.86, at least 0.87, at least 0.88, at least 0.89, at least 0.90, at least 0.91, at least 0.92, at least 0.93, at least 0.94, at least 0.95, at least 0.96, at least 0.97, at least 0.98, or at least 0.99. In various embodiments, the predictive model achieves an accuracy of at least 0.60. In various embodiments, the predictive model achieves an accuracy of at least 0.61. In various embodiments, the predictive model achieves an accuracy of at least 0.62. In various embodiments, the predictive model achieves an accuracy of at least 0.63. In various embodiments, the predictive model achieves an accuracy of at least 0.64. In various embodiments, the predictive model achieves an accuracy of at least 0.65. In various embodiments, the predictive model achieves an accuracy of at least 0.66. In various embodiments, the predictive model achieves an accuracy of at least 0.67. In various embodiments, the predictive model achieves an accuracy of at least 0.68. In various embodiments, the predictive model achieves an accuracy of at least 0.69. In various embodiments, the predictive model achieves an accuracy of at least 0.70. In various embodiments, the predictive model achieves an accuracy of at least 0.71. In various embodiments, the predictive model achieves an accuracy of at least 0.72. In various embodiments, the predictive model achieves an accuracy of at least 0.73. In various embodiments, the predictive model achieves an accuracy of at least 0.74. In various embodiments, the predictive model achieves an accuracy of at least 0.75. In various embodiments, the predictive model achieves an accuracy of at least 0.76. In various embodiments, the predictive model achieves an accuracy of at least 0.77. In various embodiments, the predictive model achieves an accuracy of at least 0.78. In various embodiments, the predictive model achieves an accuracy of at least 0.79. In various embodiments, the predictive model achieves an accuracy of at least 0.80. In various IPTS/128553107.1 Attorney Docket No: SRU-004WO embodiments, the predictive model achieves an accuracy of at least 0.81. In various embodiments, the predictive model achieves an accuracy of at least 0.82. In various embodiments, the predictive model achieves an accuracy of at least 0.83. In various embodiments, the predictive model achieves an accuracy of at least 0.84. In various embodiments, the predictive model achieves an accuracy of at least 0.85. In various embodiments, the predictive model achieves an accuracy of at least 0.86. In various embodiments, the predictive model achieves an accuracy of at least 0.87. In various embodiments, the predictive model achieves an accuracy of at least 0.88. In various embodiments, the predictive model achieves an accuracy of at least 0.89. In various embodiments, the predictive model achieves an accuracy of at least 0.90. In various embodiments, the predictive model achieves an accuracy of at least 0.91. In various embodiments, the predictive model achieves an accuracy of at least 0.92. In various embodiments, the predictive model achieves an accuracy of at least 0.93. In various embodiments, the predictive model achieves an accuracy of at least 0.94. In various embodiments, the predictive model achieves an accuracy of at least 0.95. In various embodiments, the predictive model achieves an accuracy of at least 0.96. In various embodiments, the predictive model achieves an accuracy of at least 0.97. In various embodiments, the predictive model achieves an accuracy of at least 0.98. In various embodiments, the predictive module achieves an accuracy of at least 0.99. [00148] In various embodiments, the model training module 160 trains a predictive model which achieves a true positive rate of at least 40% at a false positive rate of about 10%. [00149] FIG.4 depicts a flow diagram for training the predictive model, in accordance with an embodiment. [00150] Step 410 involves obtaining a dataset comprising feature counts of a plurality of TCRs from the subject across a plurality of cancer-associated TCR repertoire functional units (RFUs)), such as those recited in Table 1. [00151] Step 420 involves analyzing, through a ML implemented method, the feature counts across the plurality of cancer-associated TCR RFUs to train the predictive model useful for predicting presence, absence, or likelihood of a cancer. [00152] In various embodiments, steps 410-420 further comprise applying a gamma-Poisson generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. In various embodiments, applying the gamma- Poisson generalized linear model further comprises incorporating demographic covariates. In IPTS/128553107.1 Attorney Docket No: SRU-004WO various embodiments, the demographic covariates comprise age, sex, race, or any combination thereof. III.C. Deploying a Predictive model [00153] During the deployment phase, the model deployment module 170 (as shown in FIG. 1B) analyzes feature counts of TCRs from a test sample obtained from a subject of interest by applying a trained predictive model. Generally, the predictive model analyzes the feature counts of TCRs and outputs a prediction, such as a score informative for determining a presence, absence, or likelihood of cancer in the subject. [00154] In various embodiments, the score represents a combination of feature counts of TCRs in the test sample obtained from the subject (e.g., changed identity of TCRs in comparison to one or more healthy controls). In various embodiments, if all or a majority of the feature counts of TCRs are trending in a particular direction (e.g., presence, absence, or likelihood in comparison to healthy), then the subject can be deemed as having a presence of cancer. Alternatively, if all or a majority of the feature counts of TCRs are not trending in a particular direction (e.g., not present or absent in comparison to healthy), then the subject can be deemed as having an absence of cancer. [00155] In various embodiments, the score represents an aggregate score of the feature counts of the plurality of TCRs in the panel. In such embodiments, it is not necessary to know how the feature count of each individual TCR has changed (relative to healthy control(s)) to classify the subject as having a presence, absence, or likelihood of cancer. Rather, it is the aggregate combination of how the feature counts of the plurality of TCRs have changed relative to healthy control(s) that are determinative of whether the subject has a presence, absence, or likelihood of cancer. [00156] In various embodiments, predicting presence, absence, or likelihood of cancer in the subject involves comparing the predicted score output by the predictive model to one or more reference scores. As used herein, “reference scores” refer to previously determined scores, such as a “healthy reference score” corresponding to one or more healthy patients or a “cancer reference score” corresponding to one or more cancerous patients. For example, a healthy reference score may correspond to healthy patients, a patient’s own baseline at a prior timepoint when the patient did not exhibit cancer activity (e.g., longitudinal analysis), patients clinically diagnosed with cancer but not exhibiting cancer activity (e.g., cancer remission), or a healthy reference threshold score (e.g., a cutoff). As another example, a IPTS/128553107.1 Attorney Docket No: SRU-004WO “cancer reference score” may correspond to patients previously diagnosed with cancer, patients exhibiting cancer activity, or a cancer reference threshold score (e.g., a cutoff). In various embodiments, the threshold score can be derived from a cancer case / non-cancer control ROC curve analysis. The ROC curve can be derived using a logistic regression probability, or any other predictive method that can calculate a score that may be used for classification (e.g., for instance, a neural network). [00157] In various embodiments, a reference score can be a threshold cutoff score with a value between 0 and 1. In various embodiments, the threshold cutoff score is any of 0.001, 0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, or 0.95. In particular embodiments, the threshold cutoff score is between 0.5 and 1.0. In particular embodiments, the threshold cutoff score is between 0.6 and 0.8. In particular embodiments, the threshold cutoff score is 0.7. [00158] In various embodiments, predicting presence of absence of cancer in the subject involves determining whether the predicted score output by the predictive model is above or below the threshold cutoff score. In particular embodiments, if the predicted score is above the threshold cutoff score, the subject is determined to have a presence of cancer. If the predicted score is below the threshold cutoff score, the subject is determined to have an absence of cancer. In some embodiments, if the predicted score is above the threshold cutoff score, the subject is determined to have an absence of cancer. If the predicted score is below the threshold cutoff score, the subject is determined to have a presence of cancer. [00159] FIG.2 depicts a flow diagram for generating a cancer prediction for a subject, in accordance with an embodiment. In particular embodiments, the cancer prediction is a presence, absence, or likelihood of cancer in the subject, such as presence, absence, or likelihood of early stage cancer in the subject. [00160] Step 210 involves obtaining a dataset comprising feature counts of a plurality of TCRs from the subject. In various embodiments, the plurality of TCR comprise two or more variable regions selected from Table 1. [00161] Step 220 involves generating a feature count (e.g., a count of identities of plurality of TCRs from the subject against plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs)) for the subject by comparing the identities of plurality of TCRs from the subject against the plurality of variable regions of the cancer-associated TCR RFUs. [00162] Step 230 involves generating a cancer prediction (e.g., a prediction of presence, absence, or likelihood of cancer) for the subject by applying a predictive model to the feature IPTS/128553107.1 Attorney Docket No: SRU-004WO counts of the plurality of TCRs. The predictive model outputs a prediction, such as a score informative for determining a presence, absence, or likelihood of cancer in the subject. In various embodiments, the score output by the predictive model is compared to a threshold score to classify the subject as having a presence, absence, or likelihood of cancer. [00163] Step 240 involves determining whether to identify the subject as a candidate for undergoing one or more additional tests based on the generated cancer prediction. In various embodiments, responsive to determining that the subject likely has a presence of cancer, step 240 can involve performing a performing a second analysis to predict presence, absence, or likelihood of the early stage cancer or non-early stage cancer in a subject. In such embodiments, the predictive model at step 240 may be a high sensitivity predictive model that enables the rapid screening out of subjects who do not have cancer with high accuracy. Step 240 may involve a second analysis that further distinguishes the remaining subjects as having a presence, absence, or likelihood of cancer. Here, the second analysis can achieve a higher specificity in comparison to a specificity of the predictive model, thereby enabling the identification of the true positives (e.g., those subjects truly having a presence of cancer). In various embodiments, the one or more additional tests includes one or more of further blood molecular testing, a computerized tomography (CT) scan, a positron emission tomography (PET) scan, or a tissue biopsy. In various embodiments, the one or more additional tests may be sequentially performed depending on the results of the prior test. For example, responsive to determining that the subject likely has a presence of cancer, a CT scan or a PET scan can be performed. If the CT scan or PET scan further confirms a signal indicative of presence of cancer (e.g., presence of a mass in the scan), then a tissue biopsy can be subsequently performed. [00164] In various embodiments, steps 210-240 further comprise obtaining or having obtained a second dataset comprising expression levels of a plurality of biomarkers from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a second predictive model to the expression levels of the plurality of biomarkers. In various embodiments, the second predictive model is a support vector machine (SVM) model. [00165] In various embodiments, steps 210-240 further comprise obtaining or having obtained a third dataset comprising a mutational profile of a plurality of circulating tumor DNA (ctDNA) from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a third predictive model to the mutational IPTS/128553107.1 Attorney Docket No: SRU-004WO profiles of ctDNA. In various embodiments, the third predictive model is a logistic regression model. [00166] various embodiments, steps 210-240 further comprise: 1) obtaining or having obtained a second dataset comprising expression levels of a plurality of biomarkers from the subject; 2) obtaining or having obtained a third dataset comprising a mutational profile of a plurality of circulating tumor DNA (ctDNA) from the subject; 3) generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a second predictive model to the expression levels of the plurality of biomarkers; and 4) generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a third predictive model to the mutational profiles of ctDNA. In various embodiments, the second predictive model is a support vector machine (SVM) model, and the third predictive model is a logistic regression model. IV. Example Methods for Identifying Cancer-Associated T-Cell Receptor Repertoire Functional Units [00167] Additionally disclosed herein are methods for identifying TCR RFUs that are useful for generating a cancer prediction (e.g., a presence, absence, or likelihood of cancer). As described herein, TCR RFUs, such as cancer-associated TCR RFUs, can be incorporated as features of a predictive model for training the predictive model to generate a cancer prediction. Thus, a subject with a particular set of TCRs can be compared to the TCR RFUs, such as cancer-associated TCR RFUs, and further analyzed using a predictive model to generate a cancer prediction for the subject. [00168] In various embodiments, to identify TCR RFUs, methods disclosed herein involve obtaining a plurality of TCRs from samples. Here, these samples may be training samples that are obtained from individuals that are known to have cancer or not to have cancer. In particular embodiments, the samples are blood samples. In various embodiments, obtaining the plurality of TCRs from samples involves performing an assay, such as a TCR quantification assay 120 described in reference to FIG.1A. In particular embodiments, obtaining the plurality of TCRs from samples involves performing a TCR-sequencing (TCR- seq) assay to generate sequencing data of the the plurality of TCRs. [00169] In various embodiments, to identify TCR RFUs, methods involve sorting the plurality of TCRs into candidate RFUs. Here, the candidate RFUs represent a preliminary set of TCR RFUs that may be useful for generating a cancer prediction. Further steps described herein can narrow the candidate RFUs into a final set of TCR RFUs. In various IPTS/128553107.1 Attorney Docket No: SRU-004WO embodiments, sorting the plurality of TCRs into candidate RFUs involves determining one or more dissimilarity metrics representing levels of dissimilarity between certain sequences of the TCRs. For example, a dissimilarity metric can be a distance metric that enables grouping of TCRs by antigen specificity based on their sequence similarity. One example of a dissimilarity metric can be a CDR3 sequence dissimilarity metric. Another example of a dissimilarity metric can be a CDR1 and CDR2 sequence dissimilarity metric. Another example of a dissimilarity metric can be a CDR1, CDR2, and CDR2.5 sequence dissimilarity metric. Example CDR3 sequence dissimilarity metrics include TCRdist (described in Dash, P. et al., “Quantifiable predictive features define epitope-specific T cell receptor repertoires,” Nature, 547 (7661) (2017), pp.89-93, and in Mayer-Blackwell, K. et al., TCR meta- clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA- restricted clusters of SARS-CoV-2 TCRs eLife, 2021, 10:e68605, each of which is hereby incorporated by reference in its entirety), CDRdist (described in further detail in Thakkar, N., et al., “Balancing sensitivity and specificity in distinguishing TCR groups by CDR sequence similarity” BMC Bioinf, 20 (1) (2019), pp.1-14, which is hereby incorporated by reference in its entirety), iSMART (described in Zhang, H., et al., “Investigation of Antigen-Specific T- Cell Receptor Clusters in Human Cancers” Clin Cancer Res, 26(6):1359-1371 (2020), which is hereby incorporated by reference in its entirety). [00170] In various embodiments, the dissimilarity metric is further used to generate an overall dissimilarity metric. For example, the dissimilarity metric can be combined with one or more sequences of the TCR to further generate the overall dissimilarity metric. In various embodiments, the dissimilarity metric is a CDR3 dissimilarity metric and is combined with one of the V gene, the D gene, or the J gene of the TCR to generate an overall dissimilarity metric. In various embodiments, the dissimilarity metric is a CDR1, CDR2, and CDR2.5 dissimilarity metric and is combined with one of the V gene, the D gene, or the J gene of the TCR to generate an overall dissimilarity metric. In particular embodiments, the dissimilarity metric is a CDR3 dissimilarity metric and is combined with the V gene of the TCR to generate an overall dissimilarity metric. In particular embodiments, the dissimilarity metric is a CDR1, CDR2, and CDR2.5 dissimilarity metric and is combined with the V gene of the TCR to generate an overall dissimilarity metric. In particular embodiments, the CDR3 dissimilarity metric has a weight of 1, and the CDR1, CDR2, and CDR2.5 dissimilarity metric has a weight of 1/3. Thus, the overall dissimilarity metric captures differences in both the CDR3 sequences and the V genes of the TCRs. Thus, the overall dissimilarity metric IPTS/128553107.1 Attorney Docket No: SRU-004WO captures differences in both the CDR1, CDR2, and CDR2.5 sequences and the V genes of the TCRs. [00171] In various embodiments, sorting the plurality of TCRs into candidate RFUs can involve clustering the plurality of TCRs. In various embodiments, clustering the plurality of TCRs is performed using the one or more dissimilarity metrics. In some embodiments, the plurality of TCRs are clustered according to the CDR3 dissimilarity metrics. In some embodiments, the plurality of TCRs are clustered according to the overall dissimilarity metric. Thus, TCRs that exhibit similar sequences (e.g., sequences encoded by the V gene and/or CDR3 sequences) are likely to be clustered closer together into a common candidate RFU. In various embodiments, the clustering involves creating a nearest neighbor graph e.g., an approximate nearest neighbor (ANN) graph of the TCRs using the one or more dissimilarity metrics. Then TCRs in the graph are clustered to assign the TCRs into RFUs. In various embodiments, methods involve implementing a clustering algorithm based on clustering by density peaks of the graph. [00172] In various embodiments, a TCR dissimilarity index value (e.g., dc) is set to control for the clustering sparsity of TCRs in the RFUs. For example, the dissimilarity index value can be selected to 1) cluster TCRs with one conservative amino acid mismatch, 2) cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or 3) cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. Here, setting the TCR dissimilarity index value to (1) e.g., cluster TCRs with one conservative amino acid mismatch would achieve RFUs with the least TCRs. In comparison, setting the TCR dissimilarity index value to (3) e.g., cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch would yield RFUs with more TCRs. [00173] In various embodiments, the number of candidate RFUs may be too large to effectively implement for generating cancer predictions. In various embodiments, the number of candidate RFUs may exceed 10,000 candidate RFUs, 20,000 candidate RFUs, 30,000 candidate RFUs, 40,000 candidate RFUs, 50,000 candidate RFUs, 60,000 candidate RFUs, 70,000 candidate RFUs, 80,000 candidate RFUs, 90,000 candidate RFUs, 100,000 candidate RFUs, 200,000 candidate RFUs, 500,000 candidate RFUs, 1 million candidate RFUs, 2 million candidate RFUs, 3 million candidate RFUs, 4 million candidate RFUs, or 5 million candidate RFUs. In such embodiments, the candidate RFUs can undergo additional processing in the form of one or more filtering steps. The filtering steps solve several problems: 1) the filtering reduces the number of candidate RFUs, and 2) the filtering IPTS/128553107.1 Attorney Docket No: SRU-004WO identifies a reduced set of RFUs that are more likely associated with cancer, thereby increasing the cancer signal with respect to noise/non-cancer related signals. [00174] In various embodiments, a filtering step involves identifying and retaining candidate RFUs that are observed in at least a threshold number of training samples. Here, the training samples may be considered irrespective of the cancer status (e.g., known cancer or known non-cancer). This ensures that candidate RFUs appear sufficiently often across the training samples and are not arising from a minimal number of samples (e.g., due to noise or bias). In various embodiments, the threshold number of training samples includes at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 training samples. In particular embodiments, the threshold number of training samples includes at least 8 training samples. [00175] In various embodiments, a filtering step involves identifying and filtering to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a threshold number of training samples. Here, evidence of T-cell expansion is determined by estimating the number of clones that carry TCRs of the RFU. In various embodiments, evidence of expansion is present if more than 2, 4, 8, 16, 32, 64, 128, 256, or 512 clones carry TCRs of the RFU. This filtering step increases the likelihood that the retained candidate RFUs are present during an immune response, such as in the presence of cancer. In various embodiments, the threshold number of training samples is at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, or at least 18 training samples. In particular embodiments, the threshold number of training samples is at least 15 training samples. [00176] In various embodiments, a filtering step involves identifying and filtering candidate to retain candidate RFUs with a minimum amino acid-level recurrence. In various embodiments, candidate RFUs are retained if the minimum amino acid-level recurrence is greater than 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, or 32. In particular embodiments, a candidate RFU is retained if the minimum amino acid-level recurrence is greater than 2. In particular embodiments, a candidate RFU is retained if the minimum amino acid-level recurrence is greater than 4. In particular embodiments, a candidate RFU is retained if the minimum amino acid-level recurrence is greater than 8. In particular embodiments, a candidate RFU is retained if the minimum amino acid-level recurrence is greater than 16. In particular embodiments, a candidate RFU is retained if the minimum amino acid-level recurrence is greater than 32. [00177] In various embodiments, the one or more filtering steps includes 1) identifying and filtering to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a threshold number of training samples; and 2) identifying and filtering candidate to retain IPTS/128553107.1 Attorney Docket No: SRU-004WO candidate RFUs with a minimum amino acid-level recurrence. In various embodiments, the one or more filtering steps includes each of 1) identifying and retaining candidate RFUs that are observed in at least a threshold number of training samples, 2) identifying and filtering to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a threshold number of training samples, 3) identifying and filtering candidate to retain candidate RFUs with a minimum amino acid-level recurrence. FIG.24 shows example results of performing any/all of the three described filtering steps. [00178] Following the one or more filtering steps, a significantly reduced set of candidate RFUs remain. In various embodiments, methods further involve analyzing, through a generalized linear model, the reduced set of candidate RFUs to identify cancer-associated RFUs. In particular embodiments, the generalized linear model is a gamma-Poisson generalized linear model. The generalized linear model enables testing for association between candidate RFUs and the cancer status (e.g., known cancer or non-cancer) of the training samples. Furthermore, the generalized linear model accounts for variable depth of sequencing and accounts for RFU count overdispersion. In various embodiments, the generalized linear model further incorporates demographic covariates, non-limiting examples of which include age, gender, race, history of smoking, history of smoking tobacco, history of chronic obstructive pulmonary disease, prior cancer history, and/or presence of radiographic features of pulmonary nodules of the individuals from whom the training samples were obtained. In various embodiments, the candidate RFUs that are identified as enriched in cancer training samples or enriched in non-cancer training samples can serve as the TCR RFUs. [00179] In various embodiments, a first subset of the TCR RFUs are enriched in cancer samples and a second subset of the TCR RFUs are enriched in non-cancer samples. In various embodiments, the first subset of TCR RFUs may exhibit a fold enrichment in cancer samples between 0.1 and 3.0. In various embodiments, the first subset of TCR RFUs may exhibit a fold enrichment in cancer between 0.15 and 2.5, between 0.20 and 2.0, between 0.50 and 1.50. In particular embodiments, the first subset of TCR RFUs may exhibit a fold enrichment in cancer samples between 0.17 and 2.23. In various embodiments, the second subset of TCR RFUs may exhibit a fold enrichment in non-cancer samples between 0.1 and 0.2. In various embodiments, the second subset of TCR RFUs may exhibit a fold enrichment in non-cancer samples between 0.11 and 0.18 or between 0.12 and 0.16. IPTS/128553107.1 Attorney Docket No: SRU-004WO V. T-Cell Receptor Repertoire Functional Units [00180] In various embodiments, generating a cancer prediction involves implementing a plurality of TCR RFUs, such as cancer-associated RFUs. In various embodiments, generating a cancer prediction involves implementing a plurality of variable regions of the plurality of TCR RFUs. In various embodiments, generating a cancer prediction involves implementing at least one cancer-associated T-cell receptor (TCR) repertoire functional unit (RFU). In various embodiments, the cancer-associated TCR RFUs include at least one variable genes. In various embodiments, the cancer-associated TCR RFUs include at least one joining genes. In various embodiments, the cancer-associated TCR RFUs include at least one variable genes and at least one joining genes. In various embodiments, the cancer-associated TCR RFUs include a plurality of variable regions. In various embodiments, the cancer-associated TCR RFUs include at least one variable genes and a plurality of variable regions. In various embodiments, the cancer-associated TCR RFUs include at least one joining genes and a plurality of variable regions. In various embodiments, the cancer-associated TCR RFUs include at least one variable genes and at least one joining genes, and a plurality of variable regions. In various embodiments, an example plurality of variable regions can include any one of the variable regions detailed in Table 1. In other embodiments, generating a cancer prediction involves implementing a plurality of cancer-associated TCR RFUs. In such embodiments, the plurality of TCR RFUs includes more than one TCR RFU. [00181] In various embodiments, the plurality of RFUs includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, or 197 RFUs. [00182] In various embodiments, the plurality of RFUs include a subset of the RFUs shown in any of Table 1. [00183] In various embodiments, the plurality of RFUs include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, IPTS/128553107.1 Attorney Docket No: SRU-004WO 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, or 197 RFUs of RFUs shown in Table 1. [00184] Without wishing to be bound to a particular theory, the TCR is a heterodimeric protein, composed of two different polypeptide chains, alpha (α) and beta (β), each with their respective variable (V) and constant (C) regions. The variable regions are responsible for recognizing and binding to specific antigens, while the constant regions contribute to the overall structure and function of the TCR. The TCR genes are formed through a process called V(D)J recombination, which involves the recombination of variable (V), diversity (D, only in the beta chain), and joining (J) gene segments. These gene segments are numerous, allowing for a vast diversity of TCRs to be generated, which in turn helps the immune system recognize a wide array of antigens. Variable (V) genes encode portions of the variable regions of the TCR alpha and beta chains, and are responsible for determining the antigen specificity of the TCR. There are multiple V gene segments for both the alpha and beta chains, and during V(D)J recombination, one V segment from each chain is randomly selected and incorporated into the final TCR gene. Joining (J) genes encode the portions of the TCR that connect the variable and constant regions. There are multiple J gene segments for both the alpha and beta chains, and during V(D)J recombination, one J segment from each chain is randomly selected and combined with the chosen V segment. In the case of the beta chain, the selected D segment is also included in this process. The process of V(D)J recombination ensures that a diverse repertoire of TCRs is generated, which increases the likelihood that T cells can recognize and respond to a broad range of antigens. [00185] In various embodiments, the TCR RFU comprises, or consists of, a variable gene. In various embodiments, the TCR RFU comprises a TCR variable gene. In various embodiments, the TCR RFU comprises a TCR beta variable gene. In various embodiments, the TCR RFU comprises a joining gene. In various embodiments, the TCR RFU comprises a TCR joining gene. In various embodiments, the TCR RFU comprises a TCR beta joining gene. In various embodiments, the TCR RFU comprises a variable gene and a joining gene. IPTS/128553107.1 Attorney Docket No: SRU-004WO In various embodiments, the TCR RFU comprises a TCR variable gene and a TCR joining gene. In various embodiments, the TCR RFU comprises a TCR beta variable gene and a TCR beta joining gene. [00186] In various embodiments, a variable gene is selected from any one of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9. In various embodiments, a variable gene is TRBV11-3. In various embodiments, a variable gene is TRBV13. In various embodiments, a variable gene is TRBV14. In various embodiments, a variable gene is TRBV18. In various embodiments, a variable gene is TRBV19. In various embodiments, a variable gene is TRBV2. In various embodiments, a variable gene is TRBV20-1. In various embodiments, a variable gene is TRBV25-1. In various embodiments, a variable gene is TRBV27. In various embodiments, a variable gene is TRBV28. In various embodiments, a variable gene is TRBV29-1. In various embodiments, a variable gene is TRBV30. In various embodiments, a variable gene is TRBV5-1. In various embodiments, a variable gene is TRBV5-5. In various embodiments, a variable gene is TRBV5-6. In various embodiments, a variable gene is TRBV5-8. In various embodiments, a variable gene is TRBV6-1. In various embodiments, a variable gene is TRBV7-2. In various embodiments, a variable gene is TRBV6-4. In various embodiments, a variable gene is TRBV6-5. In various embodiments, a variable gene is TRBV6-6. In various embodiments, a variable gene is TRBV7-4. In various embodiments, a variable gene is TRBV7-6. In various embodiments, a variable gene is TRBV7-7. In various embodiments, a variable gene is TRBV7-8. In various embodiments, a variable gene is TRBV7-9. In various embodiments, a variable gene is TRBV9. [00187] In various embodiments, a joining gene is selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. In various embodiments, a joining gene is TRBJ1-1. In various embodiments, a joining gene is TRBJ1-2. In various embodiments, a joining gene is TRBJ1-3. In various embodiments, a joining gene is TRBJ1-4. In various embodiments, a joining gene is TRBJ1-5. In various embodiments, a joining gene is TRBJ1-6. In various embodiments, a joining gene is TRBJ2-1. In various embodiments, a joining gene is TRBJ2- 2. In various embodiments, a joining gene is TRBJ2-3. In various embodiments, a joining gene is TRBJ2-4. In various embodiments, a joining gene is TRBJ2-5. In various IPTS/128553107.1 Attorney Docket No: SRU-004WO embodiments, a joining gene is TRBJ2-6. In various embodiments, a joining gene is TRBJ2- 7. [00188] In various embodiments, a joining gene is selected from any one of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; and the joining gene is selected from any one of TRBJ1- 1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2- 4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. [00189] In various embodiments, a variable gene is TRBV11-3; and a joining gene is selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7. In various embodiments, a variable gene is TRBV11-3; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV11-3; and a joining gene is TRBJ2-2. In various embodiments, a variable gene is TRBV11-3; and a joining gene is TRBJ2-7. [00190] In various embodiments, a variable gene is TRBV13; and a joining gene is selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a variable gene is TRBV13; and a joining gene is TRBJ1-4. In various embodiments, a variable gene is TRBV13; and a joining gene is TRBJ1-5. In various embodiments, a variable gene is TRBV13; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV13; and a joining gene is TRBJ2-2. In various embodiments, a variable gene is TRBV13; and a joining gene is TRBJ2-3. In various embodiments, a variable gene is TRBV13; and a joining gene is TRBJ2-5. In various embodiments, a variable gene is TRBV13; and a joining gene is TRBJ2-7. [00191] In various embodiments, a variable gene is TRBV11-3; and a joining gene is selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7. In various embodiments, a variable gene is TRBV11-3; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV11-3; and a joining gene is TRBJ2-2. In various embodiments, a variable gene is TRBV11-3; and a joining gene is TRBJ2-7. [00192] In various embodiments, a variable gene is TRBV14; and a joining gene is selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a variable gene is TRBV14; and a joining gene is TRBJ1- 1. In various embodiments, a variable gene is TRBV14; and a joining gene is TRBJ1-4. In various embodiments, a variable gene is TRBV14; and a joining gene is TRBJ1-5. In various embodiments, a variable gene is TRBV14; and a joining gene is TRBJ2-1. In various IPTS/128553107.1 Attorney Docket No: SRU-004WO embodiments, a variable gene is TRBV14; and a joining gene is TRBJ2-2. In various embodiments, a variable gene is TRBV14; and a joining gene is TRBJ2-3. In various embodiments, a variable gene is TRBV14; and a joining gene is TRBJ2-5. In various embodiments, a variable gene is TRBV14; and a joining gene is TRBJ2-7. [00193] In various embodiments, a variable gene is TRBV18; and a joining gene is selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1-6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a variable gene is TRBV18; and a joining gene is TRBJ1- 1. In various embodiments, a variable gene is TRBV18; and a joining gene is TRBJ1-3. In various embodiments, a variable gene is TRBV18; and a joining gene is TRBJ1-5. In various embodiments, a variable gene is TRBV18; and a joining gene is TRBJ1-6. In various embodiments, a variable gene is TRBV18; and a joining gene is TRBJ2-2. In various embodiments, a variable gene is TRBV18; and a joining gene is TRBJ2-3. In various embodiments, a variable gene is TRBV18; and a joining gene is TRBJ2-5. In various embodiments, a variable gene is TRBV18; and a joining gene is TRBJ2-7. [00194] In various embodiments, a variable gene is TRBV19; and a joining gene is selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1. In various embodiments, a variable gene is TRBV19; and a joining gene is TRBJ1-2. In various embodiments, a variable gene is TRBV19; and a joining gene is TRBJ1-6. In various embodiments, a variable gene is TRBV19; and a joining gene is TRBJ2-1. [00195] In various embodiments, a variable gene is TRBV2; and a joining gene is selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7. In various embodiments, a variable gene is TRBV2; and a joining gene is TRBJ1-6. In various embodiments, a variable gene is TRBV2; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV2; and a joining gene is TRBJ2-7. [00196] In various embodiments, a variable gene is TRBV20-1; and a joining gene is selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-3, and TRBJ2-5. In various embodiments, a variable gene is TRBV20-1; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV20-1; and a joining gene is TRBJ1-5. In various embodiments, a variable gene is TRBV20-1; and a joining gene is TRBJ2-3. In various embodiments, a variable gene is TRBV20-1; and a joining gene is TRBJ2-5. [00197] In various embodiments, a variable gene is TRBV25-1; and a joining gene is selected from any one of TRBJ2-1, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a variable gene is TRBV25-1; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV25-1; and a joining gene is TRBJ2-3. In various embodiments, a IPTS/128553107.1 Attorney Docket No: SRU-004WO variable gene is TRBV25-1; and a joining gene is TRBJ2-5. In various embodiments, a variable gene is TRBV25-1; and a joining gene is TRBJ2-7. [00198] In various embodiments, a variable gene is TRBV27; and a joining gene is selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7. In various embodiments, a variable gene is TRBV27; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV27; and a joining gene is TRBJ1-2. In various embodiments, a variable gene is TRBV27; and a joining gene is TRBJ1-3. In various embodiments, a variable gene is TRBV27; and a joining gene is TRBJ1-4. In various embodiments, a variable gene is TRBV27; and a joining gene is TRBJ2- 1. In various embodiments, a variable gene is TRBV27; and a joining gene is TRBJ2-2. In various embodiments, a variable gene is TRBV27; and a joining gene is TRBJ2-3. In various embodiments, a variable gene is TRBV27; and a joining gene is TRBJ2-5. In various embodiments, a variable gene is TRBV27; and a joining gene is TRBJ2-6. In various embodiments, a variable gene is TRBV27; and a joining gene is TRBJ2-7. [00199] In various embodiments, a variable gene is TRBV28; and a joining gene is TRBJ2-3. [00200] In various embodiments, a variable gene is TRBV29-1; and a joining gene is selected from any one of TRBJ1-1, TRBJ1-4, and TRBJ2-2. In various embodiments, a variable gene is TRBV29-1; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV29-1; and a joining gene is TRBJ1-4. In various embodiments, a variable gene is TRBV29-1; and a joining gene is TRBJ2-2. [00201] In various embodiments, a variable gene is TRBV30; and a joining gene is TRBJ2-7. [00202] In various embodiments, a variable gene is TRBV5-1; and a joining gene is selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. In various embodiments, a variable gene is TRBV5-1; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV5-1; and a joining gene is TRBJ1-2. In various embodiments, a variable gene is TRBV5-1; and a joining gene is TRBJ1-3. In various embodiments, a variable gene is TRBV5-1; and a joining gene is TRBJ1-4. In various embodiments, a variable gene is TRBV5-1; and a joining gene is TRBJ1-5. In various embodiments, a variable gene is TRBV5-1; and a joining gene is TRBJ1-6. In various embodiments, a variable gene is TRBV5-1; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV5-1; and a joining gene is TRBJ2-2. In various embodiments, a variable gene is TRBV5-1; and a joining gene is TRBJ2-3. In various embodiments, a variable gene is TRBV5-1; and a joining gene is TRBJ2-4. In various embodiments, a IPTS/128553107.1 Attorney Docket No: SRU-004WO variable gene is TRBV5-1; and a joining gene is TRBJ2-5. In various embodiments, a variable gene is TRBV5-1; and a joining gene is TRBJ2-6. In various embodiments, a variable gene is TRBV5-1; and a joining gene is TRBJ2-7. [00203] In various embodiments, a variable gene is TRBV5-4; and a joining gene is selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7. In various embodiments, a variable gene is TRBV5-4; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV5-4; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV5-4; and a joining gene is TRBJ2-7. [00204] In various embodiments, a variable gene is TRBV5-5; and a joining gene is selected from any one of TRBJ1-1, and TRBJ2-1. In various embodiments, a variable gene is TRBV5- 5; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV5-5; and a joining gene is TRBJ2-1. [00205] In various embodiments, a variable gene is TRBV5-6; and a joining gene is selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7. In various embodiments, a variable gene is TRBV5-6; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV5-6; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV5-6; and a joining gene is TRBJ2-7. [00206] In various embodiments, a variable gene is TRBV5-8; and a joining gene is selected from any one of TRBJ1-1, and TRBJ2-1. In various embodiments, a variable gene is TRBV5- 8; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV5-8; and a joining gene is TRBJ2-1. [00207] In various embodiments, a variable gene is TRBV6-1; and a joining gene is selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7. In various embodiments, a variable gene is TRBV6-1; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV6-1; and a joining gene is TRBJ2-2. In various embodiments, a variable gene is TRBV6-1; and a joining gene is TRBJ2-7. [00208] In various embodiments, a variable gene is TRBV6-4; and a joining gene is selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2-7. In various embodiments, a variable gene is TRBV6-4; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV6-4; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV6-4; and a joining gene is TRBJ2-2. In various embodiments, a variable gene is TRBV6-4; and a joining gene is TRBJ2-6. In various embodiments, a variable gene is TRBV6-4; and a joining gene is TRBJ2-7. IPTS/128553107.1 Attorney Docket No: SRU-004WO [00209] In various embodiments, a variable gene is TRBV6-5; and a joining gene is TRBJ2- 3. [00210] In various embodiments, a variable gene is TRBV6-6; and a joining gene is TRBJ2- 3. [00211] In various embodiments, a variable gene is TRBV7-2; and a joining gene is selected from any one of TRBJ2-3, and TRBJ2-5. In various embodiments, a variable gene is TRBV7- 2; and a joining gene is TRBJ2-3. In various embodiments, a variable gene is TRBV7-2; and a joining gene is TRBJ2-5. [00212] In various embodiments, a variable gene is TRBV7-4; and a joining gene is TRBJ2- 1. [00213] In various embodiments, a variable gene is TRBV7-6; and a joining gene is selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7. In various embodiments, a variable gene is TRBV7-6; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV7-6; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV7-6; and a joining gene is TRBJ2-7. [00214] In various embodiments, a variable gene is TRBV7-7; and a joining gene is selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7. In various embodiments, a variable gene is TRBV7-7; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV7-7; and a joining gene is TRBJ1-4. In various embodiments, a variable gene is TRBV7-7; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV7-7; and a joining gene is TRBJ2-7. [00215] In various embodiments, a variable gene is TRBV7-8; and a joining gene is selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2-7. In various embodiments, a variable gene is TRBV7-8; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV7-8; and a joining gene is TRBJ1-5. In various embodiments, a variable gene is TRBV7-8; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV7-8; and a joining gene is TRBJ2-5. In various embodiments, a variable gene is TRBV7-8; and a joining gene is TRBJ2-7. [00216] In various embodiments, a variable gene is TRBV7-9; and a joining gene is selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7. In various embodiments, a variable gene is TRBV7-9; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV7-9; and a joining gene is TRBJ1-4. In various embodiments, a variable gene is TRBV7-9; and a joining gene is TRBJ1-5. In various embodiments, a variable gene is TRBV7-9; and a joining gene is IPTS/128553107.1 Attorney Docket No: SRU-004WO TRBJ1-6. In various embodiments, a variable gene is TRBV7-9; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV7-9; and a joining gene is TRBJ2-2. In various embodiments, a variable gene is TRBV7-9; and a joining gene is TRBJ2-3. In various embodiments, a variable gene is TRBV7-9; and a joining gene is TRBJ2-4. In various embodiments, a variable gene is TRBV7-9; and a joining gene is TRBJ2-5. In various embodiments, a variable gene is TRBV7-9; and a joining gene is TRBJ2-7. [00217] In various embodiments, a variable gene is TRBV9; and a joining gene is selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, and TRBJ2-7. In various embodiments, a variable gene is TRBV9; and a joining gene is TRBJ1-1. In various embodiments, a variable gene is TRBV9; and a joining gene is TRBJ1-4. In various embodiments, a variable gene is TRBV9; and a joining gene is TRBJ2-1. In various embodiments, a variable gene is TRBV9; and a joining gene is TRBJ2-2. In various embodiments, a variable gene is TRBV9; and a joining gene is TRBJ2-3. In various embodiments, a variable gene is TRBV9; and a joining gene is TRBJ2-7. [00218] In various embodiments, a variable region is encoded for by a variable gene selected from any one of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9. In various embodiments, a variable region is encoded for by a variable gene TRBV11-3. In various embodiments, a variable region is encoded for by a variable gene TRBV13. In various embodiments, a variable region is encoded for by a variable gene TRBV14. In various embodiments, a variable region is encoded for by a variable gene TRBV18. In various embodiments, a variable region is encoded for by a variable gene TRBV19. In various embodiments, a variable region is encoded for by a variable gene TRBV2. In various embodiments, a variable region is encoded for by a variable gene TRBV20-1. In various embodiments, a variable region is encoded for by a variable gene TRBV25-1. In various embodiments, a variable region is encoded for by a variable gene TRBV27. In various embodiments, a variable region is encoded for by a variable gene TRBV28. In various embodiments, a variable region is encoded for by a variable gene TRBV29-1. In various embodiments, a variable region is encoded for by a variable gene TRBV30. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1. In various embodiments, a variable region is encoded for by a variable gene TRBV5-5. In various embodiments, a variable region is encoded for by a IPTS/128553107.1 Attorney Docket No: SRU-004WO variable gene TRBV5-6. In various embodiments, a variable region is encoded for by a variable gene TRBV5-8. In various embodiments, a variable region is encoded for by a variable gene TRBV6-1. In various embodiments, a variable region is encoded for by a variable gene TRBV7-2. In various embodiments, a variable region is encoded for by a variable gene TRBV6-4. In various embodiments, a variable region is encoded for by a variable gene TRBV6-5. In various embodiments, a variable region is encoded for by a variable gene TRBV6-6. In various embodiments, a variable region is encoded for by a variable gene TRBV7-4. In various embodiments, a variable region is encoded for by a variable gene TRBV7-6. In various embodiments, a variable region is encoded for by a variable gene TRBV7-7. In various embodiments, a variable region is encoded for by a variable gene TRBV7-8. In various embodiments, a variable region is encoded for by a variable gene TRBV7-9. In various embodiments, a variable region is encoded for by a variable gene TRBV9. [00219] In various embodiments, a variable region is encoded for by a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. In various embodiments, a variable region is encoded for by a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a joining gene TRBJ1-2. In various embodiments, a variable region is encoded for by a joining gene TRBJ1-3. In various embodiments, a variable region is encoded for by a joining gene TRBJ1-4. In various embodiments, a variable region is encoded for by a joining gene TRBJ1-5. In various embodiments, a variable region is encoded for by a joining gene TRBJ1-6. In various embodiments, a variable region is encoded for by a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a joining gene TRBJ2-2. In various embodiments, a variable region is encoded for by a joining gene TRBJ2-3. In various embodiments, a variable region is encoded for by a joining gene TRBJ2-4. In various embodiments, a variable region is encoded for by a joining gene TRBJ2-5. In various embodiments, a variable region is encoded for by a joining gene TRBJ2-6. In various embodiments, a variable region is encoded for by a joining gene TRBJ2-7. [00220] In various embodiments, a variable region is encoded for by a joining gene selected from any one of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; and the joining gene is selected IPTS/128553107.1 Attorney Docket No: SRU-004WO from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. [00221] In various embodiments, a variable region is encoded for by a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-2. In various embodiments, a variable region is encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-7. [00222] In various embodiments, a variable region is encoded for by a variable gene TRBV13; and a joining gene selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2- 2, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV13; and a joining gene TRBJ1-4. In various embodiments, a variable region is encoded for by a variable gene TRBV13; and a joining gene TRBJ1-5. In various embodiments, a variable region is encoded for by a variable gene TRBV13; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV13; and a joining gene TRBJ2-2. In various embodiments, a variable region is encoded for by a variable gene TRBV13; and a joining gene TRBJ2-3. In various embodiments, a variable region is encoded for by a variable gene TRBV13; and a joining gene TRBJ2-5. In various embodiments, a variable region is encoded for by a variable gene TRBV13; and a joining gene TRBJ2-7. [00223] In various embodiments, a variable region is encoded for by a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-2. In various embodiments, a variable region is encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-7. [00224] In various embodiments, a variable region is encoded for by a variable gene TRBV14; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2- 1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV14; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV14; and a joining gene TRBJ1-4. In various embodiments, a variable region is encoded for by a variable gene TRBV14; and a joining gene TRBJ1-5. In various embodiments, a variable region is encoded for by a variable gene TRBV14; and a joining gene TRBJ2-1. In various embodiments, a IPTS/128553107.1 Attorney Docket No: SRU-004WO variable region is encoded for by a variable gene TRBV14; and a joining gene TRBJ2-2. In various embodiments, a variable region is encoded for by a variable gene TRBV14; and a joining gene TRBJ2-3. In various embodiments, a variable region is encoded for by a variable gene TRBV14; and a joining gene TRBJ2-5. In various embodiments, a variable region is encoded for by a variable gene TRBV14; and a joining gene TRBJ2-7. [00225] In various embodiments, a variable region is encoded for by a variable gene TRBV18; and a joining gene selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1- 6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV18; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV18; and a joining gene TRBJ1-3. In various embodiments, a variable region is encoded for by a variable gene TRBV18; and a joining gene TRBJ1-5. In various embodiments, a variable region is encoded for by a variable gene TRBV18; and a joining gene TRBJ1-6. In various embodiments, a variable region is encoded for by a variable gene TRBV18; and a joining gene TRBJ2-2. In various embodiments, a variable region is encoded for by a variable gene TRBV18; and a joining gene TRBJ2-3. In various embodiments, a variable region is encoded for by a variable gene TRBV18; and a joining gene TRBJ2-5. In various embodiments, a variable region is encoded for by a variable gene TRBV18; and a joining gene TRBJ2-7. [00226] In various embodiments, a variable region is encoded for by a variable gene TRBV19; and a joining gene selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV19; and a joining gene TRBJ1-2. In various embodiments, a variable region is encoded for by a variable gene TRBV19; and a joining gene TRBJ1-6. In various embodiments, a variable region is encoded for by a variable gene TRBV19; and a joining gene TRBJ2-1. [00227] In various embodiments, a variable region is encoded for by a variable gene TRBV2; and a joining gene selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV2; and a joining gene TRBJ1-6. In various embodiments, a variable region is encoded for by a variable gene TRBV2; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV2; and a joining gene TRBJ2-7. [00228] In various embodiments, a variable region is encoded for by a variable gene TRBV20-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-3, and TRBJ2-5. In various embodiments, a variable region is encoded for by a variable gene TRBV20-1; and a joining gene TRBJ1-1. In various embodiments, a variable region is IPTS/128553107.1 Attorney Docket No: SRU-004WO encoded for by a variable gene TRBV20-1; and a joining gene TRBJ1-5. In various embodiments, a variable region is encoded for by a variable gene TRBV20-1; and a joining gene TRBJ2-3. In various embodiments, a variable region is encoded for by a variable gene TRBV20-1; and a joining gene TRBJ2-5. [00229] In various embodiments, a variable region is encoded for by a variable gene TRBV25-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV25-1; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV25-1; and a joining gene TRBJ2-3. In various embodiments, a variable region is encoded for by a variable gene TRBV25-1; and a joining gene TRBJ2-5. In various embodiments, a variable region is encoded for by a variable gene TRBV25-1; and a joining gene TRBJ2-7. [00230] In various embodiments, a variable region is encoded for by a variable gene TRBV27; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1- 4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV27; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV27; and a joining gene TRBJ1-2. In various embodiments, a variable region is encoded for by a variable gene TRBV27; and a joining gene TRBJ1-3. In various embodiments, a variable region is encoded for by a variable gene TRBV27; and a joining gene TRBJ1-4. In various embodiments, a variable region is encoded for by a variable gene TRBV27; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV27; and a joining gene TRBJ2-2. In various embodiments, a variable region is encoded for by a variable gene TRBV27; and a joining gene TRBJ2-3. In various embodiments, a variable region is encoded for by a variable gene TRBV27; and a joining gene TRBJ2-5. In various embodiments, a variable region is encoded for by a variable gene TRBV27; and a joining gene TRBJ2-6. In various embodiments, a variable region is encoded for by a variable gene TRBV27; and a joining gene TRBJ2-7. [00231] In various embodiments, a variable region is encoded for by a variable gene TRBV28; and a joining gene TRBJ2-3. [00232] In various embodiments, a variable region is encoded for by a variable gene TRBV29-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, and TRBJ2-2. In various embodiments, a variable region is encoded for by a variable gene TRBV29-1; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a IPTS/128553107.1 Attorney Docket No: SRU-004WO variable gene TRBV29-1; and a joining gene TRBJ1-4. In various embodiments, a variable region is encoded for by a variable gene TRBV29-1; and a joining gene TRBJ2-2. [00233] In various embodiments, a variable region is encoded for by a variable gene TRBV30; and a joining gene TRBJ2-7. [00234] In various embodiments, a variable region is encoded for by a variable gene TRBV5- 1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-2. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-3. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-4. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-5. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-6. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-2. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-3. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-4. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-5. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-6. In various embodiments, a variable region is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-7. [00235] In various embodiments, a variable region is encoded for by a variable gene TRBV5- 4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV5-4; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV5-4; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV5-4; and a joining gene TRBJ2-7. [00236] In various embodiments, a variable region is encoded for by a variable gene TRBV5- 5; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV5-5; and a joining IPTS/128553107.1 Attorney Docket No: SRU-004WO gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV5-5; and a joining gene TRBJ2-1. [00237] In various embodiments, a variable region is encoded for by a variable gene TRBV5- 6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV5-6; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV5-6; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV5-6; and a joining gene TRBJ2-7. [00238] In various embodiments, a variable region is encoded for by a variable gene TRBV5- 8; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV5-8; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV5-8; and a joining gene TRBJ2-1. [00239] In various embodiments, a variable region is encoded for by a variable gene TRBV6- 1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV6-1; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV6-1; and a joining gene TRBJ2-2. In various embodiments, a variable region is encoded for by a variable gene TRBV6-1; and a joining gene TRBJ2-7. [00240] In various embodiments, a variable region is encoded for by a variable gene TRBV6- 4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV6-4; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV6-4; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV6-4; and a joining gene TRBJ2-2. In various embodiments, a variable region is encoded for by a variable gene TRBV6-4; and a joining gene TRBJ2-6. In various embodiments, a variable region is encoded for by a variable gene TRBV6-4; and a joining gene TRBJ2-7. [00241] In various embodiments, a variable region is encoded for by a variable gene TRBV6- 5; and a joining gene TRBJ2-3. [00242] In various embodiments, a variable region is encoded for by a variable gene TRBV6- 6; and a joining gene TRBJ2-3. [00243] In various embodiments, a variable region is encoded for by a variable gene TRBV7- 2; and a joining gene selected from any one of TRBJ2-3, and TRBJ2-5. In various IPTS/128553107.1 Attorney Docket No: SRU-004WO embodiments, a variable region is encoded for by a variable gene TRBV7-2; and a joining gene TRBJ2-3. In various embodiments, a variable region is encoded for by a variable gene TRBV7-2; and a joining gene TRBJ2-5. [00244] In various embodiments, a variable region is encoded for by a variable gene TRBV7- 4; and a joining gene TRBJ2-1. [00245] In various embodiments, a variable region is encoded for by a variable gene TRBV7- 6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV7-6; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV7-6; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV7-6; and a joining gene TRBJ2-7. [00246] In various embodiments, a variable region is encoded for by a variable gene TRBV7- 7; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV7-7; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV7-7; and a joining gene TRBJ1-4. In various embodiments, a variable region is encoded for by a variable gene TRBV7-7; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV7-7; and a joining gene TRBJ2-7. [00247] In various embodiments, a variable region is encoded for by a variable gene TRBV7- 8; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV7-8; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV7-8; and a joining gene TRBJ1-5. In various embodiments, a variable region is encoded for by a variable gene TRBV7-8; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV7-8; and a joining gene TRBJ2-5. In various embodiments, a variable region is encoded for by a variable gene TRBV7-8; and a joining gene TRBJ2-7. [00248] In various embodiments, a variable region is encoded for by a variable gene TRBV7- 9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ1-4. In various embodiments, a variable region is encoded for by a variable IPTS/128553107.1 Attorney Docket No: SRU-004WO gene TRBV7-9; and a joining gene TRBJ1-5. In various embodiments, a variable region is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ1-6. In various embodiments, a variable region is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-2. In various embodiments, a variable region is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-3. In various embodiments, a variable region is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-4. In various embodiments, a variable region is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-5. In various embodiments, a variable region is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-7. [00249] In various embodiments, a variable region is encoded for by a variable gene TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2- 3, and TRBJ2-7. In various embodiments, a variable region is encoded for by a variable gene TRBV9; and a joining gene TRBJ1-1. In various embodiments, a variable region is encoded for by a variable gene TRBV9; and a joining gene TRBJ1-4. In various embodiments, a variable region is encoded for by a variable gene TRBV9; and a joining gene TRBJ2-1. In various embodiments, a variable region is encoded for by a variable gene TRBV9; and a joining gene TRBJ2-2. In various embodiments, a variable region is encoded for by a variable gene TRBV9; and a joining gene TRBJ2-3. In various embodiments, a variable region is encoded for by a variable gene TRBV9; and a joining gene TRBJ2-7. [00250] In various embodiments, a CDR3 is encoded for by a variable gene selected from any one of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25- 1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9. In various embodiments, a CDR3 is encoded for by a variable gene TRBV11-3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV13. In various embodiments, a CDR3 is encoded for by a variable gene TRBV14. In various embodiments, a CDR3 is encoded for by a variable gene TRBV18. In various embodiments, a CDR3 is encoded for by a variable gene TRBV19. In various embodiments, a CDR3 is encoded for by a variable gene TRBV2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV20-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV25-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV27. In various embodiments, a CDR3 is encoded for by a variable gene TRBV28. In various embodiments, a CDR3 is encoded for by a variable gene TRBV29- IPTS/128553107.1 Attorney Docket No: SRU-004WO 1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV30. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-6. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-8. In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7- 2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-4. In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-6. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-4. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-6. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7- 8. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-9. In various embodiments, a CDR3 is encoded for by a variable gene TRBV9. [00251] In various embodiments, a CDR3 is encoded for by a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a joining gene TRBJ1-2. In various embodiments, a CDR3 is encoded for by a joining gene TRBJ1-3. In various embodiments, a CDR3 is encoded for by a joining gene TRBJ1-4. In various embodiments, a CDR3 is encoded for by a joining gene TRBJ1-5. In various embodiments, a CDR3 is encoded for by a joining gene TRBJ1-6. In various embodiments, a CDR3 is encoded for by a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a joining gene TRBJ2-2. In various embodiments, a CDR3 is encoded for by a joining gene TRBJ2-3. In various embodiments, a CDR3 is encoded for by a joining gene TRBJ2-4. In various embodiments, a CDR3 is encoded for by a joining gene TRBJ2-5. In various embodiments, a CDR3 is encoded for by a joining gene TRBJ2-6. In various embodiments, a CDR3 is encoded for by a joining gene TRBJ2-7. [00252] In various embodiments, a CDR3 is encoded for by a joining gene selected from any one of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25- 1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; and the joining gene is selected from any one IPTS/128553107.1 Attorney Docket No: SRU-004WO of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2- 3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. [00253] In various embodiments, a CDR3 is encoded for by a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-7. [00254] In various embodiments, a CDR3 is encoded for by a variable gene TRBV13; and a joining gene selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV13; and a joining gene TRBJ1-4. In various embodiments, a CDR3 is encoded for by a variable gene TRBV13; and a joining gene TRBJ1-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV13; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV13; and a joining gene TRBJ2-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV13; and a joining gene TRBJ2-3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV13; and a joining gene TRBJ2-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV13; and a joining gene TRBJ2-7. [00255] In various embodiments, a CDR3 is encoded for by a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-7. [00256] In various embodiments, a CDR3 is encoded for by a variable gene TRBV14; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV14; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV14; and a joining gene TRBJ1-4. In various embodiments, a CDR3 is encoded for by a variable gene TRBV14; and a joining gene TRBJ1-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV14; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV14; and a joining gene TRBJ2-2. In various embodiments, a CDR3 is encoded for by a IPTS/128553107.1 Attorney Docket No: SRU-004WO variable gene TRBV14; and a joining gene TRBJ2-3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV14; and a joining gene TRBJ2-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV14; and a joining gene TRBJ2-7. [00257] In various embodiments, a CDR3 is encoded for by a variable gene TRBV18; and a joining gene selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1-6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV18; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV18; and a joining gene TRBJ1-3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV18; and a joining gene TRBJ1-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV18; and a joining gene TRBJ1-6. In various embodiments, a CDR3 is encoded for by a variable gene TRBV18; and a joining gene TRBJ2-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV18; and a joining gene TRBJ2-3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV18; and a joining gene TRBJ2-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV18; and a joining gene TRBJ2-7. [00258] In various embodiments, a CDR3 is encoded for by a variable gene TRBV19; and a joining gene selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV19; and a joining gene TRBJ1-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV19; and a joining gene TRBJ1-6. In various embodiments, a CDR3 is encoded for by a variable gene TRBV19; and a joining gene TRBJ2-1. [00259] In various embodiments, a CDR3 is encoded for by a variable gene TRBV2; and a joining gene selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV2; and a joining gene TRBJ1- 6. In various embodiments, a CDR3 is encoded for by a variable gene TRBV2; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV2; and a joining gene TRBJ2-7. [00260] In various embodiments, a CDR3 is encoded for by a variable gene TRBV20-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-3, and TRBJ2-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV20-1; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV20- 1; and a joining gene TRBJ1-5. In various embodiments, a CDR3 is encoded for by a variable IPTS/128553107.1 Attorney Docket No: SRU-004WO gene TRBV20-1; and a joining gene TRBJ2-3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV20-1; and a joining gene TRBJ2-5. [00261] In various embodiments, a CDR3 is encoded for by a variable gene TRBV25-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV25-1; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV25- 1; and a joining gene TRBJ2-3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV25-1; and a joining gene TRBJ2-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV25-1; and a joining gene TRBJ2-7. [00262] In various embodiments, a CDR3 is encoded for by a variable gene TRBV27; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV27; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV27; and a joining gene TRBJ1-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV27; and a joining gene TRBJ1-3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV27; and a joining gene TRBJ1-4. In various embodiments, a CDR3 is encoded for by a variable gene TRBV27; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV27; and a joining gene TRBJ2-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV27; and a joining gene TRBJ2-3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV27; and a joining gene TRBJ2-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV27; and a joining gene TRBJ2-6. In various embodiments, a CDR3 is encoded for by a variable gene TRBV27; and a joining gene TRBJ2-7. [00263] In various embodiments, a CDR3 is encoded for by a variable gene TRBV28; and a joining gene TRBJ2-3. [00264] In various embodiments, a CDR3 is encoded for by a variable gene TRBV29-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, and TRBJ2-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV29-1; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV29-1; and a joining gene TRBJ1-4. In various embodiments, a CDR3 is encoded for by a variable gene TRBV29-1; and a joining gene TRBJ2-2. [00265] In various embodiments, a CDR3 is encoded for by a variable gene TRBV30; and a joining gene TRBJ2-7. IPTS/128553107.1 Attorney Docket No: SRU-004WO [00266] In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5- 1; and a joining gene TRBJ1-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-4. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-6. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-4. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-6. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-7. [00267] In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-4; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-4; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-4; and a joining gene TRBJ2-7. [00268] In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-5; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-5; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-5; and a joining gene TRBJ2-1. [00269] In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-6; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-6; and IPTS/128553107.1 Attorney Docket No: SRU-004WO a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-6; and a joining gene TRBJ2-7. [00270] In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-8; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-8; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV5-8; and a joining gene TRBJ2-1. [00271] In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-1; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-1; and a joining gene TRBJ2-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-1; and a joining gene TRBJ2-7. [00272] In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2- 7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-4; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-4; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-4; and a joining gene TRBJ2-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-4; and a joining gene TRBJ2-6. In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-4; and a joining gene TRBJ2-7. [00273] In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-5; and a joining gene TRBJ2-3. [00274] In various embodiments, a CDR3 is encoded for by a variable gene TRBV6-6; and a joining gene TRBJ2-3. [00275] In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-2; and a joining gene selected from any one of TRBJ2-3, and TRBJ2-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-2; and a joining gene TRBJ2-3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-2; and a joining gene TRBJ2-5. [00276] In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-4; and a joining gene TRBJ2-1. IPTS/128553107.1 Attorney Docket No: SRU-004WO [00277] In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-6; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-6; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-6; and a joining gene TRBJ2-7. [00278] In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-7; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-7; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7- 7; and a joining gene TRBJ1-4. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-7; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-7; and a joining gene TRBJ2-7. [00279] In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-8; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2- 7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-8; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-8; and a joining gene TRBJ1-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-8; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-8; and a joining gene TRBJ2-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-8; and a joining gene TRBJ2-7. [00280] In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ1-4. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ1-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ1-6. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-9; and IPTS/128553107.1 Attorney Docket No: SRU-004WO a joining gene TRBJ2-4. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-5. In various embodiments, a CDR3 is encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-7. [00281] In various embodiments, a CDR3 is encoded for by a variable gene TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, and TRBJ2-7. In various embodiments, a CDR3 is encoded for by a variable gene TRBV9; and a joining gene TRBJ1-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV9; and a joining gene TRBJ1-4. In various embodiments, a CDR3 is encoded for by a variable gene TRBV9; and a joining gene TRBJ2-1. In various embodiments, a CDR3 is encoded for by a variable gene TRBV9; and a joining gene TRBJ2-2. In various embodiments, a CDR3 is encoded for by a variable gene TRBV9; and a joining gene TRBJ2- 3. In various embodiments, a CDR3 is encoded for by a variable gene TRBV9; and a joining gene TRBJ2-7. [00282] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene selected from any one of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV11-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV13. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV14. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV18. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV19. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV20-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV25-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV27. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV28. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV29-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV30. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1. In various embodiments, a TCR RFU comprises a IPTS/128553107.1 Attorney Docket No: SRU-004WO variable region encoded for by a variable gene TRBV5-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-6. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-8. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-6. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-6. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-8. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-9. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV9. [00283] In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene TRBJ1-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene TRBJ1-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene TRBJ1-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene TRBJ1-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene TRBJ1-6. In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene TRBJ2- 1. In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene TRBJ2-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene TRBJ2-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene TRBJ2-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene TRBJ2-5. In various embodiments, a TCR RFU IPTS/128553107.1 Attorney Docket No: SRU-004WO comprises a variable region encoded for by a joining gene TRBJ2-6. In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene TRBJ2-7. [00284] In various embodiments, a TCR RFU comprises a variable region encoded for by a joining gene selected from any one of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. [00285] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-7. [00286] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV13; and a joining gene selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV13; and a joining gene TRBJ1-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV13; and a joining gene TRBJ1-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV13; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV13; and a joining gene TRBJ2-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV13; and a joining gene TRBJ2-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV13; and a joining gene TRBJ2-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV13; and a joining gene TRBJ2-7. [00287] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-1. In various embodiments, a TCR IPTS/128553107.1 Attorney Docket No: SRU-004WO RFU comprises a variable region encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV11-3; and a joining gene TRBJ2-7. [00288] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV14; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV14; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV14; and a joining gene TRBJ1-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV14; and a joining gene TRBJ1-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV14; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV14; and a joining gene TRBJ2-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV14; and a joining gene TRBJ2-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV14; and a joining gene TRBJ2-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV14; and a joining gene TRBJ2-7. [00289] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV18; and a joining gene selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1-6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV18; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV18; and a joining gene TRBJ1-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV18; and a joining gene TRBJ1-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV18; and a joining gene TRBJ1-6. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV18; and a joining gene TRBJ2-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV18; and a joining gene TRBJ2-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV18; and a joining gene TRBJ2-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV18; and a joining gene TRBJ2-7. IPTS/128553107.1 Attorney Docket No: SRU-004WO [00290] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV19; and a joining gene selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV19; and a joining gene TRBJ1-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV19; and a joining gene TRBJ1-6. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV19; and a joining gene TRBJ2-1. [00291] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV2; and a joining gene selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV2; and a joining gene TRBJ1-6. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV2; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV2; and a joining gene TRBJ2-7. [00292] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV20-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-3, and TRBJ2-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV20-1; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV20-1; and a joining gene TRBJ1-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV20-1; and a joining gene TRBJ2-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV20-1; and a joining gene TRBJ2-5. [00293] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV25-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-3, TRBJ2-5, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV25-1; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV25-1; and a joining gene TRBJ2-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV25-1; and a joining gene TRBJ2-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV25-1; and a joining gene TRBJ2-7. [00294] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV27; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, IPTS/128553107.1 Attorney Docket No: SRU-004WO TRBJ1-3, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV27; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV27; and a joining gene TRBJ1-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV27; and a joining gene TRBJ1-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV27; and a joining gene TRBJ1-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV27; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV27; and a joining gene TRBJ2-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV27; and a joining gene TRBJ2-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV27; and a joining gene TRBJ2-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV27; and a joining gene TRBJ2-6. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV27; and a joining gene TRBJ2-7. [00295] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV28; and a joining gene TRBJ2-3. [00296] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV29-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, and TRBJ2-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV29-1; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV29-1; and a joining gene TRBJ1-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV29-1; and a joining gene TRBJ2-2. [00297] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV30; and a joining gene TRBJ2-7. [00298] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-2. In various embodiments, a TCR RFU comprises a IPTS/128553107.1 Attorney Docket No: SRU-004WO variable region encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1; and a joining gene TRBJ1-6. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-6. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-1; and a joining gene TRBJ2-7. [00299] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-4; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-4; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-4; and a joining gene TRBJ2-7. [00300] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-5; and a joining gene selected from any one of TRBJ1-1, and TRBJ2- 1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-5; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-5; and a joining gene TRBJ2-1. [00301] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-6; and a joining gene TRBJ1-1. In various embodiments, a TCR IPTS/128553107.1 Attorney Docket No: SRU-004WO RFU comprises a variable region encoded for by a variable gene TRBV5-6; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-6; and a joining gene TRBJ2-7. [00302] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-8; and a joining gene selected from any one of TRBJ1-1, and TRBJ2- 1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-8; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV5-8; and a joining gene TRBJ2-1. [00303] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-1; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-1; and a joining gene TRBJ2-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-1; and a joining gene TRBJ2-7. [00304] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-4; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-4; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-4; and a joining gene TRBJ2-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-4; and a joining gene TRBJ2-6. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-4; and a joining gene TRBJ2-7. [00305] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-5; and a joining gene TRBJ2-3. [00306] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV6-6; and a joining gene TRBJ2-3. [00307] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-2; and a joining gene selected from any one of TRBJ2-3, and TRBJ2- 5. In various embodiments, a TCR RFU comprises a variable region encoded for by a IPTS/128553107.1 Attorney Docket No: SRU-004WO variable gene TRBV7-2; and a joining gene TRBJ2-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-2; and a joining gene TRBJ2-5. [00308] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-4; and a joining gene TRBJ2-1. [00309] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-6; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-6; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-6; and a joining gene TRBJ2-7. [00310] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-7; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-7; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-7; and a joining gene TRBJ1-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-7; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-7; and a joining gene TRBJ2-7. [00311] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-8; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-8; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-8; and a joining gene TRBJ1-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-8; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-8; and a joining gene TRBJ2-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-8; and a joining gene TRBJ2-7. [00312] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, IPTS/128553107.1 Attorney Docket No: SRU-004WO TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-9; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-9; and a joining gene TRBJ1-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-9; and a joining gene TRBJ1-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-9; and a joining gene TRBJ1-6. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-5. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV7-9; and a joining gene TRBJ2-7. [00313] In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, and TRBJ2-7. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV9; and a joining gene TRBJ1-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV9; and a joining gene TRBJ1-4. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV9; and a joining gene TRBJ2-1. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV9; and a joining gene TRBJ2-2. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV9; and a joining gene TRBJ2-3. In various embodiments, a TCR RFU comprises a variable region encoded for by a variable gene TRBV9; and a joining gene TRBJ2-7. [00314] In various embodiments, an RFU comprises at least 1 TCRs as provided in Table 1. In various embodiments, an RFU comprises a set of TCRs as provided in Table 1. [00315] In various embodiments, a variable region comprises a CDR3 amino acid sequence having at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at IPTS/128553107.1 Attorney Docket No: SRU-004WO least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identity to any one of the CDR3 amino acid sequences as provided in Table 1. [00316] In various embodiments, a variable region comprises a CDR3 amino acid sequence having 100% identity to any one of the CDR3 amino acid sequences as provided in Table 1. In various embodiments, a variable region comprises a CDR3 amino acid sequence of any one of the CDR3 amino acid sequences as provided in Table 1. [00317] In various embodiments, a variable region comprises a CDR3 amino acid sequence comprising a formula of CAxxxxxxxx or CSxxxxxxxx, wherein a residue “C” is a cysteine, residue “A” is an alanine, residue “S” is a serine, and residue “x” is selected from any naturally occurring amino acid residue. [00318] In various embodiments, a variable region comprises a CDR3 amino acid sequence comprising the formula of CASxxxxx, and wherein a residue “C” is a cysteine, residue “A” is an alanine, residue “S” is a serine, and residue “x” is selected from any naturally occurring amino acid residue. [00319] In various embodiments, a variable region comprises a CDR3 amino acid sequence comprising the formula of CASSxxxx, CASTxxxx, or CASRxxxx, and wherein a residue “C” is a cysteine, residue “A” is an alanine, residue “S” is a serine, and residue “x” is selected from any naturally occurring amino acid residue. [00320] In various embodiments, a centroid sequence of a TCR RFU has an amino acid sequence having at least 50%, at least 51%, at least 52%, at least 53%, at least 54%, at least 55%, at least 56%, at least 57%, at least 58%, at least 59%, at least 60%, at least 61%, at least 62%, at least 63%, at least 64%, at least 65%, at least 66%, at least 67%, at least 68%, at least 69%, at least 70%, at least 71%, at least 72%, at least 73%, at least 74%, at least 75%, at least 76%, at least 77%, at least 78%, at least 79%, at least 80%, at least 81%, at least 82%, at least 83%, at least 84%, at least 85%, at least 86%, at least 87%, at least 88%, at least 89%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99% sequence identity to any one amino acid sequence of Table 1. In various embodiments, a centroid sequence of a TCR RFU has an amino acid sequence having at least 50%, at least 51%, at least 52%, at least 53%, at least 54%, at least 55%, at least 56%, at least 57%, at least 58%, at least 59%, at least 60%, at least 61%, at least 62%, at least 63%, at least 64%, at least 65%, at least 66%, at least 67%, at least 68%, at least 69%, at least 70%, at least 71%, at least 72%, at least 73%, at least 74%, at least 75%, at least 76%, at least 77%, at least 78%, at least 79%, at least 80%, at least 81%, at least 82%, at least 83%, at least 84%, at least 85%, at least 86%, at least 87%, at least 88%, at least 89%, at least 90%, at IPTS/128553107.1 Attorney Docket No: SRU-004WO least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99% sequence identity to any one of SEQ ID NO: 1-4129. In various embodiments, a centroid sequence of a TCR RFU has an amino acid sequence of any one of SEQ ID NO: 1-4129. [00321] In various embodiments, an RFU comprises a centroid, as provided in Table 1. The subsequent description refers to example centroids in terms of numerical values (e.g., centroid 553351, centroid 732995, etc). Such centroids can be found in Table 1 and can refer to a particular V gene, a particular J gene, and one or more CDR3 sequences. [00322] In various embodiments, an RFU comprises a centroid selected from any one of 553351, 732995, 737286, 769652, 797738, 1324227, 1711543, 2004290, 2233240, 2580270, 2856591, 2999056, 3063531, 3255860, 3728614, 3881120, 3977159, 4257692, 4295192, 4386473, 4402086, 4595243, 4595333, 5560011, 5564645, 5724479, 5855377, 6219783, 6379903, 6657679, 6719723, 7431509, 7843154, 8010813, 8319549, 9777324, 10977596, 11246629, 12020807, 12249132, 13051314, 13309737, 15034475, 15115253, 15146142, 15963481, 16338265, 16620295, 17401145, 17747189, 17755682, 18110868, 18126848, 18326650, 18720516, 18835165, 19224658, 19917515, 20126163, 20196055, 21271528, 21283918, 22352938, 22523755, 22923982, 22933283, 23672149, 25060192, 25101852, 25230864, 25436087, 26105207, 26126879, 26201256, 26337696, 26390375, 26583108, 27125955, 28776705, 29069975, 29391298, 30127178, 30192241, 32124785, 32292922, 32464479, 33502141, 33512860, 33756740, 33768194, 34278058, 34471960, 34740609, 35085524, 35154900, 35388439, 36336838, 37925825, 41686305, 41984178, 42081820, 42914861, 43372928, 43857192, 44678964, 45048096, 45145387, 45434343, 45852853, 45966055, 45969070, 46175298, 46433470, 46595059, 47142066, 47192821, 47564999, 47773979, 47884962, 48497220, 48497251, 49464516, 50342477, 50365583, 50444329, 50622746, 50848050, 50866479, 51059371, 51500635, 51952782, 52426379, 52655692, 52686689, 53291353, 53895951, 54218030, 54846433, 54902696, 55298744, 57261475, 60730674, 61360812, 61453550, 61630270, 62303170, 62871617, 63263795, 64636001, 65278715, 65707312, 65999885, 66235104, 68025624, 68037534, 68513904, or 69027540. [00323] In various embodiments, a centroid selected from any one of 553351, 732995, 737286, 769652, 797738, 1324227, 1711543, 2004290, 2233240, 2580270, 2856591, 2999056, 3063531, 3255860, 3728614, 3881120, 3977159, 4257692, 4295192, 4386473, 4402086, 4595243, 4595333, 5560011, 5564645, 5724479, 5855377, 6219783, 6379903, 6657679, 6719723, 7431509, 7843154, 8010813, 8319549, 9777324, 10977596, 11246629, 12020807, 12249132, 13051314, 13309737, 15034475, 15115253, 15146142, 15963481, IPTS/128553107.1 Attorney Docket No: SRU-004WO 16338265, 16620295, 17401145, 17747189, 17755682, 18110868, 18126848, 18326650, 18720516, 18835165, 19224658, 19917515, 20126163, 20196055, 21271528, 21283918, 22352938, 22523755, 22923982, 22933283, 23672149, 25060192, 25101852, 25230864, 25436087, 26105207, 26126879, 26201256, 26337696, 26390375, 26583108, 27125955, 28776705, 29069975, 29391298, 30127178, 30192241, 32124785, 32292922, 32464479, 33502141, 33512860, 33756740, 33768194, 34278058, 34471960, 34740609, 35085524, 35154900, 35388439, 36336838, 37925825, 41686305, 41984178, 42081820, 42914861, 43372928, 43857192, 44678964, 45048096, 45145387, 45434343, 45852853, 45966055, 45969070, 46175298, 46433470, 46595059, 47142066, 47192821, 47564999, 47773979, 47884962, 48497220, 48497251, 49464516, 50342477, 50365583, 50444329, 50622746, 50848050, 50866479, 51059371, 51500635, 51952782, 52426379, 52655692, 52686689, 53291353, 53895951, 54218030, 54846433, 54902696, 55298744, 57261475, 60730674, 61360812, 61453550, 61630270, 62303170, 62871617, 63263795, 64636001, 65278715, 65707312, 65999885, 66235104, 68025624, 68037534, 68513904, or 69027540, comprises an amino acid sequence as provided in Table 1. [00324] In various embodiments, a centroid selected from any one of 553351, 732995, 737286, 769652, 797738, 1324227, 1711543, 2004290, 2233240, 2580270, 2856591, 2999056, 3063531, 3255860, 3728614, 3881120, 3977159, 4257692, 4295192, 4386473, 4402086, 4595243, 4595333, 5560011, 5564645, 5724479, 5855377, 6219783, 6379903, 6657679, 6719723, 7431509, 7843154, 8010813, 8319549, 9777324, 10977596, 11246629, 12020807, 12249132, 13051314, 13309737, 15034475, 15115253, 15146142, 15963481, 16338265, 16620295, 17401145, 17747189, 17755682, 18110868, 18126848, 18326650, 18720516, 18835165, 19224658, 19917515, 20126163, 20196055, 21271528, 21283918, 22352938, 22523755, 22923982, 22933283, 23672149, 25060192, 25101852, 25230864, 25436087, 26105207, 26126879, 26201256, 26337696, 26390375, 26583108, 27125955, 28776705, 29069975, 29391298, 30127178, 30192241, 32124785, 32292922, 32464479, 33502141, 33512860, 33756740, 33768194, 34278058, 34471960, 34740609, 35085524, 35154900, 35388439, 36336838, 37925825, 41686305, 41984178, 42081820, 42914861, 43372928, 43857192, 44678964, 45048096, 45145387, 45434343, 45852853, 45966055, 45969070, 46175298, 46433470, 46595059, 47142066, 47192821, 47564999, 47773979, 47884962, 48497220, 48497251, 49464516, 50342477, 50365583, 50444329, 50622746, 50848050, 50866479, 51059371, 51500635, 51952782, 52426379, 52655692, 52686689, 53291353, 53895951, 54218030, 54846433, 54902696, 55298744, 57261475, 60730674, 61360812, 61453550, 61630270, 62303170, 62871617, 63263795, 64636001, 65278715, IPTS/128553107.1 Attorney Docket No: SRU-004WO 65707312, 65999885, 66235104, 68025624, 68037534, 68513904, or 69027540, comprises an amino acid sequence encoded for by a variable gene selected from any one of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. [00325] In various embodiments, a set of at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, or at least IPTS/128553107.1 Attorney Docket No: SRU-004WO 197 RFUs comprises centroids selected from any one, or any set, or pair of 553351, 732995, 737286, 769652, 797738, 1324227, 1711543, 2004290, 2233240, 2580270, 2856591, 2999056, 3063531, 3255860, 3728614, 3881120, 3977159, 4257692, 4295192, 4386473, 4402086, 4595243, 4595333, 5560011, 5564645, 5724479, 5855377, 6219783, 6379903, 6657679, 6719723, 7431509, 7843154, 8010813, 8319549, 9777324, 10977596, 11246629, 12020807, 12249132, 13051314, 13309737, 15034475, 15115253, 15146142, 15963481, 16338265, 16620295, 17401145, 17747189, 17755682, 18110868, 18126848, 18326650, 18720516, 18835165, 19224658, 19917515, 20126163, 20196055, 21271528, 21283918, 22352938, 22523755, 22923982, 22933283, 23672149, 25060192, 25101852, 25230864, 25436087, 26105207, 26126879, 26201256, 26337696, 26390375, 26583108, 27125955, 28776705, 29069975, 29391298, 30127178, 30192241, 32124785, 32292922, 32464479, 33502141, 33512860, 33756740, 33768194, 34278058, 34471960, 34740609, 35085524, 35154900, 35388439, 36336838, 37925825, 41686305, 41984178, 42081820, 42914861, 43372928, 43857192, 44678964, 45048096, 45145387, 45434343, 45852853, 45966055, 45969070, 46175298, 46433470, 46595059, 47142066, 47192821, 47564999, 47773979, 47884962, 48497220, 48497251, 49464516, 50342477, 50365583, 50444329, 50622746, 50848050, 50866479, 51059371, 51500635, 51952782, 52426379, 52655692, 52686689, 53291353, 53895951, 54218030, 54846433, 54902696, 55298744, 57261475, 60730674, 61360812, 61453550, 61630270, 62303170, 62871617, 63263795, 64636001, 65278715, 65707312, 65999885, 66235104, 68025624, 68037534, 68513904, and 69027540. VI. Biomarker Panel and Biomarkers [00326] In various embodiments, generating a cancer prediction involves implementing a second dataset. In various embodiments, generating a cancer prediction involves implementing a second dataset comprising a univariate biomarker panel. Therefore, the univariate biomarker panel includes one biomarker. In various embodiments, an example univariate biomarker panel can include any one of the biomarkers provided herein. In other embodiments, generating a cancer prediction involves implementing a second dataset comprising a multivariate biomarker panel. In such embodiments, the multivariate biomarker panel includes more than one biomarker. [00327] In various embodiments, the multivariate biomarker panel includes two biomarkers. In various embodiments, an example multivariate biomarker panel can include any of the biomarker combinations provided herein. In various embodiments, the multivariate IPTS/128553107.1 Attorney Docket No: SRU-004WO biomarker panel includes 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, or 400 biomarkers. In various embodiments, the multivariate biomarker panel includes at least 2 biomarkers, at least 5 biomarkers, at least 8 biomarkers, at least 10 biomarkers, at least 12 biomarkers, at least 15 biomarkers, at least 16 biomarkers, at least 18 biomarkers, at least 20 biomarkers, at least 21 biomarkers, at least 22 biomarkers, at least 23 biomarkers, at least 24 biomarkers, at least 25 biomarkers, at least 28 biomarkers, at least 30 biomarkers, at least 35 biomarkers, at least 40 biomarkers, at least 45 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers, at least 110 biomarkers, at least 120 biomarkers, at least 130 biomarkers, at least 140 biomarkers, at least 150 biomarkers, at least 175 biomarkers, at least 200 biomarkers, at least 250 biomarkers, at least 300 biomarkers, at least 350 biomarkers, or at least 400 biomarkers. [00328] Example biomarkers included in a biomarker panel can include one or more of, two or more of, three or more of, four or more of, five or more of, six or more of, seven or more of, eight or more of, nine or more of, ten or more of, eleven or more of, twelve or more of, thirteen or more of, fourteen or more of, fifteen or more of, sixteen or more of, seventeen or more of, eighteen or more of, nineteen or more of, twenty or more of, twenty or more of, twenty two or more of, twenty three or more of, twenty four or more of, or twenty five or more of Neurotrophin-3, Complement C3, Oxidized low-density lipoprotein receptor 1, Matrix metalloproteinase-9, Macrophage colony-stimulating factor 1, Oncostatin-M, Tumor necrosis factor receptor superfamily member 1A, WAP four-disulfide core domain protein 2, C-type lectin domain family 5 member A, S-methylmethionine--homocysteine S- methyltransferase BHMT2, Urokinase plasminogen activator surface receptor, Protransforming growth factor alpha, Zinc finger protein GLI2, Neutrophil collagenase, Tumor necrosis factor receptor superfamily member 3, Interleukin-8,Monocyte differentiation antigen CD14, Protein shisa-5, CD59 glycoprotein, Neural proliferation differentiation and control protein 1, C-X-C motif chemokine 9, C-C motif chemokine 23, Collagen alpha-1(IV) chain, Placenta growth factor, Growth/differentiation factor 15, Collagen alpha-1(XVIII) chain, Natural cytotoxicity triggering receptor 3 ligand 1, Stromal cell-derived factor 1, Hepatitis A virus cellular receptor 2, Huntingtin-interacting protein 1- related protein, Retinoid-binding protein 7, Kunitz-type protease inhibitor 1, Latent- IPTS/128553107.1 Attorney Docket No: SRU-004WO transforming growth factor beta-binding protein 2, Calbindin, RNA binding protein fox-1 homolog 3, Occludin, GDNF family receptor alpha-1, Follistatin-related protein 3, Ephrin- A1, Basigin, Leucine-rich alpha-2-glycoprotein, Tumor necrosis factor receptor superfamily member 19L, Fibrinogen alpha chain, Inter-alpha-trypsin inhibitor heavy chain H3, Metalloproteinase inhibitor 1, Tumor necrosis factor receptor superfamily member 1B, Carcinoembryonic antigen-related cell adhesion molecule 8, MAM domain-containing protein 2, Interleukin-6, Folate receptor alpha, Carcinoembryonic antigen-related cell adhesion molecule 5, Osteopontin, Macrophage-capping protein, Galectin-9, NPC intracellular cholesterol transporter 2, Gamma-interferon-inducible lysosomal thiol reductase, Elastin, Macrophage metalloelastase, V-set and immunoglobulin domain-containing protein 4, Nectin-2, Mitotic spindle assembly checkpoint protein MAD1, Tumor necrosis factor receptor superfamily member 27, Tumor necrosis factor receptor superfamily member 10B, Survival of motor neuron-related-splicing factor 30, Prostasin, C-X-C motif chemokine 17, Receptor-type tyrosine-protein phosphatase F, Tumor necrosis factor receptor superfamily member 10A, Cystatin-B, Triggering receptor expressed on myeloid cells 2, Syndecan-1, Desmocollin-2, Nucleoside diphosphate kinase A, Lamin-B2, Cytoskeleton-associated protein 4, Ephrin type-B receptor 4, Layilin, Delta-like protein 1, Bone marrow proteoglycan, Seizure 6-like protein 2, Collectin-12, UL16-binding protein 2, Beta-1,4-galactosyltransferase 1, Hydroxyacylglutathione hydrolase, mitochondrial, Neutrophil gelatinase-associated lipocalin, All-trans retinoic acid-induced differentiation factor, Interleukin-1 receptor antagonist protein, Transcriptional coactivator YAP1, Tumor necrosis factor ligand superfamily member 13, Cystatin-C, Tumor necrosis factor receptor superfamily member 4, C-C motif chemokine 18, DNA-directed RNA polymerases I, II, and III subunit RPABC2, Ephrin type-A receptor 2, Signal-regulatory protein beta-1, Ganglioside GM2 activator, U2 small nuclear ribonucleoprotein B'', Inter-alpha-trypsin inhibitor heavy chain H4, Fibulin-2, Tumor necrosis factor receptor superfamily member 9, Cadherin-2, Interleukin-18-binding protein, Spliceosome-associated protein CWC15 homolog, Ephrin-A4, Glial fibrillary acidic protein, A disintegrin and metalloproteinase with thrombospondin motifs 16, Secretogranin- 1, Amphiregulin, C-C motif chemokine 14, Carcinoembryonic antigen-related cell adhesion molecule 6, Ribonuclease pancreatic, Serine protease inhibitor Kazal-type 1, CD302 antigen, Kallikrein-7, Neuropilin-2, Integrin beta-like protein 1, Myeloblastin, Agrin, Regulator of chromosome condensation, Thrombospondin-2, Protein disulfide isomerase CRELD1, EGF- containing fibulin-like extracellular matrix protein 1, Lysosome membrane protein 2, Complement component C9, Coiled-coil-helix-coiled-coil-helix domain-containing protein IPTS/128553107.1 Attorney Docket No: SRU-004WO 10, mitochondrial, EF-hand domain-containing protein D1, Fibrinogen-like protein 1, Interleukin-10 receptor subunit beta, Kallikrein-4, Septin-8, Trefoil factor 3, Cytokine receptor-like factor 1, Collagen alpha-3(VI) chain, Oxygen-dependent coproporphyrinogen- III oxidase, mitochondrial, Disintegrin and metalloproteinase domain-containing protein 8, C4b-binding protein beta chain, C-X-C motif chemokine 16, Leukocyte-associated immunoglobulin-like receptor 1, Scavenger receptor class F member 2, Serpin B8, Interleukin-4 receptor subunit alpha, CD276 antigen, Cadherin-23, Angiopoietin-2, Serine/threonine-protein kinase receptor R3, Cathepsin L2, Polypeptide N- acetylgalactosaminyltransferase 5, E3 SUMO-protein ligase RanBP2, Vasorin, von Willebrand factor A domain-containing protein 1, Ribonuclease K6, Apolipoprotein A-II, Intercellular adhesion molecule 1, Interleukin-2 receptor subunit alpha, Zinc finger and BTB domain-containing protein 17, Oncostatin-M-specific receptor subunit beta, GrpE protein homolog 1, mitochondrial, Insulin-like growth factor-binding protein 4, Vascular cell adhesion protein 1, Azurocidin, Cathepsin D, Ribonuclease T2, Complement component C1q receptor, Sushi domain-containing protein 5, SLAM family member 8, C-C motif chemokine 26, Insulin-like growth factor-binding protein 2, E3 ubiquitin-protein ligase RNF149, Tyrosine-protein kinase Mer, Protein S100-A11, Sushi, nidogen and EGF-like domain- containing protein 1, Carcinoembryonic antigen-related cell adhesion molecule 21, E3 ubiquitin-protein ligase UHRF2, Beta-Ala-His dipeptidase, Nectin-4, Polymeric immunoglobulin receptor, Sprouty-related, EVH1 domain-containing protein 2, Vasoactive intestinal polypeptide receptor 1, Galactoside 3(4)-L-fucosyltransferase and Alpha-(1,3)- fucosyltransferase 5, Protein S100-A12, Tumor necrosis factor receptor superfamily member 11B, Interferon gamma receptor 1, Nucleophosmin, Actin, aortic smooth muscle, Keratin, type I cytoskeletal 19, Sialic acid-binding Ig-like lectin 5, Lysosome-associated membrane glycoprotein 3, CD166 antigen, HLA class II histocompatibility antigen gamma chain, Proline-rich transmembrane protein 3, Integrin alpha-5, Trans-Golgi network integral membrane protein 2, CUB domain-containing protein 1, Creatine kinase B-type, Protein S100-P, Serpin A11, Paired immunoglobulin-like type 2 receptor alpha, Annexin A1, Band 3 anion transport protein, Neutrophil cytosol factor 2, Pentraxin-related protein PTX3, Lymphocyte-specific protein 1, CMRF35-like molecule 8, C-type lectin domain family 7 member A, Lysophosphatidylcholine acyltransferase 2, Neuropilin-1, MICOS complex subunit MIC25, Alpha-1-antichymotrypsin, Tumor necrosis factor receptor superfamily member 21, Dipeptidyl peptidase 1, Leukocyte immunoglobulin-like receptor subfamily B member 4, Nibrin, Complement decay-accelerating factor, Beta-2-microglobulin, Arginase-1, IPTS/128553107.1 Attorney Docket No: SRU-004WO Tumor necrosis factor receptor superfamily member 16, 26S proteasome non-ATPase regulatory subunit 1, Signal recognition particle 14 kDa protein, Integrin beta-6, AMP deaminase 3, CMRF35-like molecule 2, Polycystin-2, Stanniocalcin-2, GTP cyclohydrolase 1 feedback regulatory protein, Peptidoglycan recognition protein 1, Paired immunoglobulin- like type 2 receptor beta, Cadherin-3, Nicotinamide riboside kinase 2, Mothers against decapentaplegic homolog 1, Discoidin, CUB and LCCL domain-containing protein 2, Cysteine-rich motor neuron 1 protein, Heparan-sulfate 6-O-sulfotransferase 2, Tumor necrosis factor receptor superfamily member 8, 1,25-dihydroxyvitamin D(3) 24-hydroxylase, mitochondrial, BH3-interacting domain death agonist, Glutaredoxin-1, Tumor necrosis factor receptor superfamily member 14, Dipeptidase 2, Coagulation factor IX, Prostaglandin-H2 D- isomerase, Complement C2, Erythroid membrane-associated protein, Insulin-like growth factor-binding protein-like 1, Cystatin-SN, Elongin-A, Mucin-13, Interleukin-1 receptor type 1, Protein S100-A3, Phosphoinositide-3-kinase-interacting protein 1, Vascular non- inflammatory molecule 2, Thiopurine S-methyltransferase, Angiopoietin-related protein 3, Asialoglycoprotein receptor 1, Bone morphogenetic protein 4, C-type lectin domain family 4 member D, Basement membrane-specific heparan sulfate proteoglycan core protein, C-C motif chemokine 3, CMRF35-like molecule 1, Collagen alpha-1(XXVIII) chain, C-X-C motif chemokine 10, Glutaminyl-peptide cyclotransferase, TGF-beta receptor type-2, Collagen alpha-1(XXIV) chain, Cadherin-6, CMRF35-like molecule 6, Follistatin, Myosin-binding protein C, fast-type, BTB/POZ domain-containing protein KCTD5, Granulocyte colony- stimulating factor, Interleukin-27, Zinc transporter ZIP14, Interleukin-7, Carbonic anhydrase 1, Torsin-1A-interacting protein 1, Chitinase-3-like protein 1, Protein DGCR6, Tenascin, C- type lectin domain family 4 member G, Colipase, Beta-enolase, Epsin-1, Receptor-type tyrosine-protein phosphatase N2, Pro-adrenomedullin, Leukotriene A-4 hydrolase, Treacle protein, T-cell immunoglobulin and mucin domain-containing protein 4, C-C motif chemokine 28, Kallikrein-11, Kallikrein-6, Lymphatic vessel endothelial hyaluronic acid receptor 1, Protein-glutamine gamma-glutamyltransferase 2, Secreted frizzled-related protein 3, Disintegrin and metalloproteinase domain-containing protein 9, Alpha-hemoglobin- stabilizing protein, C-C motif chemokine 2, Egl nine homolog 1, Macrophage mannose receptor 1, Microtubule-associated tumor suppressor 1, 40S ribosomal protein S10, Tumor- associated calcium signal transducer 2, Serum amyloid A-4 protein, SLIT and NTRK-like protein 6, Citron Rho-interacting kinase, Tumor necrosis factor receptor superfamily member 19, MICOS complex subunit MIC60, Alpha-1-acid glycoprotein 1, Collagen triple helix repeat-containing protein 1, Dyslexia-associated protein KIAA0319, Butyrophilin subfamily IPTS/128553107.1 Attorney Docket No: SRU-004WO 2 member A1, Alpha-1B-glycoprotein, Draxin, Fibroblast growth factor 6, Semaphorin-3F, Stanniocalcin-1, Basal cell adhesion molecule, Chromatin complexes subunit BAP18, C-C motif chemokine 16, Dickkopf-related protein 3, Podocalyxin-like protein 2, von Willebrand factor, Pseudokinase FAM20A, Density-regulated protein, Insulin-like growth factor-binding protein 7, Growth/differentiation factor 8, Enolase-phosphatase E1, Tetraspanin-1, EF-hand calcium-binding domain-containing protein 14, Protein AMBP, Complement C1r subcomponent-like protein, Interleukin-5, Tumor necrosis factor ligand superfamily member 14, Hepatitis A virus cellular receptor 1, Tumor necrosis factor receptor superfamily member 12A, Collagen alpha-1(III) chain, G-patch domain and KOW motifs-containing protein, MANSC domain-containing protein 1, Protein sel-1 homolog 1, Periostin, PDZ domain- containing protein GIPC2, Dual adapter for phosphotyrosine and 3-phosphotyrosine and 3- phosphoinositide, Decorin, Tumor necrosis factor receptor superfamily member 6, Putative oxidoreductase GLYR1, Lipocalin-15, Neurofilament light polypeptide, Ubiquitin carboxyl- terminal hydrolase 28, Chondroadherin, Corticoliberin, Phenazine biosynthesis-like domain- containing protein, Proliferating cell nuclear antigen, Granulocyte-macrophage colony- stimulating factor, Lymphokine-activated killer T-cell-originated protein kinase, Brain- derived neurotrophic factor, Inactive tyrosine-protein kinase transmembrane receptor ROR1, Ficolin-1, Angiopoietin-related protein 4, Protein ZNRD2, Fractalkine, Myosin-7B, NAD kinase, Ras-related protein Rab-44, Tumor necrosis factor receptor superfamily member 11A, Tumor necrosis factor receptor superfamily member 6B, CXADR-like membrane protein, Histone deacetylase 8, Immunoglobulin superfamily member 8, Paralemmin-2, Reversion- inducing cysteine-rich protein with Kazal motifs, C-type lectin domain family 14 member A, Peptidyl-prolyl cis-trans isomerase FKBP1B, Interleukin-13 receptor subunit alpha-1, Protein Wnt-9a, Phospholipid transfer protein C2CD2L, Coiled-coil domain-containing protein 80, Phospholipase A2, membrane associated, U4/U6.U5 tri-snRNP-associated protein 1, Kin of IRRE-like protein 2, C-C motif chemokine 4, Interleukin-18 receptor 1, Neogenin, Leucine- rich repeat transmembrane protein FLRT2, Tissue factor pathway inhibitor 2, Delta(14)-sterol reductase LBR, Immunoglobulin superfamily containing leucine-rich repeat protein 2, Leukocyte cell-derived chemotaxin-2, Pancreatic prohormone, Alpha-1-antitrypsin, Brorin, Protein FAM3C, Porphobilinogen deaminase, Lamin-B1, Brain-specific serine protease 4, Calcitonin gene-related peptide 2, C-C motif chemokine 7, Cathepsin L1, Folate receptor beta, Prosaposin, Semaphorin-7A, N-acetylgalactosaminyltransferase 7, Cytosolic 5'- nucleotidase 1A, Fibroblast growth factor receptor 4, Flavin reductase (NADPH), BPI fold- containing family B member 2, CCN family member 3, G-protein coupled receptor family C IPTS/128553107.1 Attorney Docket No: SRU-004WO group 5 member C ,Phosphatidylinositol 4,5-bisphosphate 5-phosphatase A, Fibroblast growth factor receptor 2, CD83 antigen, Scrapie-responsive protein 1, Aldehyde dehydrogenase, dimeric NADP-preferring, Cytokine-like protein 1, Osteoclast-associated immunoglobulin-like receptor, Pleckstrin homology-like domain family B member 1, Tumor necrosis factor ligand superfamily member 11, Appetite-regulating hormone, Ribonucleoside- diphosphate reductase subunit M2, Adhesion G-protein coupled receptor G1, Tyrosine- protein kinase receptor UFO, Carbonic anhydrase 14, Complement factor H, Interleukin-6 receptor subunit alpha, Galectin-3, Spondin-2, Calcyphosin, dCTP pyrophosphatase 1, Macrophage scavenger receptor types I and II, Retinoic acid receptor responder protein 2, Sodium channel protein type 3 subunit alpha, VPS10 domain-containing receptor SorCS2, Secretogranin-2, Beta-crystallin B2, DnaJ homolog subfamily A member 4, Leukocyte immunoglobulin-like receptor subfamily A member 5, Renin, Cochlin, C-type lectin domain family 11 member A, Corticotropin-releasing factor-binding protein, Phenylalanine--tRNA ligase alpha subunit, Nephrin, Melanoma antigen preferentially expressed in tumors, Peroxiredoxin-2, C-X-C motif chemokine 13, Asialoglycoprotein receptor 2, Protein BRICK1, Retinoid-inducible serine carboxypeptidase, Neuroendocrine secretory protein 55, Bcl-2-like protein 15, Uncharacterized protein C9orf40, Immunoglobulin superfamily member 2, Cathepsin Z, Endothelial cell-specific molecule 1, Cadherin-17, Complement C5, Serum paraoxonase/arylesterase 1, Olfactomedin-4, Opticin, Paralemmin-1, Inactive pancreatic lipase-related protein 1, Paxillin, Ras/Rap GTPase-activating protein SynGAP, Beta-microseminoprotein, Hephaestin, Neugrin, Cell growth regulator with EF hand domain protein 1, Leukocyte immunoglobulin-like receptor subfamily B member 2, Neuritin, Branched-chain-amino-acid aminotransferase, mitochondrial, Heterogeneous nuclear ribonucleoprotein U-like protein 1, Early placenta insulin-like peptide, Myeloperoxidase, and Periplakin. [00329] In various embodiments, the biomarkers of a biomarker panel comprise two or more biomarkers selected from TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, IL6, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise two or more biomarkers selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise two or more biomarkers selected from TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various IPTS/128553107.1 Attorney Docket No: SRU-004WO embodiments, the biomarkers of a biomarker panel comprise IL6. In various embodiments, the biomarkers of a biomarker panel comprise TGFA. In various embodiments, the biomarkers of a biomarker panel comprise S100A12. In various embodiments, the biomarkers of a biomarker panel comprise OSM. In various embodiments, the biomarkers of a biomarker panel comprise TFPI2. In various embodiments, the biomarkers of a biomarker panel comprise LSP1. In various embodiments, the biomarkers of a biomarker panel comprise MDK. In various embodiments, the biomarkers of a biomarker panel comprise CXCL9. In various embodiments, the biomarkers of a biomarker panel comprise CLEC4D. In various embodiments, the biomarkers of a biomarker panel comprise HGF. In various embodiments, the biomarkers of a biomarker panel comprise VWA1. In various embodiments, the biomarkers of a biomarker panel comprise CEACAM5. In various embodiments, the biomarkers of a biomarker panel comprise MMP12. In various embodiments, the biomarkers of a biomarker panel comprise KRT19. In various embodiments, the biomarkers of a biomarker panel comprise CASP8. In various embodiments, the biomarkers of a biomarker panel comprise WFDC2. In various embodiments, the biomarkers of a biomarker panel comprise PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise ALPP. [00330] In various embodiments, the biomarkers of a biomarker panel comprise IL6 and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6 and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise TGFA and at least one more biomarker selected from IL6, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise S100A12 and at least one more biomarker selected from IL6, TGFA, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise OSM and at least one more biomarker selected from IL6, TGFA, S100A12, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise TFPI2 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, LSP1, IPTS/128553107.1 Attorney Docket No: SRU-004WO MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise LSP1 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise MDK and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise CXCL9 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise CLEC4D and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise HGF and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise VWA1 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, CEACAM5, MMP12, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise CEACAM5 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, MMP12, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise MMP12 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, KRT19, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise KRT19 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, CASP8, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise CASP8 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, WFDC2, ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise WFDC2 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, IPTS/128553107.1 Attorney Docket No: SRU-004WO ALPP, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise ALPP and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise PLAUR and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, ALPP, and WFDC2. [00331] In various embodiments, the biomarkers of a biomarker panel comprise IL6 and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6 and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise TGFA and at least one more biomarker selected from IL6, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise S100A12 and at least one more biomarker selected from IL6, TGFA, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise OSM and at least one more biomarker selected from IL6, TGFA, S100A12, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise TFPI2 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise LSP1 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise MDK and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise CXCL9 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various IPTS/128553107.1 Attorney Docket No: SRU-004WO embodiments, the biomarkers of a biomarker panel comprise CLEC4D and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise HGF and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise VWA1 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise CEACAM5 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise MMP12 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise KRT19 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise CASP8 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise WFDC2 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise PLAUR and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, and WFDC2. [00332] In various embodiments, the biomarkers of a biomarker panel comprise IL6 and at least one more biomarker selected from TGFA, S100A12, OSM, LSP1, MDK, CXCL9, HGF, CEACAM5, MMP12, KRT19, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6 and at least one more biomarker selected from TGFA, S100A12, OSM, LSP1, MDK, CXCL9, HGF, CEACAM5, MMP12, KRT19, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise TGFA and at least one more biomarker selected from IL6, S100A12, OSM, LSP1, IPTS/128553107.1 Attorney Docket No: SRU-004WO MDK, CXCL9, HGF, CEACAM5, MMP12, KRT19, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise S100A12 and at least one more biomarker selected from IL6, TGFA, OSM, LSP1, MDK, CXCL9, HGF, CEACAM5, MMP12, KRT19, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise OSM and at least one more biomarker selected from IL6, TGFA, S100A12, LSP1, MDK, CXCL9, HGF, CEACAM5, MMP12, KRT19, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise LSP1 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, MDK, CXCL9, HGF, CEACAM5, MMP12, KRT19, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise MDK and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, LSP1, CXCL9, HGF, CEACAM5, MMP12, KRT19, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise CXCL9 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, LSP1, MDK, HGF, CEACAM5, MMP12, KRT19, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise HGF and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, LSP1, MDK, CXCL9, CEACAM5, MMP12, KRT19, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise CEACAM5 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, LSP1, MDK, CXCL9, HGF, MMP12, KRT19, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise MMP12 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, LSP1, MDK, CXCL9, HGF, CEACAM5, KRT19, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise KRT19 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, LSP1, MDK, CXCL9, HGF, CEACAM5, MMP12, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise WFDC2 and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, LSP1, MDK, CXCL9, HGF, CEACAM5, MMP12, KRT19, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise PLAUR and at least one more biomarker selected from IL6, TGFA, S100A12, OSM, LSP1, MDK, CXCL9, HGF, CEACAM5, MMP12, KRT19, and WFDC2. [00333] In various embodiments, the plurality of biomarkers is selected from IL6, LSP1, MDK, MMP12; CEACAM5, IL6, MDK, MMP12, TGFA; HGF, IL6, MDK, MMP12, TGFA; CEACAM5, IL6, MDK, TGFA; IL6, MDK, MMP12, OSM; IL6, MDK, MMP12, TGFA; CEACAM5, IL6, LSP1, MDK, TGFA; HGF, IL6, MDK, MMP12, OSM; HGF, IL6, IPTS/128553107.1 Attorney Docket No: SRU-004WO LSP1, MDK, MMP12; IL6, KRT19, MDK, MMP12, TGFA; HGF, IL6, LSP1, MDK; IL6, LSP1, MDK; IL6, LSP1, MDK, TGFA; IL6, MDK, TGFA; CXCL9, IL6, LSP1, MDK; CEACAM5, IL6, MDK, OSM, TGFA; CEACAM5, HGF, IL6, MDK, TGFA; CEACAM5, IL6, MDK, OSM; CEACAM5, IL6, MDK, MMP12, OSM; HGF, IL6, LSP1, MDK, TGFA; CEACAM5, IL6, LSP1, MDK; CEACAM5, IL6, MDK, S100A12, TGFA; HGF, IL6, LSP1, MDK, OSM; CEACAM5, HGF, IL6, MDK, OSM; IL6, LSP1, MDK, MMP12, TGFA; IL6, MDK, MMP12, OSM, TGFA; CEACAM5, IL6, MDK, TGFA, WFDC2; CXCL9, IL6, LSP1, MDK, MMP12; IL6, LSP1, MDK, MMP12, OSM; IL6, KRT19, LSP1, MDK, TGFA; IL6, LSP1, MDK, TGFA, WFDC2; CEACAM5, IL6, LSP1, MDK, MMP12; CEACAM5, IL6, MDK, PLAUR, TGFA; HGF, IL6, MDK, TGFA; or IL6, MDK, TGFA, WFDC2. In various embodiments, the plurality of biomarkers comprises IL6, LSP1, MDK, and MMP12. In various embodiments, the plurality of biomarkers comprises CEACAM5, IL6, MDK, MMP12, and TGFA. In various embodiments, the plurality of biomarkers comprises HGF, IL6, MDK, MMP12, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, IL6, MDK, and TGFA. In various embodiments, the plurality of biomarkers comprises IL6, MDK, MMP12, and OSM. In various embodiments, the plurality of biomarkers comprises IL6, MDK, MMP12, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, IL6, LSP1, MDK, and TGFA. In various embodiments, the plurality of biomarkers comprises HGF, IL6, MDK, MMP12, and OSM. In various embodiments, the plurality of biomarkers comprises HGF, IL6, LSP1, MDK, and MMP12. In various embodiments, the plurality of biomarkers comprises IL6, KRT19, MDK, MMP12, and TGFA. In various embodiments, the plurality of biomarkers comprises HGF, IL6, LSP1, and MDK. In various embodiments, the plurality of biomarkers comprises IL6, LSP1, and MDK. In various embodiments, the plurality of biomarkers comprises IL6, LSP1, MDK, and TGFA. In various embodiments, the plurality of biomarkers comprises IL6, MDK, and TGFA. In various embodiments, the plurality of biomarkers comprises CXCL9, IL6, LSP1, and MDK. In various embodiments, the plurality of biomarkers comprises CEACAM5, IL6, MDK, OSM, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, HGF, IL6, MDK, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, IL6, MDK, and OSM. In various embodiments, the plurality of biomarkers comprises CEACAM5, IL6, MDK, MMP12, and OSM. In various embodiments, the plurality of biomarkers comprises HGF, IL6, LSP1, MDK, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, IL6, LSP1, and MDK. In various embodiments, the plurality of biomarkers IPTS/128553107.1 Attorney Docket No: SRU-004WO comprises CEACAM5, IL6, MDK, S100A12, and TGFA. In various embodiments, the plurality of biomarkers comprises HGF, IL6, LSP1, MDK, and OSM. In various embodiments, the plurality of biomarkers comprises CEACAM5, HGF, IL6, MDK, and OSM. In various embodiments, the plurality of biomarkers comprises IL6, LSP1, MDK, MMP12, and TGFA. In various embodiments, the plurality of biomarkers comprises IL6, MDK, MMP12, OSM, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, IL6, MDK, TGFA, and WFDC2. In various embodiments, the plurality of biomarkers comprises CXCL9, IL6, LSP1, MDK, and MMP12. In various embodiments, the plurality of biomarkers comprises IL6, LSP1, MDK, MMP12, and OSM. In various embodiments, the plurality of biomarkers comprises IL6, KRT19, LSP1, MDK, and TGFA. In various embodiments, the plurality of biomarkers comprises IL6, LSP1, MDK, TGFA, and WFDC2. In various embodiments, the plurality of biomarkers comprises CEACAM5, IL6, LSP1, MDK, and MMP12. In various embodiments, the plurality of biomarkers comprises CEACAM5, IL6, MDK, PLAUR, and TGFA. In various embodiments, the plurality of biomarkers comprises HGF, IL6, MDK, and TGFA. In various embodiments, the plurality of biomarkers comprises IL6, MDK, TGFA, and WFDC2. [00334] In various embodiments, the biomarkers of a biomarker panel comprise IL6 and MDK, and at least one more biomarker selected from MMP12, LSP1, CEACAM5, HGF, OSM, and KRT19. In various embodiments, the plurality of biomarkers comprises IL6, LSP1, MDK, and MMP12. In various embodiments, the plurality of biomarkers comprises CEACAM5, IL6, MDK, MMP12, and TGFA. In various embodiments, the plurality of biomarkers comprises HGF, IL6, MDK, MMP12, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, IL6, MDK, and TGFA. In various embodiments, the plurality of biomarkers comprises IL6, MDK, MMP12, and OSM. In various embodiments, the plurality of biomarkers comprises IL6, MDK, MMP12, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, IL6, LSP1, MDK, and TGFA. In various embodiments, the plurality of biomarkers comprises HGF, IL6, MDK, MMP12, and OSM. In various embodiments, the plurality of biomarkers comprises HGF, IL6, LSP1, MDK, and MMP12. In various embodiments, the plurality of biomarkers comprises IL6, KRT19, MDK, MMP12, and TGFA. [00335] In various embodiments, the plurality of biomarkers comprise three or more of TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, IL6, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the plurality of biomarkers comprise four or more, five or more, six or more, seven or more, eight IPTS/128553107.1 Attorney Docket No: SRU-004WO or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, or seventeen or more of TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, IL6, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the plurality of biomarkers comprise each of TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, IL6, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the plurality of biomarkers consist of TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, IL6, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. [00336] In various embodiments, the plurality of biomarkers comprise three or more of TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, IL6, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and TGFA, and at least one more biomarker selected from S100A12, OSM, TFPI2, LSP1, CXCL9, CLEC4D, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and S100A12, and at least one more biomarker selected from TGFA, OSM, TFPI2, LSP1, CXCL9, CLEC4D, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and OSM, and at least one more biomarker selected from TGFA, S100A12, TFPI2, LSP1, CXCL9, CLEC4D, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and TFPI2, and at least one more biomarker selected from TGFA, S100A12, OSM, LSP1, CXCL9, CLEC4D, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and LSP1, and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, CXCL9, CLEC4D, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and CXCL9, and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, CLEC4D, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and CLEC4D, and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, CXCL9, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the IPTS/128553107.1 Attorney Docket No: SRU-004WO biomarkers of a biomarker panel comprise IL6, MDK, and ALPP, and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and HGF, and at least one more biomarker selected from TGFA, S100A12 , OSM, TFPI2, LSP1, CXCL9, CLEC4D, ALPP, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and VWA1, and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, CXCL9, CLEC4D, ALPP, HGF, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and CEACAM5, and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, CXCL9, CLEC4D, ALPP, HGF, VWA1, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and MMP12, and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, CXCL9, CLEC4D, ALPP, HGF, VWA1, CEACAM5, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and KRT19, and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, CXCL9, CLEC4D, ALPP, HGF, VWA1, CEACAM5, MMP12, CASP8, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and CASP8, and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, CXCL9, CLEC4D, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, WFDC2, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and WFDC2, and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, CXCL9, CLEC4D, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, and PLAUR. In various embodiments, the biomarkers of a biomarker panel comprise IL6, MDK, and PLAUR, and at least one more biomarker selected from TGFA, S100A12, OSM, TFPI2, LSP1, CXCL9, CLEC4D, ALPP, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, and WFDC2. [00337] In various embodiments, the plurality of biomarkers comprise four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, or sixteen or more of TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, IL6, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the plurality of biomarkers comprise each of TGFA, S100A12, OSM, TFPI2, LSP1, MDK, IPTS/128553107.1 Attorney Docket No: SRU-004WO CXCL9, CLEC4D, IL6, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. In various embodiments, the plurality of biomarkers consist of TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, IL6, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. [00338] In various embodiments, the plurality of biomarkers comprises CEACAM5, HGF, IL6, MDK, MMP12, OSM, PLAUR, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, CXCL9, HGF, IL6, LSP1, MDK, MMP12, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, HGF, IL6, KRT19, LSP1, MDK, PLAUR, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, HGF, IL6, LSP1, MDK, OSM, PLAUR, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, HGF, IL6, LSP1, MDK, MMP12, PLAUR, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, CXCL9, HGF, IL6, LSP1, MDK, MMP12, PLAUR, S100A12, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, HGF, IL6, LSP1, MDK, MMP12, OSM, PLAUR, S100A12, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, CXCL9, HGF, IL6, KRT19, LSP1, MDK, MMP12, TGFA, and WFDC2. In various embodiments, the plurality of biomarkers comprises CEACAM5, CXCL9, HGF, IL6, KRT19, LSP1, MDK, MMP12, PLAUR, and TGFA. In various embodiments, the plurality of biomarkers comprises CEACAM5, HGF, IL6, MDK, MMP12, OSM, PLAUR, S100A12, TGFA, and WFDC2. In various embodiments, the plurality of biomarkers comprises CEACAM5, CXCL9, HGF, IL6, KRT19, LSP1, MDK, MMP12, OSM, PLAUR, S100A12, TFPI2, TGFA, VWA1, and WFDC2. In various embodiments, the plurality of biomarkers comprises CEACAM5, CLEC4D, CXCL9, HGF, IL6, KRT19, LSP1, MDK, MMP12, OSM, PLAUR, S100A12, TFPI2, TGFA, and WFDC2. In various embodiments, the plurality of biomarkers comprises CASP8, CEACAM5, CXCL9, HGF, IL6, KRT19, LSP1, MDK, MMP12, OSM, PLAUR, S100A12, TFPI2, TGFA, and VWA1. In various embodiments, the plurality of biomarkers comprises CASP8, CEACAM5, CXCL9, HGF, IL6, KRT19, LSP1, MDK, MMP12, OSM, PLAUR, TFPI2, TGFA, VWA1, and WFDC2. In various embodiments, the plurality of biomarkers comprises CEACAM5, CLEC4D, CXCL9, HGF, IL6, KRT19, LSP1, MDK, MMP12, OSM, PLAUR, S100A12, TGFA, VWA1, and WFDC2. In various embodiments, the plurality of biomarkers comprises CASP8, CEACAM5, CLEC4D, CXCL9, HGF, IL6, KRT19, LSP1, MDK, MMP12, OSM, PLAUR, S100A12, TFPI2, TGFA, VWA1, and WFDC2. IPTS/128553107.1 Attorney Docket No: SRU-004WO VII. Circulating tumor DNA [00339] In various embodiments, generating a cancer prediction involves implementing a third dataset. In various embodiments, generating a cancer prediction involves implementing a third dataset comprising an additional biomarker panel (e.g., a circulating tumor DNA (ctDNA) panel). [00340] In various embodiments, the ctDNA panel includes at least one mutation of interest. In various embodiments, an example ctDNA panel can include any of the mutations of interest provided herein. In various embodiments, the ctDNA panel includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 mutations of interest. In various embodiments, the ctDNA panel includes at least 1 mutations of interest, at least 2 mutations of interest, at least 3 mutations of interest, at least 4 mutations of interest, at least 5 mutations of interest, at least 6 mutations of interest, at least 7 mutations of interest, at least 8 mutations of interest, at least 9 mutations of interest, at least 10 mutations of interest, at least 11 mutations of interest, at least 12 mutations of interest, at least 13 mutations of interest, at least 14 mutations of interest, at least 15 mutations of interest, at least 16 mutations of interest, at least 17 mutations of interest, at least 18 mutations of interest, at least 19 mutations of interest, at least 20 mutations of interest, at least 21 mutations of interest, at least 22 mutations of interest, at least 23 mutations of interest, at least 24 mutations of interest, or at least 25 mutations of interest. [00341] In various embodiments, the mutation of interest comprises frameshift mutation, a missense mutation, a synonymous mutation, a splice site mutation, a nonsense mutation, or any combination thereof. In various embodiments, the mutation of interest is a frameshift mutation. In various embodiments, the mutation of interest is a missense mutation. In various embodiments, the mutation of interest is a synonymous mutation. In various embodiments, the mutation of interest is a splice site mutation. In various embodiments, the mutation of interest is a nonsense mutation. [00342] In various embodiments, the mutations of interest is a mutation of a gene selected from CDKN2A, MGAM, PIK3CA, EPHB1, PAK5, KEAP1, TP53, KRAS, KDM5A, ATM, PTEN, or any combination thereof. In various embodiments, the mutations of interest is a mutation of the CDKN2A gene. In various embodiments, the mutations of interest is a mutation of the MGAM gene. In various embodiments, the mutations of interest is a mutation of the PIK3CA gene. In various embodiments, the mutations of interest is a mutation of the EPHB1 gene. In various embodiments, the mutations of interest is a mutation of the PAK5 gene. In various embodiments, the mutations of interest is a mutation of the KEAP1 gene. In IPTS/128553107.1 Attorney Docket No: SRU-004WO various embodiments, the mutations of interest is a mutation of the TP53 gene. In various embodiments, the mutations of interest is a mutation of the KRAS gene. In various embodiments, the mutations of interest is a mutation of the KDM5A gene. In various embodiments, the mutations of interest is a mutation of the ATM gene. In various embodiments, the mutations of interest is a mutation of the PTEN gene. [00343] In various embodiments, the mutation of interest is the A21 frame shift mutation of the CDKN2A gene. In various embodiments, the mutation of interest is the R1097C missense mutation of the MGAM gene. In various embodiments, the mutation of interest is the H1047R missense mutation of the PIK3CA gene. In various embodiments, the mutation of interest is the R327S missense mutation of the EPHB1 gene. In various embodiments, the mutation of interest is the A434A synonymous mutation of the PAK5 gene. In various embodiments, the mutation of interest is the G462W missense mutation of the KEAP1 gene. In various embodiments, the mutation of interest is the R267P missense mutation of the TP53 gene. In various embodiments, the mutation of interest is the G105A missense mutation of the TP53 gene. In various embodiments, the mutation of interest is the R273L missense mutation of the TP53 gene. In various embodiments, the mutation of interest is the T125T synonymous mutation of the TP53 gene. In various embodiments, the mutation of interest is the S90 frame shift mutation of the TP53 gene. In various embodiments, the mutation of interest is the Y220C missense mutation of the TP53 gene. In various embodiments, the mutation of interest is the I195N missense mutation of the TP53 gene. In various embodiments, the mutation of interest is the W91C missense mutation of the TP53 gene. In various embodiments, the mutation of interest is the R306 nonsense mutation of the TP53 gene. In various embodiments, the mutation of interest is the A161T missense mutation of the TP53 gene. In various embodiments, the mutation of interest is the V272M missense mutation of the TP53 gene. In various embodiments, the mutation of interest is the D259Y missense mutation of the TP53 gene. In various embodiments, the mutation of interest is the G12A missense mutation of the KRAS gene. In various embodiments, the mutation of interest is the G12C missense mutation of the KRAS gene. In various embodiments, the mutation of interest is the R337C missense mutation of the ATM gene. [00344] In various embodiments, the mutation of interest can be selected from any one in Table 7, or any combination thereof. IPTS/128553107.1 Attorney Docket No: SRU-004WO VIII. Assays [00345] As shown in FIG.1A, the system environment 100 involves implementing a TCR quantification assay 120 for evaluating identities of the plurality of TCRs. Examples of an assay (e.g., TCR quantification assay 120) for one or more TCRs include DNA assays, amplification-based assays, polymerase chain reaction (PCR), reverse transcription PCR (RT- PCR), real-time PCR (qPCR), reverse transcription quantitative PCR (RT-qPCR), digital PCR (dPCR), reverse transcription digital PCR (RT-dPCR), loop-mediated isothermal amplification (LAMP), nucleic acid sequence-based amplification (NASBA), rolling circle amplification (RCA), and strand displacement amplification (SDA), amplification based- assays, Sanger sequencing, next-generation sequencing (NGS), Illumina sequencing, Ion Torrent sequnencing, PacBio single-molecule real-time (SMRT) sequencing, Oxford nanopore sequencing, whole-genome sequencing, whole-exome sequencing, RNA-seq, ChIP- seq, methyl-seq, targeted sequencing, or single-cell sequencing. In particular embodiments, the TCR quantification assay 120 involves performing TCR-sequencing (TCR-seq). Further example details of performing TCR-seq assays are described in WO2018107178, which is incorporated by reference in its entirety. [00346] The ImmunoSeq® assay utilizes next-generation sequencing (NGS) to profile T-cell receptor (TCR) and B-cell receptor (BCR) repertoires in high resolution. The assay enables the identification, quantification, and tracking of individual TCR and BCR sequences present in a biological sample, providing comprehensive insights into the adaptive immune system and its response to various physiological and pathological conditions. DNA is extracted from a biological sample, such as blood, tissue, or sorted immune cells (T cells or B cells). The extracted DNA undergoes targeted amplification of the TCR or BCR genes, specifically focusing on the hypervariable complementarity-determining region 3 (CDR3) that plays a crucial role in antigen recognition. This step utilizes specially designed primer sets that cover the variable (V), diversity (D), and joining (J) gene segments, enabling comprehensive coverage of the immune receptor repertoire. The amplified TCR or BCR genes are subjected to high-throughput, massively parallel sequencing using NGS platforms. This allows for the identification and quantification of individual TCR or BCR sequences within the sample. The generated sequencing data are processed and analyzed using specialized bioinformatics tools and algorithms. This includes sequence alignment, error correction, and the generation of a detailed immune receptor repertoire profile that contains information on clonality, diversity, and the relative abundance of individual TCR or BCR sequences. IPTS/128553107.1 Attorney Docket No: SRU-004WO [00347] Illumina® sequencing library construction and hybridization-based target capture using Integrated DNA Technologies (IDT) xGen™ reagents can be used to prepare the DNA samples for next-generation sequencing on Illumina platforms. The starting DNA material is first fragmented into smaller pieces, either using a mechanical method (e.g., sonication) or an enzymatic method (e.g., using a DNA shearing enzyme). The optimal size range for Illumina sequencing libraries is typically between 150 and 500 base pairs. The fragmented DNA ends are repaired to generate blunt ends, and a single adenine (A) nucleotide is added to the 3' ends. This step prepares the DNA fragments for ligation to adapter sequences that contain a complementary thymine (T) overhang. Illumina-specific adapters, which contain sequences necessary for cluster generation and indexing, are ligated to the repaired and A-tailed DNA fragments. The adapter-ligated DNA fragments are size-selected to remove excess adapters and obtain a library with a consistent insert size. This is usually achieved using magnetic beads or gel-based size selection methods. The adapter-ligated DNA library undergoes limited-cycle PCR amplification to enrich the fragments containing adapters on both ends and to incorporate sample-specific indices (barcodes) that allow for multiplexing during sequencing. [00348] At this point, the Illumina sequencing library is constructed. If the goal is to sequence specific genomic regions or genes, hybridization-based target capture using IDT xGen™ reagents can be employed. Biotinylated xGen™ Lockdown Probes, which are custom-designed to target specific genomic regions or genes of interest, are mixed with the prepared sequencing library. The library is then denatured, allowing the single-stranded DNA fragments to hybridize to the complementary probes under optimized hybridization conditions. Streptavidin-coated magnetic beads are added to the hybridization mixture. Since the xGen™ probes are biotinylated, they bind to the streptavidin on the magnetic beads, capturing the desired target sequences along with them. The magnetic beads are washed to remove non-specifically bound DNA fragments and any remaining excess probes, ensuring that only the target sequences of interest remain captured on the beads. The captured target DNA sequences are eluted from the magnetic beads and subjected to a final round of PCR amplification to generate sufficient material for sequencing on Illumina platforms. The final target-captured library is now ready for quality control, quantification, and sequencing using Illumina® next-generation sequencing instruments, such as the MiSeq, HiSeq, or NovaSeq. [00349] Additional non-limiting examples of assays include Southern blots, Northern blots, antibody-binding assays, enzyme-linked immunosorbent assays (ELISAs), flow cytometry, protein assays, Western blots, nephelometry, turbidimetry, chromatography, mass IPTS/128553107.1 Attorney Docket No: SRU-004WO spectrometry, immunoassays, including, by way of example, but not limitation, RIA, immunofluorescence, immunochemiluminescence, immunoelectrochemiluminescence, or competitive immunoassays, immunoprecipitation, and the assays described in the Examples section below. [00350] The information from the assay can be quantitative and sent to a computer system. The information can also be qualitative, such as observing patterns or fluorescence, which can be translated into a quantitative measure by a user or automatically by a reader or computer system. [00351] Various immunoassays designed to quantitate biomarkers can be used in screening including multiplex assays (e.g., an assay which simultaneously measures multiple analytes in a single cycle of the assay). Measuring the concentration of a target marker in a sample or fraction thereof can be accomplished by a variety of specific assays. For example, a conventional sandwich type assay can be used in an array, ELISA, RIA, etc. format. Other immunoassays include Ouchterlony plates that provide a simple determination of antibody binding. Additionally, Western blots can be performed on protein gels or protein spots on filters, using a detection system specific for the markers as desired, conveniently using a labeling method. [00352] Protein based analysis, using an antibody that specifically binds to a polypeptide (e.g. marker), can be used to quantify the marker level in a test sample obtained from a subject. In various embodiments, an antibody that binds to a marker can be a monoclonal antibody. In various embodiments, an antibody that binds to a marker can be a polyclonal antibody. In various embodiments, both monoclonal and polyclonal antibodies are used to bind polypeptides for the protein based analysis. [00353] For multiplex analysis of markers, arrays containing one or more marker affinity reagents, e.g. antibodies can be generated. Such an array can be constructed comprising antibodies against markers. Detection can utilize one or a panel of marker affinity reagents, e.g. a panel or cocktail of affinity reagents specific for one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty one, or more markers. [00354] In various embodiments, the multiplex assay involves the use of oligonucleotide labeled antibody probes that bind to target biomarkers and allow for subsequent quantification of biomarkers. One example of a multiplex assay that involves oligonucleotide labeled antibody probes is the Proximity Extension Assay (PEA) technology (Olink Proteomics). Briefly, a pair of oligonucleotide labeled antibodies bind to a biomarker, IPTS/128553107.1 Attorney Docket No: SRU-004WO wherein the two oligonucleotide sequences are complementary to one another. Thus, when both antibodies bind to the target biomarker, the oligonucleotide sequences hybridize with one another. Mismatched oligonucleotide sequences (which occurs due to non-specific binding of antibodies or cross-reactivity of antibodies) will not hybridize and therefore, will not result in a readout. Hybridized oligonucleotide sequences undergo nucleic acid extension and amplification, followed by quantification using microfluidic qPCR. The quantified levels correlate to the quantitative expression values of the respective biomarkers. Further details of the Olink Proximity Extension Assay (PEA) is described in Wik, L., et al. (2021). Proximity Extension Assay in Combination with Next-Generation Sequencing for High-throughput Proteome-wide Analysis. Molecular & cellular proteomics : MCP, 20, 100168, which is hereby incorporated by reference in its entirety. [00355] In various embodiments, the multiplex assay involves the use of bead conjugated antibodies (e.g., capture antibodies) that enable the binding and detection of biomarkers. One example of a multiplex assay involving bead conjugated antibodies is Luminex’s xMAP® Technology. Here, bead conjugated antibodies are added to the sample along with biotinylated detection antibodies. Both antibodies are specific to the biomarkers of interest and therefore, form an antibody-antigen sandwich. Streptavidin is further added, which binds to the biotinylated detection antibodies and enables detection of the complex. The Luminex 200™ or FlexMap® analyzer are employed to identify and quantify the amount of the biomarker in the sample. In various embodiments, the multiplex assay represents an improvement over Luminex’s xMAP® technology, such as the Multi-Analyte Profile (MAP) technology by Myriad Rules Based Medicine (RBM), Inc. [00356] In various embodiments, the multiplex assay involves the use of single molecule array (SIMOA) testing. For example, the assay may use paramagnetic particles coupled with antibodies that exhibit binding specificity to specific protein biomarkers. Detection antibodies are added which bind with the protein biomarkers to form fluorescent products. Thus, immunocomplexes including the paramagnetic bead, bound protein biomarker, and detection antibody are generated. Immunocomplexes are loaded into arrays (e.g., microarrays) in which individual immunocomplexes are separately localized. Next, enzymatic signal amplification occurs and fluorescent imaging is performed to capture the read out from the respective immunocomplexes in the microarray. This enables detection and/or quantification of individual protein biomarkers that were present in the sample. An example of such a multiplex assay is the SIMOA Bead-based assay from Quanterix™. IPTS/128553107.1 Attorney Docket No: SRU-004WO [00357] In various embodiments, the multiplex assay involves performing mass spectrometry based protein/peptide measurements. For example, in one embodiment, nanoparticles are engineered with surface physicochemical properties which enable protein biomarker binding to the surface of the magnetic nanoparticles. Here, a protein corona is formed on the surface of the nanoparticle composed of varying biomarker proteins. Nanoparticles can be synthesized with varying surface physicochemical properties to achieve differing protein coronas. Nanoparticle protein corona purification is performed using a magnet and corona proteins are digested. Mass spectrometry e.g., LC-MS/MS can be performed to determine presence and/or quantity of protein/peptide biomarkers. An example of such a multiplex assay is the Seer Proteograph Assay kit using the SP100 Automation Instrument for analyzing protein biomarkers. Further details of profiling proteomes using nanoparticle protein coronas is described in Blume, J. et al, “Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona.” Nat Commun 11, 3662 (2020), which is hereby incorporated by reference in its entirety. [00358] In various embodiments, the multiplex assay involves using an aptamer based approach. For example, the assay can use chemically modified aptamers for detecting and discovering protein biomarkers. For example, modified aptamer reagents are synthesized with a fluorophore, cleavable linker, and biotin molecule. The modified aptamer can bind and capture protein biomarkers, while the biotin molecule binds to a corresponding streptavidin bead. Bound protein biomarkers are further tagged with biotin molecules and the cleavable linker is cleaved to release the protein biomarker – aptamer conjugate from the streptavidin bead. A polyanionic competitor is added to prevent rebinding of non-specific complexes. Protein biomarkers are recaptured on streptavidin beads via the biotin molecule and fluorophores are measured to read out protein biomarker presence/quantity. An example of such a multiplex assay is the SOMAscan® assay. Further details of the SOMAscan® assay is described in Gold, L., et al., (2010). Aptamer-based multiplexed proteomic technology for biomarker discovery. PloS one, 5(12), e15004, which is hereby incorporated by reference in its entirety. [00359] In various embodiments, prior to implementation of a TCR quantification assay 120 (e.g., a sequencing-based assay), a sample obtained from a subject can be processed. In various embodiments, processing the sample enables the implementation of the TCR quantification assay 120 to more accurately evaluate identities of the plurality of TCRs in the sample. IPTS/128553107.1 Attorney Docket No: SRU-004WO [00360] In various embodiments, the sample from a subject can be processed to extract pluralities of TCRs from the sample. In one embodiment, the sample can undergo phase separation to separate the pluralities of TCRs from other portions of the sample. For example, the sample can undergo centrifugation (e.g., pelleting or density gradient centrifugation) to separate larger and/or more dense entities in the sample (e.g., cells and other macromolecules) from the pluralities of TCRs. Other examples include filtration (e.g., ultrafiltration) to phase separate the pluralities of TCRs from other portions of the sample. [00361] In various embodiments, the sample from a subject can be processed to produce a sub-sample with a fraction of the pluralities of TCRs that were in the sample. IX. Example Cancers [00362] Methods described herein involve implementing pluralities of TCRs for generating a cancer prediction, such as a prediction of presence, absence, or likelihood of cancer (e.g., early stage cancer or non-early stage cancer). In various embodiments, the pluralities of TCRs described herein are implemented to predict presence, absence, or likelihood of a cancer, such as a lung cancer. In various embodiments, the pluralities of TCRs described herein are implemented to generate a prediction informative for early detection of a cancer, such as an early stage lung cancer or non-early stage lung cancer. [00363] In various embodiments, the pluralities of TCRs described herein are implemented to predict the likelihood of cancer, wherein the likelihood is very low risk, low risk, moderate risk, high risk, or very high risk. [00364] In various embodiments, the cancer is a lung cancer. In some embodiments, the lung cancer is an adenocarcinoma, an adenosquamous cell cancer, a large cell cancer, a neuroendocrine cancer, a non-small cell lung cancer (NSCLC), a small cell cancer, or a squamous cell cancer. In some embodiments, the lung cancer is an adenocarcinoma. In some embodiments, the lung cancer is an adenosquamous cell cancer. In some embodiments, the lung cancer is a large cell cancer. In some embodiments, the lung cancer is a neuroendocrine cancer. In some embodiments, the lung cancer is a non-small cell lung cancer (NSCLC). In some embodiments, the lung cancer is a small cell cancer. In some embodiments, the lung cancer is a squamous cell cancer. [00365] In various embodiments, pluralities of TCRs described herein generate a cancer prediction for a particular stage of lung cancer, such as a stage 0, stage 1, stage 2, stage 3, or stage 4 lung cancer. In particular embodiments, pluralities of TCRs disclosed herein are useful for generating a cancer prediction informative for early detection of lung cancer, such IPTS/128553107.1 Attorney Docket No: SRU-004WO as early detection of the lung cancer while the lung cancer is a stage 0, stage 1, stage 2. In various embodiments, pluralities of TCRs described herein generate a cancer prediction for a particular subtype of lung cancer, including any one of adenocarcinoma, squamous lung cancer, neuroendocrine, small cell lung cancer, non-small cell lung cancer, large cell lung cancer, or adenosquamous carcinoma. [00366] In various embodiments, any method, non-transitory computer readable medium, system, or kit provided herein optionally comprises administering a treatment to the subject. In various embodiments, the treatment comprises a surgery, a chemotherapy, a radiation therapy, a targeted therapy, or any combination thereof. In various embodiments, the treatment comprises a surgery. In various embodiments, the treatment comprises a chemotherapy. In various embodiments, the treatment comprises a radiation therapy. In various embodiments, the treatment comprises a targeted therapy. [00367] In various embodiments, the methods disclosed herein optionally comprise administering a treatment to the subject. In various embodiments, the non-transitory computer readable medium disclosed herein optionally comprises administering a treatment to the subject. In various embodiments, the systems disclosed herein optionally comprise administering a treatment to the subject. In various embodiments, the kits disclosed herein optionally comprise administering a treatment to the subject. In various embodiments, the treatment comprises a surgery, a chemotherapy, a radiation therapy, a targeted therapy, or any combination thereof. In various embodiments, the treatment comprises a surgery. In various embodiments, the treatment comprises a chemotherapy. In various embodiments, the treatment comprises a radiation therapy. In various embodiments, the treatment comprises a targeted therapy. [00368] In various embodiments, the methods disclosed herein optionally comprise administering a treatment to the subject, wherein the treatment comprises a surgery, a chemotherapy, a radiation therapy, a targeted therapy, immunotherapy, or any combination thereof. In various embodiments, the non-transitory computer readable medium disclosed herein optionally comprises administering a treatment to the subject, wherein the treatment comprises a surgery, a chemotherapy, a radiation therapy, a targeted therapy, immunotherapy, or any combination thereof. In various embodiments, the systems disclosed herein optionally comprise administering a treatment to the subject, wherein the treatment comprises a surgery, a chemotherapy, a radiation therapy, a targeted therapy, immunotherapy, or any combination thereof. In various embodiments, the kits disclosed herein optionally comprise administering IPTS/128553107.1 Attorney Docket No: SRU-004WO a treatment to the subject, wherein the treatment comprises a surgery, a chemotherapy, a radiation therapy, a targeted therapy, immunotherapy, or any combination thereof. X. Computer Implementation [00369] The methods disclosed herein, such as the methods of generating a prediction of cancer in a subject, are, in some embodiments, performed on one or more computers. For example, the building and deployment of a predictive model to analyze pluralities of TCRs, and database storage can be implemented in hardware or software, or a combination of both. In one embodiment, a machine-readable storage medium is provided, the medium comprising a data storage material encoded with machine readable data which, when using a machine programmed with instructions for using said data, is capable of displaying any of the datasets and execution and results of a predictive model. Such data can be used for a variety of purposes, such as patient monitoring, treatment considerations, and the like. Methods disclosed herein can be implemented in computer programs executing on programmable computers, comprising a processor, a data storage system (including volatile and non-volatile memory and/or storage elements), a graphics adapter, a pointing device, a network adapter, at least one input device, and at least one output device. Program code may be applied to input data to perform the functions described above and generate output information. The output information is applied to one or more output devices, in known fashion. The computer can be, for example, a personal computer, microcomputer, or workstation of conventional design. [00370] Each program can be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. In any case, the language can be a compiled or interpreted language. Each such computer program is preferably stored on a storage media or device (e.g., ROM or magnetic diskette) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein. The system can also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein. [00371] The signature patterns and databases thereof can be provided in a variety of media to facilitate their use. “Media” refers to a manufacture that contains the signature pattern information. The databases as described herein can be recorded on computer readable media, IPTS/128553107.1 Attorney Docket No: SRU-004WO e.g. any medium that can be read and accessed directly by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy discs, hard disc storage medium, and magnetic tape; optical storage media such as CD-ROM; electrical storage media such as RAM and ROM; and hybrids of these categories such as magnetic/optical storage media. One of skill in the art can readily appreciate how any of the presently known computer readable mediums can be used to create a manufacture comprising a recording of the present database information. "Recorded" refers to a process for storing information on computer readable medium, using any such methods as known in the art. Any convenient data storage structure can be chosen, based on the means used to access the stored information. A variety of data processor programs and formats can be used for storage, e.g. word processing text file, database format, etc. [00372] FIG.5 illustrates an example computer 500 for implementing the entities shown in FIGS.1A, 1B, 2, 3, and 4. The computer 500 includes at least one processor 502 coupled to a chipset 504. The chipset 504 includes a memory controller hub 520 and an input/output (I/O) controller hub 522. A memory 506 and a graphics adapter 512 are coupled to the memory controller hub 520, and a display 518 is coupled to the graphics adapter 512. A storage device 508, an input device 514, and network adapter 516 are coupled to the I/O controller hub 522. Other embodiments of the computer 500 have different architectures. [00373] The storage device 508 is a non-transitory computer-readable storage medium such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 506 holds instructions and data used by the processor 502. The input device 514 is a touch-screen interface, a mouse, track ball, or other type of pointing device, a keyboard, or some combination thereof, and is used to input data into the computer 500. In some embodiments, the computer 500 may be configured to receive input (e.g., commands) from the input device 514 via gestures from the user. The graphics adapter 512 displays images and other information on the display 518. The network adapter 516 couples the computer 500 to one or more computer networks. [00374] The computer 500 is adapted to execute computer program modules for providing functionality described herein. As used herein, the term “module” refers to computer program logic used to provide the specified functionality. Thus, a module can be implemented in hardware, firmware, and/or software. In one embodiment, program modules are stored on the storage device 508, loaded into the memory 506, and executed by the processor 502. IPTS/128553107.1 Attorney Docket No: SRU-004WO [00375] The types of computers 500 used by the entities of FIG.1A can vary depending upon the embodiment and the processing power required by the entity. For example, the can run in a single computer 500 or multiple computers 500 communicating with each other through a network such as in a server farm. The computers 500 can lack some of the components described above, such as graphics adapters 512, and displays 518. XI. Kit Implementation [00376] Also disclosed herein are kits for generating a cancer prediction (e.g., a prediction of presence, absence, or likelihood of cancer in a subject). Such kits can include reagents for detecting pluralities of TCRs and instructions for generating the cancer prediction based on the detected pluralities of TCRs. [00377] In various embodiments, the detection reagents can be provided as part of a kit. Thus, additionally disclosed herein are kits for detecting the presence of pluralities of TCRs in a biological test sample. A kit can comprise a set of reagents for generating a dataset via at least one protein detection assay (e.g., a sequencing-based assay, such as ImmunoSeq® assay, a multiplex PCR assay, or any other assay provided herein) that analyzes the test sample from the subject. In various embodiments, the set of reagents enable detection of identities of any of variable genes, joining genes, variable regions, or CDR3 amino acid sequences described herein. In additional embodiments, the set of reagents enable detection of the plurality of TCRs as provided herein. [00378] A kit can include instructions for use of a set of reagents. For example a kit can include instructions for performing at least one sequencing-based assay, such as Sanger sequencing, next-generation sequencing (NGS), Illumina sequencing, Ion Torrent sequnencing, PacBio single-molecule real-time (SMRT) sequencing, Oxford nanopore sequencing, whole-genome sequencing, whole-exome sequencing, RNA-seq, ChIP-seq, methyl-seq, targeted sequencing, or single-cell sequencing, or an amplification-based assay, such as polymerase chain reaction (PCR), reverse transcription PCR (RT-PCR), real-time PCR (qPCR), reverse transcription quantitative PCR (RT-qPCR), digital PCR (dPCR), reverse transcription digital PCR (RT-dPCR), loop-mediated isothermal amplification (LAMP), nucleic acid sequence-based amplification (NASBA), rolling circle amplification (RCA), and strand displacement amplification (SDA), or any other assay provided herein. [00379] In another example, a kit can include instructions for performing at least one biomarker detection assay such as an immunoassay (e.g., a multiplex assay such as a Proximity Extension Assay (PEA)), a protein-binding assay, an antibody-based assay, an IPTS/128553107.1 Attorney Docket No: SRU-004WO antigen-binding protein-based assay, a protein-based array, an enzyme-linked immunosorbent assay (ELISA), flow cytometry, a protein array, a blot, a Western blot, nephelometry, turbidimetry, chromatography, mass spectrometry, enzymatic activity, proximity extension assay, and an immunoassay selected from RIA, immunofluorescence, immunochemiluminescence, immunoelectrochemiluminescence, immunoelectrophoretic, a competitive immunoassay, and immunoprecipitation. [00380] In various embodiments, the kits include instructions for practicing the methods disclosed herein (e.g., methods for training or deploying a predictive model to analyze identities of pluralities of TCRs to generate a feature score to generate a cancer prediction). These instructions can be present in the subject kits in a variety of forms, one or more of which can be present in the kit. One form in which these instructions can be present is as printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, etc. Yet another means would be a computer readable medium, e.g., diskette, CD, hard-drive, network data storage, etc., on which the information has been recorded. Yet another means that can be present is a website address which can be used via the internet to access the information at a removed site. Any convenient means can be present in the kits. XII. Systems [00381] Further disclosed herein are system for analyzing identities of plurality of TCRs, for generating feature counts of TCRs and a cancer prediction (e.g., a prediction of presence, absence, or likelihood of cancer in a subject). In various embodiments, such a system can include a set of reagents for detecting identities of a plurality of TCRs, an apparatus configured to receive a mixture of the set of reagents and a test sample obtained from a subject to measure the identities of the plurality of TCRs, and a computer system communicatively coupled to the apparatus to obtain the measured identities of the plurality of TCRs, generate a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), and to implement the predictive model to the subject feature count across the plurality of cancer-associated TCR RFUs (e.g., a prediction of presence, absence, or likelihood of cancer in the subject). [00382] The set of reagents enable the detection of identities of the plurality of TCRs in the test sample from the subject. In various embodiments, the set of reagents involve reagents IPTS/128553107.1 Attorney Docket No: SRU-004WO used to perform an assay, such as an amplification-based assay, or a sequencing-based assay as described above. For example, the reagents include one or more primers used to amplify one or more variable genes, joining genes, or variable regions. As another example, the reagents can include reagents for performing sequencing-based assays, including template RNA or DNA, primers, buffers, enzymes, deoxynucleotide triphosphates (dNTPs), ribonucleotide triphosphates (rNTPs), fluorescent labels or tags, or dyes. [00383] The apparatus is configured to detect identities of plurality of TCRs in a mixture of a reagent and test sample. For example, the apparatus can determine quantitative identity of plurality of TCRs through an amplification-based assay or assay for nucleic acid detection. The mixture of the reagent and test sample may be presented to the apparatus through various conduits, examples of which include wells of a well plate (e.g., 96 well plate), a vial, a tube, and integrated fluidic circuits. As such, the apparatus may have an opening (e.g., a slot, a cavity, an opening, a sliding tray) that can receive the container including the reagent test sample mixture and perform a reading to generate quantitative expression values of biomarkers. Examples of an apparatus include a plate reader (e.g., a luminescent plate reader, absorbance plate reader, fluorescence plate reader), a spectrometer, and a spectrophotometer. [00384] The computer system, such as example computer 300 described in FIG.3, communicates with the apparatus to receive the quantitative identities of the plurality of TCRs. The computer system generates feature counts across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), and implements, in silico, a predictive model to analyze the feature counts across the plurality of cancer-associated TCR RFUs to generate a cancer prediction (e.g., presence, absence, or likelihood of cancer in a subject). ENUMERATED EMBODIMENTS 1. A method for predicting presence, absence, or likelihood of cancer in a subject, the method comprising: obtaining or having obtained a dataset comprising identities of a plurality of T- cell receptors (TCRs) from the subject; generating a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR IPTS/128553107.1 Attorney Docket No: SRU-004WO repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs are encoded by at least: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. 2. The method of embodiment 1, wherein the identities of the plurality of TCRs from the subject comprise: a variable gene, wherein the variable gene is any one, or more, of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; a joining gene, wherein the joining gene is any one, or more, of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; and/or a plurality of variable regions, wherein the variable regions are encoded for by at least: a variable gene of: TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29- 1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; and a joining gene of: TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1- 5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. IPTS/128553107.1 Attorney Docket No: SRU-004WO 3. The method of embodiment 1, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV13; and a joining gene selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV14; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV18; and a joining gene selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1-6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV19; and a joining gene selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1; a variable gene TRBV2; and a joining gene selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7; a variable gene TRBV20-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-5, TRBJ2-3, and TRBJ2-5; a variable gene TRBV25-1; and a joining gene selected from any one of TRBJ2- 1, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV27; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-4, and TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV5-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-5; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV5-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-8; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV6-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV6-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene selected from any one of TRBJ2-3, and TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-7; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-8; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2-7; a variable gene TRBV7-9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7; or a variable gene TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, and TRBJ2-7. 4. The method of embodiment 3, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV13; and a joining gene TRBJ1-4; a variable gene TRBV13; and a joining gene TRBJ1-5; a variable gene TRBV13; and a joining gene TRBJ2-1; a variable gene TRBV13; and a joining gene TRBJ2-2; a variable gene TRBV13; and a joining gene TRBJ2-3; a variable gene TRBV13; and a joining gene TRBJ2-5; a variable gene TRBV13; and a joining gene TRBJ2-7; a variable gene TRBV11-3; and a joining gene TRBJ2-1; a variable gene TRBV11-3; and a joining gene TRBJ2-2; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV11-3; and a joining gene TRBJ2-7; a variable gene TRBV14; and a joining gene TRBJ1-1; a variable gene TRBV14; and a joining gene TRBJ1-4; a variable gene TRBV14; and a joining gene TRBJ1-5; a variable gene TRBV14; and a joining gene TRBJ2-1; a variable gene TRBV14; and a joining gene TRBJ2-2; a variable gene TRBV14; and a joining gene TRBJ2-3; a variable gene TRBV14; and a joining gene TRBJ2-5; a variable gene TRBV14; and a joining gene TRBJ2-7; a variable gene TRBV18; and a joining gene TRBJ1-1; a variable gene TRBV18; and a joining gene TRBJ1-3; a variable gene TRBV18; and a joining gene TRBJ1-5; a variable gene TRBV18; and a joining gene TRBJ1-6; a variable gene TRBV18; and a joining gene TRBJ2-2; a variable gene TRBV18; and a joining gene TRBJ2-3; a variable gene TRBV18; and a joining gene TRBJ2-5; a variable gene TRBV18; and a joining gene TRBJ2-7; a variable gene TRBV19; and a joining gene TRBJ1-2; a variable gene TRBV19; and a joining gene TRBJ1-6; a variable gene TRBV19; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ1-6; a variable gene TRBV2; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ2-7; a variable gene TRBV20-1; and a joining gene TRBJ1-1; a variable gene TRBV20-1; and a joining gene TRBJ1-5; a variable gene TRBV20-1; and a joining gene TRBJ2-3; a variable gene TRBV20-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-1; a variable gene TRBV25-1; and a joining gene TRBJ2-3; a variable gene TRBV25-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-7; a variable gene TRBV27; and a joining gene TRBJ1-1; a variable gene TRBV27; and a joining gene TRBJ1-2; a variable gene TRBV27; and a joining gene TRBJ1-3; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV27; and a joining gene TRBJ1-4; a variable gene TRBV27; and a joining gene TRBJ2-1; a variable gene TRBV27; and a joining gene TRBJ2-2; a variable gene TRBV27; and a joining gene TRBJ2-3; a variable gene TRBV27; and a joining gene TRBJ2-5; a variable gene TRBV27; and a joining gene TRBJ2-6; a variable gene TRBV27; and a joining gene TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene TRBJ1-1; a variable gene TRBV29-1; and a joining gene TRBJ1-4; a variable gene TRBV29-1; and a joining gene TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene TRBJ1-1; a variable gene TRBV5-1; and a joining gene TRBJ1-2; a variable gene TRBV5-1; and a joining gene TRBJ1-3; a variable gene TRBV5-1; and a joining gene TRBJ1-4; a variable gene TRBV5-1; and a joining gene TRBJ1-5; a variable gene TRBV5-1; and a joining gene TRBJ1-6; a variable gene TRBV5-1; and a joining gene TRBJ2-1; a variable gene TRBV5-1; and a joining gene TRBJ2-2; a variable gene TRBV5-1; and a joining gene TRBJ2-3; a variable gene TRBV5-1; and a joining gene TRBJ2-4; a variable gene TRBV5-1; and a joining gene TRBJ2-5; a variable gene TRBV5-1; and a joining gene TRBJ2-6; a variable gene TRBV5-1; and a joining gene TRBJ2-7; a variable gene TRBV5-4; and a joining gene TRBJ1-1; a variable gene TRBV5-4; and a joining gene TRBJ2-1; a variable gene TRBV5-4; and a joining gene TRBJ2-7; a variable gene TRBV5-5; and a joining gene TRBJ1-1; a variable gene TRBV5-5; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ1-1; a variable gene TRBV5-6; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ2-7; a variable gene TRBV5-8; and a joining gene TRBJ1-1; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-8; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-2; a variable gene TRBV6-1; and a joining gene TRBJ2-7; a variable gene TRBV6-4; and a joining gene TRBJ1-1; a variable gene TRBV6-4; and a joining gene TRBJ2-1; a variable gene TRBV6-4; and a joining gene TRBJ2-2; a variable gene TRBV6-4; and a joining gene TRBJ2-6; a variable gene TRBV6-4; and a joining gene TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ1-1; a variable gene TRBV7-6; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ2-7; a variable gene TRBV7-7; and a joining gene TRBJ1-1; a variable gene TRBV7-7; and a joining gene TRBJ1-4; a variable gene TRBV7-7; and a joining gene TRBJ2-1; a variable gene TRBV7-7; and a joining gene TRBJ2-7; a variable gene TRBV7-8; and a joining gene TRBJ1-1; a variable gene TRBV7-8; and a joining gene TRBJ1-5; a variable gene TRBV7-8; and a joining gene TRBJ2-1; a variable gene TRBV7-8; and a joining gene TRBJ2-5; a variable gene TRBV7-8; and a joining gene TRBJ2-7; a variable gene TRBV7-9; and a joining gene TRBJ1-1; a variable gene TRBV7-9; and a joining gene TRBJ1-4; a variable gene TRBV7-9; and a joining gene TRBJ1-5; a variable gene TRBV7-9; and a joining gene TRBJ1-6; a variable gene TRBV7-9; and a joining gene TRBJ2-1; a variable gene TRBV7-9; and a joining gene TRBJ2-2; a variable gene TRBV7-9; and a joining gene TRBJ2-3; a variable gene TRBV7-9; and a joining gene TRBJ2-4; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-9; and a joining gene TRBJ2-5; a variable gene TRBV7-9; and a joining gene TRBJ2-7; a variable gene TRBV9; and a joining gene TRBJ1-1; a variable gene TRBV9; and a joining gene TRBJ1-4; a variable gene TRBV9; and a joining gene TRBJ2-1; a variable gene TRBV9; and a joining gene TRBJ2-2; a variable gene TRBV9; and a joining gene TRBJ2-3; or a variable gene TRBV9; and a joining gene TRBJ2-7. 5. The method of any one of embodiments 1-4, wherein the plurality of variable regions of the cancer-associated TCR RFUs comprises a CDR3 amino acid sequence comprising a formula of CAxxxxxxxx or CSxxxxxxxx, wherein a residue “C” is a cysteine, residue “A” is an alanine, residue “S” is a serine, and residue “x” is selected from any naturally occurring amino acid residue 6. The method of any one of embodiments 1-5, wherein the plurality of variable regions of the cancer-associated TCR RFUs comprises the CDR3 amiono acid sequence comprising the formula of CASxxxxx, and wherein a residue “C” is a cysteine, residue “A” is an alanine, residue “S” is a serine, and residue “x” is selected from any naturally occurring amino acid residue. 7. The method of any one of embodiments 1-6, wherein the plurality of variable regions of the cancer-associated TCR RFUs comprises the CDR3 amino acid sequence comprising the formula of CASSxxxx, CASTxxxx, or CASRxxxx, and wherein a residue “C” is a cysteine, residue “A” is an alanine, residue “S” is a serine, and residue “x” is selected from any naturally occurring amino acid residue. 8. The method of any one of embodiments 1-7, wherein the plurality of variable regions of the cancer-associated TCR RFUs comprises at least one CDR3 amino acid sequence having at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identity to any one of CDR3 amino acid sequences as provided in Table 1. IPTS/128553107.1 Attorney Docket No: SRU-004WO 9. The method of any one of embodiments 1-8, wherein the plurality of variable regions of the cancer-associated TCR RFUs comprise at least one CDR3 amino acid sequence having 100% identity to any one of CDR3 amino acid sequences as provided in Table 1. 10. The method of embodiment 1, wherein the cancer-associated TCR RFUs are determined by: obtaining or having obtained TCR sequencing data for a plurality of TCRs from a plurality of training samples; assigning TCRs of the plurality of TCRs into candidate RFUs by: grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores; clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc). 11. The method of embodiment 10, wherein the cancer-associated TCR RFUs are further determined by performing one or more of: filtering candidate RFUs to retain candidate RFUs that are observed in at least a first threshold number of training samples; filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a second threshold number of training samples; filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than a threshold value. 12. The method of embodiment 11, wherein the cancer-associated TCR RFUs are further determined by: applying a gamma-Poisson generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. 13. The method of embodiment 12, wherein applying the gamma-Poisson generalized linear model further comprises incorporating demographic covariates. IPTS/128553107.1 Attorney Docket No: SRU-004WO 14. The method of embodiment 13, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 15. The method of embodiment 10, wherein: the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. 16. The method of embodiment 1, wherein the predictive model is a logistic regression model. 17. The method of embodiment 1, wherein the cancer-associated TCR RFUs comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least IPTS/128553107.1 Attorney Docket No: SRU-004WO 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, or at least 197 RFUs. 18. The method of embodiment 1, wherein a performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 19. The method of embodiment 18, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.64. 20. The method of embodiment 18, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.70. 21. The method of embodiment 18, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.71. 22. The method of embodiment 18, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.83. 23. The method of embodiment 18, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.84. 24. The method of embodiment 18, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.85. 25. The method of embodiment 1, wherein the method further comprises: obtaining or having obtained a second dataset comprising expression levels of a plurality of biomarkers from the subject; and IPTS/128553107.1 Attorney Docket No: SRU-004WO generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a second predictive model to the expression levels of the plurality of biomarkers. 26. The method of embodiment 25, wherein the second predictive model is a support vector machine (SVM) model. 27. The method of embodiment 25, wherein the plurality of biomarkers comprises at two or more biomarkers selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. 28. The method of embodiment 25, wherein a performance of the second predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 29. The method of any one of embodiments 25-28, wherein a performance metric of the second predictive model is improved in comparison to a model solely incorporating CEACAM5. 30. The method of embodiment 1, wherein the method further comprises: obtaining or having obtained a third dataset comprising a mutational profile of a plurality of circulating tumor DNA (ctDNA) from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a third predictive model to the mutational profiles of ctDNA. 31. The method of embodiment 30, wherein the third predictive model is a logistic regression model. 32. The method of embodiment 30, wherein the plurality of ctDNA comprises ctDNA selected from CDKN2A, MGAM, PIK3CA, EPHB1, PAK5, KEAP1, TP53, KRAS, KDM5A, ATM, and PTEN. 33. The method of embodiment 30, wherein the ctDNA comprises a mutation. IPTS/128553107.1 Attorney Docket No: SRU-004WO 34. The method of embodiment 33, wherein the mutation is any one of combination of a frameshift mutation, a missense mutation, a synonymous mutation, a splice site mutation, or a nonsense mutation. 35. The method of any one of embodiments 30-34, wherein the mutation is a substitution, an insertion, a deletion, or any combination thereof. 36. The method of any one of embodiments 1-35, wherein the cancer is lung cancer. 37. The method of any one of embodiments 1-36, wherein the lung cancer is an adenocarcinoma, an adenosquamous cell cancer, a large cell cancer, a neuroendocrine cancer, a non-small cell lung cancer (NSCLC), a small cell cancer, or a squamous cell cancer. 38. The method of any one of embodiments 1-37, wherein the cancer is an early stage cancer. 39. The method of any one of embodiments 1-38, wherein the cancer is stage I, stage II, stage III, and/or stage IV lung cancer. 40. The method of any one of embodiments 1-39, wherein the identities of the plurality of TCRs are determined from a test sample obtained from the subject. 41. The method of embodiment 40, wherein the test sample is a blood or buffy coat or serum sample. 42. The method of embodiment 40 or embodiment 41, wherein the subject is suspected of having an early stage cancer. 43. The method of embodiment 40 or embodiment 41, wherein the subject is not suspected of having an early stage cancer. 44. The method of any one of embodiments 1-43, wherein obtaining or having obtained the dataset comprising identities of the plurality of TCRs from the subject comprises performing an assay to determine the plurality of TCRs. 45. The method of embodiment 44, wherein the assay is an amplification-based assay. IPTS/128553107.1 Attorney Docket No: SRU-004WO 46. The method of embodiment 45, wherein the amplification-based assay is a PCR assay, RT-PCR assay, qRT-PCR assay, or multiplex PCR assay. 47. The method of any one of embodiments 1-46, wherein the analyzing and generating the subject feature count comprises performing an assay to determine the plurality of TCRs, and performing a feature count to determine the subject feature count against the cancer-associated RFUs. 48. The method of embodiment 47, wherein the assay is a sequencing-based assay. 49. The method of embodiment 48, wherein the sequencing-based assay is an RNA-seq assay. 50. The method of any one of embodiments 44-49, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising primers. 51. The method of any one of embodiments 25-29, wherein obtaining or having obtained the second dataset comprises performing an assay to determine the expression levels of the plurality of biomarkers. 52. The method of embodiment 51, wherein the assay is a Proximity Extension Assay (PEA), a xMAP Multiplex Assay, a single molecule array (SIMOA) assay, mass spectrometry based protein or peptide assay, or an aptamer-based assay. 53. The method of embodiment 51 or embodiment 52, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising antibodies. 54. The method of embodiment 53, wherein the antibodies comprise one of monoclonal and polyclonal antibodies. 55. The method of embodiment 53, wherein the antibodies comprise both monoclonal and polyclonal antibodies. 56. The method of any one of embodiments 30-35, wherein obtaining or having obtained the third dataset comprises performing an assay to determine the mutation profile of the plurality of ctDNA. IPTS/128553107.1 Attorney Docket No: SRU-004WO 57. The method of embodiment 56, wherein the assay is an NGS-based hybrid capture method assay. 58. The method of embodiment 1, wherein the method further comprises administering a treatment to the subject. 59. The method of embodiment 58, wherein the treatment comprises a surgery, a chemotherapy, a radiation therapy, a targeted therapy, an immunotherapy, or any combination thereof. 60. The method of any one of embodiments 1-59, wherein the subject is an undiagnosed subject, at risk subject, or a subject previously diagnosed with cancer. 61. A method for predicting presence, absence, or likelihood of cancer in a subject, the method comprising: obtaining or having obtained a dataset comprising identities of a plurality of T- cell receptors (TCRs) from the subject; generating a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs comprise at least one CDR3 amino acid sequence having at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identity to any one of CDR3 amino acid sequences as provided in Table 1; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. 62. The method of embodiment 61, wherein the plurality of variable regions of the cancer-associated TCR RFUs comprise at least one CDR3 amino acid sequence having 100% identity to any one of CDR3 amino acid sequences as provided in Table 1. 63. The method of embodiment 61, wherein the cancer-associated TCR RFUs are determined by: IPTS/128553107.1 Attorney Docket No: SRU-004WO obtaining or having obtained TCR sequencing data for a plurality of TCRs from a plurality of training samples; assigning TCRs of the plurality of TCRs into candidate RFUs by: grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores; clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc). 64. The method of embodiment 63, wherein the cancer-associated TCR RFUs are further determined by performing one or more of: filtering candidate RFUs to retain candidate RFUs that are observed in at least a first threshold number of training samples; filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a second threshold number of training samples; filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than a threshold value. 65. The method of embodiment 64, wherein the cancer-associated TCR RFUs are further determined by: applying a gamma-Poisson generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. 66. The method of embodiment 65, wherein applying the gamma-Poisson generalized linear model further comprises incorporating demographic covariates. 67. The method of embodiment 66, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 68. The method of embodiment 63, wherein: the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; IPTS/128553107.1 Attorney Docket No: SRU-004WO the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. 69. The method of embodiment 61, wherein the predictive model is a logistic regression model. 70. The method of embodiment 61, wherein the cancer-associated TCR RFUs comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least IPTS/128553107.1 Attorney Docket No: SRU-004WO 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, or at least 197 RFUs. 71. The method of embodiment 70, wherein a performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, at least 0.80, at least 0.81, at least 0.82, or at least 0.83. 72. The method of embodiment 71, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.64. 73. The method of embodiment 71, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.70. 74. The method of embodiment 71, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.71. 75. The method of embodiment 71, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.83. 76. The method of embodiment 71, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.84. 77. The method of embodiment 71, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.85. 78. The method of embodiment 61, wherein the method further comprises: obtaining or having obtained a second dataset comprising expression levels of a plurality of biomarkers from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a second predictive model to the expression levels of the plurality of biomarkers. 79. The method of embodiment 78, wherein the second predictive model is a support vector machine (SVM) model. IPTS/128553107.1 Attorney Docket No: SRU-004WO 80. The method of embodiment 78, wherein the plurality of biomarkers comprises at two or more biomarkers selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. 81. The method of embodiment 78, wherein a performance of the second predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 82. The method of any one of embodiments 78-81, wherein a performance metric of the second predictive model is improved in comparison to a model solely incorporating CEACAM5. 83. The method of embodiment 78, wherein the method further comprises: obtaining or having obtained a third dataset comprising a mutational profile of a plurality of circulating tumor DNA (ctDNA) from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a third predictive model to the mutational profiles of ctDNA. 84. The method of embodiment 83, wherein the third predictive model is a logistic regression model. 85. The method of embodiment 83, wherein the plurality of ctDNA comprises ctDNA selected from CDKN2A, MGAM, PIK3CA, EPHB1, PAK5, KEAP1, TP53, KRAS, KDM5A, ATM, and PTEN. 86. The method of embodiment 83, wherein the ctDNA comprises a mutation. 87. The method of embodiment 86, wherein the mutation is any one of combination of a frameshift mutation, a missense mutation, a synonymous mutation, a splice site mutation, or a nonsense mutation. 88. The method of any one of embodiments 83-87, wherein the mutation is a substitution, an insertion, a deletion, or any combination thereof. IPTS/128553107.1 Attorney Docket No: SRU-004WO 89. The method of any one of embodiments 61-88, wherein the cancer is lung cancer. 90. The method of any one of embodiments 61-89, wherein the lung cancer is an adenocarcinoma, an adenosquamous cell cancer, a large cell cancer, a neuroendocrine cancer, a non-small cell lung cancer (NSCLC), a small cell cancer, or a squamous cell cancer. 91. The method of any one of embodiments 61-90, wherein the cancer is an early stage cancer. 92. The method of any one of embodiments 61-91, wherein the cancer is stage I, stage II, stage III, and/or stage IV lung cancer. 93. The method of any one of embodiments 61-92, wherein the identities of the plurality of TCRs are determined from a test sample obtained from the subject. 94. The method of embodiment 93, wherein the test sample is a blood or serum sample. 95. The method of embodiment 93 or embodiment 94, wherein the subject is suspected of having an early stage cancer. 96. The method of embodiment 93 or embodiment 94, wherein the subject is not suspected of having an early stage cancer. 97. The method of any one of embodiments 61-96, wherein obtaining or having obtained the dataset comprising identities of the plurality of TCRs from the subject comprises performing an assay to determine the plurality of TCRs. 98. The method of embodiment 97, wherein the assay is an amplification-based assay. 99. The method of embodiment 98, wherein the amplification-based assay is a PCR assay, RT-PCR assay, qRT-PCR assay, or multiplex PCR assay. 100. The method of any one of embodiments 61-99, wherein the analyzing and generating the subject feature count comprises performing an assay to determine the plurality of TCRs, and performing a feature count to determine the subject feature count against the cancer-associated RFUs. 101. The method of embodiment 100, wherein the assay is a sequencing-based assay. IPTS/128553107.1 Attorney Docket No: SRU-004WO 102. The method of embodiment 101, wherein the sequencing-based assay is an RNA- seq assay. 103. The method of any one of embodiments 97-102, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising primers. 104. The method of any one of embodiments 78-82, wherein obtaining or having obtained the second dataset comprises performing an assay to determine the expression levels of the plurality of biomarkers. 105. The method of embodiment 104, wherein the assay is a Proximity Extension Assay (PEA), a xMAP Multiplex Assay, a single molecule array (SIMOA) assay, mass spectrometry based protein or peptide assay, or an aptamer-based assay. 106. The method of embodiment 104 or embodiment 105, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising antibodies. 107. The method of embodiment 106, wherein the antibodies comprise one of monoclonal and polyclonal antibodies. 108. The method of embodiment 106, wherein the antibodies comprise both monoclonal and polyclonal antibodies. 109. The method of any one of embodiments 83-88, wherein obtaining or having obtained the third dataset comprises performing an assay to determine the mutation profile of the plurality of ctDNA. 110. The method of embodiment 109, wherein the assay is an NGS-based hybrid capture method assay. 111. The method of embodiment 61, wherein the method further comprises administering a treatment to the subject. 112. The method of embodiment 111, wherein the treatment comprises a surgery, a chemotherapy, a radiation therapy, a targeted therapy, an immunotherapy, or any combination thereof. IPTS/128553107.1 Attorney Docket No: SRU-004WO 113. The method of any one of embodiments 61-112, wherein the subject is an undiagnosed subject, at risk subject, or a subject previously diagnosed with cancer. 114. A non-transitory computer-readable storage medium, the computer-readable storage medium comprising instructions that when executed by a processor, cause the processor to: obtain or having obtained a dataset comprising identities of a plurality of T-cell receptors (TCRs) from the subject; generate a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs are encoded by at least: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; and generate a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. 115. The non-transitory computer readable medium of embodiment 114, wherein the identities of the plurality of TCRs from the subject comprise: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; IPTS/128553107.1 Attorney Docket No: SRU-004WO a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; and/or a plurality of variable regions, wherein the variable regions are encoded for by at least: a variable gene of: TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29- 1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; and a joining gene of: TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1- 5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. 116. The non-transitory computer readable medium of embodiment 115, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV13; and a joining gene selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV14; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV18; and a joining gene selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1-6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV19; and a joining gene selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1; a variable gene TRBV2; and a joining gene selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7; a variable gene TRBV20-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-5, TRBJ2-3, and TRBJ2-5; a variable gene TRBV25-1; and a joining gene selected from any one of TRBJ2- 1, TRBJ2-3, TRBJ2-5, and TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV27; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-4, and TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV5-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-5; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV5-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-8; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV6-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV6-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene selected from any one of TRBJ2-3, and TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-7; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-8; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7; or a variable gene TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, and TRBJ2-7. 117. The non-transitory computer readable medium of embodiment 116, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV13; and a joining gene TRBJ1-4; a variable gene TRBV13; and a joining gene TRBJ1-5; a variable gene TRBV13; and a joining gene TRBJ2-1; a variable gene TRBV13; and a joining gene TRBJ2-2; a variable gene TRBV13; and a joining gene TRBJ2-3; a variable gene TRBV13; and a joining gene TRBJ2-5; a variable gene TRBV13; and a joining gene TRBJ2-7; a variable gene TRBV11-3; and a joining gene TRBJ2-1; a variable gene TRBV11-3; and a joining gene TRBJ2-2; a variable gene TRBV11-3; and a joining gene TRBJ2-7; a variable gene TRBV14; and a joining gene TRBJ1-1; a variable gene TRBV14; and a joining gene TRBJ1-4; a variable gene TRBV14; and a joining gene TRBJ1-5; a variable gene TRBV14; and a joining gene TRBJ2-1; a variable gene TRBV14; and a joining gene TRBJ2-2; a variable gene TRBV14; and a joining gene TRBJ2-3; a variable gene TRBV14; and a joining gene TRBJ2-5; a variable gene TRBV14; and a joining gene TRBJ2-7; a variable gene TRBV18; and a joining gene TRBJ1-1; a variable gene TRBV18; and a joining gene TRBJ1-3; a variable gene TRBV18; and a joining gene TRBJ1-5; a variable gene TRBV18; and a joining gene TRBJ1-6; a variable gene TRBV18; and a joining gene TRBJ2-2; a variable gene TRBV18; and a joining gene TRBJ2-3; a variable gene TRBV18; and a joining gene TRBJ2-5; a variable gene TRBV18; and a joining gene TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV19; and a joining gene TRBJ1-2; a variable gene TRBV19; and a joining gene TRBJ1-6; a variable gene TRBV19; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ1-6; a variable gene TRBV2; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ2-7; a variable gene TRBV20-1; and a joining gene TRBJ1-1; a variable gene TRBV20-1; and a joining gene TRBJ1-5; a variable gene TRBV20-1; and a joining gene TRBJ2-3; a variable gene TRBV20-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-1; a variable gene TRBV25-1; and a joining gene TRBJ2-3; a variable gene TRBV25-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-7; a variable gene TRBV27; and a joining gene TRBJ1-1; a variable gene TRBV27; and a joining gene TRBJ1-2; a variable gene TRBV27; and a joining gene TRBJ1-3; a variable gene TRBV27; and a joining gene TRBJ1-4; a variable gene TRBV27; and a joining gene TRBJ2-1; a variable gene TRBV27; and a joining gene TRBJ2-2; a variable gene TRBV27; and a joining gene TRBJ2-3; a variable gene TRBV27; and a joining gene TRBJ2-5; a variable gene TRBV27; and a joining gene TRBJ2-6; a variable gene TRBV27; and a joining gene TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene TRBJ1-1; a variable gene TRBV29-1; and a joining gene TRBJ1-4; a variable gene TRBV29-1; and a joining gene TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene TRBJ1-1; a variable gene TRBV5-1; and a joining gene TRBJ1-2; a variable gene TRBV5-1; and a joining gene TRBJ1-3; a variable gene TRBV5-1; and a joining gene TRBJ1-4; a variable gene TRBV5-1; and a joining gene TRBJ1-5; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-1; and a joining gene TRBJ1-6; a variable gene TRBV5-1; and a joining gene TRBJ2-1; a variable gene TRBV5-1; and a joining gene TRBJ2-2; a variable gene TRBV5-1; and a joining gene TRBJ2-3; a variable gene TRBV5-1; and a joining gene TRBJ2-4; a variable gene TRBV5-1; and a joining gene TRBJ2-5; a variable gene TRBV5-1; and a joining gene TRBJ2-6; a variable gene TRBV5-1; and a joining gene TRBJ2-7; a variable gene TRBV5-4; and a joining gene TRBJ1-1; a variable gene TRBV5-4; and a joining gene TRBJ2-1; a variable gene TRBV5-4; and a joining gene TRBJ2-7; a variable gene TRBV5-5; and a joining gene TRBJ1-1; a variable gene TRBV5-5; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ1-1; a variable gene TRBV5-6; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ2-7; a variable gene TRBV5-8; and a joining gene TRBJ1-1; a variable gene TRBV5-8; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-2; a variable gene TRBV6-1; and a joining gene TRBJ2-7; a variable gene TRBV6-4; and a joining gene TRBJ1-1; a variable gene TRBV6-4; and a joining gene TRBJ2-1; a variable gene TRBV6-4; and a joining gene TRBJ2-2; a variable gene TRBV6-4; and a joining gene TRBJ2-6; a variable gene TRBV6-4; and a joining gene TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ1-1; a variable gene TRBV7-6; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-7; and a joining gene TRBJ1-1; a variable gene TRBV7-7; and a joining gene TRBJ1-4; a variable gene TRBV7-7; and a joining gene TRBJ2-1; a variable gene TRBV7-7; and a joining gene TRBJ2-7; a variable gene TRBV7-8; and a joining gene TRBJ1-1; a variable gene TRBV7-8; and a joining gene TRBJ1-5; a variable gene TRBV7-8; and a joining gene TRBJ2-1; a variable gene TRBV7-8; and a joining gene TRBJ2-5; a variable gene TRBV7-8; and a joining gene TRBJ2-7; a variable gene TRBV7-9; and a joining gene TRBJ1-1; a variable gene TRBV7-9; and a joining gene TRBJ1-4; a variable gene TRBV7-9; and a joining gene TRBJ1-5; a variable gene TRBV7-9; and a joining gene TRBJ1-6; a variable gene TRBV7-9; and a joining gene TRBJ2-1; a variable gene TRBV7-9; and a joining gene TRBJ2-2; a variable gene TRBV7-9; and a joining gene TRBJ2-3; a variable gene TRBV7-9; and a joining gene TRBJ2-4; a variable gene TRBV7-9; and a joining gene TRBJ2-5; a variable gene TRBV7-9; and a joining gene TRBJ2-7; a variable gene TRBV9; and a joining gene TRBJ1-1; a variable gene TRBV9; and a joining gene TRBJ1-4; a variable gene TRBV9; and a joining gene TRBJ2-1; a variable gene TRBV9; and a joining gene TRBJ2-2; a variable gene TRBV9; and a joining gene TRBJ2-3; or a variable gene TRBV9; and a joining gene TRBJ2-7. 118. The non-transitory computer readable medium of embodiment 114, wherein the cancer-associated TCR RFUs are determined by: obtaining or having obtained TCR sequencing data for a plurality of TCRs from a plurality of training samples; assigning TCRs of the plurality of TCRs into candidate RFUs by: grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and IPTS/128553107.1 Attorney Docket No: SRU-004WO combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores; clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc). 119. The non-transitory computer readable medium of embodiment 118, wherein the cancer-associated TCR RFUs are further determined by performing one or more of: filtering candidate RFUs to retain candidate RFUs that are observed in at least a first threshold number of training samples; filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a second threshold number of training samples; filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than a threshold value. 120. The non-transitory computer readable medium of embodiment 118, wherein the cancer- associated TCR RFUs are further determined by: applying a gamma-Poisson generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. 121. The non-transitory computer readable medium of embodiment 120, wherein applying the gamma-Poisson generalized linear model further comprises incorporating demographic covariates. 122. The non-transitory computer readable medium of embodiment 121, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 123. The non-transitory computer readable medium of embodiment 118, wherein: the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. IPTS/128553107.1 Attorney Docket No: SRU-004WO 124. The non-transitory computer readable medium of embodiment 114, wherein the predictive model is a logistic regression model. 125. The non-transitory computer readable medium of embodiment 114, wherein the cancer-associated TCR RFUs comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, or at least 197 RFUs. IPTS/128553107.1 Attorney Docket No: SRU-004WO 126. The non-transitory computer readable medium of embodiment 114, wherein a performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 127. The non-transitory computer readable medium of embodiment 126, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.64. 128. The non-transitory computer readable medium of embodiment 126, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.70. 129. The non-transitory computer readable medium of embodiment 126, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.71. 130. The non-transitory computer readable medium of embodiment 126, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.83. 131. The non-transitory computer readable medium of embodiment 126, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.84. 132. The non-transitory computer readable medium of embodiment 126, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.85. 133. The non-transitory computer readable medium of embodiment 114, wherein the non-transitory computer readable medium further comprises: obtaining or having obtained a second dataset comprising expression levels of a plurality of biomarkers from the subject; and IPTS/128553107.1 Attorney Docket No: SRU-004WO generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a second predictive model to the expression levels of the plurality of biomarkers. 134. The non-transitory computer readable medium of embodiment 133, wherein the second predictive model is a support vector machine (SVM) model. 135. The non-transitory computer readable medium of embodiment 133, wherein the plurality of biomarkers comprises at two or more biomarkers selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. 136. The non-transitory computer readable medium of embodiment 133, wherein a performance of the second predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 137. The non-transitory computer readable medium of any one of embodiments 133-136, wherein a performance metric of the second predictive model is improved in comparison to a model solely incorporating CEACAM5. 138. The non-transitory computer readable medium of embodiment 114, wherein the non-transitory computer readable medium further comprises: obtaining or having obtained a third dataset comprising a mutational profile of a plurality of circulating tumor DNA (ctDNA) from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a third predictive model to the mutational profiles of ctDNA. 139. The non-transitory computer readable medium of embodiment 138, wherein the third predictive model is a logistic regression model. 140. The non-transitory computer readable medium of embodiment 138, wherein the plurality of ctDNA comprises ctDNA selected from CDKN2A, MGAM, PIK3CA, EPHB1, PAK5, KEAP1, TP53, KRAS, KDM5A, ATM, and PTEN. IPTS/128553107.1 Attorney Docket No: SRU-004WO 141. The non-transitory computer readable medium of embodiment 138, wherein the ctDNA comprises a mutation. 142. The non-transitory computer readable medium of embodiment 141, wherein the mutation is any one of combination of a frameshift mutation, a missense mutation, a synonymous mutation, a splice site mutation, or a nonsense mutation. 143. The non-transitory computer readable medium of any one of embodiments 138-142, wherein the mutation is a substitution, an insertion, a deletion, or any combination thereof. 144. The non-transitory computer readable medium of any one of embodiments 114-143, wherein the cancer is lung cancer. 145. The non-transitory computer readable medium of any one of embodiments 114-144, wherein the lung cancer is an adenocarcinoma, an adenosquamous cell cancer, a large cell cancer, a neuroendocrine cancer, a non-small cell lung cancer (NSCLC), a small cell cancer, or a squamous cell cancer. 146. The non-transitory computer readable medium of any one of embodiments 114-145, wherein the cancer is an early stage cancer. 147. The non-transitory computer readable medium of any one of embodiments 114-146, wherein the cancer is stage I, stage II, stage III, and/or stage IV lung cancer. 148. The non-transitory computer readable medium of any one of embodiments 114-147, wherein the identities of the plurality of TCRs are determined from a test sample obtained from the subject. 149. The non-transitory computer readable medium of embodiment 148, wherein the test sample is a blood or serum sample. 150. The non-transitory computer readable medium of embodiment 148 or embodiment 149, wherein the subject is suspected of having an early stage cancer. 151. The non-transitory computer readable medium of embodiment 148 or embodiment 149, wherein the subject is not suspected of having an early stage cancer. IPTS/128553107.1 Attorney Docket No: SRU-004WO 152. The non-transitory computer readable medium of any one of embodiments 114-151, wherein obtaining or having obtained the dataset comprising identities of the plurality of TCRs from the subject comprises performing an assay to determine the plurality of TCRs. 153. The non-transitory computer readable medium of embodiment 152, wherein the assay is an amplification-based assay. 154. The non-transitory computer readable medium of embodiment 153, wherein the amplification-based assay is a PCR assay, RT-PCR assay, qRT-PCR assay, or multiplex PCR assay. 155. The non-transitory computer readable medium of any one of embodiments 114-154, wherein the analyzing and generating the subject feature count comprises performing an assay to determine the plurality of TCRs, and performing a feature count to determine the subject feature count against the cancer-associated RFUs. 156. The non-transitory computer readable medium of embodiment 155, wherein the assay is a sequencing-based assay. 157. The non-transitory computer readable medium of embodiment 156, wherein the sequencing-based assay is an RNA-seq assay. 158. The non-transitory computer readable medium of any one of embodiments 152-157, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising primers. 159. The non-transitory computer readable medium of any one of embodiments 133-137, wherein obtaining or having obtained the second dataset comprises performing an assay to determine the expression levels of the plurality of biomarkers. 160. The non-transitory computer readable medium of embodiment 159, wherein the assay is a Proximity Extension Assay (PEA), a xMAP Multiplex Assay, a single molecule array (SIMOA) assay, mass spectrometry based protein or peptide assay, or an aptamer-based assay. IPTS/128553107.1 Attorney Docket No: SRU-004WO 161. The non-transitory computer readable medium of embodiment 159 or embodiment 160, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising antibodies. 162. The non-transitory computer readable medium of embodiment 161, wherein the antibodies comprise one of monoclonal and polyclonal antibodies. 163. The non-transitory computer readable medium of embodiment 161, wherein the antibodies comprise both monoclonal and polyclonal antibodies. 164. The non-transitory computer readable medium of any one of embodiments 138-193, wherein obtaining or having obtained the third dataset comprises performing an assay to determine the mutation profile of the plurality of ctDNA. 165. The non-transitory computer readable medium of embodiment 164, wherein the assay is an NGS-based hybrid capture non-transitory computer readable medium assay. 166. The non-transitory computer readable medium of any one of embodiments 114-165, wherein the subject is an undiagnosed subject, at risk subject, or a subject previously diagnosed with cancer. 167. A system comprising: a set of reagents used for determining identities of a plurality of T-cell receptors (TCRs) from a test sample from the subject; an apparatus configured to receive a mixture of one or more reagents in the set and the test sample and to measure the identities of a plurality of T-cell receptors (TCRs) from the test sample; and a computer system communicatively coupled to the apparatus to: obtain a dataset comprising identities of a plurality of T-cell receptors (TCRs) from the test sample; generate a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs are encoded by at least: IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29- 1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1- 5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; and generate a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. 168. The system of embodiment 167, wherein the identities of the plurality of TCRs from the subject comprise: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; and/or a plurality of variable regions, wherein the variable regions are encoded for by at least: a variable gene of: TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29- 1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; and a joining gene of: TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1- 5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7. IPTS/128553107.1 Attorney Docket No: SRU-004WO 169. The system of embodiment 168, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV13; and a joining gene selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV14; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV18; and a joining gene selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1-6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV19; and a joining gene selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1; a variable gene TRBV2; and a joining gene selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7; a variable gene TRBV20-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-5, TRBJ2-3, and TRBJ2-5; a variable gene TRBV25-1; and a joining gene selected from any one of TRBJ2- 1, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV27; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-4, and TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV5-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-5; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV5-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-8; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV6-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV6-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene selected from any one of TRBJ2-3, and TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-7; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-8; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2-7; a variable gene TRBV7-9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7; or a variable gene TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, and TRBJ2-7. 170. The system of embodiment 169, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV13; and a joining gene TRBJ1-4; a variable gene TRBV13; and a joining gene TRBJ1-5; a variable gene TRBV13; and a joining gene TRBJ2-1; a variable gene TRBV13; and a joining gene TRBJ2-2; a variable gene TRBV13; and a joining gene TRBJ2-3; a variable gene TRBV13; and a joining gene TRBJ2-5; a variable gene TRBV13; and a joining gene TRBJ2-7; a variable gene TRBV11-3; and a joining gene TRBJ2-1; a variable gene TRBV11-3; and a joining gene TRBJ2-2; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV11-3; and a joining gene TRBJ2-7; a variable gene TRBV14; and a joining gene TRBJ1-1; a variable gene TRBV14; and a joining gene TRBJ1-4; a variable gene TRBV14; and a joining gene TRBJ1-5; a variable gene TRBV14; and a joining gene TRBJ2-1; a variable gene TRBV14; and a joining gene TRBJ2-2; a variable gene TRBV14; and a joining gene TRBJ2-3; a variable gene TRBV14; and a joining gene TRBJ2-5; a variable gene TRBV14; and a joining gene TRBJ2-7; a variable gene TRBV18; and a joining gene TRBJ1-1; a variable gene TRBV18; and a joining gene TRBJ1-3; a variable gene TRBV18; and a joining gene TRBJ1-5; a variable gene TRBV18; and a joining gene TRBJ1-6; a variable gene TRBV18; and a joining gene TRBJ2-2; a variable gene TRBV18; and a joining gene TRBJ2-3; a variable gene TRBV18; and a joining gene TRBJ2-5; a variable gene TRBV18; and a joining gene TRBJ2-7; a variable gene TRBV19; and a joining gene TRBJ1-2; a variable gene TRBV19; and a joining gene TRBJ1-6; a variable gene TRBV19; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ1-6; a variable gene TRBV2; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ2-7; a variable gene TRBV20-1; and a joining gene TRBJ1-1; a variable gene TRBV20-1; and a joining gene TRBJ1-5; a variable gene TRBV20-1; and a joining gene TRBJ2-3; a variable gene TRBV20-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-1; a variable gene TRBV25-1; and a joining gene TRBJ2-3; a variable gene TRBV25-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-7; a variable gene TRBV27; and a joining gene TRBJ1-1; a variable gene TRBV27; and a joining gene TRBJ1-2; a variable gene TRBV27; and a joining gene TRBJ1-3; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV27; and a joining gene TRBJ1-4; a variable gene TRBV27; and a joining gene TRBJ2-1; a variable gene TRBV27; and a joining gene TRBJ2-2; a variable gene TRBV27; and a joining gene TRBJ2-3; a variable gene TRBV27; and a joining gene TRBJ2-5; a variable gene TRBV27; and a joining gene TRBJ2-6; a variable gene TRBV27; and a joining gene TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene TRBJ1-1; a variable gene TRBV29-1; and a joining gene TRBJ1-4; a variable gene TRBV29-1; and a joining gene TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene TRBJ1-1; a variable gene TRBV5-1; and a joining gene TRBJ1-2; a variable gene TRBV5-1; and a joining gene TRBJ1-3; a variable gene TRBV5-1; and a joining gene TRBJ1-4; a variable gene TRBV5-1; and a joining gene TRBJ1-5; a variable gene TRBV5-1; and a joining gene TRBJ1-6; a variable gene TRBV5-1; and a joining gene TRBJ2-1; a variable gene TRBV5-1; and a joining gene TRBJ2-2; a variable gene TRBV5-1; and a joining gene TRBJ2-3; a variable gene TRBV5-1; and a joining gene TRBJ2-4; a variable gene TRBV5-1; and a joining gene TRBJ2-5; a variable gene TRBV5-1; and a joining gene TRBJ2-6; a variable gene TRBV5-1; and a joining gene TRBJ2-7; a variable gene TRBV5-4; and a joining gene TRBJ1-1; a variable gene TRBV5-4; and a joining gene TRBJ2-1; a variable gene TRBV5-4; and a joining gene TRBJ2-7; a variable gene TRBV5-5; and a joining gene TRBJ1-1; a variable gene TRBV5-5; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ1-1; a variable gene TRBV5-6; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ2-7; a variable gene TRBV5-8; and a joining gene TRBJ1-1; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-8; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-2; a variable gene TRBV6-1; and a joining gene TRBJ2-7; a variable gene TRBV6-4; and a joining gene TRBJ1-1; a variable gene TRBV6-4; and a joining gene TRBJ2-1; a variable gene TRBV6-4; and a joining gene TRBJ2-2; a variable gene TRBV6-4; and a joining gene TRBJ2-6; a variable gene TRBV6-4; and a joining gene TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ1-1; a variable gene TRBV7-6; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ2-7; a variable gene TRBV7-7; and a joining gene TRBJ1-1; a variable gene TRBV7-7; and a joining gene TRBJ1-4; a variable gene TRBV7-7; and a joining gene TRBJ2-1; a variable gene TRBV7-7; and a joining gene TRBJ2-7; a variable gene TRBV7-8; and a joining gene TRBJ1-1; a variable gene TRBV7-8; and a joining gene TRBJ1-5; a variable gene TRBV7-8; and a joining gene TRBJ2-1; a variable gene TRBV7-8; and a joining gene TRBJ2-5; a variable gene TRBV7-8; and a joining gene TRBJ2-7; a variable gene TRBV7-9; and a joining gene TRBJ1-1; a variable gene TRBV7-9; and a joining gene TRBJ1-4; a variable gene TRBV7-9; and a joining gene TRBJ1-5; a variable gene TRBV7-9; and a joining gene TRBJ1-6; a variable gene TRBV7-9; and a joining gene TRBJ2-1; a variable gene TRBV7-9; and a joining gene TRBJ2-2; a variable gene TRBV7-9; and a joining gene TRBJ2-3; a variable gene TRBV7-9; and a joining gene TRBJ2-4; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-9; and a joining gene TRBJ2-5; a variable gene TRBV7-9; and a joining gene TRBJ2-7; a variable gene TRBV9; and a joining gene TRBJ1-1; a variable gene TRBV9; and a joining gene TRBJ1-4; a variable gene TRBV9; and a joining gene TRBJ2-1; a variable gene TRBV9; and a joining gene TRBJ2-2; a variable gene TRBV9; and a joining gene TRBJ2-3; or a variable gene TRBV9; and a joining gene TRBJ2-7. 171. The system of embodiment 167, wherein the cancer-associated TCR RFUs are determined by: obtaining or having obtained TCR sequencing data for a plurality of TCRs from a plurality of training samples; assigning TCRs of the plurality of TCRs into candidate RFUs by: grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores; clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc). 172. The system of embodiment 171, wherein the cancer-associated TCR RFUs are further determined by performing one or more of: filtering candidate RFUs to retain candidate RFUs that are observed in at least a first threshold number of training samples; filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a second threshold number of training samples; filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than a threshold value. 173. The system of embodiment 172, wherein the cancer-associated TCR RFUs are further determined by: IPTS/128553107.1 Attorney Docket No: SRU-004WO applying a gamma-Poisson generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. 174. The system of embodiment 173, wherein applying the gamma-Poisson generalized linear model further comprises incorporating demographic covariates. 175. The system of embodiment 174, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 176. The system of embodiment 171, wherein: the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. 177. The system of embodiment 167, wherein the predictive model is a logistic regression model. 178. The system of embodiment 167, wherein the cancer-associated TCR RFUs comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least IPTS/128553107.1 Attorney Docket No: SRU-004WO 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, or at least 197 RFUs. 179. The system of embodiment 167, wherein a performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 180. The system of embodiment 179, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.64. 181. The system of embodiment 179, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.70. 182. The system of embodiment 179, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.71. 183. The system of embodiment 179, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.83. 184. The system of embodiment 179, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.84. IPTS/128553107.1 Attorney Docket No: SRU-004WO 185. The system of embodiment 179, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.85. 186. The system of embodiment 167, wherein the system further comprises: obtaining or having obtained a second dataset comprising expression levels of a plurality of biomarkers from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a second predictive model to the expression levels of the plurality of biomarkers. 187. The system of embodiment 186, wherein the second predictive model is a support vector machine (SVM) model. 188. The system of embodiment 186, wherein the plurality of biomarkers comprises at two or more biomarkers selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. 189. The system of embodiment 186, wherein a performance of the second predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 190. The system of any one of embodiments 186-189, wherein a performance metric of the second predictive model is improved in comparison to a model solely incorporating CEACAM5. 191. The system of embodiment 167, wherein the system further comprises: obtaining or having obtained a third dataset comprising a mutational profile of a plurality of circulating tumor DNA (ctDNA) from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a third predictive model to the mutational profiles of ctDNA. 192. The system of embodiment 191, wherein the third predictive model is a logistic regression model. IPTS/128553107.1 Attorney Docket No: SRU-004WO 193. The system of embodiment 191, wherein the plurality of ctDNA comprises ctDNA selected from CDKN2A, MGAM, PIK3CA, EPHB1, PAK5, KEAP1, TP53, KRAS, KDM5A, ATM, and PTEN. 194. The system of embodiment 191, wherein the ctDNA comprises a mutation. 195. The system of embodiment 194, wherein the mutation is any one of combination of a frameshift mutation, a missense mutation, a synonymous mutation, a splice site mutation, or a nonsense mutation. 196. The system of any one of embodiments 191-195, wherein the mutation is a substitution, an insertion, a deletion, or any combination thereof. 197. The system of any one of embodiments 167-196, wherein the cancer is lung cancer. 198. The system of any one of embodiments 167-197, wherein the lung cancer is an adenocarcinoma, an adenosquamous cell cancer, a large cell cancer, a neuroendocrine cancer, a non-small cell lung cancer (NSCLC), a small cell cancer, or a squamous cell cancer. 199. The system of any one of embodiments 167-198, wherein the cancer is an early stage cancer. 200. The system of any one of embodiments 167-199, wherein the cancer is stage I, stage II, stage III, and/or stage IV lung cancer. 201. The system of any one of embodiments 167-200, wherein the identities of the plurality of TCRs are determined from a test sample obtained from the subject. 202. The system of embodiment 201, wherein the test sample is a blood or serum sample. 203. The system of embodiment 201 or embodiment 202, wherein the subject is suspected of having an early stage cancer. 204. The system of embodiment 201 or embodiment 202, wherein the subject is not suspected of having an early stage cancer. IPTS/128553107.1 Attorney Docket No: SRU-004WO 205. The system of any one of embodiments 167-204, wherein obtaining or having obtained the dataset comprising identities of the plurality of TCRs from the subject comprises performing an assay to determine the plurality of TCRs. 206. The system of embodiment 205, wherein the assay is an amplification-based assay. 207. The system of embodiment 206, wherein the amplification-based assay is a PCR assay, RT-PCR assay, qRT-PCR assay, or multiplex PCR assay. 208. The system of any one of embodiments 167-207, wherein the analyzing and generating the subject feature count comprises performing an assay to determine the plurality of TCRs, and performing a feature count to determine the subject feature count against the cancer-associated RFUs. 209. The system of embodiment 208, wherein the assay is a sequencing-based assay. 210. The system of embodiment 209, wherein the sequencing-based assay is an RNA-seq assay. 211. The system of any one of embodiments 205-210, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising primers. 212. The system of any one of embodiments 186-190, wherein obtaining or having obtained the second dataset comprises performing an assay to determine the expression levels of the plurality of biomarkers. 213. The system of embodiment 212, wherein the assay is a Proximity Extension Assay (PEA), a xMAP Multiplex Assay, a single molecule array (SIMOA) assay, mass spectrometry based protein or peptide assay, or an aptamer-based assay. 214. The system of embodiment 212 or embodiment 213, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising antibodies. 215. The system of embodiment 214, wherein the antibodies comprise one of monoclonal and polyclonal antibodies. 216. The system of embodiment 214, wherein the antibodies comprise both monoclonal and polyclonal antibodies. IPTS/128553107.1 Attorney Docket No: SRU-004WO 217. The system of any one of embodiments 191-196, wherein obtaining or having obtained the third dataset comprises performing an assay to determine the mutation profile of the plurality of ctDNA. 218. The system of embodiment 217, wherein the assay is an NGS-based hybrid capture system assay. 219. The system of any one of embodiments 167-218, wherein the subject is an undiagnosed subject, at risk subject, or a subject previously diagnosed with cancer. 220. A kit for predicting presence, absence, or likelihood of cancer in a subject, the kit comprising: a set of reagents for determining identities of a plurality of T-cell receptors (TCRs) from a test sample from the subject; and instructions for using the set of reagents to: generate a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the sample from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs are encoded by at least: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29- 1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1- 5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; and generate a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. 221. The kit of embodiment 220, wherein the identities of the plurality of TCRs from the subject comprise: IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; and/or a plurality of variable regions, wherein the variable regions are encoded for by at least: a variable gene of: TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29- 1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; and a joining gene of: TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1- 5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2- 6, and TRBJ2-7. 222. The kit of embodiment 221, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV13; and a joining gene selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV14; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV18; and a joining gene selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1-6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV19; and a joining gene selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1; a variable gene TRBV2; and a joining gene selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV20-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-5, TRBJ2-3, and TRBJ2-5; a variable gene TRBV25-1; and a joining gene selected from any one of TRBJ2- 1, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV27; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-4, and TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV5-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-5; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV5-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-8; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV6-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV6-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene selected from any one of TRBJ2-3, and TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-7; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-8; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2-7; a variable gene TRBV7-9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7; or a variable gene TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, and TRBJ2-7. 223. The kit of embodiment 222, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV13; and a joining gene TRBJ1-4; a variable gene TRBV13; and a joining gene TRBJ1-5; a variable gene TRBV13; and a joining gene TRBJ2-1; a variable gene TRBV13; and a joining gene TRBJ2-2; a variable gene TRBV13; and a joining gene TRBJ2-3; a variable gene TRBV13; and a joining gene TRBJ2-5; a variable gene TRBV13; and a joining gene TRBJ2-7; a variable gene TRBV11-3; and a joining gene TRBJ2-1; a variable gene TRBV11-3; and a joining gene TRBJ2-2; a variable gene TRBV11-3; and a joining gene TRBJ2-7; a variable gene TRBV14; and a joining gene TRBJ1-1; a variable gene TRBV14; and a joining gene TRBJ1-4; a variable gene TRBV14; and a joining gene TRBJ1-5; a variable gene TRBV14; and a joining gene TRBJ2-1; a variable gene TRBV14; and a joining gene TRBJ2-2; a variable gene TRBV14; and a joining gene TRBJ2-3; a variable gene TRBV14; and a joining gene TRBJ2-5; a variable gene TRBV14; and a joining gene TRBJ2-7; a variable gene TRBV18; and a joining gene TRBJ1-1; a variable gene TRBV18; and a joining gene TRBJ1-3; a variable gene TRBV18; and a joining gene TRBJ1-5; a variable gene TRBV18; and a joining gene TRBJ1-6; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV18; and a joining gene TRBJ2-2; a variable gene TRBV18; and a joining gene TRBJ2-3; a variable gene TRBV18; and a joining gene TRBJ2-5; a variable gene TRBV18; and a joining gene TRBJ2-7; a variable gene TRBV19; and a joining gene TRBJ1-2; a variable gene TRBV19; and a joining gene TRBJ1-6; a variable gene TRBV19; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ1-6; a variable gene TRBV2; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ2-7; a variable gene TRBV20-1; and a joining gene TRBJ1-1; a variable gene TRBV20-1; and a joining gene TRBJ1-5; a variable gene TRBV20-1; and a joining gene TRBJ2-3; a variable gene TRBV20-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-1; a variable gene TRBV25-1; and a joining gene TRBJ2-3; a variable gene TRBV25-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-7; a variable gene TRBV27; and a joining gene TRBJ1-1; a variable gene TRBV27; and a joining gene TRBJ1-2; a variable gene TRBV27; and a joining gene TRBJ1-3; a variable gene TRBV27; and a joining gene TRBJ1-4; a variable gene TRBV27; and a joining gene TRBJ2-1; a variable gene TRBV27; and a joining gene TRBJ2-2; a variable gene TRBV27; and a joining gene TRBJ2-3; a variable gene TRBV27; and a joining gene TRBJ2-5; a variable gene TRBV27; and a joining gene TRBJ2-6; a variable gene TRBV27; and a joining gene TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene TRBJ1-1; a variable gene TRBV29-1; and a joining gene TRBJ1-4; a variable gene TRBV29-1; and a joining gene TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene TRBJ1-1; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-1; and a joining gene TRBJ1-2; a variable gene TRBV5-1; and a joining gene TRBJ1-3; a variable gene TRBV5-1; and a joining gene TRBJ1-4; a variable gene TRBV5-1; and a joining gene TRBJ1-5; a variable gene TRBV5-1; and a joining gene TRBJ1-6; a variable gene TRBV5-1; and a joining gene TRBJ2-1; a variable gene TRBV5-1; and a joining gene TRBJ2-2; a variable gene TRBV5-1; and a joining gene TRBJ2-3; a variable gene TRBV5-1; and a joining gene TRBJ2-4; a variable gene TRBV5-1; and a joining gene TRBJ2-5; a variable gene TRBV5-1; and a joining gene TRBJ2-6; a variable gene TRBV5-1; and a joining gene TRBJ2-7; a variable gene TRBV5-4; and a joining gene TRBJ1-1; a variable gene TRBV5-4; and a joining gene TRBJ2-1; a variable gene TRBV5-4; and a joining gene TRBJ2-7; a variable gene TRBV5-5; and a joining gene TRBJ1-1; a variable gene TRBV5-5; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ1-1; a variable gene TRBV5-6; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ2-7; a variable gene TRBV5-8; and a joining gene TRBJ1-1; a variable gene TRBV5-8; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-2; a variable gene TRBV6-1; and a joining gene TRBJ2-7; a variable gene TRBV6-4; and a joining gene TRBJ1-1; a variable gene TRBV6-4; and a joining gene TRBJ2-1; a variable gene TRBV6-4; and a joining gene TRBJ2-2; a variable gene TRBV6-4; and a joining gene TRBJ2-6; a variable gene TRBV6-4; and a joining gene TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-5; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ1-1; a variable gene TRBV7-6; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ2-7; a variable gene TRBV7-7; and a joining gene TRBJ1-1; a variable gene TRBV7-7; and a joining gene TRBJ1-4; a variable gene TRBV7-7; and a joining gene TRBJ2-1; a variable gene TRBV7-7; and a joining gene TRBJ2-7; a variable gene TRBV7-8; and a joining gene TRBJ1-1; a variable gene TRBV7-8; and a joining gene TRBJ1-5; a variable gene TRBV7-8; and a joining gene TRBJ2-1; a variable gene TRBV7-8; and a joining gene TRBJ2-5; a variable gene TRBV7-8; and a joining gene TRBJ2-7; a variable gene TRBV7-9; and a joining gene TRBJ1-1; a variable gene TRBV7-9; and a joining gene TRBJ1-4; a variable gene TRBV7-9; and a joining gene TRBJ1-5; a variable gene TRBV7-9; and a joining gene TRBJ1-6; a variable gene TRBV7-9; and a joining gene TRBJ2-1; a variable gene TRBV7-9; and a joining gene TRBJ2-2; a variable gene TRBV7-9; and a joining gene TRBJ2-3; a variable gene TRBV7-9; and a joining gene TRBJ2-4; a variable gene TRBV7-9; and a joining gene TRBJ2-5; a variable gene TRBV7-9; and a joining gene TRBJ2-7; a variable gene TRBV9; and a joining gene TRBJ1-1; a variable gene TRBV9; and a joining gene TRBJ1-4; a variable gene TRBV9; and a joining gene TRBJ2-1; a variable gene TRBV9; and a joining gene TRBJ2-2; a variable gene TRBV9; and a joining gene TRBJ2-3; or a variable gene TRBV9; and a joining gene TRBJ2-7. 224. The kit of embodiment 220, wherein the cancer-associated TCR RFUs are determined by: obtaining or having obtained TCR sequencing data for a plurality of TCRs from a plurality of training samples; IPTS/128553107.1 Attorney Docket No: SRU-004WO assigning TCRs of the plurality of TCRs into candidate RFUs by: grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores; clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc). 225. The kit of embodiment 224, wherein the cancer-associated TCR RFUs are further determined by performing one or more of: filtering candidate RFUs to retain candidate RFUs that are observed in at least a first threshold number of training samples; filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a second threshold number of training samples; filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than a threshold value. 226. The kit of embodiment 224, wherein the cancer-associated TCR RFUs are further determined by: applying a gamma-Poisson generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. 227. The kit of embodiment 226, wherein applying the gamma-Poisson generalized linear model further comprises incorporating demographic covariates. 228. The kit of embodiment 227, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 229. The kit of embodiment 224, wherein: the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or IPTS/128553107.1 Attorney Docket No: SRU-004WO the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. 230. The kit of embodiment 220, wherein the predictive model is a logistic regression model. 231. The kit of embodiment 220, wherein the cancer-associated TCR RFUs comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, or at least 197 RFUs. IPTS/128553107.1 Attorney Docket No: SRU-004WO 232. The kit of embodiment 220, wherein a performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 233. The kit of embodiment 232, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.64. 234. The kit of embodiment 232, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.70. 235. The kit of embodiment 232, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.71. 236. The kit of embodiment 232, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.83. 237. The kit of embodiment 232, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.84. 238. The kit of embodiment 232, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.85. 239. The kit of embodiment 220, wherein the kit further comprises: obtaining or having obtained a second dataset comprising expression levels of a plurality of biomarkers from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a second predictive model to the expression levels of the plurality of biomarkers. 240. The kit of embodiment 239, wherein the second predictive model is a support vector machine (SVM) model. 241. The kit of embodiment 239, wherein the plurality of biomarkers comprises at two or more biomarkers selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, IPTS/128553107.1 Attorney Docket No: SRU-004WO CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. 242. The kit of embodiment 239, wherein a performance of the second predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 243. The kit of any one of embodiments 239-242, wherein a performance metric of the second predictive model is improved in comparison to a model solely incorporating CEACAM5. 244. The kit of embodiment 220, wherein the kit further comprises: obtaining or having obtained a third dataset comprising a mutational profile of a plurality of circulating tumor DNA (ctDNA) from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a third predictive model to the mutational profiles of ctDNA. 245. The kit of embodiment 244, wherein the third predictive model is a logistic regression model. 246. The kit of embodiment 244, wherein the plurality of ctDNA comprises ctDNA selected from CDKN2A, MGAM, PIK3CA, EPHB1, PAK5, KEAP1, TP53, KRAS, KDM5A, ATM, and PTEN. 247. The kit of embodiment 244, wherein the ctDNA comprises a mutation. 248. The kit of embodiment 247, wherein the mutation is any one of combination of a frameshift mutation, a missense mutation, a synonymous mutation, a splice site mutation, or a nonsense mutation. 249. The kit of any one of embodiments 244-248, wherein the mutation is a substitution, an insertion, a deletion, or any combination thereof. 250. The kit of any one of embodiments 220-249, wherein the cancer is lung cancer. IPTS/128553107.1 Attorney Docket No: SRU-004WO 251. The kit of any one of embodiments 220-250, wherein the lung cancer is an adenocarcinoma, an adenosquamous cell cancer, a large cell cancer, a neuroendocrine cancer, a non-small cell lung cancer (NSCLC), a small cell cancer, or a squamous cell cancer. 252. The kit of any one of embodiments 220-251, wherein the cancer is an early stage cancer. 253. The kit of any one of embodiments 220-252, wherein the cancer is stage I, stage II, stage III, and/or stage IV lung cancer. 254. The kit of any one of embodiments 220-253, wherein the identities of the plurality of TCRs are determined from a test sample obtained from the subject. 255. The kit of embodiment 254, wherein the test sample is a blood or serum sample. 256. The kit of embodiment 254 or embodiment 255, wherein the subject is suspected of having an early stage cancer. 257. The kit of embodiment 254 or embodiment 255, wherein the subject is not suspected of having an early stage cancer. 258. The kit of any one of embodiments 220-257, wherein obtaining or having obtained the dataset comprising identities of the plurality of TCRs from the subject comprises performing an assay to determine the plurality of TCRs. 259. The kit of embodiment 258, wherein the assay is an amplification-based assay. 260. The kit of embodiment 259, wherein the amplification-based assay is a PCR assay, RT-PCR assay, qRT-PCR assay, or multiplex PCR assay. 261. The kit of any one of embodiments 220-260, wherein the analyzing and generating the subject feature count comprises performing an assay to determine the plurality of TCRs, and performing a feature count to determine the subject feature count against the cancer-associated RFUs. 262. The kit of embodiment 261, wherein the assay is a sequencing-based assay. IPTS/128553107.1 Attorney Docket No: SRU-004WO 263. The kit of embodiment 262, wherein the sequencing-based assay is an RNA-seq assay. 264. The kit of any one of embodiments 258-263, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising primers. 265. The kit of any one of embodiments 239-243, wherein obtaining or having obtained the second dataset comprises performing an assay to determine the expression levels of the plurality of biomarkers. 266. The kit of embodiment 265, wherein the assay is a Proximity Extension Assay (PEA), a xMAP Multiplex Assay, a single molecule array (SIMOA) assay, mass spectrometry based protein or peptide assay, or an aptamer-based assay. 267. The kit of embodiment 265 or embodiment 266, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising antibodies. 268. The kit of embodiment 267, wherein the antibodies comprise one of monoclonal and polyclonal antibodies. 269. The kit of embodiment 267, wherein the antibodies comprise both monoclonal and polyclonal antibodies. 270. The kit of any one of embodiments 244-249, wherein obtaining or having obtained the third dataset comprises performing an assay to determine the mutation profile of the plurality of ctDNA. 271. The kit of embodiment 270, wherein the assay is an NGS-based hybrid capture kit assay. 272. The kit of any one of embodiments 220-271, wherein the subject is an undiagnosed subject, at risk subject, or a subject previously diagnosed with cancer. 273. A method for developing cancer-associated TCR repertoire functional units (RFUs), the method comprising: obtaining or having obtained TCR sequencing data of a plurality of TCRs from a plurality of training samples; sorting the plurality of TCRs into candidate RFUs by: IPTS/128553107.1 Attorney Docket No: SRU-004WO clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc); further processing candidate RFUs by performing one or more of: filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a first threshold number of training samples; and/or filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, or 32; and analyzing, through a generalized linear model, the candidate RFUs to identify cancer- associated RFUs. 274. The method of embodiment 273, wherein the overall dissimilarity scores are generated by: grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores. 275. The method of embodiment 274, wherein further processing candidate RFUs further comprises: filtering candidate RFUs to retain candidate RFUs that are observed in at least a second threshold number of training samples. 276. The method of any one of embodiments 273-275, wherein analyzing, through the generalized linear model further comprises incorporating demographic covariates. 277. The method of embodiment 276, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 278. The method of embodiment 273, wherein the generalized linear model is a gamma- Poisson generalized linear model. IPTS/128553107.1 Attorney Docket No: SRU-004WO 279. The method of embodiment 273, wherein the obtaining or having obtained the TCR sequencing data of a plurality of TCRs comprises performing an assay to determine TCR sequencing data of a plurality of TCRs. 280. The method of embodiment 279, wherein the assay is an amplification-based assay. 281. The method of embodiment 280, wherein the amplification-based assay is a PCR assay, RT-PCR assay, qRT-PCR assay, or multiplex PCR assay. 282. The method of embodiment 273, wherein: the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. 283. The method of embodiment 273, wherein the T-cell expansion is determined by estimating the number of T-cells carrying a TCR, wherein the TCR is any TCR provided herein. 284. The method of embodiment 283, wherein the T-cell expansion is present if more than 2, 4, 8, 16, 32, 64, 128, 256, or 512 clones carry TCRs, such as those provided herein. 285. The method of any one of embodiments 273-284, wherein the minimum amino acid- level recurrence is greater than 0, 1, 2, or 3. 286. The method of any one of embodiments 273-285, wherein the minimum amino acid- level recurrence is equal to 4. 287. The method of embodiment 273, wherein the first threshold number of training samples is at least 200, at least 210, at least 220, at least 230, at least 240, at least 250, at least 260, at least 270, at least 280, at least 290, or at least 300. 288. The method of embodiment 273, wherein the second threshold number of training samples is at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at IPTS/128553107.1 Attorney Docket No: SRU-004WO least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, or at least 50. 289. A method for developing a predictive model for predicting presence, absence, or likelihood of cancer, the model comprising: obtaining or having obtained feature counts across a plurality of cancer-associated TCR repertoire functional units (RFUs), wherein a plurality of variable regions of the cancer-associated TCR RFUs are encoded by at least: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, and TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; and analyzing, through a ML implemented method, the feature counts across the plurality of cancer-associated TCR RFUs to train the predictive model useful for predicting presence, absence, or likelihood of a cancer. 290. The method of embodiment 289, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV13; and a joining gene selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV14; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV18; and a joining gene selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1-6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV19; and a joining gene selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1; a variable gene TRBV2; and a joining gene selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7; a variable gene TRBV20-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-5, TRBJ2-3, and TRBJ2-5; a variable gene TRBV25-1; and a joining gene selected from any one of TRBJ2- 1, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV27; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-4, and TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV5-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-5; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV5-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-8; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV6-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV6-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene selected from any one of TRBJ2-3, and TRBJ2-5; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-7; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-8; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2-7; a variable gene TRBV7-9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7; or a variable gene TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, and TRBJ2-7. 291. The method of embodiment 290, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV13; and a joining gene TRBJ1-4; a variable gene TRBV13; and a joining gene TRBJ1-5; a variable gene TRBV13; and a joining gene TRBJ2-1; a variable gene TRBV13; and a joining gene TRBJ2-2; a variable gene TRBV13; and a joining gene TRBJ2-3; a variable gene TRBV13; and a joining gene TRBJ2-5; a variable gene TRBV13; and a joining gene TRBJ2-7; a variable gene TRBV11-3; and a joining gene TRBJ2-1; a variable gene TRBV11-3; and a joining gene TRBJ2-2; a variable gene TRBV11-3; and a joining gene TRBJ2-7; a variable gene TRBV14; and a joining gene TRBJ1-1; a variable gene TRBV14; and a joining gene TRBJ1-4; a variable gene TRBV14; and a joining gene TRBJ1-5; a variable gene TRBV14; and a joining gene TRBJ2-1; a variable gene TRBV14; and a joining gene TRBJ2-2; a variable gene TRBV14; and a joining gene TRBJ2-3; a variable gene TRBV14; and a joining gene TRBJ2-5; a variable gene TRBV14; and a joining gene TRBJ2-7; a variable gene TRBV18; and a joining gene TRBJ1-1; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV18; and a joining gene TRBJ1-3; a variable gene TRBV18; and a joining gene TRBJ1-5; a variable gene TRBV18; and a joining gene TRBJ1-6; a variable gene TRBV18; and a joining gene TRBJ2-2; a variable gene TRBV18; and a joining gene TRBJ2-3; a variable gene TRBV18; and a joining gene TRBJ2-5; a variable gene TRBV18; and a joining gene TRBJ2-7; a variable gene TRBV19; and a joining gene TRBJ1-2; a variable gene TRBV19; and a joining gene TRBJ1-6; a variable gene TRBV19; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ1-6; a variable gene TRBV2; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ2-7; a variable gene TRBV20-1; and a joining gene TRBJ1-1; a variable gene TRBV20-1; and a joining gene TRBJ1-5; a variable gene TRBV20-1; and a joining gene TRBJ2-3; a variable gene TRBV20-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-1; a variable gene TRBV25-1; and a joining gene TRBJ2-3; a variable gene TRBV25-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-7; a variable gene TRBV27; and a joining gene TRBJ1-1; a variable gene TRBV27; and a joining gene TRBJ1-2; a variable gene TRBV27; and a joining gene TRBJ1-3; a variable gene TRBV27; and a joining gene TRBJ1-4; a variable gene TRBV27; and a joining gene TRBJ2-1; a variable gene TRBV27; and a joining gene TRBJ2-2; a variable gene TRBV27; and a joining gene TRBJ2-3; a variable gene TRBV27; and a joining gene TRBJ2-5; a variable gene TRBV27; and a joining gene TRBJ2-6; a variable gene TRBV27; and a joining gene TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene TRBJ1-1; a variable gene TRBV29-1; and a joining gene TRBJ1-4; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV29-1; and a joining gene TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene TRBJ1-1; a variable gene TRBV5-1; and a joining gene TRBJ1-2; a variable gene TRBV5-1; and a joining gene TRBJ1-3; a variable gene TRBV5-1; and a joining gene TRBJ1-4; a variable gene TRBV5-1; and a joining gene TRBJ1-5; a variable gene TRBV5-1; and a joining gene TRBJ1-6; a variable gene TRBV5-1; and a joining gene TRBJ2-1; a variable gene TRBV5-1; and a joining gene TRBJ2-2; a variable gene TRBV5-1; and a joining gene TRBJ2-3; a variable gene TRBV5-1; and a joining gene TRBJ2-4; a variable gene TRBV5-1; and a joining gene TRBJ2-5; a variable gene TRBV5-1; and a joining gene TRBJ2-6; a variable gene TRBV5-1; and a joining gene TRBJ2-7; a variable gene TRBV5-4; and a joining gene TRBJ1-1; a variable gene TRBV5-4; and a joining gene TRBJ2-1; a variable gene TRBV5-4; and a joining gene TRBJ2-7; a variable gene TRBV5-5; and a joining gene TRBJ1-1; a variable gene TRBV5-5; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ1-1; a variable gene TRBV5-6; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ2-7; a variable gene TRBV5-8; and a joining gene TRBJ1-1; a variable gene TRBV5-8; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-2; a variable gene TRBV6-1; and a joining gene TRBJ2-7; a variable gene TRBV6-4; and a joining gene TRBJ1-1; a variable gene TRBV6-4; and a joining gene TRBJ2-1; a variable gene TRBV6-4; and a joining gene TRBJ2-2; a variable gene TRBV6-4; and a joining gene TRBJ2-6; a variable gene TRBV6-4; and a joining gene TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ1-1; a variable gene TRBV7-6; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ2-7; a variable gene TRBV7-7; and a joining gene TRBJ1-1; a variable gene TRBV7-7; and a joining gene TRBJ1-4; a variable gene TRBV7-7; and a joining gene TRBJ2-1; a variable gene TRBV7-7; and a joining gene TRBJ2-7; a variable gene TRBV7-8; and a joining gene TRBJ1-1; a variable gene TRBV7-8; and a joining gene TRBJ1-5; a variable gene TRBV7-8; and a joining gene TRBJ2-1; a variable gene TRBV7-8; and a joining gene TRBJ2-5; a variable gene TRBV7-8; and a joining gene TRBJ2-7; a variable gene TRBV7-9; and a joining gene TRBJ1-1; a variable gene TRBV7-9; and a joining gene TRBJ1-4; a variable gene TRBV7-9; and a joining gene TRBJ1-5; a variable gene TRBV7-9; and a joining gene TRBJ1-6; a variable gene TRBV7-9; and a joining gene TRBJ2-1; a variable gene TRBV7-9; and a joining gene TRBJ2-2; a variable gene TRBV7-9; and a joining gene TRBJ2-3; a variable gene TRBV7-9; and a joining gene TRBJ2-4; a variable gene TRBV7-9; and a joining gene TRBJ2-5; a variable gene TRBV7-9; and a joining gene TRBJ2-7; a variable gene TRBV9; and a joining gene TRBJ1-1; a variable gene TRBV9; and a joining gene TRBJ1-4; a variable gene TRBV9; and a joining gene TRBJ2-1; a variable gene TRBV9; and a joining gene TRBJ2-2; a variable gene TRBV9; and a joining gene TRBJ2-3; or a variable gene TRBV9; and a joining gene TRBJ2-7. IPTS/128553107.1 Attorney Docket No: SRU-004WO 292. The method of embodiment 289, wherein the cancer-associated TCR RFUs are determined by: obtaining or having obtained TCR sequencing data for a plurality of TCRs from a plurality of training samples; assigning TCRs of the plurality of TCRs into candidate RFUs by: grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores; clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc). 293. The method of embodiment 292, wherein the cancer-associated TCR RFUs are further determined by performing one or more of: filtering candidate RFUs to retain candidate RFUs that are observed in at least a first threshold number of training samples; filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a second threshold number of training samples; filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than a threshold value. 294. The method of embodiment 292, wherein the cancer-associated TCR RFUs are further determined by: applying a gamma-Poisson generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. 295. The method of embodiment 294, wherein applying the gamma-Poisson generalized linear model further comprises incorporating demographic covariates. 296. The method of embodiment 295, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 297. The method of embodiment 292, wherein: IPTS/128553107.1 Attorney Docket No: SRU-004WO the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. 298. The method of embodiment 289, wherein the predictive model is a logistic regression model. 299. The method of embodiment 289, wherein the cancer-associated TCR RFUs comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least IPTS/128553107.1 Attorney Docket No: SRU-004WO 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, or at least 197 RFUs. 300. The method of embodiment 289, wherein a performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 301. The method of embodiment 300, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.64. 302. The method of embodiment 300, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.70. 303. The method of embodiment 300, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.71. 304. The method of embodiment 300, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.83. 305. The method of embodiment 300, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.84. 306. The method of embodiment 300, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.85. 307. The method of embodiment 300, wherein the cancer is lung cancer. 308. The method of any one of embodiments 289-307, wherein the lung cancer is an adenocarcinoma, an adenosquamous cell cancer, a large cell cancer, a neuroendocrine cancer, a non-small cell lung cancer (NSCLC), a small cell cancer, or a squamous cell cancer. IPTS/128553107.1 Attorney Docket No: SRU-004WO 309. The method of any one of embodiments 289-308, wherein the cancer is an early stage cancer. 310. The method of any one of embodiments 289-309, wherein the cancer is stage I, stage II, stage III, and/or stage IV lung cancer. 311. The method of any one of embodiments 289-310, wherein obtaining or having obtained the dataset comprising identities of the plurality of TCRs from the subject comprises performing an assay to determine the feature count against the cancer- associated RFUs. 312. The method of embodiment 311, wherein the assay is an amplification-based assay and/or a sequencing-based assay. 313. The method of embodiment 312, wherein the amplification-based assay is a PCR assay, RT-PCR assay, qRT-PCR assay, or multiplex PCR assay. 314. The method of embodiment 313, wherein the sequencing-based assay is an RNA- seq assay. 315. The method of any one of embodiments 289-314, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising primers. EXAMPLES [00385] Below are examples of specific embodiments. The examples are offered for illustrative purposes only and are not intended to limit the scope. Efforts have been made to ensure accuracy with respect to numbers used, but some experimental error and deviation should be allowed for. Example 1: Dataset for Discovery of Lung-Cancer Associated TCR RFUs [00386] A cohort of blood samples from 155 patients diagnosed with lung cancer was assembled for discovery of RFUs associated with cancer status. The cohort was enriched for subjects with early-stage disease (FIG.8) and spanned all major lung cancer subtypes (FIG. 8). [00387] Blood from 183 subjects without lung cancer were collected as control samples, enriching for older individuals (FIG.7) with a history of smoking (FIG.7) to match the cancer cases. ImmunoSeq® assay was used to sequence 80,694,598 TCRs, of which IPTS/128553107.1 Attorney Docket No: SRU-004WO 65,238,142 were productive (median 183,255.5 TCRs/sample). The assay yielded a median of 94,177.5 TCR clonotypes per sample, which was comparable between cancer patients and non-cancer controls (FIG.7). After filtering for productive TCRs with a resolved variable gene and a CDR3 length of 10-16 residues, 57,492,160 TCRs derived from 32,059,449 TCR clonotypes were taken forward to RFU discovery and cancer-control association analysis. Example 2: Discovery of Lung-Cancer Associated RFUs [00388] A sample size of more than 32 million TCR clonotype data points was prohibitive to standard clustering algorithms such as hierarchical clustering or Gaussian mixture models due to their iterative nature and reliance on a distance matrix. To group the TCRs into RFUs, an approximate nearest neighbor graph on the TCRs using a CDR3 sequence dissimilarity metric was first created. The three N-terminal and two C-terminal residues in the CDR3 were excluded and the variable gene of each TCR was combined with the CDR3 dissimilarity into an overall dissimilarity score. This method was built upon a fast non-parametric clustering algorithm, CFSFDP originally described in Rodriguez, A. et al., Clustering by fast search and find of density peaks, Science, 2014, 344(6191), pp.1492-1496, which is hereby incorporated by reference in its entirety. The original algorithm involves a dissimilarity matrix between all data points, which is computationally prohibitive for a dataset of tens of millions of TCRs. The following improvements to the original CFSFDP algorithm were implemented: • Instead of computing and storing a pairwise dissimilarity matrix exhaustively, the methodology was updated by building an approximate nearest neighbor (ANN) index using a TCR dissimilarity metric described in Zhang et al, “GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation,” Nature Communications, 2021, 12(4699), which is hereby incorporated by reference in its entirety. The ANN was used to query the nearest neighbors of a given TCR in the index. • In the original CFSFDP algorithm, the density of each data point was calculated by exhaustively enumerating the number of neighbors within a dissimilarity cutoff of d, which is an O(n²) operation for n data points. Instead, the updated methodology uses an ANN index to (approximately) search for all the neighbors of each TCR within d. Since an ANN index returns neighbors in the order of similarity, this process only involves a small number of operations per TCR on the ANN index. IPTS/128553107.1 Attorney Docket No: SRU-004WO • Similarly, the ANN index was queried to search for the nearest TCR of higher density for each TCR instead of performing an exhaustive pairwise search. • The methodology implemented a computationally cheaper step by rejecting the direct clustering of two TCRs if their dissimilarity score exceeded d. [00389] An approximate, logarithmic time nonparametric clustering algorithm was implemented based on clustering by density peaks to the approximate nearest neighbor graph to assign the TCRs into RFUs (or as non-clustered singletons TCRs). Several candidate RFU sets were generated by varying the maximum TCR dissimilarity cutoff (parameter dc) which controls clustering sparsity. Three dc settings were used: 11, 12, and 22, which corresponded to one conservative amino acid mismatch, one mismatch of any kind or an insertion/deletion (indel), or one mismatch or indel and an additional conservative mismatch allowed respectively. Three additional dc settings (1.1, 1.2, and 2.2, approximately corresponding to any conservative amino acid mismatch for CDR3 length greater than or equal to 15 amino acid residues; any amino acid mismatch for CDR3 length greater than or equal to 15 amino acid residues; and any amino acid mismatch for CDR3 length greater than or equal to 11 amino acid residues, and any two amino acid mismatches for CDR3 length greater than or equal to 16, respectively, assuming the TCRs have matching CDR1, CDR2, and CDR2.5 sequences) were also tested by normalizing the distances above by the number of considered residues in CDR3 alignment after the N and C-terminal trimming. [00390] The clustering analysis generated 81,134 to 2,542,738 RFUs depending on dc setting (FIG.9A-9C), with between 18.8x10^6 and 30.6x10^6 TCRs being clustered with at least one other TCR (FIGs.9A-9C). RFUs followed a power law distribution in size (FIGs.9A- 9C), with a small number of large RFUs and large number of small RFUs. To assess the biological relevance of the TCR clustering, RFUs harboring TCRs that have previously been associated with CMV or influenza infection were manually inspected. RFUs centered around high prevalence TCRs targeting common influenza and CMV antigens (FIG.10 and FIG.22), surrounded by near-identical, recurrent TCRs. The clustered TCRs showed evidence of strong expansion consistent with acute response in some individuals and likely low-level latent responses in a significant subset of the cohort. [00391] The large number of RFUs and their predominantly small size presented a significant multiple testing burden imposed by small RFUs for which there was no statistical power to detect association with cancer at the current sample size. To address this for the case-control analysis, the set of candidate RFUs was restricted to RFUs observed in at least 15 individuals. IPTS/128553107.1 Attorney Docket No: SRU-004WO This resulted in between 1,114 and 199,895 RFUs being tested for cancer association depending on the dc setting. While RFUs obtained with different dc settings partially overlapped, clustering TCRs across a range of dc values allowed to find the optimal balance between the population prevalence and the degree of shared antigen specificity of each RFU for cancer association testing. To further focus the analysis on TCRs most likely to be related to cancer, the analysis was additionally restricted to RFUs with evidence of T cell expansion in at least 8 individuals (without considering cancer status) and only TCR clonotypes with minimum amino acid-level recurrence of 4 within the dataset for each RFU were tallied (FIG. 23). This filtering further ensured that the signal was above noise (as determined by permutation). [00392] The observed per-subject distribution of RFU counts was analogous to gene expression levels measured using RNA-seq, with the frequency of an RFU (gene) being computed as the sum of its TCR counts (transcripts or exons). Next, the gamma-Poisson generalized linear model was used to test for RFU association with cancer status. This model accounted for variable depth of sequencing and RFU count overdispersion and allowed incorporation of demographic covariates such as age, gender, and race into the model. Here, using the gamma-Poisson GLM was valuable to associate the RFUs with cancer status because the RFU TCR count data is analogous to gene expression from RNASeq. Furthermore, the gamma-Poisson GLM enabled incorporation of demographic covariates age, gender, race. Notably, most cancer associated RFUs were also associated with decreased counts with increasing age in the overall cohort. Thus, incorporation of the covariates further improved the cancer signal. [00393] False discovery rate (FDR) correction was applied with an FDR cutoff of 0.1 to identify statistically significant RFU hits within each dc setting. [00394] A total of 32 RFUs associated with cancer status across the six dc cutoffs was identified, including 29 that were enriched in cancer samples with fold change between 0.17 and 2.23, and 3 that were enriched in non-cancer controls with a fold change between 0.12 and 0.16 (FIG.11, and Table 3). Two recurrent patterns among cancer enriched RFUs were observed. Twenty-three RFUs showed a pattern of decreasing TCR count with increasing age in all individuals (at FDR ≤ 0.1), coupled with predominantly higher (20/23 RFUs) counts in cancer patients relative to age-matched controls (FIG.12). The second pattern, accounting for three RFUs, showed a low-level response in a minority of both cancer cases and non-cancer controls, with significantly higher TCR levels found in a subset of cancer cases. The six remaining RFUs were commonly expressed and cancer-enriched but not associated with age. IPTS/128553107.1 Attorney Docket No: SRU-004WO Seven out of the 32 RFUs had a repeated TCR centroid across different dc settings, indicating that they were either the same or nested RFUs. Some cancer-associated RFUs also showed significant association with multiple demographic covariates, highlighting the importance of the rigorous statistical model selected for the analysis (Table 4). Example 3: Cancer Prediction from RFUs [00395] A standard machine learning approach with 5-fold cross-validation was employed to evaluate the utility of the 32 cancer associated RFUs for cancer status prediction. In order to minimize RFU frequency bias arising from any demographic covariate imbalances between the cancer and control cohorts, each cancer-associated RFU’s frequency value was adjusted for the fitted effect of the demographic covariates that were statistically significantly (FDR ≤ 0.1) associated with the RFU. (FIG.24) These 32 adjusted RFU values were then used as features to train a logistic regression for cancer status prediction with forward feature selection (5-fold cross-validation). An average cross-validation ROC AUC of 0.75 was observed. The ROC AUC for stage I cancer exceeded the performance for all other stages at an AUC of 0.79 (FIG.14). Model predictions did not appear to be driven by demographic covariates (FIGs.25-28) or technical factors/batch effects (FIGs.29-30). Notably, >60% of stage I subjects were detected by the model at a specificity of 80% (test samples of each cross-validation fold; FIG.15) and the model did differentiate between lung cancer and other conditions (FIG.19). Example 4: Dataset for Validation of Lung Cancer-Associated Plasma Protein Biomarkers [00396] The TCR-based signature generated in Example 2 was used to assess for cancer early detection in the context of established tumor analytes. A total of 54 distinct protein markers previously associated with lung cancer was selected. In order to compare the predictive performance of the published protein biomarkers with the TCR signature of Example 2, circulating protein level data from 235 study subjects, including 110 cancer patients and 125 non-cancer controls, were generated. Demographic and tumor properties distribution of these subjects matched the overall distribution closely (FIGs.7-8). Olink Oncology and Inflammation Explore® panels, which assay 731 protein markers associated with these biological pathways, were used. Out of the 54 protein markers, 26 were covered by these panels and were used for replication of the published results (Table 5). [00397] Of the 26 proteins, 18 were significantly associated with cancer status in the study cohort at FDR<0.05. Notably, 17 of the 18 were positively associated with cancer status in IPTS/128553107.1 Attorney Docket No: SRU-004WO both the published report and in the study dataset. One additional protein (ALPP) was associated with cancer status in our cohort (FDR<0.05) but in the opposite direction (positive in the published study but negative in our data). The 8 remaining proteins tested were not significantly associated with cancer status in our dataset. The 17 successfully replicated protein biomarkers were advanced to subsequent analyses. Example 5: Cancer Detection With Recurrent ctDNA Lung Cancer Driver Mutations [00398] Next, a ctDNA assessment approach was used to further evaluate the TCR RFU- based cancer prediction in the context of established plasma-based early detection methods. ctDNA data was generated for 97 subjects comprising 58 cancer patients and 39 non-cancer controls. Targeted sequencing on 237 mutation hotspots in 154 lung cancer driver genes was performed (Table 6) using commercially available Illumina® sequencing library construction and hybridization target capture reagents (IDT xGen™). Matching gDNA from each subject was sequenced alongside the ctDNA samples to identify and exclude ctDNA mutations derived from clonal hematopoiesis of indeterminate potential. The average unique molecule coverage on the targeted mutation sites was >1,500x and >875x for ctDNA and gDNA samples, respectively. [00399] After standard NGS variant calling and filtering and excluding mutations found in matching gDNA samples, 25 mutations were called in total across the 97 subjects (Table 7). As expected, most mutations were called in individuals with stage III-IV disease (FIG.17), allowing the majority of stage III-IV cancer patients to be detected. In contrast, sensitivity was poor for early-stage disease (FIG.18). Example 6: Lung Cancer Prediction With a Multi-Analyte Liquid Biopsy Incorporating Immune Recognition [00400] Of the previously established 338, 235, and 97 subjects with TCR, protein, and mutation data, respectively, 85 subjects were processed for all 3 analytes. For each analyte class, it was recorded whether each sample’s cross-validation score passed the threshold determined by a given target specificity level when the sample was in the held-out set during cross-validation (Table 8). This provided an unbiased, cross-validated sensitivity estimate for each individual analyte and allowed to compare which cases are called positive by various subset of analytes (Table 8). Given the unmet need in the detection of stage I cancer and the enrichment of stage I cancers in the study dataset, the cancer cases were grouped to stage I vs. stage II-IV disease to tabulate the sensitivity results. IPTS/128553107.1 Attorney Docket No: SRU-004WO [00401] A substantial gain in sensitivity for stage I cancer was observed when TCR RFUs were added to established biomarkers, with a >25%-point increase seen at the 90% target specificity typical for single cancer type screening tests. Likewise, sensitivity increased at the highest target specificity levels required for multi-cancer early detection, with detection rate reaching ~50% of stage I lung cancer, which could not be achieved by any analyte alone (FIG.20). In contrast, TCR RFUs did not appear to improve the detection of stage II-IV cancers (FIG.21) - an observation that could potentially be explained by the immune suppression and evasion known to be associated with advanced cancers. Example 7: Further Discovery of Lung-Cancer Associated RFUs [00402] A cohort of blood samples from 252 patients diagnosed with lung cancer was assembled for discovery of RFUs associated with cancer status. The cohort was enriched for subjects with early-stage disease (FIG.32) and spanned all major lung cancer subtypes (FIG. 32). [00403] Blood from 293 subjects without lung cancer were collected as control samples, enriching for older individuals (FIG.31) with a history of smoking (FIG.31) to match the cancer cases. ImmunoSeq® assay analysis was conducted as exampled in Example 2. [00404] A sample size of 50 million TCR clonotype data points was prohibitive to standard clustering algorithms such as hierarchical clustering or Gaussian mixture models due to their iterative nature and reliance on a distance matrix. To group the TCRs into RFUs, an approximate nearest neighbor graph on the TCRs using a CDR3 sequence dissimilarity metric was first created. The three N-terminal and two C-terminal residues in the CDR3 were excluded and the variable gene of each TCR was combined with the CDR3 dissimilarity into an overall dissimilarity score. This method was built upon a fast non-parametric clustering algorithm, CFSFDP originally described in Rodriguez, A. et al., Clustering by fast search and find of density peaks, Science, 2014, 344(6191), pp.1492-1496, which is hereby incorporated by reference in its entirety. The original algorithm involves a dissimilarity matrix between all data points, which is computationally prohibitive for a dataset of tens of millions of TCRs. The following improvements to the original CFSFDP algorithm were implemented: • Instead of computing and storing a pairwise dissimilarity matrix exhaustively, the methodology was updated by building an approximate nearest neighbor (ANN) index using a TCR dissimilarity metric described in Zhang et al, “GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation,” Nature Communications, 2021, IPTS/128553107.1 Attorney Docket No: SRU-004WO 12(4699), which is hereby incorporated by reference in its entirety. The ANN was used to query the nearest neighbors of a given TCR in the index. • In the original CFSFDP algorithm, the density of each data point was calculated by exhaustively enumerating the number of neighbors within a dissimilarity cutoff of d, which is an O(n²) operation for n data points. Instead, the updated methodology uses an ANN index to (approximately) search for all the neighbors of each TCR within d. Since an ANN index returns neighbors in the order of similarity, this process only involves a small number of operations per TCR on the ANN index. • Similarly, the ANN index was queried to search for the nearest TCR of higher density for each TCR instead of performing an exhaustive pairwise search. • The methodology implemented a computationally cheaper step by rejecting the direct clustering of two TCRs if their dissimilarity score exceeded d. [00405] An approximate, logarithmic time nonparametric clustering algorithm was implemented based on clustering by density peaks to the approximate nearest neighbor graph to assign the TCRs into RFUs (or as non-clustered singletons TCRs). Several candidate RFU sets were generated by varying the maximum TCR dissimilarity cutoff (parameter dc) which controls clustering sparsity. Three dc settings were used: 11, 12, and 22, which corresponded to one conservative amino acid mismatch, one mismatch of any kind or an insertion/deletion (indel), or one mismatch or indel and an additional conservative mismatch allowed respectively. Three additional dc settings (1.1, 1.2, and 2.2, approximately corresponding to any conservative amino acid mismatch for CDR3 length greater than or equal to 15 amino acid residues; any amino acid mismatch for CDR3 length greater than or equal to 15 amino acid residues; and any amino acid mismatch for CDR3 length greater than or equal to 11 amino acid residues, and any two amino acid mismatches for CDR3 length greater than or equal to 16, respectively, assuming the TCRs have matching CDR1, CDR2, and CDR2.5 sequences) were also tested by normalizing the distances above by the number of considered residues in CDR3 alignment after the N and C-terminal trimming. [00406] The clustering analysis generated 97,477 to 3,619,644 RFUs depending on dc setting, with between 30.7x10^6 and 47.2x10^6 TCRs being clustered with at least one other TCR. RFUs followed a power law distribution in size, with a small number of large RFUs and large number of small RFUs. IPTS/128553107.1 Attorney Docket No: SRU-004WO [00407] The large number of RFUs and their predominantly small size presented a significant multiple testing burden imposed by small RFUs for which there was no statistical power to detect association with cancer at the current sample size. To address this for the case-control analysis, the set of candidate RFUs was restricted to RFUs observed in at least 15 individuals. This resulted in between 1,121 and 329,922 RFUs being tested for cancer association depending on the dc setting. While RFUs obtained with different dc settings partially overlapped, clustering TCRs across a range of dc values allowed to find the optimal balance between the population prevalence and the degree of shared antigen specificity of each RFU for cancer association testing. To further focus the analysis on TCRs most likely to be related to cancer, the analysis was additionally restricted to RFUs with evidence of T cell expansion in at least 8 individuals (without considering cancer status) and only TCR clonotypes with minimum amino acid-level recurrence of 4 within the dataset for each RFU were tallied. This filtering further ensured that the signal was above noise (as determined by permutation). [00408] The observed per-subject distribution of RFU counts was analogous to gene expression levels measured using RNA-seq, with the frequency of an RFU (gene) being computed as the sum of its TCR counts (transcripts or exons). Next, the gamma-Poisson generalized linear model was used to test for RFU association with cancer status. This model accounted for variable depth of sequencing and RFU count overdispersion and allowed incorporation of demographic covariates such as age, gender, and race into the model. Here, using the gamma-Poisson GLM was valuable to associate the RFUs with cancer status because the RFU TCR count data is analogous to gene expression from RNASeq. Furthermore, the gamma-Poisson GLM enabled incorporation of demographic covariates age, gender, race. Notably, most cancer associated RFUs were also associated with decreased counts with increasing age in the overall cohort. Thus, incorporation of the covariates further improved the cancer signal. [00409] False discovery rate (FDR) correction was applied with an FDR cutoff of 0.1 to identify statistically significant RFU hits within each dc setting. [00410] A total of 102 RFUs associated with cancer status across the six dc cutoffs was identified, including 86 that were enriched in cancer samples with fold change between 0.07 and 0.49, and 16 that were enriched in non-cancer controls with a fold change between 0.10 and 0.32 (FIG.33, and Table 9). Two recurrent patterns among cancer enriched RFUs were observed. Seventy-one RFUs showed a pattern of decreasing TCR count with increasing age in all individuals (at FDR ≤ 0.1), coupled with predominantly higher counts in cancer patients relative to age-matched controls (FIG.35). The second pattern, accounting for fifteen RFUs IPTS/128553107.1 Attorney Docket No: SRU-004WO showed an elevation of TCR counts in cancer cases vs. non-cancer controls without an accompanying correlation to subject age. (FIG.34) Some cancer-associated RFUs also showed significant association with multiple demographic covariates, highlighting the importance of the rigorous statistical model selected for the analysis (Table 10). Example 8: Cancer Prediction from RFUs [00411] A standard machine learning approach with 5-fold cross-validation was employed to evaluate the utility of the 86 positively associated cancer associated RFUs for cancer status prediction. In order to minimize RFU frequency bias arising from any demographic covariate imbalances between the cancer and control cohorts, each cancer-associated RFU’s frequency value was adjusted for the fitted effect of the demographic covariates that were statistically significantly (FDR ≤ 0.1) associated with the RFU. These 86 adjusted RFU values were then used as features to train a logistic regression for cancer status prediction with forward feature selection (5-fold cross-validation). An average cross-validation ROC AUC of 0.75 was observed. The ROC AUC for stage 0-I cancer exceeded the performance for stages II-IV at an AUC of 0.78 vs 0.68. Notably, >60% of stage 0-I subjects were detected by the model at a specificity of 80% (test samples of each cross-validation fold; FIG.36, left panel), which was higher than the fraction of Stage II-IV subjects detected. (FIG.36, right panel) TCR RFU- based model cancer predictions outperformed ctDNA and protein-based prediction for Stage I Lung Cancer and combined to achieve predictive performance superior to any analyte alone. (FIGs.38 and 39) Example 9: Lung Cancer Prediction With a Multi-Analyte Liquid Biopsy Incorporating Immune Recognition [00412] Of the previously established 545, 235, and 100 subjects with TCR, protein, and mutation data, respectively, 95 subjects were processed for all 3 analytes. For each analyte class, it was recorded whether each sample’s cross-validation score passed the threshold determined by a given target specificity level when the sample was in the held-out set during cross-validation. This provided an unbiased, cross-validated sensitivity estimate for each individual analyte and allowed to compare which cases are called positive by various subset of analytes. Given the unmet need in the detection of stage I cancer and the enrichment of stage I cancers in the study dataset, the cancer cases were grouped to stage I vs. stage II-IV disease to tabulate the sensitivity results. [00413] A substantial gain in sensitivity for stage I cancer was observed when TCR RFUs were added to established biomarkers, with a >25%-point increase seen at the 90% target IPTS/128553107.1 Attorney Docket No: SRU-004WO specificity typical for single cancer type screening tests. Likewise, sensitivity increased at the highest target specificity levels required for multi-cancer early detection, with total detection rate reaching ~50% of stage I lung cancer, which could not be achieved by any analyte alone (FIG.38). In contrast, TCR RFUs did not appear to improve the detection of stage II-IV cancers (FIG.39) - an observation that could potentially be explained by the immune suppression and evasion known to be associated with advanced cancers. [00414] Altogether, these results demonstrate that it is possible to detect the presences of lung cancer from blood by analyzing the circulating TCR repertoire. Furthermore, the TCR repertoire cancer signal is orthogonal and complementary to established tumor-derived analytes such as circulating tumor DNA and protein biomarkers. When combined with ctDNA and proteins biomarkers, TCR repertoire analysis further enables early cancer detection. Example 10: Further Discovery of Lung-Cancer Associated RFUs [00415] A cohort of blood samples from 275 patients diagnosed with lung cancer was assembled for discovery of RFUs associated with cancer status. The cohort was enriched for subjects with early-stage disease (FIG.41) and spanned all major lung cancer subtypes (FIG. 41). [00416] Blood from 304 subjects without lung cancer were collected as control samples, enriching for older individuals (FIG.40) with a history of smoking (FIG.40) to match the cancer cases. ImmunoSeq® assay analysis was conducted as exampled in Example 2. [00417] Clustering was performed as in Example 6. Candidate RFU sets were generated as in Example 6. [00418] The clustering analysis (which included an additional 25 subject repertoires not used in the case/control analysis) generated 103,622 to 4,099,223 RFUs depending on dc setting, with between 36.6x10^6 and 55.1x10^6 TCRs being clustered with at least one other TCR. RFUs followed a power law distribution in size, with a small number of large RFUs and large number of small RFUs. (FIGs.51A-51C) [00419] The large number of RFUs and their predominantly small size presented a significant multiple testing burden imposed by small RFUs for which there was no statistical power to detect association with cancer at the current sample size. To address this for the case-control analysis, the set of candidate RFUs was restricted to RFUs observed in at least 15 individuals. This resulted in between 574 and 388,978 RFUs being tested for cancer association depending on the dc setting. While RFUs obtained with different dc settings partially IPTS/128553107.1 Attorney Docket No: SRU-004WO overlapped, clustering TCRs across a range of dc values allowed to find the optimal balance between the population prevalence and the degree of shared antigen specificity of each RFU for cancer association testing. To further focus the analysis on TCRs most likely to be related to cancer, the analysis was additionally restricted to RFUs with evidence of T cell expansion in at least 8 individuals (without considering cancer status) and only TCR clonotypes with minimum amino acid-level recurrence of 4 within the dataset for each RFU were tallied. This filtering further ensured that the signal was above noise (as determined by permutation). [00420] Statistical testing was performed as in Example 6. [00421] False discovery rate (FDR) correction was applied with an FDR cutoff of 0.1 to identify statistically significant RFU hits within each dc setting. [00422] A total of 150 RFUs associated with cancer status across the six dc cutoffs was identified, including 110 that were enriched in cancer samples with fold change between 0.06 and 0.43, and 40 that were enriched in non-cancer controls with a fold change between 0.08 and 0.40 (FIG.42, and Table 11). A key recurrent pattern among cancer enriched RFUs was observed: ninety-two RFUs showed a pattern of decreasing TCR count with increasing age in all individuals (at FDR ≤ 0.1), coupled with predominantly higher counts in cancer patients relative to age-matched controls (FIG.43, Table 12). Example 11: Cancer Prediction from RFUs [00423] A standard machine learning approach with 10-fold cross-validation was employed to evaluate the utility of positively associated cancer associated RFUs for cancer status prediction. In order to minimize RFU frequency bias arising from any demographic covariate imbalances between the cancer and control cohorts, each cancer-associated RFU’s frequency value was adjusted for the fitted effect of the demographic covariates that were statistically significantly (FDR ≤ 0.1) associated with the RFU. These adjusted RFU values were then used as features to train a logistic regression for cancer status prediction with forward feature selection (5-fold cross-validation). Cross-validation was repeated 10 times with 10 different random seeds and performance of the median model by test AUC is reported. The RFU discovery and forward feature selection steps were included in the cross-validation. The ROC AUC for stage 0-I cancer exceeded the performance for stages II-IV at an AUC of 0.74 vs 0.69. (FIG.44) Notably, >50% of stage 0-I subjects were detected by the model at a specificity of 80% (test samples of each cross-validation fold; FIG.45). Importantly, cancer prediction scores were not dominated by sample source related batch effects (FIG.46) or technical factors leading to varying TCR repertoire depth (FIG.47). Intriguingly, cancer IPTS/128553107.1 Attorney Docket No: SRU-004WO prediction scores appeared able to differentiate early lung cancer from benign lung nodules (FIG.50), raising the possibility that the TCR RFU signature could be used for malignant pulmonary nodule detection and ultimately for the detection of pre-cancer in lung cancer or other tumor types. Example 12: Lung Cancer Prediction With a Multi-Analyte Liquid Biopsy Incorporating Immune Recognition [00424] Of the previously established 579, 235, and 112 subjects with TCR, protein, and mutation data, respectively, 95 subjects were processed for all 3 analytes. For each analyte class, it was recorded whether each sample’s cross-validation score passed the threshold determined by a given target specificity level when the sample was in the held-out set during cross-validation. This provided an unbiased, cross-validated sensitivity estimate for each individual analyte and allowed to compare which cases are called positive by various subset of analytes. Given the unmet need in the detection of stage I cancer and the enrichment of stage I cancers in the study dataset, the cancer cases were grouped to stage I vs. stage II-IV disease to tabulate the sensitivity results. [00425] A substantial gain in sensitivity for stage I cancer was observed when TCR RFUs were added to established biomarkers, with a >20%-point increase seen at the 90% target specificity typical for single cancer type screening tests. Likewise, sensitivity increased at the highest target specificity levels required for multi-cancer early detection, with total detection rate reaching >40% of stage I lung cancer, which could not be achieved by any analyte alone (FIG.48). In contrast, TCR RFUs did not appear to improve the detection of stage II-IV cancers (FIG.49) - an observation that could potentially be explained by the immune suppression and evasion known to be associated with advanced cancers. Example 13: Further Discovery of Lung-Cancer Associated RFUs [00426] A cohort of blood samples from 439 patients (FIG.52) diagnosed with lung cancer was assembled for discovery of RFUs associated with cancer status. The cohort was enriched for subjects with early-stage disease and spanned all major lung cancer subtypes (FIG.53). [00427] Blood from 553 subjects without lung cancer were collected as control samples, enriching for older individuals (FIG.52) with a history of smoking (FIG.52) to match the cancer cases. [00428] Briefly, blood was collected in Streck Cell-Free DNA BCT® (processed within 48 hours) or EDTA tubes (processed with 4 hours). TCR beta chain sequencing was performed on a target 8ug of input genomic DNA using a multiplex PCR UMI-based assay covering V IPTS/128553107.1 Attorney Docket No: SRU-004WO and J genes. The multiplex PCR UMI-based assay is described in further detail in Example 16. [00429] Clustering was performed as in Example 2. Candidate RFU sets were generated as in Example 2. [00430] The clustering analysis generated 156,000 to 6,672,411 RFUs depending on dc setting, with between 74,906,969 and 103,554,297 TCRs being clustered with at least one other TCR. RFUs followed a power law distribution in size, with a small number of large RFUs and large number of small RFUs. [00431] The large number of RFUs and their predominantly small size presented a significant multiple testing burden imposed by small RFUs for which there was no statistical power to detect association with cancer at the current sample size. To address this for the case-control analysis, the set of candidate RFUs was restricted to RFUs observed in at least 15 individuals. This resulted in between 522 and 723,950 RFUs being tested for cancer association depending on the dc setting. While RFUs obtained with different dc settings partially overlapped, clustering TCRs across a range of dc values allowed to find the optimal balance between the population prevalence and the degree of shared antigen specificity of each RFU for cancer association testing. To further focus the analysis on TCRs most likely to be related to cancer, the analysis was additionally restricted to RFUs with evidence of T cell expansion in at least 10 individuals (without considering cancer status) and only TCR clonotypes with minimum amino acid-level recurrence of 4-8 within the dataset for each RFU were tallied. This filtering further ensured that the signal was above noise (as determined by permutation). [00432] Statistical testing was performed as in Example 2. [00433] False discovery rate (FDR) correction was applied with an FDR cutoff of 0.1 to identify statistically significant RFU hits within each dc setting. [00434] A total of 197 RFUs associated with cancer status across the six dc cutoffs was identified, including 110 RFUs that were enriched in cancer samples with a TCR count log2- fold change between 0.06 and 0.61, and 87 that were enriched in controls with a log2-fold change between 0.07 and 0.40 (FIG.54, and Table 14). FIG.55 shows an example TCR RFU (with centroid V gene of TRBV5-1 and CDR3 centroid of CASSLGGNQPQHF) with differential counts in cancer and non-cancer (e.g., decreased TCR counts in cancer versus control). Additionally, FIG.56 shows an example TCR RFU (with centroid V gene of TRBV6-4 and CDR3 centroid of CASSDSSGGSYNEQFF) with differential counts in cancer and non-cancer (e.g., increased TCR counts in cancer versus control). IPTS/128553107.1 Attorney Docket No: SRU-004WO [00435] The recurrent most frequent and random representative TCRs of each of the 197 RFUs are further shown in Table 1. A key recurrent pattern among cancer enriched RFUs was observed: seventy-seven RFUs showed a pattern of decreasing TCR count with increasing age in all individuals (at FDR ≤ 0.1), coupled with predominantly higher counts in cancer patients relative to age-matched controls (Table 13). Example 14: Cancer Prediction from RFUs [00436] A standard machine learning approach with 10-fold cross-validation was employed to evaluate the utility of cancer associated RFUs for cancer status prediction. The per-RFU log-ratio of the generalized linear model for each sample’s RFU counts along with its demographic covariates with cancer status set to 1 vs.0 were then used as input features. The features were shifted to zero mean and unit variance. The ML model used was a bagging model of 100 SVMs with a linear kernel with 50% feature sampling and sample bootstrapping for each estimator. Clustering, RFU discovery and ML steps were all included in the cross-validation. The ROC AUC for stage 0-I cancer exceeded the performance for stages II-IV at an AUC of 0.71 vs 0.64. (FIG.57). Notably, nearly 50% of stage 0-I subjects were detected by the model at a specificity of 80% (test samples of each cross-validation fold; FIG.58). Importantly, cancer prediction scores were not dominated by sample source related batch effects (FIG.61) or technical factors leading to varying TCR repertoire depth (FIG.62). Example 15: Lung Cancer Prediction With a Multi-Analyte Liquid Biopsy Incorporating Immune Recognition [00437] Of the previously established 992 subjects with TCR, 235 subjects with protein data, and 112 subjects with mutation data, respectively, 86 subjects were processed for all 3 analytes. For each analyte class, it was recorded whether each sample’s cross-validation score passed the threshold determined by a given target specificity level when the sample was in the held-out set during cross-validation. This provided an unbiased, cross-validated sensitivity estimate for each individual analyte and allowed to compare which cases are called positive by various subset of analytes. Given the unmet need in the detection of stage I cancer and the enrichment of stage I cancers in the study dataset, the cancer cases were grouped to stage I vs. stage II-IV disease to tabulate the sensitivity results. [00438] A substantial gain in sensitivity for stage I cancer was observed when TCR RFUs were added to established biomarkers, with a ~20%-point increase seen at the 90% target specificity typical for single cancer type screening tests. (FIG.63). In contrast, TCR RFUs IPTS/128553107.1 Attorney Docket No: SRU-004WO did not appear to improve the detection of stage II-IV cancers (FIG.64) - an observation that could potentially be explained by the immune suppression and evasion known to be associated with advanced cancers and the high performance of the plasma analytes. In FIGs. 63 and 64, at each target specificity, the ordering of the bar graphs are, from left to right, 1) mutations, 2) proteins, 3) TCRs, 4) proteins + mutations, and 5) proteins + mutations + TCRs. Example 16: Multiplex PCR Assay (mRFU) For Characterizing the Rearranged T Cell Receptor A/B Receptor Sequence. [00439] Primers were selected to target TCRV gene segments, TCRJ gene segments and identity SNPs. Gene specific primer candidates of specified length were generated in silico using GRCh38 reference genome and Primer3 software. The in silico list was manually curated to ten top candidates for each target, based on GC content and predicted melting temperature. Primer candidates were tested in the lab as single pairs using template gDNA that was positive for the relevant TCR rearrangement. The final gene specific sequence for a given V or J segment were selected based on optimizing for product yield and minimizing off target products using Agilent Tapestation images. [00440] The optimized gene specific sequences were used to generate mRFU Assay Primers by adding Illumina compatible sequences to the 5’ end of the sequence as shown in FIG.65. The V primers also contained an additional unique molecular identifier (UMI) sequence made up of 12 random nucleotides for error correction. Two primer pools were created by equimolar mixing of the V primers and the J primers. Two SNP primer pools were also prepared, one each for the forward and reverse strand primer of each targeted SNP. The forward SNP primer pool and V primer pool were mixed at 1:100 ratio, and defined as Primer Pool1. Similarly, the reverse SNP primer pool and J primer pool were mixed at 1:100 ratio, and defined as Primer Pool2. [00441] The mRFU Assay workflow was made up of 3 reactions to enrich for the target TCR rearrangements and SNP regions while adding Illumina sequencing adaptors to these sequences. The first reaction was a single primer extension using DNA polymerase and Pool1 described above at concentration of 1.5μM as shown in FIG.66. The product was heated to 98oC for 2min and snap cooled to help remove unbound primers and finally cleaned up using 1.0x Ampure to remove free primers and extension reaction reagents. [00442] As shown in FIG.67, the extension product was taken into a PCR reaction (PCR1) to enrich for the full rearrangement. This PCR used the 20 unique bases of the Illumina Read1 IPTS/128553107.1 Attorney Docket No: SRU-004WO Primer Sequence as a forward primer and the multiplexed gene specific Primer Pool2 described above as a reverse primer. A low 7 cycle PCR reaction was run with primers at 500μM to amplify the genomic regions with rearranged V-J sequences and targeted SNPs. This product was cleaned up using a dual sided Ampure clean-up; a right sided 0.5x clean-up to remove the very long genomic sequence and 1.5x left sided clean-up to remove the primer and short off target products. [00443] Lastly an 18 cycle PCR (PCR2) was run attaching Illumina P5/P7 sequences with sample barcodes is used to prepare final libraries. Samples were then sequenced on an Illumina® Novaseq targeting a depth of 50M reads per sample. IPTS/128553107.1 : O 66 ^N 0 D 2 ^IQE S FFLKE GRVL ³ _r SS d A c C e ⁴-ne ¹ _J g B _j ^_ R T e 7 n 2 e V g B ^_ _v RT 17 _i ^d 3 o 9 ^r _t 5 n 0 e 1 c 5 ^c _{_} ² _. d 2 : O 1 ^ND ^IQE S FF QE NYS GST

T T T T Q T T Q G G G T T G T T T G T T T ST T ST T G QR G G QR G QR T T T Q R Q G P P GS GL GL P GL GL GL P GL GL R G R G L R R G Q R WR S R R S S S S S S S S S S S S LS PS L L F P P P P P P P P P P P P P P P P ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S RS SS SS SS SS SS SS SS SS SS SS SS SS SS SS SS SS SS SS d A A A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ¹- ^{1 1 1 7 1 1 7 1 1 7 1 1 1} ² - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - ¹ ² - ^{1 7 7 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} ² _{- - - - - - - - - - - - - - - - - - -}ne _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² _J ² _J ² _J ² _J ² _J ² _J ² _J ² _J ² _J ² _J ² _J ² _J ² _J ² _J ² _J ² _J ² _J ² _Jg R R R R R R R R R R R R R R R B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8n V V V V V V V V V V V V V V V V 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1e B B B B B B B B B B B B B B B B V V V V V V V V V V V V V V V V V Vg R R R R R R R R R R R R R R R R B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 92 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9_i ^d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2o 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4^r _t 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0n 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0e 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 c^c _{_} ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 00 ^N 1 D 2 ^IQE S FY QT DA GRSP ³ _r SS d A c C e ³-ne ² _J g B _j ^_ R T e 8 n 1 e V g B ^_ _v RT 29 _i ^d 6 o 5 ^r _t 5 n 6 e 2 c 5 ^c _{_} ² _. d 2 : O 53 ^ND ^IQE S FFLE GT NS Q

G S Q L G A P RP G R P L G G G G G G G G G G G G G G G S P AL AL AL AL AL AL AL AL AL VL AL AL A A A L SV AL A G V _I ^S SL AL GP GP G Q G T P G G GT G S S S S S S S S S S S S S S S S S LS LS LS F F F F F Y F P P P P P P P ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S S S S S S S S S S S A SA S S S S S S S S S S d A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ^{2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3} ² - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - ³ ² - ³ ¹ - ^{3 3 3 3 3} ¹ - 1 - 1 _{- - -}ne _J g B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J B ^_ _j R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te 81 81 81 81 81 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 82 82 82 82 82 82 8 8 8 8 8 8 8 8n V V V V V V V V V V V V V V V V V V V V V V V V V V 2V 1 1 1 1 1 1 1e B B B B B B B B B B B B B B B B B B B B B B B B B B B VB VB V V V V Vg R R R R R R R R R R R R R R R R R R R R R R R R R R R R R BR B B B B^_ _v T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T RT RT RT RT 09 0 0 0 0 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 2 2 2 2 2 2 2 2 92 92 9 9 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 9 9 9 9 9 9 9 3 3 3 3 3 3 3_i ^d 4 4 4 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 5 5 5 5 5 5 1 1 1 1 1 1 1o 0 0 0 40 40 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 7 7 7 7 7 7 7 9 9 9 9 9 9 9^r _t 0 0 0 0 0 99 9 9 9 9 9 9 9 9 9 9 9 9 9 9 7 7 7 7 7 7 7 4 4 4 4 4 4 4n 2 2 2 2 2 2 92 92 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 2 2 2 2 2 2 2e 2 2 2 2 2 2 2 2 2 2 2 2 01 01 01 01 01 01 01 21 21 2 2 2 2 2 c 1 1 1 1 1^c _{_} ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 43 ^N 1 D 2 ^IQE S FFAET NP GVS ³ _r SS d A c C e ¹-ne ¹ _J g B _j ^_ R T e 7 n 2 e V g B ^_ _v RT 03 _i ^d 0 o 8 ^r _t 1 n 2 e 4 c 5 ^c _{_} ² _. d 2 : O 96 ^ND ^IQE S F I^YT N GSVT

T R H Q G G GT G G GP GP TT GT GP G GP SP SP G TP MG G GR G G G G R G T G A G G G V G P P P P P P P P P P P P P P P P SL WL LL GF S R G R G WL K QL G A S S S S S S S S S S S S S S S S S S S S PS LS LS LS S S L L P L P L L ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA RA SA SA SA SA SA SA S S A S S A S S A S S S S R S S S A SA S S S S S S d A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ³- ^{3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6}ne ¹ _{- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -} ⁶- ⁶- ¹-_J ¹ B _J ^{1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _{J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J} g R R BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te 81 81 81 81 81 81 81 81 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 ⁹-n V V V V V V V V 1V 1V 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 7e B B B B B B B B B B VB VB V V V V V V V V V V V V V V V V V V V V V Vg R R R R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 84 84 84 84 8 8 8 8 8 8 8 8 8 8 8 8 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 8 8 8 8 48 48 48 4 4 4 4 4 4 4 4 4 4 9_i ^d 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 6 6 6 6 6 6 6 8 8 8 8 8 8 8 8 8 8 1o 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 2 2 2 2 2 2 62 62 6 6 6 6 6 6 6 6 0^r _t 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 21 2 2 2 2 2 2 2 6n 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 8 8 8 8 8 8 8 8 8 8 18 18 1 1 1 1 1 0e 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 81 8 8 8 8 5 c 1 1 1 1 2^c _{_} ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 86 ^N 1 D 2 ^IQE S F Y_IT N GS GTP ³ _r SS d A c C e ³-ne ¹ _J g B _j ^_ R T e 8 n 1 e V g B ^_ _v RT 04 _i ^d 5 o 7 ^r _t 2 n 0 e 9 c 6 ^c _{_} ² _. d 2 : O 30 ^N 1 D ^IQE S FF QE NYSS

G G G Q Q G T R G G G T G G G V AL G G L A G L S G L RL _I ^GL GL G G L AL G A L L G P AL G S L AL G G T R S GL DL VL GL EL H G G G P QL AL S AL G SA GS GA P G GA QR S S S S S S S S S S S S S S S S S S S S S S S S FS TS LS LS F L P L L F ³ _r Sd A SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S A S S S A SA SA c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ¹- ¹ ne ² - ¹ ² - ¹ ² - ¹ ² - ^{1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 7 1 1 1 1 1 7 1 7 1 1} ² - 2 - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _J B _{J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹- ⁹- ⁹- ⁹- ⁹-n V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 7V 7V 7V 7 7e B B B B B B B B B B B B B B B B V Vg B B B B B B B B B B B B B B B B B B^_ _v RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 29 29 29 29 29 29 29 29 29 29 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 91 91 91 91 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9_i ^d 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1o 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 06 06 06 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0^r _t 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 60 60 6 6 6 6 6 6 6 6 6 6 6 6 6 6n 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 05 05 05 0 0 0 0 0 0 0 0 0 0 0e 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 52 52 52 5 5 5 5 5 5 5 5 c 2 2 2 2 2 2 2 2^c _{_} ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 20 ^N 2 D 2 ^IQE S FFAEAT G GV ³ _r SS d A c C e ¹-ne ¹ _J g B _j ^_ R T en 9 e V g B ^_ _v RT 59 _i ^d 2 o 0 ^r _t 2 n 6 e 6 c 1 ^c _{_} 11 d : O 73 ^N 1 D ^IQE S FY QEYSSLT

G S T T T T T T T A S Q T R T A T GL G G L VL N G L AL GL GL GR GP GL GP GL GS GL G G A T S A L GL G EL GL TL GL GS G G A T P SF GL GL N S S S G F R Q GT G SS G S S S S S S S S S S S S S S S S S S S S S S S S S S S S LS PS SS SS L R ³ _r S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S SS SSd A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁷- ¹ ne ² - ¹ ² - ¹ ² - ¹ ² - ⁵ ² - ⁵ ² - ⁵ ² - ⁵ ² - ^{5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5} ² - 2 - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹-ne V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 5V 5Vg BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 29 29 29 29 29 25 25 25 25 25 25 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 8 8 8 8 8 8 58 58 58 58 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5_i ^d 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 81 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8o 6 6 6 6 6 0 0 0 0 0 0 0 0 0 0 0 10 10 10 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1^r _t 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 01 01 0 0 0 0 0 0 0 0 0 0 0 0 0n 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 15 15 15 1 1 1 1 1 1 1 1 1 1e 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 52 52 5 5 5 5 5 5 5 5 c 2 2 2 2 2 2 2 2^c _{_} ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 63 ^N 2 D 2 ^IQE S FFLE GAAV ³ _r SS d A c C e ²-ne ² _J g B _j ^_ R T en 9 e V g B ^_ _v RT 85 _i ^d 0 o 8 ^r _t 7 n 2 e 4 c 3 ^c _{_} 11 d : O 17 ^N 1 D ^IQE S FF QE NYSS G

G G G Q G G G Q Q G G G G T G G T Q AL GL AL GL DL A G L EL G G L E G L SL G S L AL G G P L G S L G EL A G P A A L A G S L G E P L V G L E A G G L AL TL VL GL G G G G G G G S S S S S S S S S S S S S S S S S S S S S S S S S S S LS LS PS LS L L L ³ _r S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S SS SS SSd A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ¹- ¹ ne ² - ¹ ² - ¹ ² - ¹ ² - ¹ ² - ¹ ² - ¹ ² - ^{1 1 1 1 7 7 1 7 1 1 7 1 1 1 1 7 1 7 2 2 2 2 2 2 2 2} ² - 2 - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹-ne V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 5V 5V 5Vg BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 87 87 87 87 87 87 87 87 87 87 87 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 71 71 71 71 7 7 7 7 7 7 7 7 7 7 7 2 2 2 2 2 2 2 2_i ^d 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 17 1 1 1 1 1 1 1 1 1 1 8 8 8 8 8 8 8 8o 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 72 72 72 7 7 7 7 7 7 7 5 5 5 5 5 5 5 5^r _t 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21 21 2 2 2 2 2 2 2 2 2 2 2 2 2n 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 10 10 1 1 9 9 9 9 9 9 9 9e 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 03 03 7 7 7 7 7 7 7 7 c 3 3 3 3 3 3 3 3^c _{_} ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 07 ^N 2 D 2 ^IQE S F H QP Q DA D ³ _r RA d S c C e ⁵-ne ¹ _J g B _j ^_ R T 1-e 0 n 2 e V g B ^_ _v RT 55 _i ^d 0 o 6 ^r _t 6 n 9 e 5 c 4 ^c _{_} 11 d : O 50 ^N 2 D ^IQE S FFLE GT NV Q

S G G G G R G G R G G A Q Q G G D G G GS GS GT G RS GR GT Q GS G AS G G P D R S Q GR A G R G L L L G L L L R L L L L L L L L GL YL QL SL P SL S G R G G G A G G A S S S S S S S S S S S S S S S S S S S S S S LS SS SS L L L L L L L L L ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S S S S S S A SA S S S S S d A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ^{2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3}ne ² _{- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -} ³-_J ² B _J ² B _J ² B _J ^{1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J J J J J J J J J J J J J} g R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹- 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 ¹- ¹- ¹- ¹- ¹- ¹- ¹-n V V V 5V 2V 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5e B B B B B VB VB V V V V V V V V V V V V V V V V V V V V V V V V V V Vg R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 52 52 52 52 46 46 46 46 46 46 46 46 46 46 46 46 4 4 4 4 4 4 4 4 4 4 4 6 6 6 6 6 6 6 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 69 69 69 69 6 6 6 6 6 6 6 6 6 6 6 6 6 6_i ^d 5 5 5 5 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 98 9 9 9 9 9 9 0 0 0 0 0 0 0o 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 87 87 8 8 8 8 2 2 2 2 2 2 2^r _t 9 9 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 76 7 7 7 4 4 4 4 4 4 4n 7 7 7 7 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 64 64 6 1 1 1 1 1 1 1e 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 44 74 7 7 7 7 7 7 c 4 4 4 4 4 4^c _{_} ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 40 ^N 3 D 2 ^IQE S FF QE NYS GT S_I ³ _r SS d A c C e ¹-ne ² _J g B _j ^_ R T e 9 n 1 e V g B ^_ _v RT 21 _i ^d 8 o 0 ^r _t 6 n 3 e 1 c 6 ^c _{_} 11 d : O 93 ^N 2 D ^IQE S FY QT DTS G

ST G G G G R G S S S G G G Q Q Q L VL AL DL G G G A F GL EL AL L R G R GL EL S A S G T T G P LE EL VL G G S ST T ST S GS ST T GS S T T ST S S A T T S G G G S S S S S S S S S S S S S S S S S AS ES GS RS RS S P G A E P G L L L ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S R S R S S S S S S A SA S S S S S S S S d A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ³- ^{3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1} ne ² -_J ² - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - ⁷ ² - ¹ ² - ² ² - ^{2 2} ² - 2 - 2 B _{J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J} g R BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹-n V V V V V V 5V 5V 5 5 5 5 5 5 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5e B B B B B B B B VB VB V V V V V V V V V V V V V V V V V V V V V V V Vg R R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 77 77 77 77 7 7 7 7 7 7 7 7 7 7 7 7 5 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 4 74 74 74 7 7 7 7 7 7 7 7 7 7 7 7_i ^d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4 4 4 4 4 4 4 4 4 4o 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 24 24 2 2 2 2 2 2 2 1 1 1^r _t 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 3 43 4 4 4 4 4 4 6 6 6n 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 0 0 0 0 0 0 0 0 0 0 30 30 3 3 3 3 2 2 2e 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 05 05 0 0 7 7 7 c 5 5 5 5 5^c _{_} ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 83 ^N 3 D 2 ^IQE S FTLV NA GA GTT ³ _r SS d A c C e ⁶-ne ² _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 15 _i ^d 3 o 3 ^r _t 5 n 5 e c ^c _{_} 21 d : O 37 ^N 2 D ^IQE S FFLE GT NA Q

T Q Q Q G Q Q G Q S S S G S A A G G G G R A D G G G SA GS G G G Q G G T G GS P P G D G P S S S S S S S G L L L L L L S L L F V TL A F EL GL RL GL TL S SL DL VL GL EL G S S S S S S S S S S S S S S S S S S S S S S S S S LS DS DS DS D D D D D ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S S S S S S S S S S S S SS SS SS SS SSd A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ² ne ² - ² ² - ² ² - ² ² - ² ² - ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1} ² - 2 - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _J B _{J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴- ⁴- ⁴- ⁴-n V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 6V 6V 6V 6e B B B B B B B B B B B B B B B B B Vg R B B B B B B B B B B B B B B B B B^_ _v T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 57 57 57 57 57 57 57 57 57 57 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 74 74 74 74 7 7 7 7 7 7 7 7 7 7 7 7 3 3 3 3 3 3 3 3_i ^d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 41 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5o 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 16 16 16 1 1 1 1 1 1 1 1 7 7 7 7 7 7 7 7^r _t 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 62 62 6 6 6 6 6 6 3 3 3 3 3 3 3 3n 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 27 27 27 2 2 2 0 0 0 0 0 0 0 0e 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 75 75 75 8 8 8 8 8 8 8 8 c 6 6 6 6 6 6 6 6^c _{_} ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. 1 ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 27 ^N 3 D 2 ^IQE S FFAR G Q GL ³ _r SS d A c C e ¹-ne ¹ _J g B _j ^_ R T e ⁹-n 7 e V g B ^_ _v RT 25 _i ^d 6 o 9 ^r _t 6 n 7 e c ^c _{_} 21 d : O 70 ^N 3 D ^IQE S FF QE NY G GT

S G G G S G G A D D D ST L G P S GT G G S Q GA G Q R R G G G Q T T Q L R Q S Q A R T A L R L L G S D S Q G GL GL GP GL GL GL DL GS GL GL GL GL GE GL G G G G S S S S S G G G G G E G G G R D S S S S S S S S S S S S S S LS VS LS S ³ _r S S S S S A V V V V V V V V V V S S S S S S S S S S S S S S S S S SSd A A A A A S S S S S S S S S S S A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ¹- ¹ ne ² - ¹ ² - ¹ ² - ¹ ² - ^{4 4 4 4 4 4 4 4 4 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1} ² - 1 _{- - - - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J ^{1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _{J J J J J J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T 1- ¹- ¹- ^{1 1 1 1 1 1 1 1} e ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴ _{- - - - - - - - -} 6 92 92 92 92 9 9 9 9 9 9 9 ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- 7 7n V V V V V V V V V 2V 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 2 2e B B B B B B B B B B VB VB V V V V V V V V V V V V V V V V V V V V V Vg R R R R R R R R R R R R BR B B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 43 43 43 43 43 68 68 68 68 68 68 68 68 68 68 68 34 34 34 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 5 5 5 5 5 2 2 2 2 2 2 2 2 2 2 2 5 5 5 45 45 4 4 4 4 4 4 4 4 4 4 4 9 9^d _i 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 51 5 5 5 5 5 5 5 5 5 5 5 5o 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 6 6^r _t 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 17 17 17 17 17 17 1 1 1 5 5n 08 08 08 08 08 1 1 7 7 7 8 8e 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 c 6 6 6 6^c _{_} ¹ _. ¹ _. ¹ _. ¹ _. ¹ _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 60 ^N 4 D 2 ^IQE S FF QE NY GAL GP ³ _r SS d A c C e ¹-ne ² _J g B _j ^_ R T e 7 n 2 e V g B ^_ _v RT 83 _i ^d 7 o 7 ^r _t 9 n 7 e c ^c _{_} 21 d : O 14 ^N 3 D ^IQE S FF QE NY GA

L L L L L L L L L L A L L G G G G G G R G G G G G G G H G G G R G G G G G G G R G L R G D G P G E P LS LS PS SS PS LS PS SS LS PS GS SS P A S L A S L G S L A S L V A G G A S A A G G E G T V S LS LS RS LS L L L L L L L L S L L L D ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S S S S S S S S S S R A SA S S S S S S S S S A d A A A A A A A A A S c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ¹- ^{7 1 1 7 7 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 5 5 3 3 3} ² - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - ³ ² - ³ ² - ^{3 3 5 3 3 5} ² - 2 - 2 _{- - - -}ne _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ^{2 2} B _J B _J g^_ _j R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R B T R T 1- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ^{1 1 1 1 1 1}-e 72 72 72 72 72 72 72 72 72 72 72 72 72 5 5 5 _{- - - - -} 0n V V V V V V V V V V V V V V V V 5V 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 2e B B B B B B B B B B B B B B B B B VB VB V V V V V V V V V V V V V V Vg R R R R R R R R R R R R R R R R R R R BR B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT 19 19 19 19 19 19 19 19 19 19 19 19 19 06 06 06 06 06 06 06 06 06 06 06 06 06 0 0 0 0 0 0 0 0 5 5 5 5 5 5 5 5 5 5 5 5 5 8 8 8 8 8 8 8 8 8 8 8 8 8 68 68 6 6 6 6 6 2_i ^d 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 8 8 8 8 8 1o 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 55 5 5 5 5 1^r _t 8 8 8 8 8 8 8 8 8 8 8 8 8 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 52 5 5 5 8n 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 23 2 2 8e 3 3 3 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 04 ^N 4 D 2 ^IQE S FY QEY GLRP ³ _r SS d A c C e ⁷-ne ² _J g B _j ^_ R T e 7 n 2 e V g B ^_ _v RT 83 _i ^d 7 o 7 ^r _t 9 n 7 e c ^c _{_} 21 d : O 57 ^N 3 D ^IQE S FY QTEEAL G

L RP RL GR GP QP SP G RL GP AP GL R RL SS SS TS TS G AS G TS G G G AL S G G G G G T G D D D D D D D D D D D D G D D D D D D D D D D D D D GE D SL G G A G A R R R R R R R R R R R R R R S S S S S S S S S S S S S S S LS LS LS S L ³ _r A A A A A A A A A A A A A A S S S S S S S S S S S S S S S S S S SS SSd S S S S S S S S S S S S S S A A A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁵- ⁵ ne ² - ⁵ ² - ⁵ ² - ⁵ ² - ^{5 5 5 5 5 5 5 5 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 7 7 1} ² - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _{J J J J J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T 1- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹-e 02 02 02 02 02 02 02 02 02 02 02 02 0 0 ⁴- ⁴- ⁴- ⁴- ⁴- ⁴- ⁴- ⁴- ⁴- ⁴- ⁴- ⁴- ⁴- ⁴- 7 7 7 7 7 7n V V V V V V V V V V V V 2V 2 6 6 6 6 6 6 6 6 6 6 6 6 6 6 2 2 2 2 2 2e B B B B B B B B B B B B B VB VB V V V V V V V V V V V V V V V V V V Vg R R R R R R R R R R R R R R R BR B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 02 02 02 02 02 02 02 02 02 02 02 02 02 02 95 95 95 95 95 95 95 95 95 9 9 9 9 9 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 51 51 5 5 5 3 3 3 3 3 3_i ^d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 7 7 7 7 7 7 7 7 7 7 7 17 1 1 3 3 3 3 3 3o 8 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 7 7 7 7 77 77 5 5 5 5 5 5^r _t 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 95 9 9 9 9 9n 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 54 5 5 5 5e 4 4 4 4 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 47 ^N 4 D 2 ^IQE S FFLE GP GL ³ _r SS d A c C e ²-ne ² _J g B _j ^_ R T e 3 n 1 e V g B ^_ _v RT 07 _i ^d 2 o 0 ^r _t 8 n 5 e 2 c ^c _{_} 21 d : O 90 ^N 4 D ^IQE S FY QEYSS

GA GA GR GA GS G T R QR G V S G Q G G G S GT Q Q G Q D G R R T G G G G G R G G R G S G G G G G G L L L L L L GL F RL F R P R F SL R SL P AL P AL A G A A A S A E A S S S S S S S S S S S S S S S S S S S R S LS LS L L P L P P L L L P L ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S S S S S S S S S A S S S S S S S S S d A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁷- ^{1 1 1 1 7 7 7 1 1 1 1 1 1 1 1 1 1 7 1 1 1 7 1 1 1 7} ² - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - ¹ ² - ¹ ² - ^{1 1 1 1 1} ² - 2 _{- - - -}ne _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ^{2 2} B _{J J} g R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R BR B^_ _j T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T R Te 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 ⁹-7 ⁹-7 ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹-n V V V V V V V V V V V V V V V V V V V V V V 7V 7 7 7 7 7 7 7 7 7 7 7e B B B B B B B B B B B B B B B B B B B B B B B VB V V V V V V V V V Vg R R R R R R R R R R R R R R R R R R R R R R R R BR B B B B B B B B B^_ _v T T T T T T T T T T T T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 77 77 77 77 77 77 77 77 77 7 7 7 7 7 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 73 73 7 7 7_i ^d 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3o 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 5 5 5 5 5 5 5 5 5 5 5 55 5 5^r _t 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 8 8 8 8 8 8 8 8 8 8 8 8 5 5n 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 85 8e 5 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 80 ^N 5 D 2 ^IQE S FY QEYS GL ³ _r SS d A c C e ⁷-ne ² _J g B _j ^_ R T e ⁸-n 7 e V g B ^_ _v RT 41 _i ^d 6 o 8 ^r _t 2 n 7 e 3 c ^c _{_} 21 d : O 34 ^N 4 D ^IQE S FF QE NYA

G G Q V G T R A D D G T D S T S G S S PA G G R AV G GA R Q RT R G Q H R G G G R G T G G G G G G G G L P L P L P L P L P L L R EL F P EL RL S AL S A G N S R G S S S R T S S S S S S S S S S S S S S S S S S S S PS LS WS L L L L L L L L L L L ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S S S S S S S S S A SA S S S S S S S S d A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ¹- ^{1 1 7 1 1 1 1 7 7 1 1 7 1 7 1 1 1 7 1 1 1 1 1 1 1 3 3 3} ² - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 1 - 1 - ³ ¹ - ³ ¹ - ^{3 3 3} ¹ - 1 - 1 -ne _{J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J} ¹ _Jg BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- 7 7 7 7 7 7 7 7n V V V V V V V V V V V V V V V 7V 7 7 7 7 7 7 7 7 7 7 2 2 2 2 2 2 2 2e B B B B B B B B B B B B B B B B VB VB V V V V V V V V V V V V V V V Vg R R R R R R R R R R R R R R R R R R BR B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 7 9 9 9 9 9 9 9 9 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 73 76 7 7 7 7 7 7 7_i ^d 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 7 67 6 6 6 6 6 6o 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 75 7 7 7 7 7^r _t 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 6 6 6 56 5 5 5 5n 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 66 6 6 6e 6 6 6 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 24 ^N 5 D 2 ^IQE S F HLPS NYSSP ³ _r SS d A c C e ⁶-ne ¹ _J g B _j ^_ R T e 8 n 1 e V g B ^_ _v RT 37 _i ^d 4 o 6 ^r _t 8 n 3 e 4 c ^c _{_} 21 d : O 77 ^N 4 D ^IQE S F Y_IT N GS

G G G R Y T T T T S T S T T G T G G R R GT D GR GR G A R G S T G G G S G T G Q G Q T G G G V G G L F QL P QL F R F F S TL RL GL P GL S P G L G G L G G G G A V A G S S S S S S S S S S S S S S S S S FS LS L P L R S R S L L L L L L L L ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S S T S S S S S S S S A SA S S N S S S S S S S S d A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ³- ^{3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 7 1} ¹ - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - ¹ ² - ⁷ ² - ^{1 7 7 1 7 7 1} ² - 2 - 2 _{- - - - -}ne _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ^{2 2 2} B _{J J J} g R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R BR B B^_ _j T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T R T R Te 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 ¹-5 ¹-5 ¹- ¹- ¹- ¹- ¹- ¹- ¹-n V V V V V V V V V V V V V V V V V V V V V V V V V V V 5V 5 5 5 5 5 5e B B B B B B B B B B B B B B B B B B B B B B B B B B B B VB VB V V V Vg R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R BR B B B^_ _v T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T RT RT RT 97 97 97 97 97 97 97 97 97 97 97 97 90 90 90 90 90 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 6 6 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2_i ^d 7 7 6 6 6 6 6 6 6 6 6 6 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6o 5 5 75 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6^r _t 6 6 6 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4n 6 6 6 66 66 6 6 6 6 6 6 6 4 4 4 4 4 4 4 4 4 4 4 4 4 2 2 2 2 2 2 2 2 2e 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 11 11 11 11 11 11 1 1 1 c 1 1 1^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 67 ^N 5 D 2 ^IQE S F HLPS N N G Q GL ³ _r SS d A c C e ⁶-ne ¹ _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 38 _i ^d 7 o 9 ^r _t 1 n 2 e 6 c ^c _{_} 21 d : O 11 ^N 5 D ^IQE S FY QEYS G

ST G L G A T R Q Q Q Q T Q T Q Q Q Q A AV SA DA D S GE G ST G AV AA GA R GV LE AE GS D A G GT G L L L L L L R L A L L L L L P L L L T S AL GL GL GL P G G G G G G G V S S S S S S S S LT S S S S S S S S S R S S S S S S LS LS LS L L L L R L ³ _r Sd A SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S A S S S A SA SA c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁷- ⁷ ne ² - ⁷ ² - ¹ ² - ¹ ² - ^{7 1 7 1 7 1 7 1 1 1 7 1 7 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2} ² - 2 - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1} B _J B _J B _{J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹- ¹- ¹-n V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 5V 5V 5V 5 5e B B B B B B B B B B B B B B B B V Vg B B B B B B B B B B B B B B B B B B^_ _v RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 92 92 92 92 92 92 92 92 92 92 9 9 9 9 9 9 9 9 9 9 9 7 7 7 7 7 7 7 7 7 7 7 7 7 6 6 6 6 6 6 6 6 6 6 26 26 26 26 2 2 2 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0_i ^d 6 6 6 6 6 6 6 6 6 6 6 6 6 6 66 6 6 6 6 6 6 8 8 8 8 8 8 8 8 8 8 8 8 8o 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 64 64 64 6 6 6 0 0 0 0 0 0 0 0 0 0 0 0 0^r _t 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 42 42 4 2 2 2 2 2 2 2 2 2 2 2 2 2n 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21 02 02 0 0 0 0 0 0 0 0 0 0 0e 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21 21 21 2 2 2 2 2 2 2 2 c 1 1 1 1 1 1 1 1^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 01 ^N 6 D 2 ^IQE S F I^YT N GS GSL ³ _r SS d A c C e ³-ne ¹ _J g B _j ^_ R T e 7 n 2 e V g B ^_ _v RT 31 _i ^d 8 o 0 ^r _t 1 n 0 e 8 c ^c _{_} 21 d : O 54 ^N 5 D ^IQE S FTY GYVT

G G Q Q G S S G G S G R T G D P G S A G G E P GE GA G G G G G GE G D G G S N G LS G AA G S S S S S G S A G T LS SS LS LS LS LS LS LS LS LS LS L V S L A S L D S L G S L S S L G S L D S L S S LS FS S A S L G S TS SS FS DS DS DS DS DS E D G ³ _r S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S SS SS SSd A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ² ne ¹ - ² ¹ - ² ¹ - ² ¹ - ² ¹ - ⁶ ¹ - ⁶ ² - ^{6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1} ² - 2 - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴- ⁴- ⁴-ne V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 6V 6V 6Vg BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 70 70 70 70 70 70 41 41 41 41 41 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 1 1 8 8 8 8 8 8 3 3 3 3 3 13 13 13 13 1 1 1 1 1 1 1 1 1 1 1 8 8 8 8 8 8 8 8_i ^d 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 31 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4o 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 15 15 15 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3^r _t 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 50 5 5 5 5 5 6 6 6 6 6 6 6 6n 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 03 03 03 0 0 9 9 9 9 9 9 9 9e 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 31 31 5 5 5 5 5 5 5 5 c 1 1 1 1 1 1 1 1^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 44 ^N 6 D 2 ^IQE S FTY GY GSL ³ _r SS d A c C e ²-ne ¹ _J g B _j ^_ R T e 7 n 2 e V g B ^_ _v RT 57 _i ^d 4 o 4 ^r _t 3 n 0 e 5 c 1 ^c _{_} 21 d : O 97 ^N 5 D ^IQE S FF QE NYSS

GS GS G G G G G G D G G D G G G G G Q G G T G G G G G G G G G G G G DE D D GA GL GL AL SL EL GL TL GL GL AL AL VL SL SL GF GL EL GL RL GL V G G G G G G G G G S S S S S S S S S S S S S S S S S S S S S S S S S VS VS VS V V V L V V ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA NA SA SA S S A S S A S S A S S S S A SA S S d A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ¹- ¹ ne ² - ^{1 1 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1} ² - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1} B _J B _{J J J J J J J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ⁴-6 ⁴-6 ⁴-6 ⁴- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹-n V V V 6V 5V 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 9 9 9 9 9 9 9 9 9 9e B B B B B VB VB V V V V V V V V V V V V V V V V V V V V V V V V V V Vg R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 18 18 18 18 56 56 56 56 56 56 56 56 56 56 56 56 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 2 2 2 2 2 2 2 2 2 2 2 2 62 62 62 62 6 6 6 6 9 9 9 9 9 9 9 9 9 9_i ^d 3 3 3 3 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 28 2 2 2 2 2 2 2 2 2 2 2 2 2o 6 6 6 6 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 83 83 8 0 0 0 0 0 0 0 0 0 0^r _t 9 9 9 9 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 33 2 2 2 2 2 2 2 2 2 2n 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 66 66 6 6 6 6 6 6 6 6e 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 61 61 6 6 6 6 6 6 c 1 1 1 1 1 1^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 87 ^N 6 D 2 ^IQE S FY QE G G GVL ³ _r SS d A c C e ⁷-ne ² _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 35 _i ^d 2 o 5 ^r _t 1 n 1 e 5 c 1 ^c _{_} 21 d : O 31 ^N 6 D ^IQE S FFAETE G

G G G GA G G A GT GA D GS GA GA GT GA GA G G G R QV A P Q V GT D G D GA A GS T K AR S Q Q Q A Q V V L L L L L L L L S L L L L S AL P RL A AL DL RL A AL T A G G E G S S S S S S S S S S S S S S S S S S S S S S S S S VS LS LS PS S L L L L ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S S S S S S S S S S S S SS SS SS SS SSd A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ¹- ¹ ne ¹ - ² ¹ - ² ² - ² ² - ² ² - ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 6 6 6 6} ² - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1} B _J B _{J J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te 9 9 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ⁹-7 ⁹-7 ⁹- ⁹-ne V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 7V 7Vg BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 59 59 61 61 61 61 61 61 61 61 61 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 8 8 8 8 2 2 5 5 5 5 5 5 5 5 5 15 15 15 15 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3_i ^d 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 5 5 5 5 5 5 5 5 5 5 5 5 5 5 9 9 9 9o 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 02 02 02 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2^r _t 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 27 27 2 2 2 2 2 2 2 2 2 5 5 5 5n 6 6 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 78 78 78 7 7 7 7 7 7 3 3 3 3e 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 81 81 8 8 8 8 2 2 2 2 c 1 1 1 1 2 2 2 2^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 21 ^N 7 D 2 ^IQE S FF QE NYS GSS D ³ _r SS d A c C e ¹-ne ² _J g B _j ^_ R T e ⁴-n 6 e V g B ^_ _v RT 18 _i ^d 4 o 3 ^r _t 6 n 9 e 5 c 1 ^c _{_} 21 d : O 74 ^N 6 D ^IQE S F HLPS NYF G Q

Q R Q Q Q Q G T G D T R G G G G G T G GS SS S G S G G A S S S G G A G G G Q N S S G S G S G G T D S SS SS LS LS P G S LS P D S L E S L G S L R S L G S L D S LS F G S L T S L V S L V S L G S L A S LS DS DS DS DS DS ES DS DS D E D E V D ³ _r Sd A SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SV SA SA SA SA SA SA S S A S S A S S A S S A S S A S S A SA c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁶- ⁶ ne ¹ - ⁶ ¹ - ⁶ ¹ - ⁶ ¹ - ^{6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6} ¹ - 1 - 1 - 1 _{- - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J ¹ B _J ^{1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _J B _{J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴- ⁴- ⁴- ⁴- ⁴-n V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 6V 6V 6V 6 6e B B B B B B B B B B B B B B B B V Vg B B B B B B B B B B B B B B B B B B^_ _v RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 83 83 83 83 83 83 83 83 83 83 8 8 8 8 8 8 8 8 8 8 2 2 2 2 2 2 2 2 2 2 2 2 2 2 9 9 9 9 9 9 9 9 9 9 39 39 39 39 3 3 3 3 3 3 8 8 8 8 8 8 8 8 8 8 8 8 8 8_i ^d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 92 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9o 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 25 25 25 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3^r _t 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 53 53 2 2 2 2 2 2 2 2 2 2 2 2 2 2n 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 92 92 92 9 9 9 9 9 9 9 9 9 9 9e 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 22 22 22 2 2 2 2 2 2 2 2 c 2 2 2 2 2 2 2 2^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 64 ^N 7 D 2 ^IQE S FFLE GSRF ³ _r SS d A c C e ²-ne ² _J g B _j ^_ R T e 7 n 2 e V g B ^_ _v RT 85 _i ^d 6 o 4 ^r _t 2 n 2 e 9 c 1 ^c _{_} 21 d : O 18 ^N 6 D ^IQE S FF QE NYSS G

G A G S T Q Q Q Q V S T R Q T T G G GA GA L G G G G G G ST G G GV GL SV GA G G G T Q G T G Q Q GT Q LS LS LS LS RS LS LS SS PS FS PS LS LS LS LS FS L G S T R S LS LS LS FS ^G _I S ^G _I S ^G _I S ^G _I G S MS TS ^G _I G S T ^G _I GT R ^R _I ³ _r Sd A SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S S A SA SA c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ¹- ¹ ne ² - ⁷ ² - ¹ ² - ^{7 1 1 7 7 7 7 1 1 1 1 1 1 1 1 1 1 7 6 6 6 6 6 6 6 6 6 6 6 6} ² - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1} B _J B _{J J J J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- ⁶- 9 9 9 9 9 9 9 9 9 9 9 9n 5V 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1e B VB VB V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V Vg R R R BR BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 65 65 65 65 65 65 65 65 65 65 65 65 65 65 6 6 6 6 6 6 6 6 5 5 5 5 5 5 5 5 5 5 5 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 52 52 52 52 5 5 5 5 7 7 7 7 7 7 7 7 7 7 7 7_i ^d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 21 2 2 2 9 9 9 9 9 9 9 9 9 9 9 9o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 10 1 9 9 9 9 9 9 9 9 9 9 9 9^r _t 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 02 6 6 6 6 6 6 6 6 6 6 6 6n 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 09 09 09 0 0 0 0 0 0 0 0 0e 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 92 92 9 9 9 9 9 9 9 c 2 2 2 2 2 2 2^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 08 ^N 7 D 2 ^IQE S F HLPS NA G Q_I ³ _r SS d A c C e ⁶-ne ¹ _J g B _j ^_ R T e 9 n 1 e V g B ^_ _v RT 70 _i ^d 2 o 5 ^r _t 0 n 1 e 6 c 2 ^c _{_} 21 d : O 51 ^N 7 D ^IQE S F HLPS N G Q

R R R G G G R R R G Q Q Q Q Q Q G G V TP G R R R R G G Q G Q G Q T G T A R ^G _I QF QF QF QF QF QF QF QL RL QF QY RL QF GL GL GL GP GS VL GL VL GS G G G G G R G S S S S S S S S S S S S S S S S S S S S S S S S S S LS PS LS AS WS L L ³ _r S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S SS SS SS SSd A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁶- ⁶ ne ¹ - ⁶ ¹ - ⁶ ¹ - ⁵ ¹ - ⁵ ² - ⁵ ² - ^{5 5 5 5 5 5 5 5 5 3 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7} ² - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te 91 91 91 91 ²-7 ²-7 ²-7 ²-7 ²-7 ²-7 ²-7 ²-7 ²-7 ²-7 ²-7 ²-7 ²-7 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-n V V V V V V V V V V V V V V V V 5e V V V V V V V V V V V V V V V V V Vg BR BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 57 57 57 57 97 97 97 97 97 97 97 97 9 9 9 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 9 9 9 4 4 4 4 4 4 4 4 74 74 74 74 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0_i ^d 9 9 9 9 4 4 4 4 4 4 4 4 4 4 4 4 44 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9o 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 45 45 45 4 4 4 4 4 4 4 4 4 4 4 4 4 4^r _t 0 0 0 0 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 51 5 5 5 5 5 5 5 5 5 5 5 5 5n 9 9 9 9 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 15 15 15 1 1 1 1 1 1 1 1 1 1e 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 53 53 5 5 5 5 5 5 5 5 c 3 3 3 3 3 3 3 3^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 41 ^N 8 D 2 ^IQE S FFLE GA GV ³ _r SS d A c C e ²-ne ² _J g B _j ^_ R T en 9 e V g B ^_ _v RT 55 _i ^d 9 o 5 ^r _t 2 n 1 e 7 c 2 ^c _{_} 21 d : O 94 ^N 7 D ^IQE S FY QEYSF

G Q G S Q G S G Q S S SR Q G G G S G T Q G Q Q Q T G R G R Q G G G D VL GP GF GE G G RE GE G EE G R V D G D T G G V Q R L GL GL GL GL GL GL GF GL DL G D T G E G G S S S S S S S S S S S S R S S K R S S S S S S S S S S LS FS LS L L L L ³ _r Sd A SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA GA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S A SA c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁷- ⁷ ne ² - ⁷ ² - ^{6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5} ² - 1 - 1 _{- - - - - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J ^{1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1} B _{J J J J J J J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 2 2 2 2 2 2 ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ¹- ¹- ¹- ¹-n V V V V V V V V V 2V 2V 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 5 5 5 5e B B B B B B B B B B B VB V V V V V V V V V V V V V V V V V V V V V Vg R R R R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 00 00 00 93 93 93 93 93 93 93 93 93 93 93 93 93 93 83 83 83 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 4 4 4 4 4 4 4 4 4 4 4 4 4 4 8 8 8 38 38 38 3 3 3 3 3 3 3 9 9 9 9_i ^d 4 4 4 8 8 8 8 8 8 8 8 8 8 8 8 8 8 6 6 6 6 6 6 8 8 8 8 8 8 8 2 2 2 2o 5 5 5 8 8 8 8 8 8 8 8 8 8 8 8 8 8 3 3 3 3 3 3 63 63 6 6 6 6 6 5 5 5 5^r _t 1 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 33 3 3 3 3 7 7 7 7n 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 36 36 3 3 1 1 1 1e 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 63 6 6 6 6 6 c 3 4 4 4 4^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 84 ^N 8 D 2 ^IQE S FFLE GT N Q GL ³ _r SS d A c C e ²-ne ² _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 14 _i ^d 2 o 2 ^r _t 9 n 1 e 0 c 3 ^c _{_} 21 d : O 38 ^N 7 D ^IQE S F H QP Q NS G

G T A G A G T S T G G G S G Q D L D G G L AL EL TL GL DL GL T G L SL EL GL GL G G L AL TL GL GL GL Q G G T T T D G G G P A G L G L GL VL QL GL G V A G S D G A T G S S S S S S S S S S S S S S S S S S S S S S S S LS LS LS LS L L L L F T ³ _r Sd A SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S A S S S A SA SA c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁵- ⁵ ne ¹ - ⁵ ¹ - ⁵ ¹ - ⁵ ¹ - ^{5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4} ¹ - 1 - 1 - 1 _{- - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J ¹ B _J ^{1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1} B _J B _J B _{J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹- ¹- ¹-n V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 5V 5V 5V 5 5e B B B B B B B B B B B B B B B B V Vg B B B B B B B B B B B B B B B B B B^_ _v RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 89 89 89 89 89 89 89 89 89 89 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 92 22 22 22 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2_i ^d 5 5 5 5 5 5 5 5 5 5 5 7 7 7 27 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2o 7 7 7 7 7 7 7 7 7 7 7 9 9 9 9 79 79 79 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7^r _t 1 1 1 1 1 1 1 1 1 1 1 4 4 4 4 4 4 4 94 94 9 9 9 9 9 9 9 9 9 9 9 9 9 9n 6 6 6 6 6 6 6 6 6 6 6 8 8 8 8 8 8 8 8 8 48 48 48 4 4 4 4 4 4 4 4 4 4 4e 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 84 84 84 8 8 8 8 8 8 8 8 c 4 4 4 4 4 4 4 4^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 28 ^N 8 D 2 ^IQE S FFAET N G GL ³ _r SS d A c C e ¹-ne ¹ _J g B _j ^_ R T e ⁹-n 7 e V g B ^_ _v RT 14 _i ^d 1 o 2 ^r _t 0 n 5 e 3 c 3 ^c _{_} 21 d : O 71 ^N 8 D ^IQE S FFLKE NTA

S L G G G P T G L L L L L L L L L G L TL GL G T S G GS G D L Q G T A P AL Q G L L G VL D G L T L R L P GL GL GL GL GL GP G L L L G P P DL GL AL GS GL G G G G LA G G G S S S S S S S S S S ET S S S S S S S S S S S S S S S LS LS SS L L A P K ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S S S S S S S S S S S S SS SS SS SS SSd A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁴- ⁴ ne ¹ - ⁴ ¹ - ⁴ ¹ - ⁴ ¹ - ⁴ ¹ - ^{4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 3 5 5 5 5 3 5 5} ¹ - 1 - 1 - 1 _{- - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J ¹ B _J ^{1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _J B _{J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹- ¹-n V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 5V 5V 5V 5e B B B B B B B B B B B B B B B B B Vg R B B B B B B B B B B B B B B B B B^_ _v T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 02 02 02 02 02 02 02 02 02 02 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 22 22 22 22 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5_i ^d 7 7 7 7 7 7 7 7 7 7 7 7 7 7 27 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2o 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 79 79 79 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7^r _t 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 94 94 9 9 9 9 9 9 9 9 9 9 9 9 9 9n 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 48 48 48 4 4 4 4 4 4 4 4 4 4 4e 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 84 84 84 8 8 8 8 8 8 8 8 c 4 4 4 4 4 4 4 4^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 61 ^N 9 D 2 ^IQE S FFAETSP GEL ³ _r SS d A c C e ¹-ne ¹ _J g B _j ^_ R T e ⁹-n 7 e V g B ^_ _v RT 14 _i ^d 1 o 2 ^r _t 0 n 5 e 3 c 3 ^c _{_} 21 d : O 15 ^N 8 D ^IQE S FY QTER G GA

L V A L L A L T G L G G G G G G D G R V G LS G G G G AL G G G G AL G GA G AV G G GA G G G G G G G G G_I L S Y Y S Y Y G S Y Y R P S R L L L L L L AL GL GL A S E G G G G A S S S S S R S S R S S S S S S S S S S S S S S S S LS LS LS LS L S L L L ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S S S S S S S S S S S S SS SS SS SS SSd A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁵- ⁵ ne ² - ⁵ ² - ³ ² - ³ ² - ³ ² - ^{3 3 3 3 3 3 3 3 3 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5} ² - 2 - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _J B _{J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ⁵-6 ⁵-6 ⁵-6 ⁵-6 ⁵-6 ⁵-6 ⁵-6 ⁵-6 ⁵-6 ⁵-6 ⁵-6 ⁶-6 ⁵-6 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹- ¹-n V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 5V 5V 5V 5e B B B B B B B B B B B B B B B B B Vg R B B B B B B B B B B B B B B B B B^_ _v T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 15 15 15 92 92 92 92 92 92 92 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 2 2 2 3 3 3 3 3 3 3 23 23 23 23 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7_i ^d 7 7 7 4 4 4 4 4 4 4 4 4 4 4 34 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3o 9 9 9 4 4 4 4 4 4 4 4 4 4 4 4 44 62 62 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6^r _t 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 24 24 2 2 2 2 2 2 2 2 2 2 2 2 2 2n 8 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 42 42 42 4 4 4 4 4 4 4 4 4 4 4e 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 25 25 25 2 2 2 2 2 2 2 2 c 5 5 5 5 5 5 5 5^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 05 ^N 9 D 2 ^IQE S FY QE GALE Q ³ _r SS d A c C e ³-ne ² _J g B _j ^_ R T e 4 n 1 e V g B ^_ _v RT 49 _i ^d 1 o 8 ^r _t 6 n 7 e 3 c 3 ^c _{_} 21 d : O 58 ^N 8 D ^IQE S FY QTE Q G

I^A Q WS S D G L G G G L G T T T T T T T T L R G L EL GL D V R RF D G L A G L RL RS _I ^SL G G L T D L V G T G L _I ^Q AL S T S G G G G Y EL Q QV GL Q T G P RL TL G G G G G G G G S S S S S S S S S S S S S S R S S S S S S S S R S S LS LS SS SS L L P L ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S S S S S S S S S S S S SS SS SS SSd A A A A A A A A A V A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁵- ³ ne ² - ⁵ ² - ⁵ ² - ⁵ ² - ⁵ ² - ⁵ ² - ^{5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 7 7 7 7 7 7 7 1} ² - 2 - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹- ¹-n V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 5V 5V 5V 5e B B B B B B B B B B B B B B B B B Vg R B B B B B B B B B B B B B B B B B^_ _v T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 97 97 97 97 97 97 97 97 97 97 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 3 3 3 3 3 3 3 3 3 3 73 73 73 73 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8_i ^d 6 6 6 6 6 6 6 6 6 6 6 6 6 6 36 3 3 3 3 3 3 3 3 3 3 3 6 6 6 6 6 6 6 6o 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 62 62 62 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6^r _t 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 24 24 2 2 2 2 2 2 8 8 8 8 8 8 8 8n 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 42 42 42 4 4 4 6 6 6 6 6 6 6 6e 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 25 25 25 2 2 2 2 2 2 2 2 c 5 5 5 5 5 5 5 5^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 48 ^N 9 D 2 ^IQE S FFAET N G QL ³ _r SS d A c C e ¹-ne ¹ _J g B _j ^_ R T e 4 n 1 e V g B ^_ _v RT 82 _i ^d 9 o 2 ^r _t 7 n 3 e 3 c 4 ^c _{_} 21 d : O 91 ^N 9 D ^IQE S FY QEY GST

T T L S G S G G T T T G T T T T L G G G S S S G G P F L L L L F WL GL LL LL F F R S P S S D W S T G G G G A S S S G G P LS QP SL SK QL SE LL SL GL G G T G G R G G G G S S S S S S S S S S S S S S S S S S S S S S S S LS PS LS L P P S P L P ³ _r SA SA SA SA SA SV SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S A S S S S A SA S S d A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁷- ⁷ ne ² - ^{7 7 7 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7} ² - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹- ¹- ¹- 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹-n V V 5V 5V 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7e B B B B VB VB V V V V V V V V V V V V V V V V V V V V V V V V V V V Vg R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 98 98 98 98 98 47 47 47 47 47 47 47 47 47 47 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 76 76 76 76 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1_i ^d 6 6 6 6 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 60 6 6 6 7 7 7 7 7 7 7 7 7 7 7o 8 8 8 8 8 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 03 03 0 8 8 8 8 8 8 8 8 8 8 8^r _t 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 37 7 7 7 7 7 7 7 7 7 7 7n 2 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 25 25 2 2 2 2 2 2 2 2e 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 56 56 5 5 5 5 5 5 c 6 6 6 6 6 6^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 81 ^N 0 D 3 ^IQE S FFLKE NTA GVL ³ _r SS d A c C e ⁴-ne ¹ _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 35 _i ^d 8 o 2 ^r _t 5 n 8 e 5 c 4 ^c _{_} 21 d : O 35 ^N 9 D ^IQE S FY QEYT GT

G T Q G Q Q G L T G P R G L T G A G V A G AE G G G GE G G G G G G D G G G S P Q ST VT G D Q D G G G G F L S P L L L L L L L L L L QL GL VL R Q DL SL F GL GL A ^G _I G G Q S S S S S S S S S S S S S S S S S S S S S S S S RS FS FS LS S L S L L G ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S S S S S S S S S S S S SS SS SS SS SSd A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁷- ⁷ ne ² - ⁷ ² - ⁷ ² - ⁷ ² - ¹ ² - ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} ² - 2 - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _J B _{J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹- ¹-n V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 5V 5V 5V 5e B B B B B B B B B B B B B B B B B Vg R B B B B B B B B B B B B B B B B B^_ _v T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 51 51 51 51 51 51 21 21 21 21 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 7 7 7 7 7 7 3 3 3 3 13 13 13 13 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1_i ^d 8 8 8 8 8 8 7 7 7 7 7 7 7 7 37 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3o 7 7 7 7 7 7 0 0 0 0 0 0 0 0 0 70 70 70 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7^r _t 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 07 07 0 0 0 0 0 0 0 0 0 0 0 0 0 0n 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 75 75 75 7 7 7 7 7 7 7 7 7 7 7e 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 56 56 56 5 5 5 5 5 5 5 5 c 6 6 6 6 6 6 6 6^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 25 ^N 0 D 3 ^IQE S FY QT D^T _IE GL ³ _r SS d A c C e ³-ne ² _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 07 _i ^d 4 o 3 ^r _t 3 n 4 e 6 c 4 ^c _{_} 21 d : O 78 ^N 9 D ^IQE S FFLE GT NL

TA A L L G G A S G Q Q T Q T T T Q T Q G G G L L S GS ST S S G GP L G T ST GR G G G G G G G TE G G G Q S RS F D S L V S L G S L A S L A S L D S LS PS R V S L T S F G S L Q S L E S LS P A R L T S L G S LS RS PS PS PS PS PS P P P P P P P P ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S S S S S A SA SA S S S d A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ² ne ² - ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 6 6 6 6 6 6 6 6 6 6 6 6} ² - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1} B _J B _{J J J J J J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹- ¹- ¹- ¹- 8 8 8 8 8 8 8 8 8 8 8 8n 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1e VB VB V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V Vg R R BR BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 21 21 21 21 21 21 21 21 21 21 21 21 21 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 13 13 13 13 1 1 1 1 1 8 8 8 8 8 8 8 8 8 8 8 8_i ^d 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 37 3 3 3 3 8 8 8 8 8 8 8 8 8 8 8 8o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 70 70 70 7 9 9 9 9 9 9 9 9 9 9 9 9^r _t 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 07 9 9 9 9 9 9 9 9 9 9 9 9n 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 95 95 95 9 9 9 9 9 9 9 9 9e 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 56 56 5 5 5 5 5 5 5 c 6 6 6 6 6 6 6^c _{_} ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. 1 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 : O 02 ^N 1 D 3 ^IQE S F H QP Q NS G D Q ³ _r SS d A c C e ⁵-ne ¹ _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 64 _i ^d 7 o 2 ^r _t 2 n 6 e 0 c 5 ^c _{_} 21 d : O 55 ^N 0 D 1 ^IQE S FY QTE GL G

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Y D S S G G LF R V S T D S N S RT D K Y L EF QR Q A S VL R G P T D E P Q T N G R G T D E G Q Q G G G G N G S S E F L G G G G Q F A GL GL AL GL D G G A V A D GE A D G S S S S R S S S S S S S S S S S S S L S S S S LS LS L L L L R L G P L ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA _I ^S A SA SA SA SA SA S S A S S S S S S S S S A S S S S S S S S d A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁷- ^{7 7 7 7 1 1 7 7 1 7 7 7 7 7 7 7 7 5 5 5 5 5 5 5 5 5 5} ² - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - ⁵ ² - ⁵ ² - ^{3 5 5 3} ² - 2 _{- - -}ne _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J g^_ _j R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R B T R T e ⁹-7 ⁹-7 ⁹-7 ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹-n V V V 7V 7 7 7 7 7 7 7 7 7 7 7 7 7 7 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5e B B B B VB V V V V V V V V V V V V V V V V V V V V V V V V V V V V Vg R R R R R BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 25 25 25 25 25 25 25 25 25 25 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 6 6 6 6 6 6 6 6 6 6 56 56 5 5 5 5 5 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2_i ^d 9 9 9 9 9 9 9 9 9 9 9 9 6 6 6 6 6 6 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2o 6 6 6 6 6 6 6 6 6 6 6 6 96 9 9 9 9 9 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4^r _t 7 7 7 7 7 7 7 7 7 7 7 7 7 6 6 6 6 6 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2n 7 7 7 7 7 31 31 31 31 31 31 31 31 31 31 31 31 31 31 3 3e 1 1 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 22 ^N 2 D 3 ^IQE S FFLE GT DRP ³ _r SS d A c C e ²-ne ² _J g B _j ^_ R T e 8 n 1 e V g B ^_ _v RT 33 _i ^d 4 o 6 ^r _t 4 n 8 e 4 c 5 ^c _{_} 21 d : O 75 ^N 1 D 1 ^IQE S FY QTE GAL G

G S G S Q A Q Q G G R _I T R GL DS GL GL D G A T T L GL EL AL RF GF GT G S L G Y L R F AL RL VL G EL GL G G G E P E G V Q P G A D D S G T L D P G GA G H PL S S S S S S S S S S S S S S S S S S S LS LS LS R L WR F L S L L V S P ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S S S S S S S S A SA S S S S S S S d A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁵- ^{5 5 5 5 5 5 3 5 3 5 3 5 5 5 5 1 5 5 5 3 7 5 3 5 5 5 5 5 5}ne ² - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 _{- - - - - - - - - - - - - -} ⁵- ⁵- ⁵- ⁵-_J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _{J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J} g R BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹-n V V V V V V V V V V V 5V 5V 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5e B B B B B B B B B B B B B VB VB V V V V V V V V V V V V V V V V V V Vg R R R R R R R R R R R R R R R BR B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 7 7 7 7 7 7 7 7 7 7 7 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 22 22 2 2 2 2 2 2 2 2 2_i ^d 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 24 2 2 2 2 2 2 2 2o 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 42 42 4 4 4 4 4 4^r _t 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 23 2 2 2 2 2n 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 31 3 3 3 3e 1 1 1 1 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 65 ^N 2 D 3 ^IQE S FFLE GT NS D ³ _r SS d A c C e ²-ne ² _J g B _j ^_ R T e ⁴-n 6 e V g B ^_ _v RT 40 _i ^d 1 o 5 ^r _t 3 n 2 e 6 c 6 ^c _{_} 21 d : O 19 ^N 1 D 1 ^IQE S FY QTEL

D H T G Q T G G T T A R Q G Q Q G EL G Y P E S L TL GL GL NL G G L EL GL G G T G G G P EL GL DL DL RL QL QL AP G G G Q N G R A R T P P V A V V V V G NP G TR S S S S S S S S S S S S S S S S S S S S PS LS LS G L L L L L L P P P P ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S S S S S S S S A SA S S S S S S S d A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁵- ^{5 3 5 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4}ne ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² -_J ² - ² J ¹ - ² J ² - ^{2 2} J ² -_J ² -_J ² _Jg BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- 8 8 8 8n V V V V V V V V V V V V V V 5V 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1e B B B B B B B B B B B B B B B VB VB V V V V V V V V V V V V V V V V Vg R R R R R R R R R R R R R R R R R BR B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 72 72 72 72 72 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 45 45 4 4 4 4 9 9 9 9_i ^d 4 4 4 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 51 5 5 5 6 6 6 6o 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 7 7 7 7^r _t 3 3 3 3 3 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 17 1 5 5 5 5n 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 71 2 2 2 2e 4 4 4 4 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 09 ^N 2 D 3 ^IQE S FF QE N G GAL G ³ _r SS d A c C e ¹-ne ² _J g B _j ^_ R T e ⁴-n 6 e V g B ^_ _v RT 95 _i ^d 1 o 7 ^r _t 7 n 9 e 3 c ^c _{_} 22 d : O 52 ^N 2 D 1 ^IQE S FFLE GT D

N G N N T R R G S G G G E L A A L L G L L L G Q T R N S N R R Q Q T S G D S R Q L L W_I ^T P A G P P P P P P P P P P P P P P P GE LE V G G A G L G G A A G G D G S P D S S S S S S S S S S S S S S S S S ES ES E V E E E E L L P P P P P P P ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S S S S S T S S S S S S S A S S S S S S S S S S S S S d A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 7 7 7 7 7} ² - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - ¹ ² - ⁷ ² - ^{7 7 6 6 6 6 6 6 6} ² - 2 _{- - - - - - - -}ne _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ¹ B _J ^{1 1 1 1 1 1} B _{J J J J J J} g R R R R R R R R R R R R R R R R R R R R R R R R R R R R BR B B B B B^_ _j T T T T T T T T T T T T T T T T T T T T T T T T T T T T T R T R T R T R T R Te 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 ¹-6 ¹-6 ¹-6 ¹-6 ¹-6 ¹-6 ¹- ¹- ¹- ¹- ¹- ¹- 8 8 8 8 8 8 8n V V V V V V V V V V V V V V V V V V V V V 6V 6 6 6 6 6 1 1 1 1 1 1 1e B B B B B B B B B B B B B B B B B B B B B B VB V V V V V V V V V V Vg R R R R R R R R R R R R R R R R R R R R R R R BR B B B B B B B B B B^_ _v T T T T T T T T T T T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 37 3 3 3 3 3 3 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 4 74 74 7 7 7 7_i ^d 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 4 4 4 4o 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 9 9 9 9 9 9 9 9 9 9 9 9 8 8 8 68 6 6 6^r _t 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 8 8 8n 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 34 3 3e 4 4 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 42 ^N 3 D 3 ^IQE S FFAEAT G ³ _r GV d S c C e ¹-ne ¹ _J g B _j ^_ R T 1-e 9 n 2 e V g B ^_ _v RT 54 _i ^d 6 o 4 ^r _t 6 n 5 e 5 c ^c _{_} 22 d : O 95 ^N 2 D 1 ^IQE S F HLPS D G

R S G Q Q Q Y S T Q T R GP DR GP GP GP G G E P Q P G P P G Y P SL GL S N S Y N S G N G S GL YL GL SL SL G G G G A T S G G G G S G R Q G AL T G RF GR D S S S S S S S S S S S S S S S S S S LS LS P P L L F L S P WP L L L P ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S S S S S S S S S S S S S A S S S S S S S S S S S S S d A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁶- ^{6 6 6 6 6 6 6 6 1 7 7 1 7 1 7 1 7 1 7 7 7} ¹ - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - ⁷ ² - ^{1 7 7 7 7 7 1 1 1 7 1} ² - 2 _{- - - - - - - - - -}ne _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ^{2 2 2 2 2 2 2 2 2} B _{J J J J J J J J J} g R R R R R R R R R R R R R R R R R R R R R R R R R BR B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R Te 81 81 81 81 81 81 81 81 81 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 7 7 7 7 7 7n V V V V V V V V V V V V V V V V V V V V V V V V V V V V 2 2 2 2 2 2e B B B B B B B B B B B B B B B B B B B B B B B B B B B B VB V V V V Vg R R R R R R R R R R R R R R R R R R R R R R R R R R R R R B B B B B^_ _v T T T T T T T T T T T T T T T T T T T T T T T T T T T T T RT RT RT RT RT 37 37 37 37 37 37 37 37 37 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 6 6 4 4 4 4 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 80 8_i ^d 6 6 6 6 6 6 6 6 6 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0o 8 8 8 8 8 8 8 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2^r _t 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0n 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4e 4 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 85 ^N 3 D 3 ^IQE S FF QE NYS GS D ³ _r SS d A c C e ¹-ne ² _J g B _j ^_ R T e ⁴-n 6 e V g B ^_ _v RT 42 _i ^d 3 o 7 ^r _t 7 n 7 e 9 c ^c _{_} 22 d : O 39 ^N 2 D 1 ^IQE S FY QEYSP G

R G G G N G G S L G T T R _I ^A S D G GP G G A A Q L P D WG GS GR G ST G LR T R T N T P S GT L SL RL GF AL R QL GY PL LL G L S P P WL Q Y A N N G S S R S S S S R S S S S S LS SS LS LS S S P S F R F F P L WL F P L ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S R A S S A S S R S S S S S S S S S S S S S A S S S S S S S S S S S S S S S d A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁷- ^{7 7 7 7 7 1 1 7 1 7 1 1 7 1 1 7 7 7 7} ² - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - ⁷ ² - ^{1 7 7 1 1 7 1 7 7 2 2 2 2} ² - 2 _{- - - - - - - - - - - -}ne _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2} B _{J J J J J J J J J J J} g R R R R R R R R R R R R R R R R R R R R R R R BR B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R Te 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 7 7 7 7 7 7 7 7n V V V V V V V V V V V V V V V V V V V V V V V V V V 2 2 2 2 2 2 2 2e B B B B B B B B B B B B B B B B B B B B B B B B B B VB V V V V V V Vg R R R R R R R R R R R R R R R R R R R R R R R R R R R B B B B B B B^_ _v T T T T T T T T T T T T T T T T T T T T T T T T T T T RT RT RT RT RT RT RT 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 34 3 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 42 4 4_i ^d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 2 2o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 9 59 5^r _t 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 9n 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5e 4 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 29 ^N 3 D 3 ^IQE S FY QEYS DL ³ _r SS d A c C e ⁷-ne ² _J g B _j ^_ R T 3-e 1 n 1 e V g B ^_ _v RT 54 _i ^d 1 o 1 ^r _t 0 n 4 e 7 c 1 ^c _{_} 22 d : O 72 ^N 3 D 1 ^IQE S FFLE GT

N T E G G Y Q G S G Q G R A LL NR G G F S T L S T F R T L Y G P T S Y R N F F G LP GF Q A Y R N F GL GL GL Y Y Y Y S AL GL S G G S G G E G V R G G G G G G LA GT S S S S S S S S S S S S S S S S S S S S LS LS L P L L L L L L L P L R ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S S S S S S S S S S A SA S S S S S S S S S d A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ^{2 2 2 2 2 2 2 2 2 2 2 2 4 7 7 7 1 7 7 7 7 7 7 7 7 1} ² - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 1 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - ⁷ ² - ⁷ ² - ^{7 7 7 7 7} ² - 2 - 2 _{- - -}ne _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J g^_ _j R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R B T R T e 72 72 72 72 72 72 72 72 72 72 72 72 72 72 ⁸-7 ⁸-7 ⁸-7 ⁶-7 ⁸- ⁶- ⁸- ⁸- ⁸- ⁸- ⁸- ⁸- ⁸- ⁶- ⁶- ⁸- ⁸- ⁸- ⁶- ⁸-n V V V V V V V V V V V V V V V V V V 7V 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7e B B B B B B B B B B B B B B B B B B B VB VB V V V V V V V V V V V V Vg R R R R R R R R R R R R R R R R R R R R R BR B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT 34 34 34 34 34 34 34 34 34 34 34 34 34 34 97 97 97 97 97 97 97 97 97 97 97 97 97 9 9 9 9 9 9 9 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4 4 4 4 4 4 4 4 4 4 4 74 74 7 7 7 7 7_i ^d 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4o 9 9 9 9 9 9 9 9 9 9 9 9 9 9 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 42 4 4 4 4^r _t 5 5 5 5 5 5 5 5 5 5 5 5 5 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 27 2 2 2n 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 75 7 7e 5 5 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 62 ^N 4 D 3 ^IQE S FY QEYSS ³ _r SS d A c C e ⁷-ne ² _J g B _j ^_ R T e 7 n 2 e V g B ^_ _v RT 36 _i ^d 1 o 6 ^r _t 2 n 1 e 0 c 2 ^c _{_} 22 d : O 16 ^N 3 D 1 ^IQE S FF QE N

Y G T G S S Q Q G R S S N T G R G R G Q G EL R E P A G L A G L AL G G L A G L AL GL GL GL GL G G L AL GL P G P AL D G S A SL TL D R G R S AL TL E G AR S G G D AT S G S S S S S S S S S S S S S S S S S S S S S S S S LS AS LS V L L L L P S ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S S S S S A SA S S S S d A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ¹- ^{7 7 1 1 7 7 1 1 1 7 7 7 1 1 1 1 5 7 7 7 7 1 7 1 7 1 1 1 7 7 1 7}ne ² _{- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -} ⁷-_J ² B _J ² B _J ^{1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _{J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J} g R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ⁶-7 ⁸-7 ⁸-7 ⁸-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹-n V V V V V V V V V V V V V 7V 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7e B B B B B B B B B B B B B B VB VB V V V V V V V V V V V V V V V V V Vg R R R R R R R R R R R R R R R R BR B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 97 97 97 97 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 7 7 7 7 7 7 7 7 7 7 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 73 73 7 7 7 7 7 7 7 7_i ^d 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 35 3 3 3 3 3 3 3o 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 55 5 5 5 5 5 5^r _t 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 58 5 5 5 5 5n 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 85 8 8 8 8e 5 5 5 5 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 06 ^N 4 D 3 ^IQE S FY QT DT D ³ _r RS d A c C e ³-ne ² _J g B _j ^_ R T e 7 n 2 e V g B ^_ _v RT 36 _i ^d 1 o 6 ^r _t 2 n 1 e 0 c 2 ^c _{_} 22 d : O 59 ^N 3 D 1 ^IQE S FY QEYV

G G G E G G G S G P F L G G A Q R G Q G N Q G G Q G G T G T P G G G Y G S N N N S G S P TP RL GL DL AL GP EL GL GS AL GL GL AL AL GP TL RL EL GL DL G G G D G Y D G S LT S S S S S S S S S S S S S S S S S S S S S S S S LS LS L L L S L L ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S S S A SA S S d A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁷- ¹ ne ² - ^{1 7 7 7 7 7 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 1 7 1 1 1 7} ² - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J ² B _J ² B _J ² B _J ^{2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T 9- ⁹- ⁹- ⁹- ⁹- ⁹- ^{9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3}- ³- ³- ³- ³- ³- ³- ³- ³- ³-e 7 7 7 7 7 7 -7 -7 _{- - - - - - - - - - - - - - - -} 1 1 1 1 1 1 1 1 1 1n V V V V V V V V 5V 5V 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1e B B B B B B B B B B VB VB V V V V V V V V V V V V V V V V V V V V V Vg R R R R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 77 77 77 77 77 77 77 77 38 38 38 38 38 38 38 38 38 38 38 38 38 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 7 7 7 7 7 7 7 7 7 7 7 7 7 87 87 87 0 0 0 0 0 0 0 0 0 0_i ^d 5 5 5 5 5 5 5 5 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 99 9 9 9 9 9 9 9 9 9o 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 7 97 97 9 9 9 9 9 9 9^r _t 8 8 8 8 8 8 8 8 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 73 7 7 7 7 7 7n 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 36 36 3 3 3 3e 6 6 6 6 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 49 ^N 4 D 3 ^IQE S FFLE GALRL ³ _r SS d A c C e ²-ne ² _J g B _j ^_ R T e 7 n 2 e V g B ^_ _v RT 94 _i ^d 1 o 2 ^r _t 7 n 6 e 3 c 2 ^c _{_} 22 d : O 92 ^N 4 D 1 ^IQE S FY QEY

GE G GS G G N R SS G GS G GS GS S G S S F F G S T G A Q G S V V G Y Q G Q Y D GL G K G G G LS RS L R R G G S LS LS LS LS SS LS LS LS LS LS PS FS LS ^G _I S ^G _I S ^G _I S ^S _I ^G _I ^G _I ^G _I ^Q _I ^G _I ^N _I ^G _I GL P S M^I _I P ^Q _I ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S S S S S S S S S S S N S A S S S S S S S S S S S S S d A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁷- ^{1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2} ² - 2 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - ² ¹ - ² ¹ - ^{2 2 2 2 2 2 2 2 2 2} ¹ - 1 _{- - - - - - - - -}ne _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ¹ B _J ^{1 1 1 1 1 1 1 1} B _{J J J J J J J J} g R R R R R R R R R R R R R R R R R R R R R R R R R R BR B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T 3-e 1 ³- 1 11 72 72 72 72 72 72 72 72 72 72 72 72 72 72 91 91 91 91 91 91 91 91 91 9 9 9 9 9 9 9 9 9n V V V V V V V V V V V V V V V V V V V V V V V V V 1V 1 1 1 1 1 1 1 1e B B B B B B B B B B B B B B B B B B B B B B B B B B VB V V V V V V Vg R R R R R R R R R R R R R R R R R R R R R R R R R R R B B B B B B B^_ _v T T T T T T T T T T T T T T T T T T T T T T T T T T T RT RT RT RT RT RT RT 30 30 31 31 31 31 31 31 31 31 31 31 31 31 31 31 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 9 9 9 9 9 8 8 8 8 8 8 8 8 8 8 8 8 8 8 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 45 4 4_i ^d 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 5 5o 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 91 9^r _t 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1n 6 6 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 3e 8 c^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 82 ^N 5 D 3 ^IQE S FTLV NA GA GE ³ _r SS d A c C e ⁶-ne ² _J g B _j ^_ R T e ⁴-n 6 e V g B ^_ _v RT 78 _i ^d 0 o 6 ^r _t 3 n 4 e 5 c 2 ^c _{_} 22 d : O 36 ^N 4 D 1 ^IQE S FTY GV

G GT GS QR G Y A G S S G G A G G Q G G Q L P R T G G G G G G E G D GV G S S SE G G GR G G G E G G R_I R P P S M^W _I L L L L L L L AL DL GL SL NL R L G G Q S G R G G S S S S S S DT S S S S S S S S S S S S S S S S LS LS L L L F L F L L S L ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA RA SA S S A S S A S S A S S S S S S S S A SA S S S S S S d A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ^{2 2 2 2 2 2 2 6 6 6 6 6 6 6 6 6 6 6 6 6 2 2 2 2 2 2 2 2 2 2}ne ¹ _{- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -} ²- ²- ²-_J ¹ B _J ^{1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1} B _J B _{J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J} g R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te 9 ^{1 1 1 1 1 1 1 1 1} n 1 91 91 91 91 91 91 91 -5 -5 -5 -5 -5 -5 -5 -5 -5 ¹-5 ¹-5 ¹-5 ¹-5 72 72 72 72 72 72 72 72 72 72 72 72 72e VB V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V Vg R BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 94 94 94 94 94 94 94 94 41 41 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 13 13 13 1 1 1 1 1 1 1 1 7 7 7 7 7 7 7 7 7 7 7 7 7_i ^d 9 9 9 9 9 9 9 9 1 1 1 1 1 31 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4o 1 1 1 1 1 1 1 1 5 5 5 5 5 5 15 15 1 1 1 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4^r _t 3 3 3 3 3 3 3 3 0 0 0 0 0 0 0 0 50 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3n 8 8 8 8 8 8 8 8 3 3 3 3 3 3 3 3 3 03 03 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0e 1 1 1 1 1 1 1 1 1 1 1 31 31 51 51 51 51 51 51 51 51 5 5 5 5 5 c 1 1 1 1 1^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 26 ^N 5 D 3 ^IQE S FFLE GT NP G GP ³ _r SS d A c C e ²-ne ² _J g B _j ^_ R T e 8 n 1 e V g B ^_ _v RT 06 _i ^d 8 o 2 ^r _t 1 n 5 e 3 c 3 ^c _{_} 22 d : O 79 ^N 4 D 1 ^IQE S FTY GY D G

GT R Q V WQ G R G G G Q R G G S S T T S A A S G G G GT GT N G R G G G Q S S S S S S G S S G S G Q T YS G G R LS SS SS PS Q R S F S S F P S FS QS P G S L E S L T S LS DS DS DS DS DS DS DS DS D A Q N D Q S DS DS DS LS RS G G G N ³ _r S S S S S S S S S S S S S S S S S S S S S S S S S S S S S V V V GV AVd A A A A A A A A A A A A A A A A A A A A A A A A A A A A A S S S S S c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ² ne ¹ - ² ¹ - ² ¹ - ² ¹ - ² ¹ - ² ¹ - ^{2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 7 1 1 1 1 2 2 2 2 2} ¹ - 1 - 1 _{- - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J ¹ B _J ^{1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T 4^{4 4 4 4 4 4 4 4 4 4 4 1}- ¹- ¹- ¹- ¹-e 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 _{- - - - - - - - - - - -} ⁴- ⁴- 9 9 9 9 9n 2V 2V 2 2 2 2 2 2 2 2 2 2 2 2 2 6 6 6 6 6 6 6 6 6 6 6 6 6 6 2 2 2 2 2e B B VB VB V V V V V V V V V V V V V V V V V V V V V V V V V V V V V Vg R R R R BR BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 57 57 57 57 57 57 57 57 57 57 57 57 57 57 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 9 9 9 4 4 4 4 4 4 4 4 4 4 4 4 4 4 74 84 84 84 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8_i ^d 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 43 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1o 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 6 6 6 6 36 36 3 3 3 3 3 3 3 3 7 7 7 7 7^r _t 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 9 9 9 9 9 69 6 6 6 6 6 6 6 4 4 4 4 4n 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 95 95 95 9 9 9 9 7 7 7 7 7e 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 51 51 5 5 7 7 7 7 7 c 1 1 1 1 1 1 1^c _{_} ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 69 ^N 5 D 3 ^IQE S FFLE GT N GSL ³ _r SS d A c C e ²-ne ² _J g B _j ^_ R T en 9 e V g B ^_ _v RT 06 _i ^d 9 o 1 ^r _t 7 n 4 e 4 c 3 ^c _{_} 22 d : O 13 ^N 5 D 1 ^IQE S FFLE GT G

G N Q G G N N G T Q G AT G T R D G G G Q Q G S Q S Q R G R E R R G G G G NE G G GE GA G GA D G GE D Q S G G G G G E D V ³ _r GV G G G G G G G TR LS LS LS LS LS LS LS LS L L L S L R EL DL GL DL P Q QS Q Q Q Q S VS VS VS VS V A V V S S S S S S S S SS SS SS SS SS SS SS SS SS SS SS SS G SS SS SS SS d S S S S A A A A A A A A A A A A A A A A A A A A G A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ² ne ² - ² ² - ² ² - ² ² - ² ² - ² ² - ² ² - ² ² - ⁶ ² - ⁶ ¹ - ⁶ ¹ - ⁶ ¹ - ⁶ ¹ - ^{6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 5 5 5} ¹ - 1 - 1 _{- - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J ¹ B _J ^{1 1 1 1 1 1 1 1 1 2 2 2 2 2} B _J B _{J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T 1- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹-e 92 92 92 92 92 92 92 92 92 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 41 41 41 4 4ne V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 1V 1Vg BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 98 98 98 98 98 98 98 98 98 28 28 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 8 8 8 8 8 1 1 1 1 1 1 1 1 1 6 6 86 86 86 86 8 8 8 8 8 8 8 8 8 8 8 8 8 8 6 6 6 6 6_i ^d 7 7 7 7 7 7 7 7 7 5 5 5 5 5 5 65 6 6 6 6 6 6 6 6 6 6 6 6 6 8 8 8 8 8o 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 55 55 55 5 5 5 5 5 5 5 5 5 5 0 0 0 0 0^r _t 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 57 57 5 5 5 5 5 5 5 5 1 1 1 1 1n 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 77 77 77 7 7 7 7 7 1 1 1 1 1e 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 71 71 7 7 7 8 8 8 8 8 c 1 1 1 1 1 1 1 1^c _{_} ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 03 ^N 6 D 3 ^IQE S FY QEYS CPP A ³ _I _r G d A c C e ⁷-ne ² _J g B _j ^_ R T e 0 n 3 e V g B ^_ _v RT 42 _i ^d 5 o 5 ^r _t 8 n 0 e 5 c 3 ^c _{_} 22 d : O 56 ^N 5 D 1 ^IQE S FY QTE G

V DT DA EA TV Q SV D T K S E W T Y A E D V V A G L L Q L G A GA GA GT GA N G G G G A R Q R ^Q _I P Q H Q Q Q Q Q Q P Q ^Q _I Q Q Q Q L L L L L P GL GL S AL SL G S F A V E S S S S S S S S S S S S S S S S S S S S S S S S S S LS LS VS L L L S L ³ _r Sd A SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S S A SA SA c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁵- ³ ne ² - ³ ² - ⁵ ² - ^{3 5 3 5 3 5 5 3 5 3 7 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} ² - 2 - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te 41 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹-n V 1V 1V 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5e B B B VB VB V V V V V V V V V V V V V V V V V V V V V V V V V V V V Vg R R R R R BR BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 15 15 15 15 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1_i ^d 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 5 5 5 5 5 5 5 5 5 5 5 5 5 5o 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 02 02 0 0 0 0 0 0 0 0 0 0 0 0^r _t 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 7 7 7 7 7 7 7 27 2 2 2 2 2 2 2 2 2 2 2n 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 78 78 78 7 7 7 7 7 7 7 7e 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 81 81 8 8 8 8 8 8 c 1 1 1 1 1 1^c _{_} ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 46 ^N 6 D 3 ^IQE S FY QE DVF ³ _r SS d A c C e ⁷-ne ² _J g B _j ^_ R T e ⁹-n 7 e V g B ^_ _v RT 50 _i ^d 3 o 6 ^r _t 8 n 6 e 1 c 4 ^c _{_} 22 d : O 99 ^N 5 D 1 ^IQE S F HLPS N G

Q N G G G G G R I Q Q D N GR G N G N N G T G G G G G S G G G Q G S G S ^G _I G N N G G T N T G E D E E G G D G S ^G _I S ^G _I S ^G _I S ^Q _I S ^R _I S ^D _I S GS PS ^G _I S GS DS ^P _I S GR KR RR TR TR GR RR LR GR QR RR LR LS LS LS LS LS L L L ³ _r S S S S S S S S S S S S S S A S S S S S S S S S S S S S S S S SS SS SSd A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁶- ⁶ ne ¹ - ⁶ ¹ - ⁶ ¹ - ^{6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5} ¹ - 1 - 1 _{- - - - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J ^{1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1} B _{J J J J J J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te 91 91 91 91 91 91 91 91 9 9 9 9 9 9 7 7 7 7 7 7 7 7 7 7 7 7 ¹- ¹- ¹- ¹- ¹- ¹- ¹- ¹-n V V V V V V V V 1V 1V 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5e B B B B B B B B B B VB VB V V V V V V V V V V V V V V V V V V V V V Vg R R R R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 82 82 82 82 82 82 82 82 82 82 82 82 82 82 81 81 81 81 81 81 8 8 8 8 8 8 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 9 9 9 9 9 9 19 19 19 1 1 1 5 5 5 5 5 5 5 5_i ^d 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 3 9 9 9 7 7 7 7 7 7 7 7o 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 38 38 3 3 3 3 3 3 3 3 3^r _t 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 82 2 2 2 2 2 2 2 2n 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 52 52 5 5 5 5 5 5e 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 22 2 2 2 2 2 c 2 2 2 2 2^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 89 ^N 6 D 3 ^IQE S FY QEYA GYL ³ _r SS d A c C e ⁷-ne ² _J g B _j ^_ R T e ⁹-n 7 e V g B ^_ _v RT 50 _i ^d 3 o 6 ^r _t 8 n 6 e 1 c 4 ^c _{_} 22 d : O 33 ^N 6 D 1 ^IQE S F H QP Q D

G G D S S R G G Q G GS SS N GS SS G S G G A S S G G Q R R G T R G L Q EL GL GL GL DL EL EL GL GS Q S P D D D D D D G S G S E D D D GE SP SE GL GL GL DL G T G D V D G S S S S S S S S S S S S S S S S S S S S S S S S S S S LS LS LS LS L S L ³ _r S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S SS SS SSd A A A A A A A A A A V A A A A A A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁵- ⁵ ne ¹ - ⁵ ¹ - ⁵ ¹ - ⁵ ¹ - ⁵ ¹ - ⁵ ¹ - ⁵ ¹ - ^{5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1} ¹ - 1 - 1 - 2 _{- - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ² B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1} B _J B _{J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁸-7 ⁶-7 ⁶-7 ⁷-7 ⁸-7 ⁸-7 ⁸-7 ⁸-7 ⁸- ⁶- ⁸-ne V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 7V 7V 7Vg BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 55 55 55 55 55 55 55 55 55 55 28 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 7 7 7 7 7 7 7 7 7 7 9 89 89 89 89 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8_i ^d 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 93 9 9 9 9 9 9 9 2 2 2 2 2 2 2 2 2 2 2o 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 32 32 32 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3^r _t 5 5 5 5 5 5 5 5 5 5 9 9 9 9 9 9 9 9 9 29 29 2 2 3 3 3 3 3 3 3 3 3 3 3n 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 92 92 92 9 9 9 9 9 9 9 9 9 9e 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 22 22 2 2 2 2 2 2 2 2 c 2 2 2 2 2 2 2 2^c _{_} ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 23 ^N 7 D 3 ^IQE S FFLKE N G Q GP ³ _r SS d A c C e ⁴-ne ¹ _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 87 _i ^d 1 o 4 ^r _t 8 n 9 e 1 c 4 ^c _{_} 22 d : O 76 ^N 6 D 1 ^IQE S FFAET

G S Q G K Q P P P P G N D S D S G V G G RL FE P G G Q G VR G P P P P P P P P P P P P P S S R S S S S G L ³ _r S RS LS L L AL P QL RL R EL F G A A S S AS V V A A P V V S S G G ^A _I ^A _I G G AT PS D D D D D D D D S S S S S S S S S S S S S S S S S S S A SA S S S S S S S S S S S G WG G WG WG WG WWWS S S S S S S S d A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ¹- ¹ ne ¹ - ¹ ¹ - ¹ ¹ - ¹ ¹ - ¹ ¹ - ¹ ¹ - ¹ ¹ - ¹ ¹ - ^{1 1 1 1 7 7 7 7 7 7 7 7 7 7 7 7 7 2 2 2 1 2 2 2 2} ¹ - 1 - 1 _{- - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ⁶-7 ⁸-7 ⁸-7 ⁸- ⁸- ⁶- ⁶- ⁷- ⁶- ⁶- ⁶- ⁶- ⁶- 0 0 0 0 0 0 0 0 0 0 0 0 0 ⁴- ⁴- ⁴- ⁴- ⁴- ⁴- ⁴- ⁴-n V V V 7V 7V 7 7 7 7 7 7 7 7 3 3 3 3 3 3 3 3 3 3 3 3 3 6 6 6 6 6 6 6 6e B B B B B VB VB V V V V V V V V V V V V V V V V V V V V V V V V V V Vg R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 38 38 38 38 38 38 38 38 38 38 38 38 38 97 97 97 9 9 9 9 9 9 9 9 9 9 6 6 6 6 6 6 6 6 2 2 2 2 2 2 2 2 2 2 2 2 2 8 8 8 78 78 78 78 7 7 7 7 7 7 9 9 9 9 9 9 9 9_i ^d 3 3 3 3 3 3 3 3 3 3 3 3 3 6 6 6 6 6 6 6 86 8 8 8 8 8 6 6 6 6 6 6 6 6o 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 62 62 6 6 6 7 7 7 7 7 7 7 7^r _t 9 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 1 1 1 1 1 1 1 21 2 2 3 3 3 3 3 3 3 3n 2 2 2 2 2 2 2 2 2 2 2 2 2 6 6 6 6 6 6 6 6 6 6 6 16 16 3 3 3 3 3 3 3 3e 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 62 62 6 6 6 6 6 6 c 2 2 2 2 2 2^c _{_} ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 66 ^N 7 D 3 ^IQE S FY QEYS GAL ³ _r SS d A c C e ⁷-ne ² _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 29 _i ^d 1 o 7 ^r _t 5 n 8 e 3 c 4 ^c _{_} 22 d : O 10 ^N 7 D 1 ^IQE S FFLE GT N

NR GR T GR Q N DS R Q Q Q A G G G A T Q Q T G S Q G G T G G T G G Q GE D D GP D GE G D G W V D GL GL GL GL GL AL GL TL GL GP GL GP RL DL GL EL PL R T Q R S Q D S S S S S S S S S S S S S S S S S S S S S S S S S S S SS LS SS PS L E L ³ _r S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S SS SS SSd A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ² ne ² - ² ² - ² ² - ² ² - ² ² - ² ² - ² ² - ^{2 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4} ² - 2 - 2 - 1 _{- - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J ¹ B _J ^{1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1} B _J B _{J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ⁴-6 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹-ne V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 5V 5V 5Vg BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 69 69 69 69 69 69 69 69 69 69 57 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 3 73 73 73 73 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7_i ^d 7 7 7 7 7 7 7 7 7 7 0 0 0 0 0 30 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3o 3 3 3 3 3 3 3 3 3 3 9 9 9 9 9 9 09 09 09 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0^r _t 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 93 93 9 9 9 9 9 9 9 9 9 9 9 9 9n 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 36 36 36 3 3 3 3 3 3 3 3 3 3e 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 62 62 6 6 6 6 6 6 6 6 c 2 2 2 2 2 2 2 2^c _{_} ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 00 ^N 8 D 3 ^IQE S FTY GY N G DL ³ _r SS d A c C e ²-ne ¹ _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 78 _i ^d 3 o 5 ^r _t 4 n 1 e 5 c 4 ^c _{_} 22 d : O 53 ^N 7 D 1 ^IQE S FFLKER G

G S R T GS G G G A G G Y R G S G T G T L D Q D R VE D T Q G D G G SS GE G G G SA GA G GA G S Q G P G P S L E F Q L L L WWL L L L L L L L L L L L L RL GL TL V A V A G G G A S S R S R S S S S S S S S S S S S S S S S S S S S S LS LS LS LS L L L L ³ _r S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S SS SS SS SSd A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁴- ⁴ ne ¹ - ⁴ ¹ - ⁴ ¹ - ⁴ ¹ - ⁴ ¹ - ⁴ ¹ - ^{4 4 4 4 4 1 1 1 1 1 1 7 7 1 7 7 1 1 7 1 7 7 7 1 1 7 7} ¹ - 1 - 1 - 1 _{- - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹- ¹-n V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 5V 5V 5V 5e B B B B B B B B B B B B B B B B B Vg R B B B B B B B B B B B B B B B B B^_ _v T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 57 57 57 57 57 57 57 57 57 57 5 5 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 3 3 3 3 3 3 3 3 3 3 73 73 01 01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0_i ^d 0 0 0 0 0 0 0 0 0 0 0 0 3 3 13 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1o 9 9 9 9 9 9 9 9 9 9 9 9 8 8 8 38 38 38 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3^r _t 3 3 3 3 3 3 3 3 3 3 3 3 5 5 5 5 5 5 85 85 8 8 8 8 8 8 8 8 8 8 8 8 8 8n 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 56 56 56 5 5 5 5 5 5 5 5 5 5 5e 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 62 62 62 6 6 6 6 6 6 6 6 c 2 2 2 2 2 2 2 2^c _{_} ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 43 ^N 8 D 3 ^IQE S FY QT DA G GALL ³ _r SS d A c C e ³-ne ² _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 07 _i ^d 4 o 3 ^r _t 3 n 4 e 6 c 4 ^c _{_} 22 d : O 96 ^N 7 D 1 ^IQE S FY QE DV G

Q P Q Q Q Q G G Q Q Q Q Q G T G G Q Y R T G G G S G G S G S G G G V VL RP RY G GL GL EL GL GP GL GL GP GL GL GP QV TP GS AS GL GA TE GL EL G G D G A D A A E G S S S L S S S S S S S S S S S S S S S S S S S S LS LS LS LS L L L L L L ³ _r Sd A SA S S A S SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S A S S S A SA SA c C C C A Y C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁷- ⁷ ne ² - ⁷ ² - ² ² - ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3} ¹ - 1 - 1 - 1 _{- - - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J ¹ B _J ^{1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1} B _J B _J B _{J J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹- ¹- ¹- ¹- ¹- ¹-n V V V V V V V V V V V V V V V V V V V V V V V V V V V V 5V 5V 5V 5 5 5eg B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B VB VB VB^_ _v RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 80 80 80 50 50 50 50 50 50 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 7 7 7 7 7 7 07 07 07 07 0 0 0 0 0 0 0 0 0 8 8 8 8 8 8 8 8 8 8 8 8^d _i 3 3 3 6 6 6 6 6 6 6 6 6 6 76 76 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7o 8 8 8 7 7 7 7 7 7 7 7 7 7 7 7 67 67 67 6 6 6 6 4 4 4 4 4 4 4 4 4 4 4 4^r _t 5 5 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 77 77 7 7 2 2 2 2 2 2 2 2 2 2 2 2n 6 6 6 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 78 78 12 1 1 1 1 1 1 1 1 1 1 1e 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 23 23 23 2 2 2 2 2 2 2 2 c 3 3 3 3 3 3 3 3^c _{_} ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 86 ^N 8 D 3 ^IQE S FTY GY NATS ³ _r SS d A c C e ²-ne ¹ _J g B _j ^_ R T e 7 n 2 e V g B ^_ _v RT 97 _i ^d 9 o 3 ^r _t 7 n 7 e 7 c 4 ^c _{_} 22 d : O 30 ^N 8 D 1 ^IQE S F Y_IT N GY

GV VE S Q R G G G N Q G MG Q G A V G V G YA GA PA Q N G G G G GE GE G GA G GT G V G D S G T L S GT G L L L L L L L ^G _I L L L L L L L L L L DL QL P QL VL WL R P T R V E A S S S S S S S S S S S S S S S S S S S S S S S S S S PS PS LS LS L L R L ³ _r S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S SS SS SS SSd A A A A A A A A A D A A A A A A A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ³- ³ ne ¹ - ³ ¹ - ³ ¹ - ³ ¹ - ³ ¹ - ³ ¹ - ^{3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1} ¹ - 1 - 1 - 1 _{- - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J ¹ B _J ^{1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1} B _J B _{J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹- ⁹- ⁹- ⁹-n V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 7V 7V 7V 7e B B B B B B B B B B B B B B B B B Vg R B B B B B B B B B B B B B B B B B^_ _v T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 58 58 58 58 58 58 58 58 58 14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 7 7 7 7 7 7 7 7 7 1 41 41 41 41 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4_i ^d 4 4 4 4 4 4 4 4 4 2 2 2 2 2 12 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1o 2 2 2 2 2 2 2 2 2 0 0 0 0 0 0 20 20 20 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2^r _t 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 05 05 0 0 0 0 0 0 0 0 0 0 0 0 0 0n 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 53 53 53 5 5 5 5 5 5 5 5 5 5 5e 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 33 33 33 3 3 3 3 3 3 3 3 c 3 3 3 3 3 3 3 3^c _{_} ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 20 ^N 9 D 3 ^IQE S F Y_IT N GA GAP ³ _r SS d A c C e ³-ne ¹ _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 38 _i ^d 5 o 5 ^r _t 6 n 3 e 0 c 5 ^c _{_} 22 d : O 73 ^N 8 D 1 ^IQE S FFAET N WE

V D A G W R A A A R G G G G L E WG ER S G T E Q P R G N Q G V G L D L D G G QR D D G V V G G G G ³ _r S F S S PS LS F G S TS L G S LS S N S LS PS S P S LS SS Q P S PT WT S LS QS QS QS QS QS QS PS QS QS QS QS QS QS QS VS VS A S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S d A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A V A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ¹- ¹ ne ¹ - ¹ ¹ - ¹ ¹ - ¹ ¹ - ¹ ¹ - ¹ ¹ - ¹ ¹ - ^{1 1 1 1 1 1 1 1 1 1 1 7 1 7 7 7 7 1 1 1 7 7 7 1 2 2} ¹ - 1 - 1 _{- - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J ^{1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹-7 ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- ⁹- 4 4 4 4 4 4 4 4 4 4 4 4 4 4n V V V V V 7V 7V 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9e B B B B B B B VB VB V V V V V V V V V V V V V V V V V V V V V V V V Vg R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 28 28 28 2 2 2 2 2 2 2 2 2 2 2 6 6_i ^d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 81 8 8 8 8 8 8 8 8 8 8 8 8o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 8 8 8 18 18 1 1 1 1 1 1 1 1 4 4^r _t 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 0 0 0 0 0 0 80 8 8 8 8 8 8 8 1 1n 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 02 02 0 0 0 0 0 9 9e 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 24 24 2 2 2 2 2 c 4 4 4 4 4^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 63 ^N 9 D 3 ^IQE S FY QT DTSP ³ _r SS d A c C e ³-ne ² _J g B _j ^_ R T e 8 n 1 e V g B ^_ _v RT 29 _i ^d 6 o 5 ^r _t 5 n 6 e 2 c 5 ^c _{_} 22 d : O 17 ^N 8 D 1 ^IQE S FFLE GT

T G G N G A G T G G Q R M G N G G G GV G G G GV G SA GS E R V S N A V A G D G Q G G Q AL V G V V V V V V V V V V V V V V QA G GV DA T G A V E T G R A G T V R E S S S S S S S S S S S S S S S S S S AS VS V V V V V A V K V V P G V G ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S S S S S S S S S S S S S A S S S S S S S S S S S S S d A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ^{2 2 2 2 2 2 2 2 2 4 2 2 2 4 2 2 2 4 4 2 2} ² - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 2 - 1 - 2 - 2 - 2 - 1 - 2 - 2 - 2 - 1 - 1 - 2 - ² ² - ^{4 2 2 2 4 2 2 2 2 2 2} ² - 1 _{- - - - - - - - - -}ne _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J B _J ² B _J ^{2 2 1 2 2 2 2 2 2} B _{J J J J J J J J J} g R R R R R R R R R R R R R R R R R R R R R R R R R BR B B B B B B B B^_ _j T T T T T T T T T T T T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R Ten 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9V 9 9 9 9 9 9 9e B B B B B B B B B B B B B B B B B B B B B B B B B B B V V V V V V Vg R R R R R R R R R R R R R R R R R R R R R R R R R R R BR B B B B B B^_ _v T T T T T T T T T T T T T T T T T T T T T T T T T T T T RT RT RT RT RT RT 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 1 1 1 1 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 68 68 6 6_i ^d 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 8 8o 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 41 4 4^r _t 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 1n 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 92 9 9e 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 2 c 4 4^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 07 ^N 9 D 3 ^IQE S FTLV NA GSSF ³ _r SS d A c C e ⁶-ne ² _J g B _j ^_ R T e 7 n 2 e V g B ^_ _v RT 15 _i ^d 9 o 5 ^r _t 9 n 8 e 3 c 5 ^c _{_} 22 d : O 50 ^N 9 D 1 ^IQE S FFLE GART

T S G L Q S R R R R N R A G RA G G G D A D G G G G E D Q G V V R L T G V G E V V G A D D V L V S V V GV RP Q Q Q Q Q QL Q Q Q Q Q Q TL Q R R Q Q Q Q Q Q GP Q G S R S S S S S S S S S S S S S S S S S S S R S S S S S S S S QS QS Q L ³ _r S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S SS SSd A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ² ne ² - ² ² - ^{3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2} ² - 2 _{- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J ² B _J ² B _J ^{1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2} B _{J J J J J J J J J J J J J J J J J J J J J J J J J J J J} g R R R R R R BR B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te 9 9 9 9 9 9 41 41 41 41 41 41 41 41 41 41 41 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4n V V V V V V V V V V V V V V V V V 1V 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1e B B B B B B B B B B B B B B B B B B VB V V V V V V V V V V V V V V Vg R R R R R R R R R R R R R R R R R R R BR B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT 16 16 16 16 16 16 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 69 69 6 6 6 6 6 6 6 6 6 6 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 90 90 90 9 9 9 9 9 9 9_i ^d 4 4 4 4 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 8 8 8 8 8 0 0 0 0 0 0 0o 1 1 1 1 1 1 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 4 4 4 4 4 84 8 8 8 8 8 8^r _t 9 9 9 9 9 9 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 0 0 0 0 0 0 4 4 4 4 4 4n 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 5 5 5 5 5 5 05 05 0 0 0 0e 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 54 5 5 5 c 4 4 4^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 40 ^N 0 D 4 ^IQE S FFAET NS Q GF ³ _r SS d A c C e ¹-ne ¹ _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 05 _i ^d 5 o 3 ^r _t 5 n 4 e 1 c 6 ^c _{_} 22 d : O 93 ^N 9 D 1 ^IQE S FFLE GT NE

V GA G Q G G G N R S P D Q S Q G G G S E D G G S E G D G D GA G A D D G G NE G D DA G G Q G V G Y G Q Q E L L L L L L L L L AL GL G P F G GL EL R NL S P R S E E T G G G S S L S S S S S S S S S S S R S S S S S R S S S S LS LS LS ES T Q L L L ³ _r Sd A S S A TA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S S A SA SA c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ⁴ ne ² - ² ¹ - ² ¹ - ² ¹ - ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 7 7} ¹ - 1 - 1 _{- - - - - - - - - - - - - - - - - - - - - - - - - - -} _J B _J B _J B _J B _J B _J B _J ¹ B _J ¹ B _J ¹ B _J ¹ B _J ^{1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2} B _J B _{J J J J J J J J J J J J J J J J J J J J J J} g R R R R R R R R R R R R BR BR BR B B B B B B B B B B B B B B B B B B B^_ _j T T T T T T T T T T T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te 41 41 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 ¹-5 31 3 3ne V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 1V 1Vg BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _v T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 69 69 78 78 78 78 78 78 78 78 78 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 3 3 3 0 0 3 3 3 3 3 3 3 3 3 83 83 83 83 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 4 4 4_i ^d 8 8 5 5 5 5 5 5 5 5 5 5 5 5 5 35 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3o 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 54 54 54 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4^r _t 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 41 41 4 4 4 4 4 4 4 4 4 4 3 3 3n 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 15 15 15 1 1 1 1 1 1 1 4 4 4e 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 54 54 5 5 5 5 5 5 5 5 c 4 4 4 4 4 4 4 4^c _{_} ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. 2 ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 83 ^N 0 D 4 ^IQE S FF QE NY G ³ _r SS d A c C e ¹-ne ² _J g B _j ^_ R T 1-e 5 n 2 e V g B ^_ _v RT 07 _i ^d 2 o 0 ^r _t 3 n 6 e 1 c 6 ^c _{_} 22 d : O 37 ^N 9 D 1 ^IQE S FY QEYS

E G E P Q R G N G D G T S D G G T T D G G SA A G G S ^N _I N R G N G Q G R S T L S G R S R D V D V D V L LS LS LS L G S L G S L G S L G S L N S L A S L T S S S LS P S S LS S L S L R S LS SS S A S L G S LS AR GR FS RR RR PS Q Q Q Q Q Q Q ³ _r SA SA SA SA SA SA SA SA SA S S A TA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S S S S S A SA S S S S d A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ⁷- ^{5 5 5 7 7 7 1 1 3 3 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2}ne ² _{- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -} ²-_J ² B _J ² B _J ^{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} B _{J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J} g R R R BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te 31 31 31 31 31 31 31 31 31 31 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 4 4 4 4 4 4 4n V V V V V V V V V V 2V 2V 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1e B B B B B B B B B B B B VB V V V V V V V V V V V V V V V V V V V V Vg R R R R R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 34 34 34 34 34 34 34 34 34 34 07 07 07 07 07 07 07 07 07 07 07 0 0 0 0 0 0 9 9 9 9 9 9 9 3 3 3 3 3 3 3 3 3 3 0 0 0 0 0 0 0 0 0 0 0 70 70 70 7 7 7 5 5 5 5 5 5 5_i ^d 4 4 4 4 4 4 4 4 4 4 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0 0 0 0 0 0o 3 3 3 3 3 3 3 3 3 3 6 6 6 6 6 6 6 6 6 6 6 6 6 6 96 96 9 5 5 5 5 5 5 5^r _t 4 4 4 4 4 4 4 4 4 4 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 6 9 9 9 9 9 9 9n 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 95 56 5 5 5 5 5 5e 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 64 6 6 6 6 6 c 4 4 4 4 4^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 27 ^N 0 D 4 ^IQE S FFLE GT N G GLL ³ _r SS d A c C e ²-ne ² _J g B _j ^_ R T e ¹-n 5 e V g B ^_ _v RT 71 _i ^d 6 o 1 ^r _t 7 n 8 e 2 c 6 ^c _{_} 22 d : O 70 ^N 0 D 2 ^IQE S FFLE GT NL

D GA Q G E E E D T _I ^G G G G G Y G G G S S A S G G L S L A V GL Q Q G G GP S S R S G D G D Q Q Q Q GL GL GF GL GL L LL LL GL RL WL P G G GT G P D Q G D S S S S S S S S S S S S S S S S R R R S S PS PS PS P P P P P P P P Q E ³ _r SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA S S A S S A S S A S S S S S S S S A SA S S S S S S d A A A A A A c C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C Ce ²- ^{2 2 4 1 5 5 3 3 5 5 3 5 5 5 5 5 3 5 5 3 3 3 3 3 3 3 3 3 3 3}ne ² _{- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -} ³- ³- ³-_J ² B _J ^{2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1} B _{J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J} g R R BR BR B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B^_ _j T T T T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R T R Te 41 41 41 41 72 72 72 72 72 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8n V V V V V V V V V 2V 2V 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1e B B B B B B B B B B B VB V V V V V V V V V V V V V V V V V V V V V Vg R R R R R R R R R R R R BR BR B B B B B B B B B B B B B B B B B B B B^_ _v T T T T T T T T T T T T T T RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT RT 95 95 95 95 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 99 99 99 9 9 9 9 9 9 9 9 9 9 9_i ^d 5 5 5 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4 9 9 9 9 9 9 9 9 9 9 9o 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 6 6 6 46 46 4 4 4 4 4 4 4 4 4^r _t 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 65 6 6 6 6 6 6 6 6n 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 57 57 5 5 5 5 5 5e 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 74 7 7 7 7 7 c 4 4 4 4 4^c _{_} ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. ² _. d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 : O 60 ^N 1 D 4 ^IQE S FFAETV GSL ³ _r SS d A c C e ¹-ne ¹ _J g B _j ^_ R T e ⁹-n 7 e V g B ^_ _v RT 10 _i ^d 0 o 6 ^r _t 3 n 6 e 4 c 6

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0_{2 9 0} ^{2 8 4 9 9 9} ^{8 6 4 1 0 3} ₉ 8 ₅ 5 _{1 7 8 4 5 6 5 7 8 5 1 3 8 7 7 8 8 4 2 5 8 7 6} _{2 4 2 6} ^{6 0 0 2 1 2 7 8 8 1 7 6 0 9 8 4 8 1 7 5 6 8 4 2 0 9 6 5 2 1} ^{6 3 2 7} ₅ 1 ₈ 6 ₃ 3 ₃ 2 ₃ 0 ₈ 7 ₅ 7 ₀ 5 ₅ 0 ₇ 5 ₀ 5 ₁ 7 ₁ 0 ₂ 1 ₀ 4 ₃ 7 ₅ 2 ₄ 2 ₂ 4 ₉ 8 ₇ 4 ₂ 5 ₄ 0 ₈ 6 ₀ 5 ₇ 4 ₂ 9 ₇ 8 ₈ 9 _{1 3 1 4 5} _{2 2 1 8 1 3 5 1 1 3 2 2 1 4 5 2 1 1 5 2 1 9 4 7 1 5 6 1 4 5 1 6 6} ⁶ ₃ ¹ ₂ ⁹ ₁ ³ ₃ ⁸ ₁ _{4 4 8 9 7 2 7} ⁹ _{9 8 1 1 2 7 4 2 1 6 7} ¹ ₃ ⁷ _{3 4} ⁸ ^{2 9 4 6 3 5 3 4 4 8 3 9 1 9 6 7 4 4 6 6 2 3 7 5 7 8 1 6 5 4 2} _{5 7} _{4 2 8 4 3 3 1 5 5 5 3 8 7 1 9 6 5 0 5 8 7 5 4 7 9 9 4} ^{1 3 7 5 9 3 6} ^{9 6 1 4 6 4 0 2 5 7 8 8 0 3 9 8 4 6 6} ₃ ⁸ ₁ ⁹ ₈ ⁷ _{5 0 7 9 8 6 8 0 8 7 3} _{1 1 9 6 8 2 4 9 7 2 1 1 7 3 3 1 7 7 6} ³ ₃ ¹ ₅ ⁰ ₁ ⁹ ₃ ⁷ ₄ ² ₁ ⁰ ₃ ⁹ ₃ ³ ₁ ⁹ ₄ ³ ₅ ⁵ ₂ ⁵ ₁ ³ ₁ ⁴ ₂ ³ ₁ ₄- _{4 1} -⁷ - ₁ 5^{7 9 7 7 7} - 9 ₁ ^{- 7} ₁ ^{- 7} _{5 1} -⁴ - 5 ₁- ₅ ^{- 4} ₅ ^{- 7 7 9} ₂- _{1 2 4 5 4 4 9} _{6 6 2 2 2 1 2 2 2 2 5 2 7 2 6 1 2 7 6 1 6 2 2 2 1 6} ⁷ ₂ ⁷ ₂ -₅ -₆ -₆ -₆ ⁷ ₂ -_{6 9} -₆ ⁷ ₂ -₇VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVBB BB B BB BB B BB B BB BB B BB BB B BB BB B BB B BB BB B BB^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ₀ ⁵ ₆ ¹ ₈ ⁸ ₅ ¹ ₈ ⁹ ₁ ^{3 3 0 6 1 1 9 8 1 7 2 2 1 9 9 0 9 3 8 4 9 3 8} ¹ _{2 4 1 3 7 0 7 7 0 8 5 7 6 4 3 7 0 7 4 9 4 9 0 9 6 4 3 9 0 9 1} ₄ ⁴ ₂ ⁸ ₁ ⁵ ₆ ⁸ ₁ ⁴ ₃ ⁶ ₆ ⁹ ₈ ⁰ ₆ ⁰ ₈ ⁹ ₂ ⁰ ₅ ⁵ ₀ ⁹ ₆ ⁶ ₄ ² ₂ ⁵ ₆ ⁵ ₀ ⁷ ₆ ⁶ ₀ ⁷ ₂ ⁹ ₆ ⁰ ₅ ⁸ ₉ ⁴ ₂ ⁶ ₈ ⁷ ₀ ¹ ₃ ⁵ ₃ ⁶ ₈ ⁷ ₃ ^{8 2 2 2 8 4 9} ₂. ⁹ ₂ ⁰ ₀ ⁴ ₆ ⁹ ₄ ⁴ ₆ ² ₂ ⁴ ₂ ² ₃ ² ₄ ⁸ ₅ ⁷ ₇ ¹ ₅ ⁸ ₂ ¹ ₀ ⁶ ₀ ⁴ ₄ ⁹ ₄ ⁸ ₂ ⁸ ₈ ^{6 1 1 1 1 3 6 6 1 1 6 4} ₅ 5 ₀ 3 ₀ 9 ₅ 7 ₃ 2 ₉ 4 ₇ 1⁹ ₂ 3^{5 4 1 6 1 4 2 1 6} ₂ ⁷ ₂ ⁰ ₃ ⁰ ₂ ⁵ _{8 6 0 5 6 4 4 3 4 0 4 1 2 2 0 3 0 8 5 3} _{4 2 1 2 1 1 2 3 1} ⁶ ₃ ⁷ ₇ ² ₂ ⁶ ₂ ⁵ ₁ ¹ ₁ ⁸ ₂ ⁵ ₃ ⁶ ₃ ⁰ ₂ ⁷ ₁ ⁸ ₂ ⁸ ₇ ⁹ ₁ ² ₃ ⁴ ₃ ⁸ ₇ ¹ ₃ ⁰ ₂ ⁵ ₇ ¹ ₁ ¹ ₃ ⁴ ₃ ⁹ ₉ ¹ ₁ ⁵ ₅ 8₂ ₂ ^. ₂ ₁ ^. ₁ ² ₂ ² ₁ ² ₂ ₁ ^. ₂ ₂ ^. ₂ ₂ ^. ₂ ² ₂ ² ₁ ² _{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} ¹ _/ ₁ ^. ₁ ² ₁ ² ₁ ² ₁ ² ₁ ^. ₂ ^. ₂ ^. ₂ ² ₂ ² ₂ ^. ₂ ^. ₂ ^. ₂ ^. ₂ ^. ₂ ^. ₁ ² ₂ ^. ₂ ² ₂ ² ₂ ² ₂ ^. ₂ ² ₂ ² ₂ ^. ₂ ^. ₂ ^. ₂ ^S _T P _I ² ₀- ² ₀- -^E ₆ ^E ^. ₆ ₅ ^. ₅ ^. ⁴ ₀- ⁴ ₀- -^E ₇ ^E ^. ₈ ₂ ^. ₃ ^. ⁴ ₃ ⁴ ^. ₄ ₀ ^. ₀ ^. -

₃ ⁹ ₉ 8 ₂ 9 ₂ 9 ₃ 9 ₀ 9 ₃ 9 ₃ 9 ₃ 9 ₃ 9 ₃ 9 ₃ 9 ₃ 9 ₃ 9 ₃ 9 ₃ 9 ₀ 9 ₃ 9 ₃ 9 ₃ 9 ₂ 9 ₃ 9 ₃ 9 ₃ 9 _{3 3 3 3 3 0 3} _{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} ⁹ ₂ ⁹ ₂ ⁹ ₂ ⁹ ₂ ⁹ ₂ ⁹ ₂ ⁹ ₂ ₀ ⁵ ₂ 4 ₇ 4 ₅ 4 ₀ 5 ₁ 4 ₂ 5 ₂ 5 ₂ 5 ₂ 5 ₂ 5 ₀ 5 ₂ 5 ₂ 5 ₂ 5 ₂ 5 ₁ 4 ₂ 5 ₂ 5 ₂ 5 ₇ 4 ₂ 5 ₂ 5 ₁ 5 ₁ 5 ₀ 5 ₂ 5 ₂ 5 ₂ 5 _{0 2} _{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} ⁴ ₂ ⁵ ₂ ^{9 6 7 2 6 1} ₆ 8 ₁ 4⁰ ₄ 0 ₁ 4⁴ ₇ 3 ₂ ⁴ _{0 4 7 8} ³ _{8 4 6 3 3 9 4 0} _{5 4 7 4 6 5 5 1 6 3 9 1 4} ⁰ ₈ ^{5 2 7 9 1 9 1 5 3 0 5 1 4 3 9 2 0} ^{2 5 4 6 1 6 8 1 1 0 6 8 9 8} ₂ 2 ₇ 6 _{0 2 0 3 3 8 0 3 8 1 7 3 3 3 0} _{9 2 4 3 5 3 1 2 6 3 3 7 2 9 2 1} ⁴ ₃ ⁷ ₄ ² ₅ ⁵ ₁ ³ ₃ ⁸ ₂ ⁴ ₃ ⁴ ₁ ¹ ₁ ⁵ ₇ ¹ ₉ ⁹ ₃ ⁴ ₂ ⁰ ₃ ⁶ ₂ ₆ ^{8 4} ⁰ _{6 4 7 6 3 4 4 9 8 2 0 9 3 6 9 5} ₄ ⁶ ₁ ² ₁ ^{4 6 0 3 6 2 9 4 1 1 5 7 5 8 8 7 7 9 9 9 5 4 5 9 5 3 5 3} ^{4 8 0} ₃ 6 ₇ 7 _{2 7 3 6 4 9 6 2 6 6 3 4 1 7 3 5 7 8 5 5 3 3 4 4 7 3} _{6 1 3 2 3} ⁶ ₂ ³ ₁ ⁴ ₁ ⁴ ₄ ² ₂ ⁷ ₂ ⁹ ₅ ² ₂ ⁰ ₇ ⁵ ₁ ² ₁ ⁴ ₂ ¹ ₃ ⁴ ₃ ⁰ ₁ ³ ₂ ⁸ ₁ ⁴ ₂ ⁰ ₁ ⁸ ₈ ⁹ ₅ ³ ₇ ⁹ ₂ ⁸ ₁ ¹ ₂ ⁷ ₁ ₄- _{4 1 4 2 1}- ₁-₆ -₆ -₅ -₆ - ₄ ₆ -₆ ⁷ ₂ ⁴ ₁ ⁹ ₁ ⁷ ₂ ⁷ ₂ ⁷ ₂ ⁷ ₅ ₂ -_{6 9} ⁷ ₄ ₂ - ₁ _{6 2} - ₄ _{5 2} -_{6 2} ⁹ ₂ ⁷ ₂ ⁵ ₂ ⁷ ₂ ⁷ ₂ ⁷ ₂ ⁷ ₄ ₂ -_{6 2} VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVBB BB B BB BB B BB B BB BB B BB BB B BB BB B BB B^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ₇ ² ₈ ² _{7 0 2} ⁴ ₆ ⁶ ₈ ⁹ ₈ ⁰ ₅ ² ₄ ⁶ ₀ ⁸ _{5 3} ⁰ ₈ ⁰ ₆ ⁹ ₉ ⁹ ₃ ⁰ ₈ ³ ₀ ^{7 3 9 3 1 4 3 4 7} ⁵ ₈ ² ₉ ² ₉ ¹ ₃ ² ₂ ⁴ ₉ ⁹ ₉ ² ₂ ⁰ ₁ ⁴ ₃ ⁹ ₉ ⁹ ₄ ⁴ ₅ ⁶ ₆ ⁴ ₄ ¹ ₃ ² ₀ ² ₇ ⁵ ₈ ₃ ⁸ _{3 8 5 8 8 5 6 2 6 0} ₁ ¹ ₅ ^{9 3 5 0 2 5 1 6 4 8} ₂. ⁷ ₉ ³ ₄ ³ ₇ ⁷ ₄ ⁶ ₀ ⁷ ₇ ⁸ ₄ ⁴ ₄ ⁸ ₃ ⁷ ₂ ⁹ ₇ ⁸ ₂ ¹ ₆ ⁸ ₇ ⁸ ₅ ¹ ₄ ⁹ ₀ ⁹ ₇ ¹ ₀ ⁰ ₃ ⁷ _{8 1 8 8 8 6 7 6 9 5} ₃ ⁶ ₀ ^{7 5 4 7 3 2 4 7 8 9} ₂ 3^{1 2 2 8 1 5 5 2 0 5 5 4 4 0 3 2 1 8 4 4 3 5} ₈ 9 _{9 8 4 2 3 2 7 5 3} _{7 3 1 7 5 1 2 2 3 2 3 3 7 3 2 3 1 2 3 5 3 4 1} ⁵ ₆ ⁵ ₂ ¹ ₃ ¹ ₂ ³ ₂ ² ₂ ⁵ ₁ ¹ ₂ ⁵ ₅ 8₂ ₁ ^. _{1 1 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2} ¹ _/ ₁ ^. ₁ ^. ₁ ^. ₁ ² ₁ ^. ₂ ^. ₂ ^. ₂ ^. ₂ ² ₂ ^. ₁ ^. ₁ ^. ₁ ² ₁ ^. ₂ ² ₁ ² ₁ ^. ₂ ² ₁ ^. ₂ ² ₁ ² ₁ ² ₁ ² ₁ ^. ₂ ^. ₁ ^. ₁ ^. ₁ ^. ₁ ^. ₁ ^. ₁ ^S _T P _I

F ₂ ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - r d_f ⁵ ^. ₄ ^{4 6} r ^{3 5} _{0 0 5} 6 _{0 9} ⁷ ⁵ _{0 2} ⁷ ⁸ _{0 2} ⁸ ⁸ _{0 7} ⁸ ⁹ _{0 7} ⁰ ⁹ _{1 8} 1 ^e ₈ - ⁰ c ^. ₂ ^E ₀ ₉ ^. ₀ ⁰ 0 ⁸ ₀ ₁ ^. ₇ ⁰ 0 ⁷ ₀ ₅ ^. ₆ ⁰ _{0 6} ⁰ _{0 8} ⁰ _{0 8} ⁰ _{0 1} ₀ ⁷ ₁ ^. ₀ ⁷ ₁ ^. ₀ ⁴ ₉ ^. ₀ ⁴ ₉ ^. ₀ ⁸ ₆ _e u 5 2 3 3 a ^{3 4 6} 2 4 7 7 l ₇ v_p ² ^. ₀ ⁸ ₉ ^{2 0 5 9 5} 0 ⁷ ₉ ⁶ ₅ ⁶ ₅ ⁶ ₉ ⁶ ₉ ⁶ ₁ ⁶ r ^. ₉ ^{- 4} _{1 0} _E ^. ₈ ^{- 3} _{3 0} _E ^. ₁ ^{- 7} _{9 0} _E ^. ₃ ^{- 8} _{3 0} _E ^. ₄ ^{- 7} _{9 0} _E ^. ₅ ^{- 4} _{5 0} _E ^. ₈ ^{- 0} _{7 0} _E ^. ₈ - _E e_{g 7 1 6 3 4} n_a ^{7 4 3} _{4 8 1} ₇ _C ⁴ _{1 6} ¹ ₄ ² ₈ ⁰ ₃ ⁹ ₁ ⁵ ₆ h ₇ ^{5 6 5 1 5 9 3} ⁷ ₃ 7 ₈ 5 ₅ 0 ₂ 0 ₅ 3 ₈ 4 ₅ d_{l 9} 7 ₉ 7 ₈ 5 ₈ 4 ₄ 2 _{9 1} ⁷ ₃ o _{2 2 3 3} ^{3 6 6} ^{F . . .} _{3 3 1 1} _{2 0 0 0} ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ n a ₂ _e 0 1 M ₁ ¹ ₆ ⁶ ³ ₀ 1 2 5 7 3 . 9₂ e ₃ ^{8 4} ₈ ⁹ ₁ ³ ₃ ⁶ ₀ ⁷ ₇ ³ ₃ _s ⁴ _{0 9} a . ₄ ⁰ ₁ ² ₅ ⁸ ₉. ₅ ⁵ ₆. ₁ ³ ₂. ³ ₃. ₈ ⁸ _{9 4} ⁵ ₅ _b ² ₁ ⁹ ₇ ₆ ^. ₉ ⁹ ₈ ₃ ^. ₅ ⁰ ₂ ⁶ ₁ ⁷ ₀ ⁹ ₁ ⁹ ₁ ⁷ ₁ ⁹ ₀ ¹ ₈ ⁴ . 9 ⁸ ₆ ⁸ ₈ ⁸ ₂ 1_/ S ¹ ^d _{i 0} ⁴ ₅ ³ _{0 2} ⁷ _T _{9 0} ^{4 5 5 9 5 9 4 8 3 P} _I o ^{6 5 2 1} _{0 7 5 5 5 3 4 6 8 8} _{3 5 3 5} ⁵ ₆ ¹ ₀ ⁶ ₃ ⁸ ₂ ⁸ ₈ ⁴ ₆ ⁴ ₃ ⁷ ₈ ³ ₃ ⁷ ₃ ⁴ ₁ ² ₈ _{_ c 2 2} ^. _{2 2 1 2 1 2 1 2 2 2 2 2 1 2}

2 6 ₆ 0 ₈ 1 ₅ 0 ₉ 6 ₉ 2 ₈ 0 ₉ _{1 1 0 8 9 7 8} ² ¹ ₀ ^{1 1 0 0 0 3} ₉ 3 ^. ₀ ₀ - ^. ₀ ₀ - ^. ₀ ₀ - ^. ₀ ₀ - ^. ₀ ₀ - ^. ₀ ₀ - ^. ₀ ₀ - ^. ₀ - 3 ⁰ ⁰ _{1 1} ³ 5 ^{0 3} _{1 4} ³ 5 ^{0 4} _{1 4} ⁶ 4 ^{0 4} _{1 1} ⁶ 4 ^{0 2} _{1 1} ⁰ 0 ^{0 2} _{2 2} ¹ 0 ^{0 4} _{2 4} ¹ 6 ^{0 4} _{2 4} ₂ ^{0 4} ² ₀ ^{. 7} ₀ ^{. 9} ₀ ^{. 9} ₀ ^. _{0 0 0 0 2} _{8 0 9 0 4 0 4 0} ⁹ ₀ ^. ₀ ⁹ ₀ ^. ₀ ⁸ ₇ ^. ₀ ⁸ ₇ ^. ₀ ⁸ ₇ 3 1 E 4 E 2 9 1 2 2 4 4 9 1 5 8_{0 2 0 6 3 3} ² ⁶ ₀ ^{9 5 3 5 6 5 6 5 6 5 7 5} ₂ 6⁵ ^- ₃ _E ^. ₀ ₁ - ₃ _E ^. ₉ ⁶ _{0 0} - ^. ₁ ⁵ _{0 7} - ^. ₀ ₁ - ₉ _E ^. ₀ ₁ - ₉ _E ^. ₀ ₂ - ₈ _E ^. ₀ ₃ - ₁ _E ^. ₀ ₄ - _E ₆ 2 _{3 9 9 1 8 9 7} ₇ ^{7 0 1 2 3 4 6} ¹ _{3 1 6 6 7 3 0} ₃ ^{9 5 9 7 1 1 7} ⁷ _{8 2 5 1 8 3 9} ₅ ⁰ ₄ ^{9 4 0 2 1 6} ^{7 6} _{1 8 5 2 5 2} _{1 1} ^{6 9 5 1 1 6} ^{. .} _{1 1 1 3 3 3} _{0 0} ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ 9₈ 2 8 6 1 ⁷ 6 9 ₂. 9⁵ ₆ ⁹ ₁ ⁸ ₁ ¹ ₁ ⁶ _{9 5} ⁰ ₂ ⁵ _{2 2} 3 4 ^. ⁴ _{6 7} ⁵ ₀. ₅ ⁷ ₄. ₅ ² ₆. ₆ ⁴ ₆ ³ ₁ ₂ ¹ ₁ ³ _{3 7} ⁰ _{5 3} ₃ ⁵ ₅ ₉ ⁶ ₃ ⁹ ₉ ⁷ ₆ ¹ ₉ ¹ ₈ ⁴ ₉ ² ₂ ⁷ . 1 ⁸ ₀ . . 7 ¹ ₅ ^. ₅ ⁵ ₀ ⁴ ₃ ³ ₀ ⁰ ₁ ⁴ ₇ ⁸ ₂ 1_/ S ⁴ ₄ ² ₁ ⁴ ₉ ^{6 8 3 1 4 9 3 1 7 7 9 0 7 1} _T P _I 4⁷ _{4 5 2 7 8 8 2 7 9 3 7 2 0 8} _{8 1} ² ₅ ² ₂ ⁵ ₂ ⁸ ₂ ² ₁ ⁵ ₂ ⁰ ₈ ⁸ ₂ ² ₁ ³ ₂ ⁵ ₄ ⁴ ₂ ⁹ ₄ ⁷ ₁ ⁸ ₈ _{2 2 2} ^. _{2 2} ^. _{2 2 1 2 1 2 1 2 2 2 2 2}

.₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - 4_{2 8} ⁴ _{2 8} ⁶ _{2 5} ⁸ ₂ ² ₈ ² ₈ ² ₈ ² ₈ ⁸ ₈ ³ ₃ 0₀ ^{0 0 0 0 2 2} ₃ ² ₃ ² ₃ ² ₃ ² ₃ ³ ₄ ⁴ ^. ₈ 2 ₀ ^. ₈ 2 ₀ ^. ₂ ⁰ ⁹ ₀ ^. ₀ ⁰ ⁶ ₀ ^. ₂ ⁰ _{0 2} ⁰ _{0 2} ⁰ _{0 2} ⁰ _{0 5} ⁰ _{0 4} _{0 1 0 1 0 3 0 8 0} ⁴ ₁ ^. ₀ ⁴ ₁ ^. ₀ ⁴ ₁ ^. ₀ ⁴ ₁ ^. ₀ ⁷ ₄ ^. ₀ ⁴ ₅ E 7 8 2 6 1 6 0 9 2 7 3 4 3 7 9_{7 0 0} ^{6 0 4 1 3} ⁸ ₇ ^. ₅ ⁵ ₀ ⁴ ₁ ⁵ ₀ - ^. ₆ ^{- 0} ₃ ⁵ ₀ ⁵ ₄ ⁵ ₅ ⁵ ₂ ⁵ ₂ ⁵ ₆ ⁵ ₉ ⁵ E ^. ₄ ^{- 9} _{2 0} _E ^. ₅ ^{- 4} _{0 0} _E ^. ₄ ^{- 5} _{4 0} _E ^. ₄ ^{- 9} _{8 0} _E ^. ₄ ^{- 1} _{0 0} _E ^. ₅ ^{- 3} _{8 0} _E ^. ₇ ^{- 3} _{6 0} _E ^. ₇ - _E ₆ 3 ₃ 5 ₉ 2 ₂ 8 ₇ 3 ₈ 2 _{3 7 9} _{5 9 6 6 0 2} ^{7 7 4} ^{4 1 8 2 8 3} _{1 8 2} _{8 4 8 5 0 5} ^{8 2 2 1} ^{4 4 1 3 0} _{7 6 7 6} _{9 7 4 4} ^{9 6 1 1 1} ^{7 8 1} _{5 1 6 6 3 2} _{2 1} ^{7 1 1 7 2 8 8} ^. ₀ ^. ₃ ₀ ^. ₁ ₀ ^. ₃ ₀ ^. ₃ ₀ ^. ₁ ₀ ^. ₃ ₀ ^. ₁ ₀ ^. ₀ ₀ ^. ₀ 0₈ 8 6 7 9 0 8 ₂. 1₃ ⁶ ₄ ⁰ ⁰ ₅ ⁶ ⁶ ₄ ^{9 9} ₁ ^{0 6 1} ₂ ⁶ ₅ ⁹ ₂ 5 ₀ 7⁹ _{2 2} 4 ₈ ⁸ ₀ ⁵ ₆ ³ ₃ 9₀ ^. _{2 8} ₉ ² . _{5 9}. _{9 4}. _{5 3}. ₉ ⁷ _{9 8 3}. ₄ ² ₃. ₂ ^. _{9 7} ^. _{3 1} ⁵ ₅ ₃ ³ ₂ ³ ₃ ³ ₃ ³ ₆ ⁷ ₃ ⁴ ₁ ⁴ ₃ ¹ ₅ ^. ₈ ⁷ ₃ ⁴ ₃ ⁷ ₀ ⁰ ₁ ³ ₁ ⁴ ₂ ⁹ ₆ ⁷ ₁ ² ₀ ⁸ ₂ 1_/ S ⁵ _{5 5} ⁷ ₈ ¹ ₅ ⁹ ₇ ⁰ ₂ ² ₂ ³ ₂ ⁰ ₀ ⁵ ₆ ⁵ ₅ ^{7 4 7 1 8 8 4 6} _T P _I 8^{4 8 4 4 9 5 8 4} _{5 0 0 0 8 1 4 6 7} _{8 6 2 9 2 4 3 2 2} ⁴ ₂ ⁸ ₈ ⁴ ₆ ⁷ ₂ ⁴ ₆ ⁷ ₁ ⁸ ₈ ⁶ ₁ ⁹ ₅ ⁰ ₃ ³ ₂ _{2 2 2 2 1 2 1 2} ^. _{2 2} ^. _{2 2} ^. _{2 2} ^. _{2 2 1 2} ^. _{2 2}

7 ⁹ ₃ ⁴ ₂ ⁵ ₅ ^{9 9} ₇ ^{2 5 7 9} ³ ₄ ⁵ ₅ ¹ ₅ ⁵ _{6 7 9 6 4 0 3 1 7} ₁ ⁰ ⁹ ₀ ^. ₉ ⁰ ⁰ ₀ ^. ₆ ⁰ ⁵ ₀ ^. ₀ ⁰ ⁴ ₀ ² ^. ₆ ⁰ ⁵ ₀ ⁹ ^. ₁ ⁰ ² ₀ ¹ ^. ₁ ¹ ₀ ⁵ ₈ ^{2 1} ₄ ^{2 1} ₅ ₀ ⁰ ₄ ^. _{0 0} _{1 0 4 0 9 0 4 0 9 0 2 0} ⁵ ₄ ^. ₀ ⁰ ₂ ^. ₀ ² ₄ 3 7 2 1 3 1 5 1 2 2 2 3 8 1 5 8 6_{0 0 0 0 7} ^{6 5} ⁵ ₀ ^{- 9} ₅ ⁶ ^. ₀ ^{- 0} ₀ ^. ₂ ^{0 6} ¹ ₀ ^. ₉ ⁰ ₀ ⁴ ₀ ⁰ ₀ ⁹ ₆ ⁶ ₄ ⁵ ₇ ₀ _{0 5 0 0 0 7 2} ^{- 2} ₆ ⁵ ₀ ₀ _{E 6} ^{- 0} ₀ ⁶ ₀ ₈ ⁰ ₀ ⁷ ₇ _{E 6 E 0 3} ^{4 . 0 . 0 . .} _E ^. ₀ ⁸ ₉ ^. ₀ ² ₈ ₅ 9^{5 7} ₈ 5 _{4 4 4 4} _{6 3 1} ^{3 2 2 4 3} ^{2 5} _{7 2 8 5 3 5} ₈ ^{9 8 2 7 6 3 9} ⁰ ₀ 6 ₉ 0 ₁ 2 ₇ 5 ₉ 6 ₁ 0 ₃ 0 ₉ _{5 3 1 8 4 2 4} ⁸ ^{9 6 6 8 5 9} _{7 9} _{3 1 1 1 1} ^{2 8 4} ^{. . . .} _{4 3 1 2} _{0 0 0 0} ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ 5 ₂ ₁ 8 . 1₁ ⁷ 5 5 ⁵ 3 7 ⁵ ₃ 6 8 ⁵ ₇ ⁹ ₂ ₈ ^{5 8} ₀ ⁷ ₉ ⁹ ₃ ₅ ⁷ ₇ ⁷ ₄ ⁰ ₀ ⁸ ₂ ² ₁ ⁵ ⁷ ₅ ₂ ⁰ ₆ ⁵ ₉ ³ ₃ 5⁶ ₀ ₁ ^. ₄ ₇ ⁴ . 6 ⁹ ₃ ₃ ³ . 5 ⁸ ₂ ₃ ² . 2 ⁸ ₂ ₁ ⁸ . ₁ ⁷ _{1 5} . . ₂ ³ _{5 9 5} ₇ ⁹ ₃ ⁶ ₁ ^. ₂ ³ ₂ ⁵ ₂ ⁶ ₅ ² ₁ ⁰ ₈ ^. ₅ ⁹ ₆ ⁸ ₂ 1_/ S ⁶ ₀ ⁵ _{5 5} ³ ₇ ⁵ ₂ ³ ₂ ⁷ _{9 0} ⁴ _{1 5} ⁹ _{2 0} ⁰ ₀ ⁵ ₆ ⁴ ₆ ^{1 9 8} _T P _I 9^{8 4 4 2 5 2 5 1 2 8} ₉ ⁵ ₄ ¹ _{8 4 5} _{9 8 6 2 8 7 0 6 0 4 4} ⁴ ₂ ⁴ ₂ ¹ ₁ ⁸ ₁ ⁶ ₂ ⁵ ₆ ₂ ^. _{1 2 1 2 1 2} ^. _{2 2 2 2} ^. _{1 2} ^. _{1 2 2 2 1 2}

⁸ _{2 3} 0 ₇ 0 ₅ 0 ₇ 0⁰ _{2 6} ⁵ _{3 5} 0₀ ^. ₀ ₀ ^. ₀ ₀ - ^. ₀ ₀ - ^. ₀ ⁰ 0 - ^. ₀ ¹ _{0 0} ⁰ ₀ ⁰ ₀ - ^. ₀ ^. ₀ - ^. ₀ ^. ₀ - 0_{2 5} ⁰ ₅ ³ ₃ ² ₃ ⁴ ₉ ⁶ ₆ ^{8 5 4} 0 ⁴ ₂ ⁴ ₃ ⁸ ₅ ⁵ ₆ ² _{7 9 3 1 3 6 2} 2 ^. ₉ ² ⁴ ₀ ^. ₉ ² ⁴ _{0 1} ² _{0 3} ² _{0 2} ² ₀ ¹ ₄ ² ₀ ⁸ ₄ ³ ₀ ⁹ ₆ ³ ₀ ³ ₆ _{0 5 0 5} ^. ₀ ³ ₈ ^. ₀ ⁸ ₈ ^. ₀ ¹ ₀ ^. ₀ ⁵ ₈ ^. ₀ ⁸ ₉ ^. ₀ ⁹ ₂ ^. ₀ ⁵ ₇ 5 5 5 5 3 6 8 0 0 5 9 3 0_{0 0} ^{8 8 9 1 0 3} ⁰ ₀ ⁶ ^. ₂ ⁰ ⁶ ₀ ⁷ ^. ₉ ^{0 8} ₀ 8 ₀ ^. ₃ ^{0 8} ₀ 1 ₀ ^. ₉ ^{0 5} ₀ 8 ₀ ^. ₅ ^{0 2} ₀ 5 _{0 7} ⁰ ₀ ¹ ₁ ₇ ⁰ ₀ ⁹ ₁ ₅ ⁰ ₀ ² ₉ _{0 3 0 8 0 8 0 5 0 6} ^. ₀ ¹ ₂ ^. ₀ ⁶ ₉ ^. ₀ ⁶ ₁ ^. ₀ ¹ ₇ ₆ 9_{0 1} 9⁵ _{1 8 3 3 1 5 4} _{9 7} ¹ ₇ ² ₈ ⁹ ₀ ² ₆ ⁹ ₆ ⁰ ₈ ⁶ ₆ 1⁶ 7₉ ² ^{5 0} ₀ ⁵ ⁷ ₀ ¹ ₃ ⁷ ₈ 5⁵ 8₁ ⁶ ₁ 1⁹ 6₆ _{1 9 7} ⁰ ₇ ⁴ ₈ ⁵ ₄ ⁵ ^. ₀ ^{4 2} - ^. ₁ ² ^. _{2 1} ₁ ⁸ ₁ ⁰ ₁ ⁸ _{2 0} ¹ ₂ - ₀ ^. ₀ ² ₁ ₀ ^. ₀ ^. ₀ ^. ₀ ^. - ^. ₀ 1 8 2 8 2 8 ⁹ ⁴ ₄ ⁵ ₅ ³ ₀ ¹ ₉ 9 0 2 4 ⁹ ₄ ⁶ ₉ 5 ₂. 9₄ ⁹ ₂ 3 8. ⁵ ₁ 6 . ₆ ⁰ ₆. ₂ ⁵ ₄ ² ₀ . ₃ ¹ ₁ ⁴ ₆. ₆ ⁰ ₂ ⁰ ₈ ₈ ⁴ ₈ ⁷ _{6 3} 5₅ ₁ ⁴ ₅ ⁹ ₂ ⁶ ₈ ² ₁ ⁰ ₈ ⁹ ₆ ⁹ ₇ ₄ ^. ₂ ¹ ₁ ⁸ . ₉ . 2 ⁹ ₂ ⁵ ₁ ¹ ₈ ^. ₅ ⁵ ₁ ¹ ₃ ⁸ ₃ ⁸ ₂ 1_/ S ² ₂ ⁹ ₁ ⁴ ₂ ³ ₂ ² _T _{3 4} ^{2 3 8 0 7 6 1 8 1 1 1 P} _I 4^{4 2 6 1} _{4 1 5 3 7 7 5 0 2 7 0 7} _{1 3 2 6 3} ⁹ ₈ ⁶ ₂ ⁰ ₆ ⁷ ₂ ⁰ ₈ ⁰ ₃ ⁹ ₂ ⁰ ₂ ⁰ ₅ ⁵ ₁ ⁵ ₀ ⁶ ₃ ⁹ ₆ ._{2 2} ^. _{2 2} ^. _{2 2 2 2 1 2 1 2} ^. _{1 2 1 2 1 2}

3 ⁶ ₃ ² ₆ ² ₆ ⁵ ₄ ⁸ ₃ ⁸ ₃ ^{0 5 7 7} ⁰ ₆ ⁰ ₀ ⁰ ₀ ⁰ _{0 1 1 2 1 2 8 5 6 5 6} ₃ ³ ⁰ ₀ ^. ₃ ⁴ ⁰ ₀ ^. ₈ ⁴ ⁴ ₀ ^. ₈ ⁴ ⁴ ₀ ⁸ ^. ₂ ⁴ ¹ ₀ ⁸ ^. ₇ ⁴ ² ₀ ⁸ ^. ₇ ⁴ ² ₀ ⁸ ^. ₆ ⁴ ₀ ⁹ ₆ ^{4 2} ₁ ^{4 2} ₁ ₀ ⁴ ₉ ^. _{0 0} _{0 0 0 0 8 0 8 0 4 0 6 0 6 0} ¹ ₉ ^. ₀ ⁸ ₀ ^. ₀ ⁸ ₀ 9 4 0 5 4 7 3 7 7 0 4 1 ² ₁ ² ₁ ³ ₁ ⁶ ₁ ⁷ ₁ ⁴ ₁ ⁵ ₁ ⁸ ₁ ⁹ ^{5 0 7 0 1 0 6 0 3 0 8 0 3 0 6 0 6} ₁ ₀ 1 ₀ ^. ₄ 8 ₀ ^. ₈ 0 ₀ ^. ₄ 4 ₀ ^. ₇ 4 ₀ ^. ₈ 7 ₀ ₀ ^. ₇ ₀ ¹ _{0 0 0 7} ⁰ _{0 6} ⁰ _{0 3} _{9 0 0 0 9 0 6 0 3 0 3} ^. ₀ ⁰ ₇ ^. ₀ ⁴ ₇ ^. ₀ ⁹ ₅ ^. ₀ ³ ₇ ₈ 0_{6 2 2 2 4} 8^{3 0} ₄ ⁰ ₉ ⁹ _{2 3 7} 9 ₄ 8 ₆ 5⁹ _{3 4} 0 0₀ 5⁴ ₈ ³ ₃ ⁷ _{6 2} 3 ₇ 8 ₄ ³ _{5 5} 4_{3 2} 8¹ ₄ ⁶ ₆ ⁰ _{4 7} 9 ₇ ³ ₅ ^{3 4} 9₁ 4_{1 7} ² ₂ ¹ ₁ ⁵ _{1 5} ⁸ ₄ ⁵ ₇ ^{8 7} 1₄ .₀ ³ ₂ ^. ₀ ^. ₀ ^{. 7} ₀ ⁰ ₁ ³ ₁ ^{. 4} ₂ - ^. ₀ - - ₀ - ^. ₀ ^. ₀ ^. _{0 0} - ^. ₀ 2 2 ^{1 0} 0 4 2 4 7 ₂. 5 _{5 2} ³ ₄ ⁶ ₃ ⁹ ₂ ₅ ⁵ ₉ ⁵ ₉ ^{1 3} ₆ ⁷ ₅ ⁸ ₇ ³ ₀ ⁴ ₉ ^{5 1 3} ₃ 3 ₆ ⁴ ₇ ³ ₇ ⁶ ₇ ² ₇ ⁸ _{1 4 4} ⁵ ⁰ . 1 ⁶ ₃ ⁶ 1 ⁰ ₆ ₂ ^. ₅ ⁷ ₉ ₂ ^. ₇ ₅ ⁹ . 0 ⁸ ₁ ₂ ² . 3 ³ ₈ ₁ ¹ . 1 ² ₃ . ₃ . ₇ ⁷ _{8 1} ⁵ _{5 3 5} ₉ ⁵ ₄ ² ₆ ⁶ ₀ ¹ ₃ ⁰ ₈ ^. ₉ ³ ₈ ^. ₆ ⁷ ₇ ⁸ ₂ 1_/ S 0 ⁴ _T ₄ ⁹ ₈ ⁹ ₄ ⁸ ₅ ⁸ ₂ ¹ ₇ ⁸ ₈ ⁷ ₆ ⁶ ₈ ² ₄ ¹ ₆ ² ₃ ¹ ₀ ¹ ₇ ¹ _{5 0} ¹ ₆ ^{9 3 9 P} _I 6^{2 2 6 5 5 5 1 7 8 6 5 7} ₂ ⁶ ₃ ⁹ _{7 4 4} _{4 2 2 2 6 1 0 1 6 2 0 3 6} ⁰ ₂ ⁰ ₅ ⁷ ₁ ⁸ ₉ ⁸ ₂ ⁴ ₂ _{2 1 2} ^. _{2 2} ^. _{2 2} ^. _{2 2 2 2 2 2} ^. _{2 2} ^. _{2 2} ^. _{2 2} ^. _{2 2}

6 ³ ₂ ⁰ ₅ ⁸ ₉ ¹ ₁ ³ ₄ ^{3 4 0 6} ² ₆ ⁹ ₉ ² _{9 2 2 2 4 2 9 3 5 5 6} ₁ ⁴ ⁸ ₀ ^. ₇ ⁴ ⁶ ₀ ^. ₀ ⁴ ¹ ₀ ⁹ ^. ₉ ⁵ ⁴ ₀ ⁷ ^. ₉ ⁵ ² ₀ ⁸ ^. ₃ ⁵ ⁷ ₀ ⁸ ^. ₃ ⁵ ₀ ⁵ ₉ ^{5 2} ₂ ^{5 6} ₈ ₀ ⁷ ₉ ^. _{0 0} _{0 0 9 0 8 0 3 0 2 0 9 0} ⁹ ₇ ^. ₀ ⁴ ₀ ^. ₀ ⁷ ₄ 0 3 8 1 3 2 8 4 1 7 3 8 5 4 3 6 0_{2 2 2 2 2 2} ^{0 3} ³ ₉ ⁰ ¹ ₀ ⁵ ^. ₂ ^{0 6} ⁷ ₀ ^. ₂ ^{0 8} ³ ₀ ^. ₁ ^{0 1} ³ ₀ ₄ ^. ₅ ⁰ ₀ ² ₅ ⁰ ₀ ¹ ₀ ⁰ ₀ ³ ₃ ₀ ⁰ ₀ ⁹ ₃ ₅ ⁰ ₀ ⁸ ₂ _{0 0 9 0 8 0 0} ⁹ ₉ ^. ₀ ² ₉ ^. ₀ ⁸ ₆ ^. ₀ ² ₅ ^. ₀ ⁴ ₈ ^. ₀ ² ₇ _{7 4 4} 4₁ ^{9 4} _{7 2} 8 ₃ 2 ₆ 7 ₉ 3 ₈ 7 ₃ 3¹ ₂ 6³ _{4 3} 9 ₀ 7 ₇ 5 ₄ 7 _{8 9} _{0 0 2 4 6 2} ^{4 1} ^{5 8 2} ₈ ^{1 8 1} _{2 5} _{3 4 9 1 2} ^{2 9 0} ^{8 5 1} ₁ ⁵ _{9 3 1} _{2 1 2} ^{6 7 7 1 8} ^{. .} _{0 1 0 1 3 0} ₀ ^. ₀ - ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ 5₅ 2 9 ³ ₂ 8 ⁹ ₆ 1 ² ₂ ₉ ⁸ ₈ . 9⁹ ₈ ⁸ ₂ ⁹ 8 ^{9 7} ₂ ₁ ² ₁ ⁴ ₆ ⁵ ₀ 2₇ ¹ ₄ ⁴ ₉ ^{6 6} ₂ 3^{. 0} _{3 9 7} ^. ₃ 5⁹ ₄ ₅ ^. ₅ ₅ ⁸ . 5 ³ ₁ ₁ ⁵ . 0 ⁷ ₉ ⁸ 3 ⁷ ₈ ₀ ^. ₈ . _{3 7 4} . ₆ ⁹ _{8 5 5 7 5} ₅ ⁹ ₅ ⁵ ₁ ² ₅ ² ₄ ⁵ ₉ ¹ ₁ ⁶ ₅ ^. ₃ ⁴ ₆ ¹ ₂ ⁴ ₃ ⁸ ₂ 1_/ S 9 ⁶ ₃ ⁰ ₄ ¹ ₄ ⁹ ₉ ¹ ₀ ³ ₀ ⁶ _{4 9} ⁴ ₁ ⁸ ₆ ⁵ ₂ ⁴ ₄ ^{3 9 9 7 3} _T P _I 6^{9 7 2 1 4 5 8 6} ₈ ¹ ₃ ⁷ _{2 0 3 3 9} _{8 1 0 3 3 3 3 7 3} ⁰ ₂ ⁸ ₅ ⁵ ₇ ² ₀ ¹ ₁ ² ₀ ¹ ₃ ² ₅ ₂ ^. _{1 2 2 2} ^. _{2 2 2 2 2 2 2 2} ^. _{2 2 2 2 2 2}

7 ⁶ ₇ ⁸ ₃ ⁸ ₂ ⁸ ₂ ⁸ ₂ ^{8 1 4 7} ⁴ ₅ ⁴ ₅ ⁶ _{5 5 5 5 2 6 8 7 7 8 5} ₀ ⁵ ² ₀ ^. ₀ ⁵ ² ₀ ^. ₂ ⁵ ⁸ ₀ ⁸ ^. ₁ ⁵ ² ₀ ⁸ ^. ₁ ⁵ ² ₀ ⁸ ^. ₁ ⁵ ² ₀ ⁸ ^. ₁ ⁵ ₀ ⁰ ₇ ^{5 9} ₅ ^{5 5} ₇ ₀ ² ₇ ^. _{0 0} _{8 0 8 0 2 0 7 0 7 0 7 0} ⁰ ₅ ^. ₀ ⁵ ₉ ^. ₀ ⁵ ₃ 2 8 3 9 6 2 7 3 6 4 1 5 0 4 3 7 2_{2 0 0 0 0 2} ^{1 3} ² ₅ ⁰ ⁸ ₀ ² ^. ₉ ⁰ ⁰ ₀ ^. ₃ ⁰ ³ ₀ ¹ ^. ₀ ^{0 3} ² ₀ ^. ₄ ^{0 5} ⁴ ₀ ₆ ^. ₄ ⁰ _{0 4} ⁰ ₀ ⁸ ₃ ₅ ⁰ ₀ ¹ ₃ ₆ ⁰ ₀ ⁵ ₁ _{0 0 2 0 8 0 7 0 0} ⁷ ₉ ^. ₀ ¹ ₆ ^. ₀ ⁵ ₁ ^. ₀ ¹ ₈ ^. ₀ ⁴ ₀ ₅ 5 ₁ 6 _{6 2} 2⁹ _{7 9 6 3 4} _{2 4 6} ^{1 2 4 4 1} ⁷ ₇ ² _{5 3 7 0 0} ₅ ^{1 1 3 6 0 1 1} ⁹ ₉ ₂ ⁶ ₉ 8 ₀ 4⁶ _{2 0} 4 ₂ 2 ₃ 9 ₉ 3³ ₉ 9 ₀ 4 ₅ 3² _{3 1} 8 ₀ 6 _{3 0} _{1 0 3 4 3 2} ^{1 4} ^{. . . . .} _{0 3 1} _{0 0 0 0} - ^. ₀ ^. ₀ ^. ₀ ^. ₀ 8 ₂. 6₇ ² ₉ 3 ¹ ₉ ⁴ ₂ ¹ ₀ ³ ₂ ² ₉ 5 ⁹ ₂ ₇ ⁴ ₃ ⁹ ₆ ³ ₅ ⁰ ^{. 8} _{2 0} ⁰ ¹ ₀ ^{9 4 6 2} ₈ ³ ₃ 7 ₈ 8 ₆ 8 ₉ ⁴ ₅ ⁵ ² . 5 ⁴ ₄ ₁ ⁹ ₉ ₃ ¹ ₉ ₁ ⁶ . 9 ¹ ₅ ₁ ³ ₇ ₉ ^. ₈ ₂ ⁵ ₅ ₂ ^. ₁ ₃ ⁵ ₆ ₅ ^. _{8 8} ⁹ _{8 5} . _{2 5} ₂ ⁴ ₀ ^. ₅ ⁹ ₅ ^. ₃ ⁴ ₆ ⁴ ₁ ⁶ ₀ ⁸ ₂ 1_/ S ⁸ _T ₀ ⁴ _{8 9} ¹ _{5 1} ⁷ _{9 7} ³ ₄ ² ₈ ³ ₇ ² ₇ ⁷ _{4 0} ⁶ _{0 2} ⁷ ₇ ⁴ ₆ ⁸ ₄ ⁶ ₈ ^P _I 8₃ ⁹ ₉ ⁴ ₉ ¹ ₁ ⁹ ₇ ¹ ₇ ⁵ ₈ ² ₃ ² ₉ ² ₁ ² ₉ ⁸ ₇ ¹ ₃ ¹ ₅ ² ₂ ⁵ ₁ ⁴ ₉ ⁵ ₂ ⁹ ₉ ₂ ^. _{2 2} ^. _{2 2} ^. _{1 1} ^. _{1 1} ^. _{1 1} ^. _{1 1 1 2} ^. _{2 2} ^. _{2 2}

.₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - 5 ^{7 8 7 7 7 6} ⁵ _{8 5} ₇ ^{5 5} _{8 9} 5¹ _{9 9} 9 _{9 9} 9 _{9 9} 9 _{0 3} ¹ ³ _{7 5} ² ⁰ _{8 5} ² ⁹ _{8 5} 9⁵ ₀ ^. ₇ 5 ₀ ^. ₉ ⁵ ⁴ ₀ ^. ₁ ⁵ ⁷ ₀ ^. ₁ ⁵ _{0 1} ⁶ _{0 8} ⁶ _{0 5} ⁶ _{0 8} ⁶ _{0 8} _{3 0 3 0 2 0 0 0} ⁷ ₀ ^. ₀ ⁷ ₀ ^. ₀ ⁰ ₀ ^. ₀ ⁰ ₄ ^. ₀ ⁶ ₇ ^. ₀ ⁶ ₇ 4 5 4 7 8 4 3 6 3 1 8 8 3 3_{3 0 0} ^{6 7 1 2 3} ⁶ ₉ ^{0 4 0 0 4 3} ₀ ⁷ ₂ ² ₄ ² ₃ ¹ ₃ ³ ² ₀ ^. ₃ 3 ₀ ^. ₁ 8 ₀ ^. ₆ ⁰ ² ₀ ^. ₁ ⁰ ⁸ _{0 2} ⁰ _{0 1} ⁰ _{0 0} ⁰ _{0 6} ⁰ _{0 6} _{0 0 9 0 6 0 9 0 2} ^. ₀ ⁷ ₇ ^. ₀ ³ ₅ ^. ₀ ¹ ₈ ^. ₀ ⁰ ₅ ^. ₀ ³ ₇ ₇ 3 ₃ 6 ₆ 5 ₁ 7 _{8 7 9 7 9} _{6 1 8 3} ^{8 1 8 5 2} ^{5 4 3 4} _{8 6 5 6 1} _{9 9 8 5} ^{1 9 8 3 1} ^{0 2 3} _{5 8 7 7 7} _{3 4 6} ^{0 0 0 4 2 7} ^{5 3} _{0 5 1 9 0 3} _{1 1} ^{0 5 7 7 7 4 1} ^. _{2 2 1 0 0 1 3} ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ 9₈ 6 6 ³ 7 1 0 ₂. 0₁ ⁶ ₀ ² 7 ^{5 4} _{0 0} ⁵ ^{0 9} _{0 9} ⁴ ² ₇ ² ₂ ^{5 2} ₉ ⁹ ₂ 7⁹ _{5 6} 6⁶ ₉ ³ ₃ 5⁹ ₂ ₉ ^. _{2 1} ₉ ³ . 7 ³ _{4 7} ₁ ⁹ . ₃ ⁸ _{5 2 8}. ₁. ₉ ^. _{7 3 4}. ₄ ⁹ _{8 5 5} ₁ ⁷ ₂ ⁸ ₅ ^. ₆ ⁸ ₀ ⁷ ₂ ² ₆ ⁴ ₇ ⁶ ₆ ¹ ₂ ⁸ ₁ ⁴ ₁ ⁰ ₆ ^. ₃ ⁴ ₆ ⁸ ₂ 1_/ S ⁹ ₈ ⁸ ₃ ⁰ ₅ ⁷ ₂ ² ₄ ⁹ ₇ ⁶ ₀ ⁸ ₂ ⁸ ₅ ¹ _{6 3} ⁸ ₇ ^{0 8 0 1 9 9 9} _T P _I 2^{0 0 5 4 5 9 4 9} ₄ ⁴ _{8 5 6 4 9 0 3} _{2 3 1 2 3 3 9 3 7} ⁴ ₅ ⁰ ₃ ⁶ ₆ ³ ₂ ⁴ ₄ ² ₃ ¹ ₃ ¹ ₁ ² ₀ ₂ ^. _{2 2 2 2} ^. _{1 2} ^. _{1 2} ^. _{1 2 1 2} ^. _{2 2 1 2 1 2}

₀ ^. ₀ ₀ ^. ₀ ^. _{0 0} - ^. ₀ ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - 5 ^{3 4 0 0 0 0} ¹ _{1 6} ₀ ^{7 8} _{5 6} 7⁵ ₁ 8¹ _{2 5} 2 _{2 5} 2 _{2 5} ⁹ ² _{5 9} ³ ⁹ _{7 3} ⁹ ³ _{9 1} 4⁵ ₀ ^. ₇ 7 ₀ ^. ₃ 0 ₀ ^. ₄ ⁸ ⁸ ₀ ^. ₄ ⁸ ² _{0 4} ⁸ _{0 4} ⁸ _{0 5} ⁸ _{0 6} ⁸ _{0 7} _{1 0 3 0 9 0 8 0 8} ^. ₀ ² ₈ ^. ₀ ² ₈ ^. ₀ ⁹ ₅ ^. ₀ ⁹ ₇ ^. ₀ ⁹ ₇ 3 6 0 0 9 2 6 7 0 4 6 0 8 3_{5 4 4} ^{8 8 8 0 0} ³ ₈ ^{0 0 9 0 7 0} ₄ 0⁴ ₄ ⁵ _{5 1} ³ ₁ ⁸ ⁰ ₀ ^. ₇ 2 ₀ ^. ₅ 2 ₀ ^. ₈ 8 ₀ ^. ₅ 1 ₀ ^. ₂ ⁰ ⁶ _{0 4} ⁰ _{0 9} ⁰ _{0 0} ⁰ _{0 9} _{1 0 2 0 3 0 3 0 5 0 7} ^. ₀ ⁰ ₈ ^. ₀ ⁶ ₄ ^. ₀ ⁰ ₉ ^. ₀ ¹ ₁ ₅ 7 ₅ ₁ ¹ _{3 3 1 7 1} 9 ₃ ¹ 0 ⁴ ⁶ _{5 9} ⁸ ₂ ⁶ _{2 5} 6 ₈ ⁶ ₀ ^{3 3 1} 8 ⁷ ₃ ⁸ ₁ ⁰ _{3 9 7} ₃ ² ₁ ^{7 3 3} ⁰ ₈ ⁹ 1 ⁹ ^. _{0 9} ⁶ ₃ ⁴ _{6 7 2} ₀ ⁴ ₃ ^{8 7 6 8} 2 ⁴ ₁ ⁵ ₃ 6 ₂ 7 ₈ 2 ₀ 0 - ^. ₀ - ^. ₀ ^. _{0 1} - ^. ₁ ₀ ^. ₁ ₀ ^. ₃ ₀ ^. ₃ ₀ ^. ₀ 3 6 ⁸ 4 6 ⁸ ₂. 4₉ ⁰ _{9 1} ⁰ ₈ ⁰ ₂ ⁷ ₁ ⁶ ₉ ³ ₇ ⁹ ₂ 8^{9 3} ₄ ⁸ ₇ 8³ ₈ ⁹ ₂ ⁹ ₇ ⁴ ₉ ³ ₃ _{6 3} 2 . 0 ⁶ _{4 4}. ₇ ⁸ _{3 6 1}. _{8 2}. ₆ ⁷ _{0 7} ⁹ _{6 9} ⁴ _{8 5} ¹ _{1 5} ⁵ ₅ ₄ ⁵ ₀ ⁹ ₆ ⁹ ₁ ^. ₃ ³ ₃ ³ ₁ ² ₉ ⁰ ₂ ⁷ ₇ ^. ₇ ⁰ ₃ ^. ₉ ⁶ ₉ ^. ₄ ⁰ ₉ ^. ₅ ³ ₈ ⁸ ₂ 1_/ S ⁰ ₈ ¹ ₀ ³ ₀ ⁰ _{3 8} ⁷ ₃ ⁷ ₃ ⁶ ₀ ⁷ ₈ ³ ₈ ^{5 4 9 7 3 3 1} _T P _I 2^{4 5 4 8 3 1 5} ₄ ⁹ _{9 5 8 8 4 8 2 5} _{7 3 3 5 1 3 5 8} ⁹ ₁ ³ ₁ ⁵ ₆ ⁵ ₈ ⁵ ₂ ⁰ ₈ ¹ ₃ ² ₈ ¹ ₂ ⁵ ₆ _{2 1 2} ^. _{2 2 1 2 1 2 1 2 1 2} ^. _{2 2} ^. _{1 1} ^. _{1 2}

5 ₈ ^{. 6} ₁ ^{. 6} ₈ ^. _{s s s s s} _{2 0 5 0 1 0} ⁶ ₅ ^D _F ^e _y ^e _y ^e _y ^e _y ^e _y ₄ ³ ⁴ _{9 2} ³ _{1 9} ^{0 d} _e ₉ ⁸ ₄ ³ ₂ ⁰ ₀ ³ ₆ ₂ ^{9 2} ₈ m 1⁶ ₄ ₅ ^{3 5} _r ₅ 5₁ 5₉ 4₂ 8₅

6 ⁷ ₆ ^{7 7} ³ _{7 7 6 7 6 a 2} ₉ ^{9 3} ⁷ ₀ ^. ₉ ^{9 3} ⁷ ₀ ^. ₉ ^{9 3} ⁷ ₀ ^. ₉ ^h ⁷ _{e g p} ^{l 2} _{1 r} o_{t 1} _{2 0 2 0 2 0 2} ^m _a ⁿ _{i a} _r A- ⁱ n o ⁰ M _{r b} ⁿ ⁱ _i _h - e 7 8 9 _r ^m _r ^t _{c 0} - o^{t n} _t ^e _to 4^e o^{f a} _f ¹ _S ⁿ _{i c i} ^y _c ^r _p 2 ₁ ⁵ 9 ^{0 5} ₁ ⁶ ¹ ₁ ³ _k ^r _s ^t _a ^a _f 7 ₀ ^. ₄ ⁰ ⁵ ₀ ^. ₂ ⁰ ² ₀ ^. ₁ 2 _a ⁿ _a ^h _t ⁿ _i ^t _{s e} ^y _a o h _c ⁱ m ^r _t w ^e _t o u w _{p fi} _{2 0 5 0 0 0 6} o ^{c s} ⁱ o o o _{n s} ^h _t m^c _e _b ^r _P ^r _g ^r _P O ⁱ _T ^a _p ^y _L ^p _s ₉ 1 ₇ ₀ ⁶ ₁ ⁷ ₁ 1₅ ⁷ ⁰ ₇ ⁴ ₄ ₃ ^{8 4} _t n o_i ^{5 1 5 7} ⁸ _{6 7} o 1 ^{4 9} ^. ₃ ^. ₁ ^r ^. _P ^s _{s 3 1 2 0} ¹ ₄ ₀ ⁱ _n ^e _c . ¹ o ₁ ⁵ ⁰ ₀ ⁷ ⁸ ₃ ³ ¹ ₈ ² ₃ _{0 0} - U ^c _a n _{P P P} ⁴ _P ³ _P 6 ₂. 9₅ ⁰ ₉ 9 ⁹ ¹ ₂ ₈ ⁴ ₁ ^{2 3} ₃ ₄ ⁸ ₄ 4 . ⁷ ₆ ⁹ ₉. ₁ ^. _{5 1} ⁵ ₅ ₁ ⁵ ₁ ⁵ ₅ ₁ ^. ₃ ⁸ ₁ ⁰ ₁ ⁶ _{8 e} ^l _y _b ^a _s ^A _F A 0 ₂ ⁸ 0 ^{M I} _P ¹ _{P 2} 1_/ s _S 4₆ ^{4 3 7 4 8 7 a} ¹ _{2 2 7 6 5 0 T a} ^G _T ¹ _S O ^F _T ^S _L ^S _T P _I ₇ ² ₂ ⁶ ₆ ⁵ ₁ ⁴ ₉ ¹ ₂ ⁸ ₅ ₂ ^. _{1 2} ^. _{1 2} ^. _{1 2}

_s ^e _s _y ⁹ ₆ ^. ₁ ^. ² ₀ ¹ ^. ₂ ^. ₁ ³ ₃ ^. ₀ ^.

⁹ ₀ ² ₁ ³ ₁ ⁴ ₂ ³ ₃ ⁹ ₄ ^{1 6 7 1 9} ^{0 0 0} _{5 6 9 2 3} _{0 0 0} ⁰ ₀ ⁰ ₀ ⁰ ₀ ⁰ ₀ ⁰ ₀ ^{0 1 1} ^{. .} ₀ ^. ₀ ^. _{0 0 0} _{0 0} ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ₄ ^h _t w - ^{c l} _l ^e ^y _e o^r _d n ⁿ _i n ⁱ _a ^e _i _t n ^e _{c l} u o ^o _{y d c} e^e _{s I} ⁹ ^f _{it 9} ⁿ _i l_{i 6}- , ^p _g ^a _r m^r _p ^r _b ^e _{t l}o _e ^a _t ^e ₁ o _{e p} ^l _e n ^t _c mD n ^e ⁱ _s a _y ^{t e} _t ^b _e o _g ^a _l m ^g _a ^s _a ^y _{t a} ^t ₈n mⁱ ⁱ _k ^e _l ^a _f n _{r k}u _{e t} a _l _h ^a _t ^y _c ^l _li ^d Aⁿ _i ^m _e ^e _r o - n _k ^C _{- p a b} ^{e o h} _p ^l _e , _e ^l _e -_e o ^{e i e n} _i o ⁿ _i ^{n i o} _l ⁿ _i ^s _i ^l _r ^{l p} _s ⁿ _e ^t _{a r} W_r ⁿ _{i e s} ^o _r ^{l t k} _{s a} ^d X^m _e ^y _t ^m _a ^{c p} o^t o^{t a} _t ^c _r ^g _it ^e _h ^c _a ^t _a ^{r o} _t ^p _s M - _C ^h _c - m C ^o _e ^e _t _d m ⁿ _I ^k _l o A^h _a _p ^l _{e c} n p H^a _f o _c n v ^a _f ^o _{c 1} ^a _C ⁿ _a ^d _{a e e} _{a 5} M m K^y _c ^a _C ¹ ₄ ⁵ ₂ ⁸ _{I 0} X ^{1 7} ₈ ⁰ ₁ B ¹ ₃ ^{0 7 0} ₉ ⁷ ₁ ³ ₃ ₇ W ^{2 1 2 C} _{0 2} _P ^{7 9 7 7} ₄ ² _P ⁰ _Q ⁸ ₅ _Q ⁰ ₅ _P ⁰ ₄ _P ¹ _P ⁶ _{6 9 8} _Q ⁰ _P ³ _P ⁰ _P ¹ _Q ₂ ₅ . 9₂ 3 9 ^D M 4₁ ^A _{2 3} _{9 8} ⁵ ₅K ^L _C ^C _{P F} A _C ¹ _P ¹ _T ^P _S ⁸ ₂D M ^X _E _C ^L ₆ ^P _L _C ^L _I G W A A H V ^E M C R ¹ _/ M K ^A _C ^S _T P _I

_s ^e _y ⁷ ₉ ^. ₀ ⁹ ₂ ^. ₁ ² ₃ ^. ₀ ⁰ ₆ ² ₀ ^. ₀

^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ _r ₂ ^{e h} _t n_i ^e _c ⁿ ^s _{i b} i^e _{t 2} ^m w e - _r _r ^{o e a}f _s o - n _e ^o _r ⁷ ₃ _t n _r _c o _e ^e _g ^u _s ^o _{r d} ^r ^{c n y} _{li y} -_{t p} ⁿ _it ⁱ _b ^{g f} _{i 1} o ^m _{l t} o _e - _e ⁿ _iu ^r _p _f ^s o _r ⁶ _{e a 6} ^r _a ^d _e ^e _i ^l _g ^A _r ^a _i n _e ^d _e ^{n e} _t ^{o e} _{d a} o _{r 1}- n ^g _il m_r n ⁿ _a ^{t o} ₃ ^{a l} _e mⁱ _k m^t _a o_P ⁱ _f n l ⁱ _a n^{i n} _i ^t _a o^t _p nⁱ _r o _r ^a _fr ^e _b o ^t _{c a} ⁱ _p o _l A c ^o _i ^t _e ^y _l ^{u h} _{t r} ^C- o ^o _{s a} ⁱ _c ^r _b ^r _poA^u _k m s m ^o _r ^s _a ^vi _t ^e _{c c}u mo^t _c ^e _p ^m _e ^m _l ^a _fr ^o _{s 1} ^g _n ^r _{c i c} m ^s _a ^o _d o^t _c X^{m o} ^- _{e s} ^o _s ^m _e ^c _y Wⁱ _d ^o _d U^l _p ^c _a ^e _r M ^u _T ^a _f ^u _s m ^u _P ^u _s ^s _a A A ^e _S ^a _{f 2} Vⁿ _e ^a _{f C} ^h _c ^y _L ^s _a m^l _g _{0 0} ³ ₂ ² ₅ ² ₄ ^{8 5 7} _{I L 3} X ² _{9 B} ^V ^{5 X 2} ₁ B X _Q ₄ ⁴ ¹ _{3 E} ⁰ ₈ ^W _I ⁵ ₁ ^{R 6} ₅ U U _Q ⁰ _Q ⁸ _Q ⁴ _P ⁸ ₆ _Q O D ¹ _P ⁶ _Q ⁹ _Q ₂. 2 ⁹ ₂ ² _{1 2} _{C R 6} G A ^T _P ^A ₃ ^{A 7 3} 1 ³ ₃ _P ⁵ ₅ ^D U ¹ _C ^L _S ^P G B _F ^L _C M ⁸ ₂ _F A W ^L U P M ^A _T _F ^F _S N G ^G _E A ^C _S V ^{X 1} C ^A _/ _L ^S _T P _I

m ^{o 1} _{7 7 7 7 7 7 7 7 9 9 8 8} _{r r} ^{3 6 1 1 1 1 1 1 1 1 1 1 2 1 1} ^h _{r r r r r r r r r r r r r r r} _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ₅ 3 ₅ _P ³ _P ⁸ ₂ _E A A ^F _E ₁ G G G ^A ₁ ^A ₁ ^A ₁ ^B ₃ ² ^{L 1} _T D D D D _L ^N _{E B} K ^K _L ^K _L ^C _P ^H _R ^H _R ^H _R ^I _R ^I _R ^I _R ^I _R ^X _S ^M _T ^M _T ^M _T G A A A A A A A A A A A A A A A A 8 5 ₄ 7^{3 7} ₄ 1^{0 4 0 4 1 7} _{1 8 0} _{9 2 7 1 3 2 2 3 0 1 1} ² ₃ ^{5 3 7 2} ^{4 0 8 2 0 3 7 9 4 9 2 0} _{5 1 8 4} _{5 8 3 6 4 0 1 4 1 4 0 4} ¹ ₃ ^{7 5 5} _d ^{8 7 8 2 8 2 2 8 3 7 8 4 4} ₄ 2 _{4 6} ⁿ _{0 4 3 4 2 9 9 6 7 7 7 5 7 8} ³ ₈ ³ _e ³ ₁ ⁰ ₁ ⁹ ₂ ⁹ ₂ ¹ ₁ ⁶ ₄ ⁶ ₄ ³ ₇ ⁶ ₂ ⁶ ₂ ⁶ ₂ ⁴ ₇ ⁵ ₂ ⁰ ₁ ⁰ ₈ ₁ ⁰ ₁ ₈ 3 ₈ 4^{3 7} ₄ 9^{0 4} _{1 8 0} _{8 0 5 9 1 0 0} ⁰ ₁ ^{4 1 7 2 5 1 5 0} _t ^{4 0 7 0 0 2 6 8} _{8 9 9 1 3 0 7 3} ₅ ^r _a ⁸ _{8 3 6 4 0 1 4} ² ₁ ^{7 0 9 0 7 5 5} 0 ⁷ ₄ ⁸ ₃ ² ₄ ⁸ ₂ ² ₉ ² ₉ ⁸ _{4 0 3 3 4 4 6} ₆ ^{3 7 8 4 4 2 3 3} ₂.^t _s ³ ₁ ⁰ ₁ ⁹ ₂ ⁹ ₂ ¹ ₁ ⁶ ₄ ⁶ ₄ ³ ₇ ₇ ⁶ ₇ ₂ ⁶ ₇ ₂ ⁶ ₅ ₂ ⁴ ₇ ₇ ⁵ ₈ ₂ ⁰ ₈ ₁ ⁰ ₈ ₁ ⁰ ₁ ⁹ ₂ 3₃ 5₅m ^o _r ⁹ ₄ _r ¹ _r ² _r ^{2 5} ₉ 1 ₉ 1^{5 1 1 1} ₅ 1² ₁ 1 ₁ 1 ₁ ⁸ ¹ ₂ 1_/ ^h _c ^h _c ^h _{r r r r r r r r r r r r r} _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^S _T P _I

⁰ ₂ ^B _T ^B _Z ₈ ⁶ ₅ ¹ ₅ ³ ₄ ¹ ₁ ₈ ⁴ ₄ ¹ ₅ ³ ₄ ¹ ₁ ³ _r ¹ ^h _r _{c c}

₂ ¹ ₁ - _{5 1} ^A A ₂ ^A _{2 P P} B B ₄ ¹ _BN ^L ₂ R ^C _{F F} D ⁹ _{1 6} N N ^{B B A} N^X _T ^M ₂ ^L _C ^O _C ^R M C ^V _{I C} A A N ₇ H K K K _{E E} R R ^K _T ^{C D D D} _L N A B B B B B^R _E B B B _{C C C C} ^D _C ^D _C ^R _C ^R _C ^T _C ^T _C ₄ ³ _{0 9 6 2 6 1} ₄ ⁷ ₀ ⁸ ₁ ¹ ₅ ³ ₉ ² ₃ ⁹ ₃ ⁶ ₆ ¹ ₁ ⁰ ₀ ⁵ ₇ ⁸ ₇ ⁴ ₇ ⁷ ₄ ⁶ _{5 4 6} ^{7 0} ^{2 6 0 2 8 2 3 1 0 2 1 6 5 2 7} _{8 7} _{5 1 5 2 3 6 5 8 6 8 9 3 9} ^{5 7 2 6} ^{5 1 1 6 5 8 7 7 3 5 7} _{1 4 8 6 7 4} _{1 7 1 0 2 2 0 0 1} ^{2 5 7 7 6 8 8 2} ^{1 4 1 0 3 0} ₁ ⁴ _{6 8 8 7 9 9 3 3 3 2} _{1 4 1 4 2 1} ⁴ ₁ ⁰ ₁ ⁹ ₆ ¹ ₄ ⁸ ₆ ² ₉ ¹ ₂ ¹ ₂ ⁷ ₃ ⁷ ₃ ⁰ ₂ ¹ ₄ ₄ ^{1 7} ₀ 6^{1 3} ₉ 0 ₆ 7 ₂ 4 ₆ 9^{0 5 8 4} ₁ _{3 8 0 3 7 2 2 5 9 8 5 5 5} ^{1 6 5 0} ^{2 4 0 1 7 2 3 1 9 0 0} _{4 3 4 6 7 5} _{5 1 5 2 3 6 5 8 5} ^{5 4 9 6 3 5 2 5} ⁵ ₁ ¹ ¹ ₇ ¹ ₁ ⁶ ₀ ⁵ ₂ ⁸ _{8 9 3 9 0 4 7 5 7 4} ₂ ⁷ ₀ ⁷ ₀ ³ ₁ ⁵ ₆ ⁷ ₈ ² ₈ ⁵ ₇ ⁷ ₉ ⁷ ₉ ⁶ ₃ ⁸ ₃ ⁸ ₃ ² _{2 2}. ₁ ⁴ ₄ ¹ ₁ ⁰ ₄ ³ ₂ ⁰ ₁ ⁴ ₁ ⁴ ₁ ⁰ ₁ ⁹ ₆ ¹ ₄ ⁸ ₆ ² ₉ ¹ ₂ ¹ ₂ ⁷ ₃ ⁷ ₃ ⁰ ₂ ¹ ₄ ⁹ ₂ 3₃ 5₅ _{2 5 2 3 1 9 6} ⁸ ¹ _r ¹ _r ² _r ^X _r ² _r ¹ _r ⁷ _r ⁷ _r ^X _r ¹ _r ^{1 1 7 9 9} ₆ 1 ₆ 1^{2 3} ₂ 1_/ ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _{r r r r r r r r r} _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^S _T P _I

G ^C ₃ K^I _P ^I ₅ ⁰ ₁ ⁸ ₆ ⁸ ₆ ⁰ _{1 1} ₅ ⁸ ₉ ⁷ ₁ ₆ ⁸ ₆ ⁰ _{1 1} ⁷ _r ⁷

^h _r _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c 1 A³ _{1 2 T} ³ B T ³ _T _{L 1} ² ₂ ² X R M M M ^P _{2 0 3 7 7 7} _S ^P _S ^{R R R R 3} A A A A^{U U} D N N N ^X _T U U _{F F F F} G^{G G G} _F ⁰ ^L ₃ P H P H P H H _{C C} D D D D D D D _{E E E E E E E E} ^P _E ^P _E ₃ ³ ₉ 0 ₉ 0^{2 4 5} ₈ 1^{7 9} ₇ _{1 0 3 9 3 1 4 4 3} ^{8 2 3 5 0 1 8 5} ^{3 8 5 9 4 3 8 9 5 0} _{3 8 3 8 3 3 1 8} _{0 1 5 7 4 7 5 2} ^{8 4 8 3 5 1 5 7 9} ^{5 2 7 3 3 8 0} _{8 4 4 1 1 1 9 0 9 0 0} _{4 2 2 1 2 7} ^{8 1 7 7 8 9 0 6 1 5 1 1} ^{2 0 6 0 5} _{3 7 8 1 1 1 1 2 1 2 2 4 4} _{2 1 1 1 2} ² ₃ ¹ ₁ ¹ ₃ ⁴ ₃ ⁵ ₅ ⁵ ₅ ⁵ ₅ ⁵ ₅ ⁰ ₂ ¹ ₄ ⁹ ₈ ³ ₉ ³ ₉ ³ ₉ ₃ ¹ ₉ ₀ ⁸ ₉ ₈ ⁸ ₁ ² ₇ ⁴ ₁ ⁵ ₈ ₉ ⁹ ₂ ⁷ ₂ ⁹ ₁ ⁹ ₁ ² ₇ ₆ ¹ ₂ ⁵ ₆ ⁰ ₁ ¹ ₁ ^{8 5} ^{3 7 5 8 3 1 8 9 5 9 7 3 7 3 4} _{9 6} _{0 1 5 7 4 7 5 0 6 3 4 1 1 1} ^{0 4 5 8} ⁵ ₄ ² ₂ ⁷ ₂ ³ ₁ ³ ₂ ⁸ ₇ ⁰ ₃ ⁷ ₇ ⁰ ₈ ⁷ ₁ ^{7 8 9 0} ₉ 6 ₀ 1 ₉ 5 ₀ 1 ₀ 1 ₂. ² ₂ ⁰ ₁ ⁶ ₁ ⁰ ₁ ⁵ ₂ ² ₃ ¹ ₁ ¹ ₃ ⁴ ₃ ⁵ ₁ ₅ ⁵ ₁ ₅ ⁵ ₁ ₅ ⁵ ₂ ₅ ⁰ ₁ ₂ ¹ ₂ ₄ ⁹ ₂ ₈ ³ ₄ ₉ ³ ₄ ₉ ³ ₉ ⁹ ₂ 3₃ 5₅ ² _r ⁷ _r ¹ ₉ _r ¹ _r ² ₀ _r ² ₂ _r ¹ _r ^{6 6 7 7 7 7 1} ₂ ⁸ ^{2 3 6 6 6} ₂ 1_/ ^h _c ^h _c ^h _c ^h _c ^h _{r r r r r r r r r r r r} _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^S _T P _I

_T ^R _P ¹ ^T _P ³ ₃ ⁶ ₁ ₁ ⁵ ₄ ² ₄ ³ ₁ ⁵ ₁ ₁ ⁵ ₄ ² ₄ ₀ ² _r ⁷

^h _r _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c _{1 2 4 4 7 7 7}BH B B B V ¹ _{2 2 3 3} ^A B B B ₆ ¹ _{P S S 2} _T W W W X X X ^R _F ^R _F ^R _F ^{1 A} _T A A ₂ N^P _T X _E ^R _E ^R _E ^R _E ^{T A B B B G G G L O} A N N ^S _P ^I _R _{E F F F F F F F F F} G G G G G ₅ ⁷ ₇ ⁵ ₂ ₁ ⁹ ₉ ₂ ⁸ ₀ ⁸ ₆ ₁ ⁹ ₄ ₄ ⁷ ₂ ₂ ⁹ ₆ ₂ ⁴ ₂ ₀ ⁶ ₀ ₀ ⁸ _{5 7} ³ ₂ ⁰ _{2 1} ^{5 5} ^{2 0 7 7 0 1 6 8 7 8 8 9 0 1} _{3 2 8 9} _{3 2 7 8 9 1 2 2 4 8 9 8 8} ^{3 1 4 8 4} ^{1 1 3 3 3 6 3 3 3 4 4 1} _{2 1 4 9 6 2} _{5 7 1 1 8 6 2 2 2 1 1} ^{7 7 4 5 0 3 3} ^{3 9 1 1 1 8 5 5 5 2} _{0 3 4 7 8 9 1 6} _{1 3 2 2 1 1 1 1 1 1} ² ₁ ⁸ ₁ ⁸ ₂ ¹ ₇ ⁰ ₈ ⁸ ₅ ⁸ ₅ ³ ₇ ⁷ ₉ ₅ ^{5 5} _{2 9 6 7 2 6} ² ₉ ⁷ ₁ ⁶ ₉ ⁸ ₉ ⁷ ₃ ⁷ _{2 0} _{6 0} ⁷ ₁ ² ₉ ⁴ ₉ ⁶ _{4 7} ³ ₀ ⁰ _{0 7} ⁵ ₁ ⁵ _{0 8 9} ₃ ⁸ ₁ ⁷ ₇ ⁶ ₈ ⁸ ₈ ¹ ₁ ⁶ ₂ ⁸ ₂ ⁶ ₄ ⁷ ₈ ⁸ ₉ ⁷ ₇ ⁹ ₇ ⁰ ₂ ⁵ ₉ ⁰ ₄ ³ ₉ ^{6 2} ¹ ₅ ¹ ₇ ³ ₁ ³ ₁ ³ ₈ ⁶ ₆ ³ ₂ ³ ₂ ³ ₂ ⁴ ₁ ⁴ ₁ ¹ ₀ ⁷ ₃ ^{7 3 5 0} ₅ 3 ₁ 3 ₂. ³ ₁ ⁹ ₃ ¹ ₂ ¹ ₂ ¹ ₁ ⁸ ₁ ⁵ ₁ ⁵ ₁ ⁵ ₁ ² ₁ ² ₁ ⁸ ₁ ⁸ ₄ ₂ ¹ ₇ ₇ ⁰ ₈ ₈ ⁸ ₉ ₅ ⁸ ₁ ₅ ³ ₆ ₇ ⁷ ₉ ⁹ ₂ 3₃ 5₅ _{7 2 0} ⁸ ³ _r ¹ _r ² _r ² _r ¹ _r ⁴ _r ⁴ _r ^{4 4 1} ₀ 1⁴ ₃ 1³ ₀ 1 ₀ 2 ₀ 2 ₇ 1 ₆ 1 ₂ 1_/ ^h _c ^h _c ^h _c ^h _c ^h _{r r r r r r r r r r r r} _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^S _T P _I

₂ ^D ₁ _T ^E _S ² ₃ ¹ ⁷ ₀ ² ₁ ⁷ _{4 1} ² ₁ ⁶ ₀ ² ₁ ⁷ _{4 1} ³ _r ²

^h _r _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^A ₂ ^A ₂ ^A ₂ ^{7 A} ₅ ^{A A A 1 1 1 1 1}N N _{C S S 1 3} ^A _{B 5 5 6 P P P P P} ^I _R ^I N R ^I ₄ _R -₁ A A A ^F M M M M D R R H _Z H D _I ^K _I ^N D D D D ^A _E ^A _E ^A _E ^A _E ^A _E G G G H H H H _I K K K K K K K K K ₅ ^{4 4} ₉ 4^{3 2 9 9 1} ¹ ₀ 6 ₀ 2 ₁ 7 ₅ 9^{4 6} ₄ 8 ₈ 8 ₉ 9^{2 6 2} ₀ 5 ₉ 2 ₀ ¹ ⁸ ₅ ¹ ¹ ₃ ⁰ ₆ _{5 1 7 6 1 3 4 4 2 9 6 8 9 3 9} ^{6 9} ^{3 4 4 5 9 9 3 2 9 8 2 6 2} _{9 2 9 9} _{6 6 6 1 7 3 4 8 7 6 3} ^{6 8 8 9 9 9} ^{7 7 7 6 7 3 3 0 9 1} _{0 5 0 4 4 4 4 4} _{9 9 9 2 4 5 5 2 3 4} ² ₃ ⁵ ₃ ⁵ ₃ ⁵ ₄ ⁰ ₁ ⁰ ₁ ⁰ ₁ ⁰ ₁ ⁰ ₁ _{5 0 0} ⁹ ₁ ⁴ ₉ ₃ ² ₃ ³ ₆ ² ₇ ⁹ ₈ ⁹ ₇ ¹ ₈ ^{1 1 0} ^{9 4 0 5 8 4 6 8 7 8 2 6 2 3} _{3 1 4} _{3 0 6 6 1 1 2 4 2 9 4 6} ^{1 6 0 5 8} ³ ₆ ⁴ ₆ ⁴ ₆ ⁵ ₁ ⁹ ₇ ⁸ ₃ ^{2 2 9 8 1 5} ₇ 1 ₃ 6 ₉ 8 ₉ 8 ₂ 9 ₉ 9 ₉ 9 ₂. ⁷ ₉ ⁷ ₉ ⁷ _{4 8 7 6 3 0 5 0 4 4 4 4 4} ₉ ⁶ ₂ ⁷ ₄ ³ ₅ ³ ₅ ⁰ ₂ ⁹ ₃ ¹ ₄ ² ₃ ⁵ ₃ ⁵ ₃ ⁵ ₄ ⁰ ₁ ⁰ ₁ ⁰ ₁ ⁰ ₁ ⁰ ₁ ⁹ ₂ 3₃ 5₅ _{6 6 6 2 1 1 7 2 2 2 9} ⁸ ¹ _r ¹ _r ¹ _r ⁶ _r ¹ _r ¹ _r ¹ _r ² _r ¹ _r ^{7 1 1 1 X 1} ₉ 1 ₉ 1 ₉ 1 ₉ 1 ₂ 1_/ ^h _c ^h _c ^h _c ^h _c ^h _c ^h _{r r r r r r r r r r} _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^S _T P _I

₂ GA^T _S ₀ ⁹ ₅ ³ ₈ ⁰ ₄ ² _{1 1} ₀ ⁷ ₄ ³ ₈ ⁰ ₄ ² _{1 1} ^X _r

^h _r _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c _{C 1 4 4 4 1} _{5 5} ² D^T _T ² _{T S S 2 2} K K K K K S² _P ² _P ² _P ² _P ³ _P M M M M A A A_I ^F _L ^F _L A A M M R R ^R _{S T} A A A A A ^T _E A G G G G K K K K K K K K ^A _L M M M M M M M M M M ^{4 6 7} ₉ 4^{5 1 8} ₁ 9^{3 3 5 7 5 3} ₀ 5 ₇ 1 ₃ 4 ₇ 9 ₆ _{3 2 2 5 1 0 8 4 0 6 2 8 7 6 0 0 9} ¹ ^{2 1 8 2 0 4 3 1 4 1 5 8 3 4 2 3} _{8 1} _{3 2 5 8 9 7 5 6 8 5 1 7 5 5 7} ^{4 6 6} ^{3 6 7 1 3 2 4 7 8 3 8 0 2 6} _{7 5 5 7} _{7 0 0 2 0 2 2 7 9 4 0 1 1} ^{7 9 0 0 0} ^{4 3 3 5 9 5 5 1 0 6 2 2} _{8 6 1 2 2 2} _{5 7 7 1 4 2 2 1 2 6 1 1} ² ₁ ⁶ ₅ ¹ ₁ ⁴ ₁ ⁴ ₁ ⁴ ₁ ⁴ ₁ ^{4 6 7} _{9 1 1 8 3 7 6} _{1 0} ^{2 5 1 8 7 3 3 5 7 5 3 3 0 2 7 9} ¹ _{0 4 9 8 6 3 8 4 0 6 5 4 9 8 8 7 9} ₃ ⁰ ₂ ⁷ ₅ ² ₈ ⁸ ₈ ² ₇ ² ₅ ¹ ₆ ² ₈ ⁰ ₅ ⁴ ₁ ⁷ ₇ ² ₅ ³ ₅ ¹ ₇ ² ₇ ⁴ ₅ ⁶ ₅ ⁵ ³ ₇ ⁶ ₀ ⁷ ₀ ¹ ₂ ³ ₀ ² ₂ ⁴ ₂ ⁷ ₇ ⁸ ₉ ³ ₄ ⁸ ₀ ⁰ ₁ ² ₁ ⁶ ₈ ^{7 9 0 0} ₇ 0 ₂. ⁴ ₅ ³ ₇ ³ ₇ ⁵ ₁ ⁹ ₄ ⁵ ₂ ⁵ ₂ ¹ ₁ ⁰ ₂ ⁶ ₆ ² ₁ ² ₁ ² ₁ ⁶ ₆ ₅ ¹ ₁ ₁ ⁴ ₂ ₁ ⁴ ₂ ₁ ⁴ ₂ ₁ ⁴ ₁ ⁹ ₂ 3₃ 5₅ _{3 3 2 2} ⁸ ⁴ _r ¹ _r ¹ _r ⁷ _r ¹ _r ¹ ₂ _r ¹ ₂ _r ¹ ₃ 1 ₅ 1 ₇ 1 ₇ 1 ₇ 1^{5 7 7 7 7 7} ₂ 1_/ ^h _c ^h _c ^h _c ^h _c ^h _{r r r r r r r r r r r r} _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^S _T P _I

r_a ^a _i v ^{r a} _i ^{r a} _i ^r v _ ^_ _a t v _{a a s} _ v_ v_ uo T _f _C ⁱ _h ^e ^s _s ^e _s ^e _s ^m _y E _e ⁿ F_e ⁿ m ^s _e ⁿ _e n s_i ^s _si ^s _si on ₅ K^A _P ₁ ⁶ ₄ ⁴ ₄ ⁵ ₉ ₁ ⁴ ₃ ⁴ ₄ ⁵ ₉ ₀ ² _r

^h _r _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c A _{T T} G A A C A M _{7 1 R} ^{N G} _CA ⁶ _I ^H ₂ ^{I O} _{C T C} S^{1 C}GG K _{L S T} Y _{C F C}G M M M M M M N N F^A _T ^G _C _{8 4 7 E} ^{C C} ^{4 3 1 6 2 6} ₀ 7 R _{T C C} A _{C C} _{5 8 0 1 9 7} ⁴ ^{2 1 5 5 5} _{1 8} ₈ ⁰ _{1 0 1 4} ⁸ ₃ ⁷ ₅ ⁵ ₄ ₂ ¹ ⁴ ₈ ² ₀ ⁰ ₄ ² ₁ ⁷ ₇ ³ ₀ ¹ ₂ ⁷ ₄ ⁴ ₅ ⁴ ₃ ² ₃ ⁷ ₀ ₁ ² ₁ ⁷ ₃ ⁷ ₅ ¹ ₁ ² ₁ ⁶ ₁ ¹ _{3 S} ⁷ ^O ₉ ⁰ ^{1 1} ₂ ^{2 1 6} ⁴ ₃ 8 ₉ 7 ₇ 9 _{5 1 6} _{P 2 5 1 8 1 2} ³ ₁ ³ ₇ ⁵ _{9 3} ₈ ² ₄ 1¹ _{7 0} _{4 7 8} ^{6 2 4 5 4} ² _{9 7 6 0 6} R ₈ ¹ ₁ ³ ₀ ³ ₁ ^{4 8 7 4} M ⁹ _r ⁷ _r ³ _r ³ _r ² _r ⁰ _{4 3 5 4} ^H ₂ ¹ ₈ ^{2 0 2 7 3 1} _C O ^h _c ^h _c ^h _c ^h _c ^h _{c 0 2}. ⁴ ₁ ² ₀ ₁ ⁷ ₄ ₃ ⁷ ₁ ₅ ¹ ₇ ₁ ² _{0 2} ₁ ⁶ ₁ ¹ ₃ ⁹ ₂ .₇ ^{u 3} ₃ _S ^D _{5 7 7} ⁵ ₅ _{0 7 7 I 5 0} ⁸ ⁷ _r ¹ _e m u^t _c ^{8 4 6 8 7} ₂ 1_/ ^h _r ³ _r ¹ _r ¹ _r ⁸ _r ¹ _r ¹ _r _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^h _c ^l _{b r} a _T ^e _S ^{e j} ₁ _b ⁰ ₀ _S ⁰ ₃ _S ⁰ ₁ _S ⁰ ₀ _S ⁰ _S ^S _T P _I ^V _I ^e _cn ^a _{c r} _tn ^a _i ^r _a v_ ^e _s ⁿ _e ^s _si m ⁶ ₄ ^y _l G^p ^. _r _p ^T ₂ ^A _E K¹ _P ⁵ ₀ ^. _{0 9} ⁷ ₂ ⁶ ₂ ₅ ⁵ ₁ A

G G A A A _C A A _C _{C C C C C} G A _T A _C G _{C T C} ⁷ ₉ ² ₈ ⁵ ₀ ⁰ ₈ ⁹ ₉ ⁰ ₁ ⁷ ₈ ^{4 9 0 3 7 0} ^{8 3 6 3} _{9 0 7 1 8 8} ₄ ^{5 6 4 4 6 3 5 4 3} ^{0 5} ₇ 6 ₇ 6 ₇ 6 ₇ 6 ₇ 6 ₇ 6 _{7 7 7 7 7 7} _{1 9 7 0 7 5 7 2 7 4 7 1 7 2} ⁶ _{7 7} ⁶ _{7 6} ⁶ _{7 4} ⁶ _{7 1} ⁶ _{7 2} ⁶ _{7 6} ¹ _r ¹ _r ¹ _r ¹ _r ^{1 1 1 1 1 1 1 1 1} ^h _{r r r r r r r r r} _{c 9} ^h _{c 7} ^h _{c 7} ^h _{c 7} ^h _{c 7} ^h _{c 7} ^h _{c 7} ^h _{c 7} ^h _{c 7} ^h _{c 7} ^h _{c 7} ^h _{c 7} ^h _{c 7 2}. 9₂ 3₃ _{9 5 1 9 0 5 1 9 6 4 3 5 3} ⁵ ₅ ⁸ ₀ ⁴ ⁰ ₃ ⁹ ₀ ^{8 7 8 8 7 8 6 6 1 5 8} ₂ 1_/ _S ⁰ _S ⁰ ₀ _S ⁰ ₀ _S ⁰ ₁ _S ⁰ ₀ _S ⁰ ₀ _S ⁰ ₁ _S ⁰ _{3 3 3 0} _S ⁰ _S ⁰ _S ⁰ _S ⁰ _S ^S _T P _I

⁶ ₂ ^. ₀ ⁰ ₁ ^. ₀

n_o ^o _c _r ⁴ N ⁸ ₁ ³ ₉ ² ₁ ⁰ ₃ ⁷ ₄ ⁸ ₄ ⁸ ₆ ⁹ ₉ ⁴ ₄ ⁷ ₁ ⁸ ₂ ⁵ ₃ ⁸ ₇ c _{t 3} ⁴ ₁ ⁰ ₅ ³ ₁ ² ₄ ⁰ ₄ ⁰ ₁ ⁰ ₁ ⁵ ₁ ⁷ ₁ ⁵ ₆ ⁶ ₁ ¹ ₆ ⁰ ₁ ⁰ ₀ ^. _{0 t} _r n e_c u o_{c 8} 5^{9 6} ₈ 3 ₁ 1 ₇ ⁷ _{1 9 6 1 7} n _{4 0 8 9 4} ⁷ ₁ ^{8 4 4 5 8 1 9} ^a _r ^{2 0 9 6 2} _{8 8 8 1 9 6 0} _C ^c _{t 6 1} ^{1 2 2 1 2 0 1 7 5} ⁰ _{3 9 3 3 8 7 1 1 5 1 4 8} ₀ ^. ₀ ^d _io _s ^r _{t 3 4 4 1 5 4 4}R ^{n -} ₇ -₆ ⁷ ₂ -₆ ⁷ ₂ ⁷ ₂ ⁷ ₂ -₅ -₆ -₆ ⁷ ₂ -₆ ⁷ ₂ ⁷ ^C _e _T ^c _{e 2} n U^e V V V V V V V V V V V V V V g B B B B B B B B B B B B B B + _F _s ^R n R V _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^o _it ^a _t u ^{6 3 7 2 2 1 1 1} ^d _{i 7} 6 ₇ 8 ₁ 7 ₃ 7 ₂ 4 ₅ 5 ₈ 8 _{0 2 7 5 7} ⁶ ₄ ² _{8 2}.M o + ^r _{t 1} _s ⁷ ₂ 6 _{4 2 5 1 1} ⁴ ₆ ³ ₉ ⁸ ₂ ⁵ ₃ ⁹ ₀ ⁹ ₂ ⁸ ₁ ⁹ ₂ ⁿ _i ^. _{9 2} ⁷ ₄ ⁴ ₆ ⁴ ₁ ⁰ ₄ ⁰ ₄ ⁰ ₇ ⁵ ₂ ⁶ ₂ ⁴ ₈ ⁷ ₆ ² ₀ ⁰ ₄ ³ 9 n e _c ⁰ ₁ ⁰ ₇ ⁶ ₉ ⁶ ₁ ⁷ ₁ ⁰ ₂ ⁰ ₂ ¹ ₂ ⁴ ₁ ⁰ ₇ ¹ ₁ ⁹ ₁ ⁰ ₂ ⁰ ₃ ₂ ⁵ ₅ ^e _t o _e ⁸ ₂ ^r _P ^l _b ¹ ^a _T ^c _d ¹ ₁ ² ₁ ² ₁ ² ₁ ² ₁ ² _/ ₁ ² ₁ ² ₁ ² ₂ ² ₂ ² ₂ ² ₂ ² ₂ ² ₂ ^S _T P _I

² ₀- -^E ₃ ^. ₉ ^. ³ ₀- -^E ₉ ^. ₁ ^. ⁷ ₁ ^. ₀ ^. ₃ ⁸ ₁ ₅ ⁵ ₁

⁶ ₉ ² ^{4 3} _{2 5 5} ⁵ _{1 4 9 9 4 8 4 9 4} _{8 1} ^{2 6 1 4 4 5 9 0 0 2 3 5 3 3} ³ ₃ ⁴ ₄ ⁸ ₁ ₄ ⁰ ₆ ₁ ⁶ ₇ ⁴ ₇ ₄ ² ₆ ₁ ² ₁ ₆ ⁴ ₂ ₁ ⁸ ₃ ₁ ⁸ ₃ ₁ ³ ₀ ₂ ⁴ ₈ ₁ ¹ ₃ ₁ ⁹ ₇ ₉ ⁴ ₉ ₄ ⁹ ₇ ₅ ⁴ ₁ ² _{8 1} ⁰ ₈ ⁶ ⁹ _{4 7 1 3 0 1 2 6} ₂ ³ ₆ ^{2 9 5 0 2 9 9 5 6 5 6 0 4} ^{6 3} _{8 1 5 6 5 5 7 7 4 5 2 8 5 8} _{2 6} ⁹ ₃ ² ₇ ⁶ ₅ ³ ₆ ⁹ ₉ ⁸ ₄ ⁰ ₁ ² ₁ ³ ₁ ⁶ ₁ ⁰ ₁ ² ₉ ⁷ ₆ ⁴ ₃ ⁶ ₄ ⁰ ₁ ⁷ ₆ ^{- 7} ₄- ₄- _{6 5 1 1- 4 4} _{2 5 2 6 6} -₅ ⁷ ₂ ⁷ ₂ -₆ -₅ ⁹ ₂ -₆ ³ ₁ ⁷ _{2 2} ⁷ ₂ ⁷ ₂ -₆ V V V V V V V V V V V V V V V V V VB B B B B B B B B B B B B B B B B B^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ³ ₀ ⁰ ₆ ⁵ ₇ ⁴ _{2 7} ⁰ ₆ ⁷ _{0 2 3 9 7} ⁷ ₂ ⁸ ₁ ⁸ ₈ ³ ₉ ^{2 3} ⁶ ₉ ⁷ ₉ ² ₄ ⁹ ₂ ⁸ ₂ ⁷ ₉ ⁹ ₈ ⁰ ₆ ³ ₉ ² ₉ ^{2 8 7 3 9 5} ₅ 5 ₈ 2 ₂. ^{0 9 7 0 6 9 1} _{9 2 5 8 0 4 1 2} ⁹ ₂ ₃ ¹ _{1 3 3 2 1 9} ⁹ ₆ ⁵ ₂ ¹ ₃ ¹ ₂ ⁶ ₂ ⁸ ₆ ⁸ ₈ ⁰ ₃ ² ₅ ⁰ ₄ ² ₆ ³ ₃ ₂ ⁰ ₁ ² ₁ ³ ₂ ⁰ ₇ ⁰ ₁ ⁶ ₁ ⁰ ₁ ⁴ ₁ ⁸ ₃ ³ ₆ ⁰ ₇ ⁰ ₁ ³ ₁ ⁶ ₁ ⁷ ₁ ⁰ ₂ ² ₂ ⁵ ₅ 8₂ ² ₁ ₂ ^. ₁ ₁ ^. ₁ ₁ ^. ₂ ₁ ^. ₂ ₁ ^. _{2 2 2 2 2 2 2 2 2 2 2 2} ¹ _/ ₁ ^. ₁ ^. ₂ ^. ₂ ^. ₂ ^. ₂ ^. ₂ ^. ₂ ^. ₂ ^. ₂ ^. ₂ ^. ₂ ^. ₂ ^S _T P _I

n _r ^a _c ^d _f 0₆ 6 1 ₅ 0 1 ₅ 0 2₇ 3 9₅ 4 2₇ 3 2₇ 3 2₄ 6^_ _s ^. _r ⁹ ₀ ^. ₄ ⁹ ¹ ₀ ^. ₂ ⁹ _{0 2} ⁶ _{0 8} ¹ _{0 3} ⁶ _{0 8} ⁶ _{0 8} ⁷ _{0 8} _i ^e _{c 0 5 0} ⁰ ₀ ^. ₀ ⁰ ₀ ^. ₀ ⁸ ₉ ^. ₀ ¹ ₃ ^. ₀ ⁸ ₉ ^. ₀ ⁸ ₉ ^. ₀ ⁵ ₂ ^e _{c e}u 1 ^{a 4} 4 7 4 4 - 6 8 0 n _c ^l _{a 1 1} ⁷ ₁ ⁷ _{0 E} ⁷ ₀ ⁶ ₀ ⁰ ₁ ^{_ v} _p ⁰ ₀ ^. ₅ ⁰ ⁷ ₀ ^. ₁ ⁰ _{0 1} ⁰ _{0 4} ⁹ ₉ ⁰ _{0 1} ⁰ _{0 9} ⁰ _{0 4}s _i ^. _{r 0 6 0} ⁰ ₀ ^. ₀ ⁹ ₀ ^. ₀ ⁹ ₄ ^. ₄ ⁵ ₀ ^. ₀ ⁸ ₆ ^. ₀ ² ₂ ^. ₀ ³ ₅ cn ^{a 2} _{c g} ^h ^{_ o} _C ⁹ ₂ ^{7 1 6 1 4 1 7} l s . ^d _l ^e _g ⁶ ₁ ⁸ ₅ ₆ ² ₁ ⁷ _{4 4} ₄ ² _{1 3} ² ₂ ¹ ₁ ₄ ¹ ₄ ⁷ ₄ ₃ ⁴ ₃ ² ₉ ₅ ³ ₂ ⁸ ₁ ₅ ⁶ _{1 5} _i ^r _e ^o _F ⁿ _a ^. ₀ ⁴ ₄ ^. ₀ ⁷ _{3 -} ^. ₀ ⁵ ₁ ^. ₀ ⁸ ₉ ^. ₀ ² ₉ ^. ₀ ¹ ₁ ^. ₀ ⁶ _{0 -} ^. ₀ ⁷ ₁ ^. _r ^e n _c ^a _e 1 4⁹ ₁ ⁶ ₁ 2 0 ² ₇ 2 4 n ₅ ⁷ ₀ ⁹ ₀ ⁸ ₆ ⁷ ₈ ^{5 0} ₈ ⁰ ₂ ^a _c M ² ^_ ₅ ^{. . 7} ₀ ⁶ _{3 1} ⁶ ₆ ³ ₃ _s ^e _s _i ^a . b ³ ₇ ³ ₉ ₇ ⁷ ₁ ³ ₃ ₂ ⁰ ₃ ⁸ . 2 ¹ ₁ ⁹ . ⁸ 5 ³ ₁ ¹ ₀ ₈ ^. ₅ ⁹ . 5 ² ₁ ² . 4 ⁶ _{5 9 2}. 9^r ₂ _tn ^{9 5 1 1 6 8 7 3} ₃ ^e _c ^d _{i 6} o ⁰ ₁ ⁰ ₂ ₆ ⁴ ₂ ₁ ³ ₃ ₉ ⁸ ₃ ₃ ² ₂ ₉ ³ _{9 2 7 6 2} ⁸ ₈ ⁸ ₁ ⁰ ₃ ⁸ ₈ ⁵ 6 ² ₉ ⁰ ₇ ⁸ ₂ ⁰ ₁ ⁷ ₅ ³ ₁ ³ ₈ ⁶ ₁ ⁹ _{0 5} 8₂ 1 _{d _ c} ^. _{2 2} ^. _{2 2} ^. _{2 2} ^. _{2 2} ^. _{2 2} ^. _{2 2} ^. _{2 2} ^. _/ _{2 2} ^S _T P _I ² ₁ ⁵ _{5 4} ² ₈ ³ _{0 8} ₇ ¹ ₇ ⁸ ₀ ⁸ ₅ ⁶ ₁ ⁵ ₉ ⁶ ₉ ⁶ ₀

⁷ ₆ ^{7 7 8 0} ⁰ ₅ ₀ ³ ₃ 4 ₅ 6 ₄ 1 ₆ 6 ₅ 1 ₅ 5 ^. ₀ ₀ - ^. ₀ ₀ - ^. ₀ ₀ - ^. ₀ ₀ - ^. ₀ ₀ - ^. ₀ ₀ - ^. ₀ ₀ - ^. ₀ -₅ ⁴ 9₅ 4 2₇ 3 1 1 1 6 6 5₈ ₃ ¹ ₃ ^{6 5 6 5 6 5 6 2 3 2 3 6 2} ¹ _{0 0 8 0 3 0 3 0 3 0 5 0 5 0 5} ₃ ^. ₀ ¹ ₃ ^. ₀ ⁸ ₉ ^. ₀ ² ₀ ^. ₀ ² ₀ ^. ₀ ² ₀ ^. ₀ ² ₃ ^. ₀ ² ₃ ^. ₀ ⁷ ₇ - 9 ^{- -} 0 0 E ⁴ _{0 E} ^{1 - - 2} ^{0 0 1} _{E 0 E E 0} _{7 5} ^{1 0} ₅ ^{3 6 0} ⁷ ₅ ^{. 5} ₀ ^{. 8} ₁ ^. _{7 0 2 4 0 8} _{0 8 0 0 0 5} ⁵ ₀ ^. ₇ ⁵ ₀ ^. ₀ ¹ ₆ ^. ₅ ⁵ ₀ ^. ₅ ⁵ ₀ ^. ₀ ⁴ ₄ 9₉ ⁰ ₇ ⁰ ₀ ⁷ ₁ ^{2 1 0 9} ^{3 1 9 6} _{8 5 7 1} ₀ ⁸ ₈ ^{6 9 4 9 8 1 4 4 2 1 8 2} ⁸ _{1 3 7 4 2 2 3 2 4 2 0 4 5 6 2} ₈ ^. ₀ ⁵ ₅ ^. ₀ ⁹ ₃ ^. ₀ ¹ ₁ ^. ₂ ⁰ ₂ ^. ₀ ⁸ ₃ ^. ₂ ⁶ ₅ ^. ₀ ⁶ ₂ ^. ₀ ⁶ ₂ 1 6 5 4 ^{1 5} ₆ ⁴ ₀ 3 ⁰ ₇ ⁶ ₂ ⁶ ₂ 4 ₃ ₄ ⁷ ₁ ¹ ₄ ⁴ ₅ ³ ₀ ³ ₃ ⁵ ₆ . ⁵ ₈. ⁰ ₀ ⁹ ₃ ⁷ ₄. ⁹ ₃ ^{6 0} ^{2 5 7 6 1 . . 5 6 5 . 3} ₅ ^{. 9} ₂ _{0 5 5 1 4 6 2 0 5 1 3 0 6 5 0} ^. ₂ ⁵ _{5 2}. 9₂ ³ ₉ ⁰ ₄ ² ₅ ² ₆ ³ ₈ ⁶ ₂ ^{9 0 1 7 9 0 7 5 0 4 3} ₃ ⁵ ₄ ⁰ ₂ ⁵ _{7 1 6 9 0 1 6 3 7 3 2} ⁵ 1 ² ₂ ² ₂ ⁰ ₇ ⁸ ₂ ⁰ ₁ ⁷ ₉ ⁶ ₁ ⁹ ₈ ⁰ ₁ ⁷ ₉ ² ₁ ² ₄ ³ ₂ ⁹ _{2 5} 8₂ 1 ₂ ^. _{2 2} ^. _{2 2} ^. _{1 2} ^. _{1 2} ^. _{1 2} ^. _{1 1} ^. _{1 1} ^. _/ _{1 1} ^S _T P _I ⁰ ₅ ³ _{7 1} ¹ ₅ ⁰ _{8 3} ₆ ⁷ ₇ ² ₉ ⁴ ₁ ^. ₀ ⁸ ₅ ⁸ _{4 4} ¹ ₄ ⁴ ₂ ³ ₅ ⁷ ₇ ₇ ⁹ ₇

5 ³ ₆ ⁶ ₅ ⁹ ₉ ⁷ ₅ ^{0 6 6} ^{3 0 3 0 0} _{2 6 6} _{0 0 0 0 0} ^{1 3 1} ^. - ^. - ^. - ^. - ^. _{0 0 0} _{0 0 0 0 0} - ^. ₀ - ^. ₀ - ^. ₀ -₁ ⁶ 2₇ 8 5₅ 2 5₅ 2 2₁ 2₁ 2₇ 1 ₉ 5₁ ₈ ⁰ ₆ ⁷ ₆ ^{7 9 6 9 6 9 6 3 2 6 0} ² ₀ ^. _{0 0 6 0 8 0 8 0 9 0 1 0 6} _{0 0} ⁵ ₉ ^. ₀ ¹ ₂ ^. ₀ ¹ ₂ ^. ₀ ² ₀ ^. ₀ ² ₀ ^. ₀ ⁶ ₅ ^. ₀ ⁹ ₈ ^. ₀ ⁴ ₀ - 7 7 2 4 2 - 8 E ⁴ ₀ ^{6 4 6 9 4} ^{0 0} _{0 1 1 1 E 0} ₇ ⁰ ₇ ^{0 0 0 1 0} ⁵ ₈ ^{. 6} ₀ ^. _{0 0 4 0 7 0 3 0 0 9} _{1 8 0 0 5} ^. ₀ ⁵ ₁ ^. ₀ ⁹ ₉ ^. ₀ ³ ₃ ^. ₀ ⁵ ₈ ^. ₆ ⁵ ₀ ^. ₀ ⁹ ₉ 4₅ ³ ₃ ⁸ ₀ ⁰ ₁ ^{1 2 4 4} ^{5 9 6 8} _{0 3 2 9} ₈ ⁵ ₁ ⁶ ₅ ^{6 6 5 3 6 7 9 1 6 2 3} ⁷ ₄ ^. _{1 3 5 1 6 2 8 1 8 4 9 3 7} _{2 0} ³ ₁ ^. ₀ ¹ ₅ ^. ₀ ⁹ ₄ ^. ₀ ⁶ ₀ ^. ₀ ¹ ₇ ^. ₀ ² ₅ ^. ₀ ⁸ ₅ ^. ₀ ⁷ ₄ 1₁ 4 1₉ ¹ 3 5 ⁰ 5 8 3 1⁰ ₅ ⁰ ₅ ⁷ ₈ 1 ⁹ ₃ ² ₃ ₄. ⁸ ₅ ₁. ³ ₇ ₂. ⁹ ₄ ⁶ ₂ ¹ ₇ ³ ₆ . ^. ₇ ⁴ ₅ ⁹ ₅ ₅ ^. ₉ ² ₆ ⁰ ₆ ⁴ ₁ ⁷ ₁ ⁴ ₆ ² ₇ ² ₃ ¹ _{1 5} ² _{3 3} ^. ₉ ⁸ ₇ ^. ₆ ⁷ _{2 2}. 9₂ ₂ ⁶ _{2 7} ⁴ ₈ ¹ ₅ ⁷ ₆ ¹ ₇ ² ₀ ⁶ ₄ ⁰ ₄ ² ₈ ⁰ ₃ ³ ₀ ^{6 7 3} ₃ ³ ₉ ⁰ ₇ ⁸ ₂ ¹ ₁ ⁵ ₃ ⁹ ₁ ⁹ ₀ ⁰ ₂ ⁹ ₂ ⁰ ₂ ⁸ ₁ ¹ ₂ ⁶ ₂ ₉ ⁰ ₇ ₇ ⁸ ₄ ₂ ⁶ ₁ ₉ ^{7 5} 4₅ 8₂ 1_/ _{2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2} ^S _T P _I ¹ ₅ ⁷ _{7 5} ⁷ ₅ ³ _{0 3} ₁ ³ ₁ ¹ ₈ ² ₄ ^. ₁ ⁹ ₈ ⁷ _{6 2} ³ ₁ ⁰ ₂

₀ ^. ₀ ₀ n ^c - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^M _L ^a _{c f} __s ² _g ⁶ ₀ ^{7 7} ^. ₀ ^. ₀ ^. ¹ G_i ^o _{l 0 0 0} ₀ ⁵ ₉ ⁹ ₁ ⁷ ₁ ² ^{0 2} ₅ ^{6 5} ₁ ^{0 7} ₈ ^{0 7} ₄ ₅ ^{1 1} _{1 t} ² ₀ ₁ ^. ₀ ⁶ ₀ ₂ ^. ₀ ⁰ ₃ ₀ ^. ₀ ⁴ ₀ ₈ ^. ₀ ⁰ ₉ c 7 ^. ₀ ⁷ ₄ n n i ⁴ ₁

₁ ³ ₈ 3₆ 4 ⁷ ₀ ⁴ ₆ 5₃ 8⁴ ₂ 5₁ 8³ ₈ 5₁ 6⁰ ₆ 6 _r 0⁶ ₁ ^c _tr ₄ ^. ₀ ⁷ ₀ ₂ ^. ₀ ⁴ ₀ ₉ ^. ₀ ⁷ _{0 0} ₄ ^. ₀ ⁴ ₀ ^. ₀ ² ₄ ^e _{c t 2} ⁹ ₄ ⁰ ₉ n n ⁰ ₄ ⁵ ₀ ⁴ 6 ^a u 8 ₁ 1 3 6 9 _C ^o _{c 9} ⁴ ₅ ⁷ ₆ ₀ ¹ ₁ ⁹ ₀ ³ ₀ -^{0 0 0} _E _{7 5} ^{0 8} ⁵ _{0 0 0 6 0 3 2} ₄ ^. _{0 3} ^. ₀ ⁹ ₂ ^. ₀ ⁸ ₅ ^. ₀ ¹ ₉ ^. ₅ ⁶ ₀ V d_i 3 ⁵ ₈ ² ₇ ^{9 6} ₀ ^{0 4 5} o^r ^{6 3} ₃ ³ _{0 7} ⁷ _t n ₅- ₅- ₅-⁵ ₁ ₅ ^. ₈ ₀ ⁴ ₁ ₇ ^. ₉ ³ 0 ⁶ ₂ ₉ ^. ₇ ⁴ 0 ³ ₁ _{0 -} ^. ₂ ⁶ 0 ⁰ ₀ ₇ ^. ₈ ₂ ² _e ₀ ^c _{6 6 6} V V V U_e _F n B B B e _g ^{R R R} 0 7 7 ^{9 3} R _{T T T} 4₁ ⁹ ₆ ⁴ _{8 7 6} 1₁ ⁵ ₆ ¹ ₆ ⁷ ₄ ¹ ^. ₇ ⁶ . ₀ ² . . ₇ ⁰ ₃ ² 6 ⁸ ₅ ⁴ ₄ ⁸ ₄ ² ₁ ⁹ ₁ ⁸ ₁ ⁹ ₉ ^. ₀ ⁴ ₅ ^d _{i 2} ¹ ₁ ⁶ ₉ o ⁷ ₂ r_{t 2} ³ ⁴ ₆ ³ ₅ . 0 ¹ ₂ ⁵ ₅ ⁹ ₂ ³ ₃ ⁴ ₁ ⁷ ₂ ⁰ ₄ ² ₅ ⁰ ₄ ^{2 0 1 7 6} . n e^{7 7 1 3} ₃ ⁷ ₂ ⁷ ₁ ⁴ _{8 7 0 9 7} ₅ ⁰ ₂ ⁵ ₁ ⁰ ₂ ⁸ ₁ ¹ ₂ ⁴ ₆ ⁰ ₁ ⁶ ₁ ¹ _{1 c 3 2 2} ⁵ ₅ _e ⁸ 2_{1 2 1 2 1 2 1 1} ^l ₂ 1_/ _{2 1 b} a _T ^c _d ² ₂ ² ₁ ² ₂ ^S _T P _I ² ₀- -^E ₉ ^. ₇ ^. ³ ₀- -^E ₃ ^. ₆ ^. ₁ ⁷ ₀ ^. ₀ ^. ₄ ⁰ ₃ ₄ ⁹ ₂ ⁴ ₇ ³ ₂ ¹ ₅

⁵ ₃ ⁵ ₉ ⁴ ₂ ³ ₇ ⁹ _{2 9} ² ₁ ⁹ ₇ ⁶ ₈ ^{6 4 0 9} ³ ₇ ^{6 5 1 6 9 8 9 1} ₇ 4 ₁ 8 ₅ 3 ₄ 6 ₄ 3 ₁ 6 ₉ 1 ₈ 7 ₆ 3 ₈ 5 ⁴ ₅ 4 ₅ 9 ₃ 0 ₉ 1 ₁ 4 ₅ 0 ₃ 8 ₁ 7 ₈ 6 ₀ 3 _{2 7 8 6 3 8 0 5} _{4 2 1 2 2 8 3 3 1 2 3} ¹ ₂ ⁵ ₂ ⁷ ₃ ² ₈ ² ₈ ⁸ ₉ ⁷ ₁ ⁴ ₁ _{5 1 1}- ₅- ₁- ₅ ^{- 9} ₉ ₁ - ₁ ₇ - - -_{6 6 9 9 6 6 9 6} ⁹ ₁ ⁹ _{2 9} ⁷ ₂ ⁴ ₁ ⁴ ₁ ⁷ ₂ ⁷ ₂ ⁹ ₂ V V V V V V V V V V V V V V V V V V VB B B B B B B B B B B B B B B B B B B^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ¹ ₁ ⁵ ₃ ⁶ ₁ ⁷ ₃ ⁹ _{3 2} ³ ₆ ⁶ ₆ ⁶ ₅ ⁸ ₇ ⁸ _{4 6} ⁴ ₂ ⁴ ₂ ⁸ ₂ ⁴ ⁵ ₄ ² ₄ ⁴ ₆ ³ ₃ ⁶ ₆ ⁰ ₇ ^{7 8 7 2 5 6 6 5 4 4 7} ₄ 9 ₉ 0 ₂. ⁶ ₄ ⁵ ₀ ⁶ ₁ ⁷ ₈ ⁶ ₃ ⁷ ₃ ₄ ⁰ ₇ ₈ ⁴ ₂ ₉ ⁷ ₂ ₅ ⁶ ₄ ₅ ¹ ₆ 5 ₄ 4 ₈ 0 ₄ 5 ₄ 5 ₃ 5 ₅ 1 ₄ 8⁹ ₂ ³ ₂ ⁰ ₂ ³ ₃ ³ _{6 6 0 5 2 2 8 8 5} ³ 3 ¹ ₁ ⁶ ₃ ⁰ ₆ ⁴ ₃ ³ ₁ ⁴ ₃ ² ₃ ⁷ ₁ ⁸ ₃ ¹ ₉ ⁹ ₃ ⁹ ₃ ¹ ₃ ⁸ ₂ ¹ _{2 3} 5₅ 8₂ ₂ ^. ₂ ₂ ^. ₂ ² ₂ ₂ ^. ₂ ² ₂ ² ₁ ² ₂ ² ₂ ¹ 2 ^. ₂ ₂ ^. ₂ ₂ ^. _{2 /} ₂ ² ₂ ^. ₂ ² ₁ ² ₁ ² ₂ ² ₂ ² ₂ ² ₁ ^S _T P _I ² ₀- -^E ₈ ^. ₆ ^. ³ ₀- -^E ₁ ^. ₄ ^. ₁ ² ₁ ^. ₁ ₀ ^. ₄ ⁰ ₃ ₄ ⁹ ₂ ₉ ⁹ ₅ ¹ ₈

₀ ⁹ ₄ 9 _{8 7 7 9 5 1 8 1 7 2 5 5 6 7 7 4} _{5 5} ⁵ ₉ ⁷ ₂ ⁸ ₇ ⁶ ₁ ⁵ ₅ ⁶ ₆ ⁷ ₃ ⁹ ₁ ⁹ ₁ ² ₀ ^{9 8 8 2 3 6 4} ^{4 3 7 6} ₁ ⁸ ₄ ³ ₆ ⁰ _{4 5 8 4 3 0 3} _{7 2 1 3} ⁹ ₁ ⁴ ₅ ⁰ ₅ ⁷ ₁ ⁶ ₅ ¹ ₂ ⁹ ₅ ⁷ ₂ ⁹ ₄ ⁹ ₇ ² ₉ ³ ₁ ₁- ₁- ₁ ^{7 9 7 7 7 3} ₉ -_{2 2 2 2 2 1} ⁷ ₂ ⁴ ₂ ⁷ ₂ -₇ ⁷ ₂ ⁷ ₂ ⁹ ₂ ⁷ ₂ ⁷ ₂ ⁷ ₂ ⁷ ₂ ⁷ ₂ ⁴ ₁ V V V V V V V V V V V V V V V V V V VB B B B B B B B B B B B B B B B B B B^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ⁴ ₅ ⁵ ₃ ³ ₃ ⁴ ₄ ⁸ ₇ ⁸ ₇ ^{2 2 7 8 7 2 6 5 5 4} ⁶ _{9 2 9 5 6 1 8 5 4 7 5 5 2} ₁ ⁷ ₀ ⁷ ₃ ⁹ ₅ ⁷ ₃ ⁴ ₂ ⁵ ₃ ¹ ₆ ³ ₇ ⁰ ₂ ^{5 8 9 5 2 4 8 8 4} ₂. ³ ₂ ¹ ₉ ¹ ₉ ¹ ₈ ⁰ ₂ ⁶ ₉ ⁶ ₂ ³ ₇ ⁹ ₁ ³ ₈ ₂ ⁷ ₈ ₈ ¹ ₆ ₆ ² ₂ ₆ ⁴ ₉ 1 ₁ 3 ₅ 2 ₅ 2 ₄ 5⁹ ₂ ⁵ ₁ ⁶ ₂ ² ₃ ⁸ ₂ ⁸ ₁ ⁵ ₂ ⁹ _{2 8 1 5 5 2} ³ 1 ¹ ₂ ³ ₃ ⁰ ₄ ⁷ ₂ ¹ ₁ ⁶ ₂ ⁴ ₃ ⁰ ₂ ¹ ₁ ⁴ ₁ ⁴ ₁ ⁹ _{3 3} 5₅ 8₂ ₂ ^. ₂ ² ₂ ₂ ^. ₂ ² ₁ ² ₁ ² ₂ ₂ ^. ₂ ² ₁ ² ₂ ₁ ^. ₂ ² ₂ ₁ ^. _{2 2 2} ¹ _/ ₂ ^. ₂ ² ₂ ² ₂ ² ₁ ² ₁ ^. ₂ ^. ₂ ^S _T P _I ⁴ ₀- -^E ₆ ^. ₆ ^. ⁶ ₀- -^E ₉ ^. ₁ ₈ ^. ⁵ ₁ ^. ₁ ₀ ^. ₄ ⁰ ₃ ₄ ⁹ ₂ ⁵ ₆ ⁴ ₅ ⁰ ₁ ¹

_{5 0 9 9} ⁷ ^{8 5 5 2} _{1 1 6 5 5 5 3 0 1 4 8} _{5 2 8 3} ⁴ ₃ ² ₇ ⁸ ₄ ⁰ ₈ ⁹ ₁ ³ ₅ ⁹ ₂ ⁰ ₆ ⁵ ₀ ² ₅ ^{4 8 8 3 3} ^{4 8 4 5 0 0 9} ₆ ⁷ ₁ ² ₉ ⁵ _{9 9 9 9 6} _{9 1 6 6 4 3 5} ⁵ ₅ ³ ₂ ⁹ ₇ ¹ ₅ ⁸ ₄ ⁰ ₁ ⁵ ₅ ⁰ ₅ ³ ₂ ₂- ₁- ₁- _{1 1} ⁷ ₄ ^{- 7} _{1 4} - -_{2 6 2} ⁷ ₂ ⁰ ₁ ⁷ ₂ -₅ -₆ ⁹ ₁ ⁷ ₂ ⁹ ₂ ⁷ ₂ ⁵ ₂ ⁷ ₂ ⁵ ₂ ⁵ ₂ ⁷ ₂ ⁷ ₂ ⁷ ₂ V V V V V V V V V V V V V V V V V V VB B B B B B B B B B B B B B B B B B B^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ⁷ ₆ ⁶ ⁸ ₂ ⁸ ₀ ⁰ ₃ ⁴ ₁ ³ ₃ ⁸ ₀ ⁷ ₇ ¹ ₄ ⁸ ₀ ⁰ ₃ ⁸ ₇ ⁶ ₅ ¹ ₇ ⁷ ₈ ⁷ ₈ ⁵ ₇ ⁰ ₆ ¹ ₇ ₀ ⁶ ₃ ⁹ ₅ ⁸ ₄ ⁵ ₁ ⁷ ₆ ⁶ ₀ ² ₄ ^{4 9 5 7 8 0 7 7 4 9 0} ₂. ⁶ ₀ ¹ ₅ ¹ ₀ ¹ ₂ ⁸ ₈ ⁰ ₄ ⁹ ₀ ¹ ₇ ₇ ¹ ₅ ₈ ¹ ₈ ₀ ⁷ ₃ ₀ ⁰ ₀ ₂ ⁹ ₇ 2 ₈ 5 ₈ 5 ₈ 6 ₄ 4 ₇ 2⁹ ₂ ¹ ₁ ² ₃ ⁰ ₃ ⁷ ₁ ⁸ ₁ ⁰ _{0 2 9 9 8 0 2} ³ 4 ¹ ₃ ⁰ ₄ ³ ₃ ⁰ ₃ ⁷ ₁ ⁸ ₁ ² ₂ ⁷ ₃ ⁰ ₂ ⁰ ₂ ⁵ ₁ ⁴ ₃ ⁷ _{3 3} 5₅ 8₂ ² ₂ ² ₂ ² ₂ ₁ ^. ₂ ² ₂ ₂ ^. ₁ ₁ ^. ₂ ₁ ^. ₂ ² ₂ ₁ ^. ₂ ₂ ^. _{2 2 2 2} ¹ _/ ₂ ^. ₂ ² ₂ ^. ₂ ² ₁ ^. ₂ ² ₁ ² ₂ ^. ₁ ^S _T P _I ² ₀- -^E ₈ ^. ₁ ₆ ^. ³ ₀- -^E ₅ ^. ₁ ₄ ^. ⁸ ₁ ^. ₁ ₀ ^. - ₄ ⁰ ₃ ₁ ⁹ ₂ ¹ ₁ ⁹ ₄ ¹ ₁

4 ^{5 9} ^{0 8 0} _{3 1 3 3 5 1 1 0 1 3 2 2} _{8 1 5} ⁰ ₉ ⁶ ₂ ⁸ ₉ ⁵ ₁ ⁴ ₁ ⁶ ₂ ² ₃ ⁹ ₇ ⁵ ₄ ³ ₀ ^{3 2 5 4 3 4} ^{6 4 0 1 4 1} ₁ ⁰ ₁ ⁸ ₁ ¹ _{3 9 2 8 3 0} _{4 9 2 1 7 1} ⁷ ₂ ⁴ ₈ ⁰ ₃ ² ₃ ⁶ ₁ ⁸ ₂ ⁰ ₄ ⁷ ₂ ⁰ ₅ ⁶ ₄ ₁ ⁷ ₁- - 5 _{1 1 1}- _{9 1}- _{1 4} _{2 7 2} ⁷ _{2 2} ⁷ ₂ -_{7 2} ⁷ ₂ -₅ ⁵ ₂ ⁹ ₁ -₇ ⁵ ₂ ⁷ ₂ -₅ ⁷ ₂ -₆ ⁷ ₂ V V V V V V V V V V V V V V V V V V VB B B B B B B B B B B B B B B B B B B^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ⁶ ₇ ⁹ ₆ ⁷ ₉ ¹ ₀ ¹ ₈ ⁶ ₄ ^{9 4 1 0 4 1 6 0 0 0 1 4} ⁴ _{6 9 0 5 6 4 6 1 4 0 4 8 7} ₁ ³ ₇ ⁵ ₈ ⁹ ₅ ⁶ ₈ ⁶ ₂ ³ ₇ ⁴ ₇ ⁹ ₅ ³ ₁ ^{5 4 7 6 7 4 0 9 1} ₂. ³ ₁ ^{4 8 5 0 7 4 7 5 2} ₂ 7 ₇ 1 ₂ 7 ₉ 1 ₃ 5 ₄ 2 ₂ 5 ₄ 9 ₉ 1⁹ ₂ ¹ ₄ ₁ ⁵ ₈ ₂ ⁰ ₄ ₂ ¹ ₅ ₂ ⁶ ₈ ₃ ⁶ ₄ ₃ ⁵ ₁ ₂ ⁷ _{4 7 2 8 5 6 8 1 3 9 7} ³ 2 ¹ ₂ ⁰ ₁ ⁹ ₃ ³ ₃ ³ ₁ ³ ₃ ¹ ₃ ⁸ ₂ ⁴ ₂ ² ₁ ³ _{2 3} 5₅ 8₂ ₂ ^. ₂ ² ₂ ₁ ^. ₂ ² ₂ ₂ ^. ₂ ₂ ^. ₂ ₂ ^. ₂ ² ₁ ² ₂ ₁ ^. ₁ ² _{2 2 2 2 2} ¹ _/ ₁ ^. ₂ ^. ₁ ² ₁ ^. ₂ ^. ₂ ² ₁ ² ₁ ^. ₂ ^S _T P _I ² ₀- -^E ₉ ^. ₅ ^. ⁴ ₀- -^E ₆ ^. ₆ ^. ¹ ₂ ¹ ^. ₀ ^. ₄ ⁰ ₃ ₄ ⁹ ₂ ₃ ⁹ ₅ ⁸ ₂

₃ ^{2 0} ₈ 1 ₈ 6¹ ₃ 5 ₇ 3⁰ ₅ 9 _{3 3 9 8 5 3} ₄ ⁶ _{1 4 2 7 6 8 2 8} ⁹ ₀ ¹ ₅ ³ ₃ ⁵ ₈ ⁰ ₇ ³ ₅ ⁷ ₆ ² ₅ ⁸ ₅ ³ ₁ ₂ ⁹ ₃ ⁸ ₄ ⁷ ₅ ¹ ₅ ⁰ ₂ ³ ₂ ⁹ ₄ ⁰ ₁ ² ₅ ¹ ₁ ⁴ ₅ ⁵ ₁ ¹ ₆ ⁷ ₉ ⁰ ₃ ⁴ ₄ ⁴ ₂ ⁰ ₁ ₁- ₁- ₁ ⁷ ₅ ^{- 7} ₂- ₂ ^{- 8} _{2 2} - _{4 1 4} _{2 6 2 7 6 1} ⁵ ₂ -₆ ⁷ ₂ -₆ ⁵ ₂ ⁷ ₂ ⁵ ₂ ⁷ ₂ -_{6 2} -₅ ⁴ ₁ -₆ V V V V V V V V V V V V V V V V V V VB B B B B B B B B B B B B B B B B B B^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ⁰ ₄ ⁴ ₁ ⁰ ₄ ³ ₃ ³ ₆ ⁰ ₁ ^{3 8 4 5 2 7 7 8 0 7 5} ⁷ ₃ ⁷ _{3 0 2 7 0 2 9 8 0 0 0 4} ₀ ⁷ ₃ ⁴ ₄ ⁸ ₆ ¹ ₄ ⁶ ₉ ⁸ ₆ ⁵ ₁ ² ₀ ⁵ ₃ ^{0 1 3 1 0 4 4 8} ₂. ⁵ ₈ ^{6 5 3 2 7 1 2 6 7 4} ₀ 0 ₈ 5 ₇ 9 ₅ 0 ₁ 0 ₄ 2 ₉ 5 ₅ 9⁹ ₂ ¹ ₉ ₃ ⁶ ₈ ₁ ¹ ₀ ₃ ⁴ ₃ ₈ ⁵ ₆ ₁ ³ ₆ ₃ ³ ₃ ₃ ⁵ _{8 7 7 7 7 1 0 5 1 8 5} ³ 1 ⁷ ₉ ⁸ ₂ ⁸ ₂ ⁵ ₂ ⁰ ₄ ³ ₃ ² ₁ ⁰ ₃ ⁸ ₂ ⁴ ₁ ¹ _{3 3} 5₅ 8₂ ₁ ^. ₂ ₁ ^. ₂ ₂ ^. ₁ ² ₂ ² ₂ ² ₁ ² ₂ ² ₁ ₁ ^. ₂ ₁ ^. ₂ ² ₂ ₁ ^. _{2 2 2} ¹ _/ ₁ ^. ₂ ^. ₂ ^. ₁ ² ₁ ² ₁ ² ₂ ² ₂ ^S _T P _I ² ₀- -^E ₂ ^. ₉ ^. ³ ₀- -^E ₂ ^. ₈ ^. ² ₁ ^. ₁ ₀ - ^. - ₄ ⁰ ₃ ₄ ⁹ ₂ ₈ ⁴ ₇ ¹ ₄

_{1 6 5} ⁸ ^{0 3 0} _{2 7 8 9 0 6 4 1 6 9 3 2} _{9 7 4} ⁸ ₃ ⁸ ₂ ⁵ ₂ ⁶ ₈ ⁹ ₀ ⁴ ₄ ⁵ ₄ ² ₈ ¹ ₅ ^{4 3 6 3 5 7 8} ^{2 7 8 1} ₃ ³ ₂ ⁸ ₂ ⁹ _{9 9 4 7 8 0 7} _{3 3 1 2} ⁵ ₃ ⁶ ₃ ² ₃ ² ₃ ¹ ₃ ⁶ ₇ ³ ₇ ⁴ ₅ ⁰ ₃ ⁴ ₁ ⁹ ₁ ⁸ ₁ ₄ ^{- 4} _{4 4 4 4 4 1 1 4 1} _{2 2 6 2 1} ⁷ _{2 2} -₆ -₆ -₆ -₆ -_{6 2} -₅ -₅ -_{6 2} ⁹ ₁ -₅ V V V V V V V V V V V V V V V V V V VB B B B B B B B B B B B B B B B B B B^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ⁰ ₄ ⁸ _{0 2} ⁰ ₄ ¹ ₈ ³ ₉ ^{7 2 4 2 2 9 0 4 7 9 9} ⁶ _{4 5 2 5 3 5 2 7 0 5 3 4 4} ₄ ⁰ ₁ ⁰ ₂ ⁹ ₆ ⁰ ₈ ³ ₅ ⁷ ₁ ¹ ₈ ⁰ ₂ ^{9 6 1 5 3 4 9 7 5 6} ₂. ⁶ ₀ ⁰ ₅ ⁹ ₁ ³ ₇ ³ ₃ ⁶ ₇ ⁸ ₉ ⁹ ₁ ⁹ ₁ ₁ ³ ₄ ₄ ⁴ ₈ ₈ ⁹ ₄ ₁ ² ₀ 5 ₄ 2 ₁ 3 ₅ 0 ₆ 0 ₆ 0⁹ ₂ ¹ ₃ ⁰ ₃ ³ ₂ ³ ₁ ¹ _{9 0 1 4 1 8 8} ³ 3 ¹ ₁ ⁸ ₃ ¹ ₂ ³ ₂ ⁰ ₄ ⁰ ₂ ¹ ₂ ⁰ ₃ ⁸ ₂ ⁸ ₂ ⁰ ₄ ⁷ ₂ ⁴ ₃ ⁶ _{1 3} 5₅ 8₂ ₂ ^. ₂ ² ₂ ₂ ^. ₁ ² ₁ ² ₁ ² ₁ ² ₂ ² ₂ ₂ ^. ₂ ² ₂ ¹ 2 ^. ₂ ² _{2 2 /} ₁ ² ₁ ² ₁ ² ₂ ² ₁ ^. ₂ ^. ₂ ² ₂ ^S _T P _I ¹ ₀- -^E ₀ ^. ₁ ^. ³ ₀- -^E ₆ ^. ₁ ^. ₁ ⁰ ₃ ^. ₀ ^. ₄ ⁰ ₃ ₃ ⁹ ₂ ³ ₃ ⁴ ₈

6_{2 2 8} ^{7 3 0} ^{2 4 6 6 0 9 1} _{5 9 5 3 6 3 5 3} _{8 3 2 4 5 3 4} ⁴ ₆ ⁰ ₁ ² ₉ ¹ ₇ ² ₆ ⁴ ₀ ¹ ₇ ⁶ ₄ ⁴ ₄ ^{5 7 4} ^{1 7 9 1 0 2 6 0} ₄ ⁵ ₃ ⁰ ₁ ⁰ _{6 2 3} _{8 5 1 9 2 2 1 1} ⁹ ₄ ⁴ ₃ ³ ₄ ⁰ ₅ ⁰ ₁ ¹ ₄ ⁸ ₁ ⁸ ₂ ^{7 7} ₄- ₄- ₄ ^{- 3} ₄- ₄ ^{- 7} _{4 5} _{2 2 6 2 6 6 1 6 6 2 2 2} -₆ -₆ ⁷ ₂ ⁷ ₂ ⁷ _{2 2} ⁸ ₁ V V V V V V V V V V V V V V V V V V VB B B B B B B B B B B B B B B B B B B^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ⁶ _{0 0} ⁹ _{7 2} ³ ₄ ⁰ ₃ ³ ₇ ⁵ ₄ ⁵ ₄ ² _{2 2} ⁴ ₄ ^{9 7 4 8 7 6} ⁷ ₈ ⁹ ₈ ⁹ ₅ ⁹ ₁ ⁷ ₇ ⁷ ₈ ¹ _{8 1 2 4 3 6 7} ₃ ⁸ ₅ ⁸ ₅ ⁵ ₁ ⁹ ₁ ⁴ ₇ ² ₀ ² ₆ ⁴ ₆ ^{4 3 1 9} ₂. ² ₅ ¹ ₆ ³ ₉ ⁸ ₀ ^{4 3 1 9 9 2 8 7 1 7 1} ₇ 7 ₀ 1 ₅ 7 ₆ 5⁹ ₂ ⁵ ₃ ² ₅ ⁸ ₁ ⁰ ₉ ₈ ⁸ ₁ ₃ ¹ ₀ ₄ ⁵ ₅ ₃ ¹ ₅ ₃ ¹ ₈ ₃ ³ ₀ ₃ ⁰ _{3 4 2 4 3 9 1 8} ³ 8 ⁶ ₃ ¹ ₂ ⁰ ₃ ⁸ ₁ ⁶ ₃ ⁸ ₁ ⁴ ₃ ⁹ _{2 3} 5₅ 8₂ ₁ ^. ₂ ₁ ^. ₁ ² ₁ ² ₂ ₁ ^. ₂ ² ₂ ₂ ^. ₂ ₂ ^. ₂ ² ₁ ² ₂ ₂ ^. ₁ ₂ ^. _{2 2 2 1} ¹ _/ ₁ ² ₂ ^. ₁ ^. ₁ ^. ₂ ^. ₁ ² ₂ ² ₁ ^S _T P _I ² ₀- -^E ₈ ^. ₁ ^. ⁵ ₀- -^E ₇ ^. ₈ ^. ₁ ⁷ ₃ ^. ₀ ^. -

₃ ⁰ ₄ 0 ₄ 0 ₄ 0 ₄ 0 ₄ 0 ₄ 0 _{4 4 9 4 4 4 9} _{3 3 3 3 3 3 3} ⁰ ₃ ⁰ ₃ ⁹ ₂ ⁰ ₃ ⁰ ₃ ⁰ ₃ ⁸ ₂ ₂ ⁹ ₄ 9 ₄ 9 ₄ 9 ₄ 9 ₄ 9 ₄ 9 ₄ 9 ₄ 9 ₁ 7 ₄ 9 ₂ 9 _{4 1} _{2 2 2 2 2 2 2 2 2 2 2 2} ⁹ ₂ ⁸ ₂ _{3 0} ⁴ ₁ ^{5 7 0 7} ⁴ _{7 6 8 4 8 6 4} ₀ ⁰ ₂ ^{6 1 0 4 3 0 4 6 3 7 1 7} ^{9 6} ₁ 5 ₉ 7 _{5 8 6 0 6 3 7 3 0 6} _{5 4 5 1} ⁴ ₂ ⁷ ₁ ⁵ ₂ ⁵ ₁ ¹ ₁ ⁵ ₂ ⁸ ₆ ⁸ ₇ ⁴ ₉ ⁶ ₄ _{8 5} ⁶ ₂ ⁷ ₄ ^{1 3} ^{8 8 6 8 6} ₅ 3 ₇ 7 ₅ 0 ₄ 4 _{7 0} _{9 9 5 3 7 3 6 0 8} ⁸ ₂ ⁷ ₆ ^{7 3 4} ^{6 6 5 4 9 4 0 3 4 1 6} ₄ 8 ₃ 7 ₂ _{5 3 4 1 1 1 2 1 9 2 5 6 7} ⁴ ₄ ⁷ ₁- ₁- ₁- ₁- _{1 1 4 4 1 4 1} _{2 5 5 5 5} -₅ -₅ -_{6 2} -₆ -₅ -₆ -₅ ⁰ ₃ V V V V V V V V V V V V V VB B B B B B B B B B B B B B^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ^R _T ⁵ ₀ ² _{6 5} ⁷ ₉ ⁵ _{3 4} ⁹ ₈ ^{9 7 9 9 6} ² ₈ ⁹ ₈ ⁶ ₆ ⁸ ₅ ⁰ ₂ ¹ ₇ ³ ₈ ₂ ² _{4 1 2 1 6 3} ₀ ^{7 8 7 7 0 5} ₂. ⁶ ₁ ⁹ ₂ ⁷ ₄ ⁷ ₉ ¹ ₆ ⁸ ₂ ¹ ₀ ¹ _{2 4 0 2 2 1} ₄ ⁶ ₁ ⁹ ₇ ^{3 3 9 6 9} ₂ ⁹ ₂ ⁴ ₂ ² ₁ ⁰ ₂ ⁴ ₁ ⁴ ₄ ² ₃ ¹ ₂ ⁵ ₃ ⁸ _{8 7 1 4} ³ 1 ⁸ ₂ ⁷ ₆ ⁴ ₅ ⁹ _{2 3} 5₅ 8₂ ₂ ^. _{2 2 2 1 1 2 2} ¹ _/ ₁ ² ₂ ² ₁ ^. ₂ ² ₂ ² ₂ ^. ₂ ^. ₁ ² ₂ ^. ₁ ² ₂ ^. ₁ ^. ₂ ^. ₁ ^S _T P _I

^a f _c ^. _r 0 0⁷ ₁ 0 0⁷ ₁ 0 0³ 0 0⁴ 0 0⁴ 0 0⁴ 0 0⁶ 1 0³ ^_ _s ^e _c ^r ₀ ^{. 1} ₀ ^{. 1} ₀ ^. ₄ 4³ ₀ ^. ₇ 5¹ ₀ ^. ₇ 5¹ ₀ ^. _{7 0 1 0 0} _i n _{d 0 5 3 0 5 3 0 6 0 0 6 2 0 6 2 0} ⁵ ₆ ¹ ₂ ^. ₀ ⁴ ₈ ⁴ ₁ ^. ₀ ³ ₂ ⁷ ₆cn ^l ^a _a ⁶ _c ^v ₃ ⁵ ₈ ⁶ ₀ ⁹ ₀ ⁶ ₀ ⁶ ₀ ⁶ ₀ ⁹ ₆ ⁵ ₀ ⁸ ₂ ⁰ ₉ ⁶ ₀ ⁷ ₆ _s ^{7 6 2} - ⁸ - ⁶ - ³ - ^{0 6 7 5} ^_ _p _i .^r _{e e 6} u ^. _{0 2 2 8 0 5 0 5} - ⁴ _{8 0} ₁ - _E ^. ₃ ^E ₈ ^. ₁ ^E ₂ ^. ₈ ^E ₅ ^. ₁ ^E ₄ ^. ₇ - _E ^. ₁ ^E ₁ ^. ₁ - _E ^o _l ^. _r a ⁵ ^h ₅ ^{9 8 4 2 9 9 1} ⁴ ₄ 7 ₇ 0 ₅ 1 ₈ 5 ₅ 5 ₅ 7 ₁ ^e _{c C} d_{l 2} 9 ₅ 5 _{6 9 1 6 7} ⁰ ₃n ^a _c ^o _{9 7} ⁰ ₄ ² ₂ ⁶ ₀ ⁷ ₂ ⁷ ₉ ⁹ ₇ ^_ _F _s ^{2 e} _g ² ₃ ⁷ n ^. ₁ ² ₃ ⁵ ₁ ³ ₂ ¹ ₃ ¹ ₃ ⁴ ₁ _{i g 0 9} ^. _{0 7} ^. _{0 1} ^. _{0 1} ^. _{0 3} ^. _{0 4} ^. _{0 2} ^. _{0 9} ^e _c e 5 1⁹ ₆ 1 8 9 1 2 5 0 3 8 n M e _{0 4 6 8} ^{3 4 2} s ⁵ _{9 4 1 6} ^a _c ³ ^_ ₀ ^{. 7} ₉ ³ ₉ ⁷ ₅ ² ₄ ⁴ ₂ ⁹ ₃ _s ^a _{i b} . . _r ⁿ _a ⁷ ₇ ₁ ⁶ ₄ ₈ ¹ ₅ ₄ ¹ . 2 ¹ ₁ ₂ ⁵ . 7 ⁰ ₆ ₉ ⁷ . 9 ⁰ ₈ ₁ ¹ . 7 ⁹ ₁ ⁴ . 6 ¹ ₇ . ₈ ₂ ⁴ ₆ ⁹ ₈ ⁸ _{5 2}. 9₂t ³n ^{e d} _i 4 _c ^o _r ¹ 2 ₃ 5 2 ⁰ ₁ ⁹ 0 8 ⁴9 3 ⁴ ₄ ⁰ 5 6 ¹ 8 3 ⁵ ₉ ⁵ 0 4 ¹ 8 1 ⁰ ₆ ⁷ 6 6 ³6 3 ⁹ ₁ ⁰ 1 7 58 58 ₅ ₁ ¹ ₄ ⁸ ₃ ⁰ ₃ ¹ ₃ ⁵ ₉ ⁵ ₄ ⁴ ₁ ⁵ ₂ ⁵ ₅ ⁸ ₂ 1_/ _{d _ c 2 2 2 2 1 2 2 2 2 2 2 2 2 . 2 2 . 2} ^S _T P _I ₉ ⁹ ₅ ¹ ₉ ⁸ ₅ ⁵ ₆ ⁴ ₁ ⁶ ₇ ⁶ ₈ 2 ⁴ ₅ ⁶ _{8 9} 2 ¹ ₂ ³ ₀ ³ ₀ ^. ₀ ⁵ ₄ -_E ⁴ ₈ ⁸ ₀ ⁹ ₀-^E ₉ ⁶ ₀ ⁹ ₁ ⁴

₁ ⁰ ₅ ⁹ ₃ ⁹ ₆ ⁰ ₇ ⁷ ₀ ^{8 7 2} ^{1 0 1 0 0 3} ₇ 0 _{1 4} _{0 0 0 0 0 0 0} ^{1 1} ^{. . . . . .} - ^. _{0 0} _{0 8 - 0 8 - 0 1 - 0 6 - 0 9 - 0 0 5 -} ^. _{0 3 -} ^. ₀ ³ ₀ 1 0³ ₀ 1 0³ ₀ 1 0³ ₀ 1 0³ 1 0³ 1 0⁴ 1 0⁴ 1 0⁸ ^{3 7} ₀ ^{. 3 7} ₀ ^{. 3 7} ₀ ^{. 3 7} ₀ ^. ₀ 3⁷ ₀ ^. ₀ 3⁷ ₀ ^. ₄ 6⁵ ₀ ^. ₄ 6⁵ _{0 6} _{2 6 0 2 6 0 2 6 0 2 6 0 2 6 0 2 6 0 4 7 0 4 7} ^. ₀ ¹ ₈ ⁵ ₄ ⁶ ₀ ² ₇ ^{8 3 8 2 4 9 8}- ^{8 5} 7_{0 3 5} - ⁰ ₁ ⁵ ₀ ^{1 5} 8_{0 0} - ^{1 5} 4_{0 7} - ^{2 5} _{0 4} - ^{3 6} _{0 0} - ^{4 6} _{0 7} - ^{6 5} ₀-^{E . E . -} _{0 7 8 0} _{6 1 2 1 E} ^. ₁ ^E ₄ ^. ₁ ^E ₆ ^. ₁ ^E ₃ ^. ₈ ^E ₄ ^. ₅ ^E ₃ ^. ₃ ^E ₄ 9₅ ^{1 1 8 3 4 7 1} ⁹ _{0 9 6 1 3 9 7} ₉ ⁹ ₀ ^{2 3 7 1 4 5} ^{8 9} ₀ 7 _{7 4 0 6 9} _{2 2 2} ⁵ ₃ ^{6 2 2 5} ^{6 5 9 7} ₉ 0 _{6 7 9} _{1 2 2 1 3} ⁷ ₁ ⁵ ₁ ⁹ ₁ ^. _{0 7} ^. ₀ ^. _{0 6} ^. _{0 2} ^. _{0 2} ^. _{0 6} ^. ₁ _{0 6} ^. _{0 1} 2 0 4 5 ² ₂ ⁹ ₀ 5 8 8 2 0 6 ⁸ ₁ ² ₂ ^{4 5} ₆ 3^{8 4} ₆ ^{5 8} ₀ ⁸ ₂ _{2 8} ³ . ₂. ₄ ⁸ _{4 2 4} ⁹ _{2 8} ⁸ _{6 7} ^. _{9 5} ₅ ⁹ _{2 7} ⁰ ₁ ³ ₇ ₉ ^. ₉ ¹ . 6 ² ₄ ⁸ . 4 ⁹ ₁ ⁹ . 0 ⁶ ₄ ⁷ . 6 ⁷ ₇ ⁹ ₈ ² ₂ ⁰ _{3 2}. 9₂ 38 27 1 3 85 20 ₃ 5^{4 0 8 3 8 3 7 7 4 6} 97 84 09 87 ₅ _{1 3 1 0 7 3 9 9 3 0} ⁹ ₄ ⁷ ₃ ⁷ ₂ ¹ ₇ ⁸ ₃ ⁷ ₄ ³ ₄ ⁵ ₁ ⁸ ₆ ⁵ ₇ ⁰ ₃ ⁵ ₁ ⁸ ₀ ¹ ₃ ³ ₅ ⁰ ₄ ⁸ ₂ 1_/ _{. 2 2 . 2 2 . 2 2 . 2 2 . 2 2 . 2 1 2 1 2 2 . 2} ^S _T P _I

⁴ ₀ ⁷ ₀ ⁰ ₄ ² ₈ ⁷ ₆ ⁷ ₇ ⁵ ₀ ⁷ ^{3 4 1 0 3 1 4} ₆ _{0 0 0 0 0 0 0} ³ ^{. . .} _{0 5 -} ^. _{0 4 -} ^. _{0 8 -} ^. _{0 6 -} ^. ₀ _{- 0 2 - 0 3 - 0 7 -} ^. _{0 1} 2 0₀ ⁷ ^. ₄ 2 0⁶ 0 ⁰ ₀ ⁶ ₀ ₄ ^. ₂ 2 0⁵ 0 ⁹ ₀ ⁴ ₀ ₁ ^. ₄ 3 0⁶ 0 ⁶ ₁ ⁸ ₀ ₁ ^. ₃ 3 0⁹ 0 ⁵ ₀ _{2 7} ^. ₅ 3 0⁹ 0 ² ₆ ⁶ ₀ ₃ ^. ₅ 3 0⁷ 0 ² ₆ ⁶ ₀ ₃ ^. ₄ 4 0⁹ 0 ³ ₈ ⁹ ₀ ₇ ^. ₉ ₀ ⁴ ₅ ⁵ ₁ 0₈ ^{5 2} ₁ ^{6 9} ₁ ^{5 3} ₈ ^{5 1} ₃ ^{5 9} ₈ ^{5 9} ₆ ^{6 1} _{0 1} 7_{7 0}- ⁷ ₀- ⁸ ₀- ⁵ ₀- _{0 0 0} ⁷ ^. ₁ ^E ₀ ₃ ^. ₄ ^E ₅ ₃ ^. ₂ ^E ₁ ⁸ 3 ^. ₆ ^E ₄ - ⁶ 7 ^. ₇ ^E ₃ - ³ 7 ^. ₇ ^E ₆ - ⁰ 9 ^. ₄ ^E ₀ ₈ ^. ₄ ₀ ⁰ ₀ 1₂ ³ ⁹ ₇ ⁵ ₈ ⁷ ₂ ⁶ ₂ ⁶ ₀ ³ ₉ ⁵ ₈ ₀ ³ ⁶ ₉ ² ₂ ⁸ ₂ ³ ₆ ⁷ ₉ ² ₇ ⁴ ₁ ₉ ⁷ ⁹ ₉ ⁴ ₀ ⁴ ₆ ³ ₇ ² ₁ ⁶ ₀ ⁷ ₄ ₂ ⁰ ^. ₄ ⁰ ₂ ⁶ ₁ ⁸ ₂ ⁵ ₃ ⁸ ₃ ⁸ ₂ _{0 7} ^. _{0 8} ^. _{0 1} ^. _{0 5} ^. _{0 8} ^. _{0 9} ^. _{0 5} ^. _{0 8} 2 0₉ ⁹ ₂ ⁰ 1 3 ⁸ 2 5 6₃ ⁸ ₇ ₇ ^{3 7} ₃ ² _{2 8} 9⁷ ₄ ⁴ ₉ . 9 ₆ ⁶ ₀ _{2 7} ^. _{1 3} ⁹ ₆. ₁ ⁰ _{8 1} . ₈ ⁸ _{4 2} ¹ ₄. ₈ ⁹ ₄. ₄ ₁ ⁶ ₃ ^. ₇ ⁴ ₈ ⁴ ₂ ⁴ ₃ ¹ ₃ ⁷ ₉ ⁷ ₃ ⁶ ₁ ^. ₄ ⁵ ₃ ⁰ ₁ ⁸ ₀ ⁷ ₃ ³ _{8 2}. 9₂ 3 99 77 30 08 1 ₃ 5^{8 5 9 7 2 4 2 2} 0 34 42 1 1 ₅ _{1 3 7 6 3 1 2 5 4 2} ⁰ ₉ ⁶ ₅ ³ ₂ ² _{9 2} ⁶ ₃ ⁷ ₇ ⁴ ₄ ¹ ₂ ⁰ ₁ ⁹ ₈ ¹ ₂ ⁸ ₉ ² ₅ ⁸ ₂ 1_/ _{1 2 1 . 1 1 2 2 2 2 . 2 2 . 2 1 . 2 2 2} ^S _T P _I ₆ ⁷ ₁ ¹ ₂ ¹ ₉ ⁵ ₉ ³ ₂ ⁵ ₉ ⁹ ₈ 9 ¹ ₂ ⁴ _{2 1} ⁵ ₆ -_E ⁴ ₁ ⁰ ₂ ² ₂-^E ₇ ⁰ ₁ ⁸ ₉ ⁶

₆ ³ ₂ ⁰ ₉ ⁶ ₈ ⁷ ₄ ^{5 4 7 3} ^{3 0 0 0 0} _{6 6 3 7} _{0 0 0 0 0} ^{0 1 1 0} ^{. .} - ^. _{0 3} ^. _{0 3 -} ^. _{0 0 0 0} _{0 4 - 0 8 0 2 -} ^. _{0 7 -} ^. _{0 8 -} ^. _{0 1 -} ^. _{0 1} ⁰ ₆ 7 0⁴ ^{3 4} ₀ ^. ₆ 0 1⁶ ⁷ ₆ 2 1⁴ ₈ 7 1⁶ ₇ 7 1⁰ ₀ 7 1⁰ ₀ 7 1⁰ ₀ 8 1² ₅ ₁ ¹ ₀ ₁ ^. ₀ ⁰ ₈ ³ ₀ ₄ ^. ₀ ⁷ _{0 0 0 0 0} _{0 8 0 7} ² ₀ ^. ₀ ¹ ₇ ⁵ ₉ ^. ₀ ⁵ ₉ ¹ ₇ ^. ₀ ⁵ ₉ ¹ ₇ ^. ₀ ⁵ ₉ ¹ ₇ ^. ₀ ² _{1 2} ⁵ ₀ ¹ ₀ ¹ ⁷ _{0 5} ³ ⁷ _{0 8} ⁴ ⁶ _{0 4} ⁰ ₂ ⁵ ₀ ⁰ ₈ ⁵ ₀ ⁸ ₇ ⁵ ₀ ⁵ _{0 6}- ⁰ ₅ ⁰ ₈ ⁰ ₆ ^{0 3} ₁ ⁸ - ⁵ - ² - ^{0 9} ^E ₀ ^{. 1} ₀ ^{. 1} ₀ ^{. 1} ₀ ^{. 7} _{6 6 9 0 5} _{7 0 6 0 7 0 1 0 4} ^. ₈ ^E ₂ ^. ₈ ^E ₈ ^. ₇ ^E ₇ ^. ₀ ⁴ ₁ ₇ ⁹ ₉ ⁴ ₇ ⁴ ₅ ^{5 9 3 7} ^{2 3 0 2} ₇ 2 _{2 5 3} _{4 9 2 2 2} ² ₁ ^{7 2} ^{4 3 1 6 9 7} ₆ 8 ₅ ₅ 7 _{4 1 8 4 9 2} ⁷ ₈ ₀ ^{7 3 3 9 6 1 7} ^. ₁ ^. _{1 1 1 3 2 1} _{1 0 0 6 -} ^. _{0 4} ^. _{0 4} ^. _{0 3} ^. ₀ ^. _{0 6} ^. _{0 4} 5₈ 8 8 3 3 7 0 8 7⁰ ₀ 3 1 8_{3 1} . ³ _{4 3} ⁴ ₀ ₉. ₅ ⁰ ₁ _{6 4} ¹ ₄ ⁷ 5₃ ² ₉ ³ _{9 6} ² 2 ⁸ ₈ ⁶ ₂ _{7 1} ¹ _{2 7} ₄ ⁹ ₁ ⁴ ₆ ¹ ₂ ⁷ . 8 ¹ ₄ ³ . 8 ⁵ ₄ ⁶ . 7 ⁴ ₂ ¹ ₃ . . 4 ^. ₃ ¹ ₁ ⁸ ₃ ⁶ ₂ ⁴ ₁ ⁹ _{4 2}. 9₂ 31 0₂ 97 1 4 ₃ 5^{8 2 0 4 0 8 7} 25 57 1 2 87 49 ₅ _{9 5 2 5 2 5 8} ⁸ ₂ ⁴ ₂ ⁰ ₀ ⁴ ₃ ² _{4 5} ³ ₁ ² ₇ ⁶ ₆ ⁹ ₂ ⁸ ₆ ⁵ ₀ ¹ ₃ ³ ₅ ⁰ ₄ ¹ ₂ ⁵ ₅ ¹ ₀ ⁸ ₂ 1_/ _{2 2 . 2 1 2 2 . 2 2 2 1 . 2 1 . 2 1 . 2 2 2} ^S _T P _I ₂ ⁵ ₁ ² ₈ ⁶ ₀ ⁹ ₃ ⁴ ₈ ¹ ₄ ⁴ ₃ 3 ⁰ ₆ ⁴ _{0 9} 5 ⁰ ₅ ¹ ₁ ³ ₀ ^. ₀ ⁷ ₈ ⁷ ₄ ¹ ₂ ⁶ ₅ ₆ ⁶ ₉ ⁷ ₉ ¹ ₂ ² ₅ ⁹

₄ ³ ₅ ³ ₇ ⁹ ₉ ³ ₄ ³ ₅ ^{3 4 4} ^{0 0 0 0 1 1} ₄ 0 _{7 8} _{0 0 0 0 0 0 0} ^{0 0} ^{. . . . . .} - ^. _{0 0} _{0 9 - 0 3 - 0 8 - 0 6 - 0 2 - 0 9 0 4 -} ^. _{0 3 -} ^. _{0 7} ⁹ ₃ 9 1⁴ ₂ 9 1⁵ ₉ 0 2⁸ 0 2⁹ 2 2⁴ 2 2⁹ 2 2⁸ 2 2⁸ ^{1 9} ₀ ^{. 3 4} ₀ ^{. 3 1} ₀ ^. ₇ 1¹ ₀ ^. ₅ 5⁴ ₀ ^. ₁ 3⁶ ₀ ^. ₁ 8⁵ ₀ ^. ₆ 9¹ _{0 6} _{7 2 0 0 1 0 5 1 0 0 8 0 0 3 0 1 2 0 1 6 0 4 6} ^. ₀ ⁹ ₄ ¹ ₆ ₂ ^{6 5 5 8 1 6 4 4} ⁴ _{0 2 0 3 0 5 3} ₈ ^{0 9} ₃ ^{0 3 0 5 0 5} _{0 0 5 0 5 0 4 0 9} - ^{0 0 0 7 0 9 0 7} ⁴ ₀ ^{. 0} ₀ ^. _{2 0 3 8 0 8 0 8 0 9 0 9} _{3 0 0 0} ³ ₁ ^. ₀ ³ ₆ ^. ₇ ^E ₇ ^. ₀ ³ ₃ ^. ₀ ⁵ ₆ ^. ₀ ⁷ ₆ ^. ₀ ¹ ₉ 6₄ ^{5 1 0 3 3 0} ³ _{7 2 1 8 7 0} ⁰ ₅ ₃ ⁹ ₆ ^{3 0 9 6 2 9} ^{8 7} ₄ 9 _{9 2 2 8 5} _{4 8 1} ³ ₈ ^{0 8 2 7} ^{0 7 5 6} ₉ 9 _{5 0 1} _{1 1 2 3 2} ³ ₁ ⁸ ₁ ⁴ ₂ ^. _{0 7} ^. _{0 1} ^. _{0 4} ^. _{0 2} ^. _{0 7} ^. _{0 4} ^. ₂ _{0 7} ^. _{0 9} 9 8 1 9 ² ₂ ⁵ ₆ ⁰ ₉ ² ₄ 0 5 1 5 ² ₂ ⁴ ₃ 1⁸ ₀ ⁰ ₅ ⁸ ₇ ⁵ ₉ _{5 7 1} ¹ _{2 7} ⁵ ₆ ⁹ ₉ ³ ₉ ⁶ _{0 8} ¹ _{2 7} ⁷ ⁴ . ₁ ⁸ ₇ ⁵ . 2 ⁴ ₁ ⁹ ₀ ₄ ^. ₅ ⁰ ₁ ₄ ^. ₅ . . _{6 4} ₃ ³ ₅ ^. ₅ ⁰ ₃ ⁸ ₃ ⁰ ₆ ⁴ ₁ ⁹ ₄ ^. ₅ ⁶ _{0 2}. 9₂ 34 87 86 ₃ 5^{1 6 1 3 8 6} 71 93 69 68 49 75 ₅ _{3 7 3 5 2 3} ² ₇ ⁶ ₄ ⁰ ₄ ⁸ ₅ ² ₂ ⁸ ₁ ⁰ ₁ ⁸ ₃ ² ₅ ⁸ _{1 0} ¹ ₁ ⁸ _{1 1} ¹ ₂ ⁵ ₅ ¹ ₀ ⁸ ₂ ³ ₄ ⁴ ₇ ⁸ ₂ 1_/ _{2 2 2 2 . 2 2 . 2 1 . 1 1 . 2 2 . 2 1 2 1 2} ^S _T P _I ₄ ⁴ ₃ ¹ ₂ ¹ ₃ ² ₁ ₁ ⁹ ₁ ⁵ ₃ ² ₅ ¹ ₃ ^. ₀ ⁴ ₁ ⁶ ₇ ⁶ ₈ 6 ⁸ ₆ ³ _{5 3} 0 ⁵ ₃ ¹ ₅ ⁵ ₀ ^. ₀ ¹ ₇ ⁹ ₅ ³ ₉ ⁸ ₉ ₉ ⁰ ₉ ⁹ ₇ ⁷ ₂ ⁵ ₅ ¹

₇ ⁵ ₈ ⁴ ₂ ⁶ ₅ ⁶ ₅ ⁸ ₄ ^{6 5 4} ^{0 0 0 0 0 0} _{7 6 2} _{0 0 0 0 0 0} ^{0 0 0} ^{. . . .} _{0 6 -} ^. _{0 4} ^. _{0 0 0} _{0 1 0 7 - 0 5 0 7 -} ^. _{0 5 -} ^. _{0 9 -} ^. _{0 2} ⁴ ₆ 3 2⁴ 3 2⁴ 6 2³ 6 2² 7 2⁵ 8⁶ 0⁴ 0² ^{0 7} ₀ ^. ₉ 7⁵ ₀ ^. ₉ 7⁵ ₀ ^. ₇ 4⁷ ₀ ^. ₅ 8⁴ ₀ ^. ₈ ² ^{2 3} _{0 2} ³ _{0 4} ³ _{0 7} _{9 4 0 7 2 0 7 2 0 6 1 0 7 1 0 4 4} ^. ₀ ¹ ₂ ⁴ ₉ ^. ₀ ⁴ ₀ ⁷ ₂ ^. ₀ ⁷ ₇ ⁷ ₈ 5_{0 4} ⁶ _{0 8} ⁸ _{0 9} ⁷ _{0 3} ⁸ _{0 9} ⁸ ₂ ⁰ ₈ ² ₃ ₆ ^{0 7 0 7 0 0 0 1 0 4} ₀ 0² ₁ 0¹ ₀ 0⁹ ⁰ ₀ ^. _{5 0 5 0 9 0 5 0 9 0 4 0 4 0 2} _{3 0} ⁸ ₉ ^. ₀ ⁴ ₁ ^. ₀ ⁹ ₉ ^. ₀ ⁵ ₄ ^. ₀ ⁷ ₁ ^. ₀ ⁷ ₉ ^. ₀ ⁵ ₁ ^. ₀ ³ ₂ 4₅ ⁴ ₃ ⁴ ₁ ² ₆ ⁸ ₃ ¹ ₀ ^{6 3} ⁶ ₉ ^{1 4 2 7 2} ₅ 0 ₈ 8¹ _{5 0 7 7 8 3 7} ₂ ⁴ ₉ ^{6 7 0 6 5 2} ^{2 7} ₁ 7 _{1 2 0 7 1} _{1 0 1} ² ₁ ⁰ ₂ ^{3 9 3} ^{. . .} _{0 6} ^. _{0 -} ^. ₁ _{0 8} ^. _{1 4} _{2 - 0 5 0 5 - 0 2} ^. ₀ ^. _{0 4} 8 9 8 5 4 4 ⁵ ₇ ⁷ ₇ ⁶ ₃ ¹ ₉ 6 ^{2 9} ² ₂ ⁸ _{1 7} 6 ₇ ₀ ³ ₆ ₅ ⁰ _{2 4} ⁵ _{8 3} ⁶ _{6 2} ⁹ _{8 6} ⁸ ₈ ⁵ ₉ ^{2 7} ⁰ . ₄ ¹ ₅ ⁷ . 6 ² ₇ ⁷ . 5 ² . ₃ . _{3 6 8 4 8} ₁ ⁶ ₁ ² ₄ ⁶ ₃ ^. ₇ ⁵ ₄ ⁵ ₄ ⁵ ₈ ^. ₇ ⁰ ₁ ^. ₂ ⁰ _{9 2}. 9₂ 37 1 4 4 ₃ 5^{0 4 8 4 0} 0 56 27 84 1 3 66 45 ₅ _{6 1 2 2 0} ⁶ ₃ ⁷ ₇ ⁹ ₃ ⁶ ₃ ⁸ ₀ ¹ ₈ ⁸ ₁ ³ ₀ ⁸ ₇ ³ ₃ ⁷ ₁ ¹ ₄ ³ ₃ ⁷ ₉ ⁷ ₉ ³ ₃ ⁹ ₁ ⁰ ₁ ⁹ ₂ ¹ ₆ ⁶ ₃ ⁸ ₂ 1_/ _{. 2 1 2 1 2 2 . 2 1 2 1 2 1 2 1 2 1 . 2} ^S _T P _I ₁ ⁶ ₅ ⁷ ₃ ⁸ ₁ ⁶ ₅ ⁵ ₈ ⁴ ₆ ⁹ ₉ 9 ⁶ ₇ ⁶ _{1 4} 3 ⁶ ₇ ⁵ ₁ ⁸ ₀ ^. ₀ ² ₂ ⁷ ₁ ⁵ ₉ ⁵ ₉ ₁ ⁹ ₉ ¹ ₄ ² ₄ ¹ ₈ ⁶

₆ ² ₃ ³ ₈ ⁵ ₈ ⁴ ₄ ⁴ ₂ ^{3 9 4} ^{0 0 1 0 0 1} ₈ 0 _{1 8} _{0 0 0 0 0 0 0} ^{4 0} ^{. . . . . .} - ^. _{0 0} _{0 8 - 0 9 - 0 6 - 0 2 - 0 8 - 0 9 0 7 -} ^. _{0 7} ^. _{0 9} ² ₀ 1 3² ₀ 7 3³ ₂ 7 3⁰ ₁ 2 4⁴ 2 4⁴ 2 4⁴ 4 4⁸ 4 4² ^{9 1} ₀ ^{. 9 1} ₀ ^{. 7 2} ₀ ^{. 5 6} ₀ ^. ₀ 7⁴ ₀ ^. ₀ 7⁴ ₀ ^. ₀ 7⁴ ₀ ^. ₂ 1² ₀ ^. ₈ _{1 5 0 1 5 0 4 8 0 7 9 0 0 1 0 0 1 0 0 1 0 0 8 0} ³ ₅ ³ ₀ ₈ ^{1 3 3 7 6 6 4 0} ⁰ _{1 4} 0 _{1 8} 8 _{0 8 1 1 8 1 1 0 9 2 9} ₇ ⁰ ₇ ⁰ ₇ ^{0 3} ₅ ^{0 9} ₄ ^{0 8 0 7 0 5 0 4} ⁸ ₀ ^{. 0} ₀ ^{. 5} ₀ ^. _{0 0 4 0 3 0 7 0 1} _{8 0 8 0 3 0} ⁹ ₁ ^. ₀ ⁶ ₃ ^. ₀ ⁹ ₈ ^. ₀ ³ ₇ ^. ₀ ⁵ ₂ ^. ₀ ⁶ ₈ 4₅ ^{0 4 4 3 5 7 7} ⁰ ₄ 7 _{2 0 2 7 3 2} _{4 3} ¹ ₉ ⁶ ₄ ^{2 7 5 5} ^{5 5 5 8} ₄ 7 _{7 2 2} _{3 3 4 2 0} ⁹ ₈ ^{2 3} ^{7 1 3 6 5 5} ₄ 7 ₇ _{0 3 3 1} ⁰ ^. _{1 1 2 1} _{8 0 8} ^. _{0 2} ^. _{0 4} ^. _{0 7} ^. _{0 6} ^. _{0 1} ^. _{0 9 -} ^. _{0 3} 5₀ ² ₃ ³ ₆ 6 5 8 4 5 5 3₂ ¹ ₇ ³ _{0 1} ⁴ ⁶ ₃ ⁶ ⁰ ₄ ⁴ ⁴ ₅ ² ₇ ₈ ^. ¹ _{0 1} ⁰ ₆ ⁴ _{8 7 3 5 3 8} ⁵ _{4 3} ³ _{9 6} ₅ ⁰ ₅ ⁵ ₄ ₀ ^. ₂ ⁶ ₃ ₇ ^. ₃ ⁴ . 5 ⁸ ₁ ⁷ . 7 ⁶ . . . 1 ⁸ ₅ ⁷ ₁ ² ₂ ⁴ ₂ ³ ₉ ⁷ ₅ ⁵ _{3 2}. 9₂ 32 53 01 44 ₃ 5^{9 2 1 5 6 5 3 3} 05 24 72 1 0 1 4 ₅ _{1 4 2 5 9 3 1 7} ⁸ ₁ ⁶ ₁ ⁷ ₂ ⁷ ₁ ⁸ ₇ ⁰ ₃ ⁹ ₃ ⁴ ₅ ⁴ ₂ ⁰ ₄ ⁴ ₁ ⁷ ₇ ³ ₂ ² _{9 2} ⁸ ₂ ⁴ ₂ ⁰ ₀ ⁸ ₂ 1_/ _{2 2 2 2 . 2 1 . 2 2 . 2 2 . 2 2 . 2 1 . 2 2 2} ^S _T P _I ₆ ⁵ ₃ ³ ₁ ² ₂ ² ₁ ⁴ ₇ ⁶ ₀ ¹ ₃ 5 ⁸ ₅ ⁸ _{1 7} 1 ⁵ ₀ ⁵ ₂ ⁸ ₀ ^. _{0 8} -^E ₅ ³ ₁ ⁵ ₁-^E ₃ ⁷ ₇ ⁰ ₅ ²

₃ ⁷ ₇ ⁶ ₅ ⁰ ₅ ¹ ₈ ¹ ₀ ¹ ₃ ¹ ₉ ⁷ ³ ₀ ^{0 0 0 0 1 0 0} ₇ 2 ^. ₀ _{0 9 -} ^. ₀ _{0 9} ^. ₀ _{0 4 -} ^. ₀ _{0 1} ^. ₀ _{0 2} ^. ₀ _{0 4 -} ^. ₀ _{0 9} ^. ₀ _{0 7 -} ^. _{0 2} ² ₈ 4 4² ₈ 4 4⁷ ₄ 4 4⁶ ₆ 6 4⁴ ₈ 1 5⁶ ₁ 6 5⁰ ₅ 6 5⁰ ₅ 7 5⁷ ^{3 3} ₀ ^. ₀ ³ ₅ ³ ₀ ₀ ^. ₀ ⁹ ₅ ⁹ ₀ ₉ ^. _{0 0 0 0 0 5} _{5 0 0} ² ₆ ¹ ₈ ^. ₀ ⁶ ₆ ⁷ ₆ ^. ₀ ⁶ ₀ ² ₉ ^. ₀ ⁵ ₆ ² ₁ ^. ₀ ⁵ ₆ ² ₁ ^. ₀ ⁴ _{8 8} ₈ ¹ ₆ ⁹ ₃ ⁰ ₇ ¹ ₂ ^{8 1 2 6} ⁰ ₂ ⁸ ₁ ⁵ ₂ ⁵ ₂ ⁵ _{1 7} 5 _{2 8 2 4 0 9} ₈ ⁰ ₄ ⁰ ₃ ⁰ ₄ ⁰ ₉ ⁰ ₂ ^{0 8} ₅ ^{0 8} ₆ ^{0 1} ⁴ ₀ ^{. 7} ₀ ^{. 4} ₀ ^{. 9} ₀ ^{. 8} _{0 0 0 0 5} _{3 0 0 0 2 0 0 0 8} ^. ₀ ⁷ ₂ ^. ₀ ⁹ ₆ ^. ₀ ² ₅ ^. ₀ ⁹ ₂ 2₇ ² ₁ ⁵ ₃ ⁷ ₀ ^{1 5 9 4} ^{0 8 4 1} ₈ 0 _{8 6 1} _{4 4 6 1 0} ⁴ ₅ ^{5 1} ^{3 2 7 8 1 5} ₉ 7 ₀ ₅ 5 _{3 9 6 9 0 6} ⁸ ₈ ₁ ^{3 2 0 0 7 2 5} ^. ₁ ^. ₁ ^. _{1 1 0 1 2} _{4 0 8 - 0 7 0 -} ^. _{0 8 -} ^. _{0 8} ^. _{0 4 -} ^. _{0 3} ^. _{0 4} 4 3 7 9 ³ 5 4 3 5 ⁷ ₉ ⁴ ₁ ⁰ ₂ ⁸ ₉ 8 0⁶ ₉ 5^{9 6 6 2} _{7 2} ³ ₁ _{2 8 9 9 4 8 8 7 1} ⁰ _{1 4} ^. _{8 9} ⁰ _{9 9} ² ¹ . ₂ ⁷ ₁ ³ . 7 ⁸ ₁ ⁴ . 1 ⁵ ₁ ⁹ . 6 ³ ₉ ⁰ . . _{0 2} ₉ ⁶ ₈ ¹ ₀ ⁶ ₄ ⁴ ₂ ² ₂ ¹ ₃ ^. ₉ ⁵ _{1 2}. 9₂ 38 56 84 ₃ 5^{5 5 2 3 6 3} 25 1 7 03 23 61 01 ₅ _{9 4 3 1 2 3} ⁷ ₁ ¹ ₄ ⁹ ₁ ³ ₆ ² ₉ ⁷ ₂ ⁵ ₀ ⁷ ₃ ⁸ ₂ ⁰ _{5 7} ⁷ ₂ ⁶ ₁ ¹ ₁ ³ ₃ ⁴ ₇ ³ ₆ ² ₁ ⁵ ₀ ⁷ ₈ ⁸ ₂ 1_/ _{2 2 2 2 . 2 2 . 2 2 . 2 1 2 1 2 1 2 1 . 2} ^S _T P _I ₂ ⁰ ₀ ⁵ ₇ ¹ ₄ ⁴ ₀ ⁴ ₄ ³ ₃ ⁴ ₈ 8 ¹ ₁ ⁸ _{8 1} ⁹ ₉ ⁹ ₆ ⁹ _{0 5} ₆ ¹ ₉ ⁵ ₃

⁵ ₆ ⁷ ₂ ³ ₃ ^{5 4 9 9 0} ^{0 1 5} ₀ 1 _{0 2 0 5} _{0 0 0 0} ¹ ₀ ^{1 1 0} ^{. . . . .} ₀ ^. _{0 0} _{7 - 0 6 - 0 2 - 0 3 0 8 - 0 6 - 0 7 -} ^. _{0 4 -} ^. _{0 6} ⁸ ₆ 8 5⁸ ₆ 8 5⁸ ₆ 8 5⁸ 8 5⁸ 8 5⁸ 8 5⁸ 0 6³ 2 6⁴ ^{9 2} ₀ ^{. 9 2} _{0 0 7 0 7 0 7 0 7 0 7 0 7} _{5 2 0 5 2} ^. ₀ ⁹ ₅ ² ₂ ^. ₀ ⁷ ₉ ⁹ ₁ ^. ₀ ⁷ ₉ ⁹ ₁ ^. ₀ ⁷ ₉ ⁹ ₁ ^. ₀ ⁷ ₉ ⁹ ₁ ^. ₀ ⁷ ₈ ² ₄ ^. ₀ ¹ ₅ ⁷ ₆ ₆ ^{8 7 6 4 6 6 9} ⁵ _{0 5} 8 _{0 6 0 7 0 9 0 4 0 2 6} ¹ _{3 6} ₂ ⁰ ₉ ^{0 1} ₃ ^{0 5 0 3 0 4 0 4 0 2 0 2} ⁰ ₀ ^{. 6} ₀ ^. _{0 4 0 1 0 5 0 0 0 6 0 9} _{5 0 4 0} ⁸ ₂ ^. ₀ ⁸ ₈ ^. ₀ ¹ ₄ ^. ₀ ³ ₆ ^. ₀ ⁴ ₄ ^. ₀ ⁹ ₆ ^. ₀ ⁶ ₆ ³ ₀ ⁸ ₃ ⁹ ₃ ⁹ ₇ ⁴ ₄ ⁴ ₇ ¹ ₅ ^{0 2} ^{9 3 3 9 0 4 5} _{4 4} _{5 6 3 1 8 1 0} ^{2 1} ^{0 8 2 2 9 9 1} _{9 3} _{4 7 8 1 1 8 5} ^{2 6} ^{5 7 5 0 4 0 2} _{6 8} _{1 1 1 4 2 2 3} ^{3 7} ^{. . . . . . .} _{2 1} _{0 2 0 2 0 2 0 7 - 0 1 0 9 0 8} ^. _{0 8} ^. _{0 2} 5 4 0 6 ^{9 9} ₂ ³ ₇ 4 ⁶ ₄ ⁰ ₅ ⁹ ₂ 0 ₁ ₃ ^{8 4} ₅ ³ ₆ ¹ ₉ 1⁴ ₆ ⁰ ₈ ³ ₆ ₃ . _{6 2}. ⁸ _{6 4} ⁶ _{3 8 1}. ₇ ⁰ _{4 7} ^{1 3} ⁷ ₁ ⁶ ₁ ⁹ ₄ ³ _{3 5} ^. ₃ ³ ₂ ^. ₇ ⁵ ₄ ⁹ ₁ ⁹ ₃ ^. ₃ ³ ₆ ₇ ^. ₆ ⁴ ₆ ₃ ^. ₇ ⁴ _{5 2}. 9₂ 33 20 47 78 06 87 3 ₃ 5^{1 0 7 7 1 0 6 3 8 4 9 1 0} 4 72 85 ₅ _{2 5 3 2 7 4 0 3 1 9 1 3 9} ⁸ ₀ ¹ ₃ ³ ₅ ⁰ ₄ ⁶ ₃ ⁷ ₇ ⁴ ₄ ⁸ ₂ ⁰ ₇ ⁸ ₂ ⁰ ₂ ⁸ ₈ ⁷ ₉ ⁸ ₂ 1_/ _{. 2 1 . 2 1 . 2 1 . 1 1 . 1 1 . 1 1 . 1 2 . 2 2 . 2} ^S _T P _I ₇ ⁰ ₁ ⁴ ₄ ₆ ⁶ ₀ ⁶ ₅ ¹ ₆ ⁴ ₂ 8 ⁴ ₄ ⁴ _{0 8} 9 ⁴ ₄ ³ ₆ ² ₀ ^. ₀ ¹ ₃ ⁶ ₉ ⁸ ₀ ⁴ ₆ ₂ ¹ ₅ ⁰ ₇ ⁶ ₃ ⁷ ₉ ³

₁ ⁷ ₄ ³ ₇ ² ₂ ⁷ ₇ ⁴ ₀ ⁷ ₅ ^{8 3} ^{1 0 0 1 0 1 0} ₆ 3 ₀ _{0 0 0 0 0 0 0 0} ¹ ^{. . . . . . .} - ^. ₀ _{0 7 - 0 9 0 3 - 0 2 0 8 - 0 7 - 0 4 0 1 -} ^. _{0 5} ⁷ ₂ 5 6⁵ ₆ 5 6⁵ ₆ 7 6⁵ ₆ 8 6⁴ ₅ 8 6⁴ ₅ 8 6⁴ 8 6⁴ 8 6⁴ ⁶ ₅ ⁶ ₀ ₃ ^. ₀ ⁸ ₈ ⁵ ₀ ₁ ^. ₀ ⁸ ₈ ⁵ ₀ ₁ ^. ₀ ² ₂ ⁵ ₀ ₉ ^. ₀ ⁶ ₁ ⁹ ₀ ₄ ^. ₀ ⁶ ₁ ⁹ ₀ ₄ ^. ₅ ₀ ⁶ ₁ ⁹ ₀ ₄ ^. ₅ ₀ ⁶ ₁ ⁹ ₀ ₄ ^. ₅ ₀ ⁶ ₁ ⁹ ₄ ₃ ⁸ ₆ ⁸ ₃ ^{0 8 5 0 2 2} ⁶ ₂ ¹ ₂ ⁰ _{1 6} 6 ₃ ⁸ _{4 7 4 7 4 1 4 3} ₆ ⁰ ₇ ⁰ ₆ ⁰ ₇ ⁰ ₇ ^{0 5} ₂ ^{0 3} ₂ ^{0 5 0 3} ¹ ₀ ^{. 1} ₀ ^{. 7} ₀ ^{. 6} ₀ ^. _{0 0 0 2 0 3} _{9 0 8 0 6 0 5 0} ² ₄ ^. ₀ ⁶ ₃ ^. ₀ ⁶ ₆ ^. ₀ ² ₅ ^. ₀ ⁹ ₆ 1₅ ² ₅ ⁶ ₈ ^{1 4 3 9 1} ^{6 9 4} ₆ 9 _{3 2 1 9} _{7 7 2 5} ⁵ ₈ ⁹ ₂ ^{6 5} ^{2 4 3 1 3 3} ₃ 8 ₂ ₅ 1 _{6 3 6 8 6 8} ⁰ ₆ ₁ ^{2 4 9 7 1 8 7} ^. ₁ ^. ₂ ^. _{0 1 1 2 1} _{1 0 7 - 0 1 0 3 -} ^. _{0 3} ^. _{0 4} ^. _{0 1} ^. _{0 6} ^. _{0 1} 4₆ 5 9³ ₄ 2 ⁴ ₇ 8 ⁹ ₇ 5 6_{0 0} 6⁵ ₆ ⁷ ₅ ⁰ ^{4 4} _{7 9} ⁷ ^{2 3} _{7 5} ₄ ^. ⁷ _{2 6 0 2} ² _{2 5 6} ⁴ _{9 9 3} ⁸ ₉ ⁰ _{6 1} ₂ ⁰ ₁ ⁸ . 0 ¹ ₂ ⁵ ₆ ₂ ^. ₅ ¹ . 5 ⁶ ₈ ⁰ ₇ ₇ ^. . ₃ . 8 ³ ₇ ⁹ ₃ ⁵ ₂ ^. ₄ ⁷ ₁ ⁰ ₁ ⁴ _{5 2}. 9₂ 31 50 50 70 ₃ 5^{9 4 1 4 0 1 8 5} 09 1 4 26 86 97 ₅ _{1 7 2 8 9 3 0 0 2} ⁰ ₀ ⁵ ₀ ⁰ ₈ ¹ _{8 2} ¹ ₁ ¹ ₃ ⁶ ₇ ⁵ ₁ ¹ ₃ ⁴ ₅ ⁰ ₂ ⁴ ₄ ² ₃ ⁰ ₂ ⁸ ₅ ⁷ ₈ ⁸ ₂ 1_/ _{. 2 1 2 1 2 1 . 2 2 . 2 2 . 2 2 . 2 2 . 2 2 . 2} ^S _T P _I ₅ ⁹ ₄ ¹ ₉ ₉ ⁶ ₄ ² ₇ ⁹ ₂ ⁸ ₂ 5 ⁴ ₇ ⁰ _{6 8} ⁷ ₀ ¹ ₇ ⁹ ₃ ⁵ ₁ ₅ ⁷ ₆ ⁴ ₇ ¹ ₂ ⁶ ₂ ⁷

₅ ³ ₄ ⁰ ₇ ⁹ ₂ ¹ ₄ ² ₈ ³ ₉ ^{1 5} ^{0 1 0 0 0 0 0} _{7 0} _{0 0 0 0 0 0 0} ^{0 1} ^{. . .} _{4 -} ^. _{0 6 -} ^. _{0 7} ^. _{0 8} ^. _{0 0} _{0 1 - 0 8 - 0 0} ^. _{0 8 -} ^. _{0 8} ⁴ ₅ 8 6 8 8^{6 9} ₀ ⁴ ^. ₅ ⁶ ^{6 9} ₀ ⁴ ^. ₅ ⁶ ^{6 9} ₀ ⁴ ₅ 9 6₀ ⁴ ₈ 0 7₀ ⁶ ₇ 0 7⁶ ₇ 0 7⁶ ₇ 0 7⁶ ₇ ₄ ^. ₀ ⁶ ₁ ⁹ ₄ ^. ₀ ⁰ ₆ ⁷ ₁ ^. _{0 0 0} _{1 4 0 1 4 0 1 0} ³ ₁ ² ₅ ^. ₀ ³ ₁ ² ₅ ^. ₀ ³ ₁ ² ₅ ^. ₀ ³ ₁ ² ₅ ₃ ⁵ ⁰ _{4 3} ⁶ ₁ ⁷ ₅ ⁹ ₁ ^{7 7} ₇ ⁵ ₉ ⁷ ₁ ₂ ⁰ ⁵ ₀ ¹ ₄ . ₇ ⁰ ⁷ ₀ ⁸ ₄ . ₃ ⁰ ⁶ ₀ ³ ₄ . ₉ ^{0 0} ₅ 1 ₀ ^. ₇ ^{0 5} ₅ 2 ₀ ^. ₆ ^{0 9} ₅ 3 ₀ ^. ₁ ^{0 1} ₃ 3 _{0 8} ⁰ ₀ ⁵ ₀ _{6 0 3 0 7 0 3 0 6 0 7 0 6} ^. ₀ ⁸ ₂ ^. ₀ ⁵ ₂ 1₆ ⁵ ₄ ⁶ ₆ ⁷ ₂ ⁶ ₄ ² ₈ ² ₉ ⁹ ⁰ ₀ ^{8 8 0 2 7 4} ₃ 2² _{8 8 6 8 3 8 6} ₂ ⁸ ₂ ^{9 6 9 9 4 3} ^{6 9} ₅ 7 _{9 8 5 6 0} _{1 0 0} ⁸ ₀ ^{2 8 8 3} ^{. . . .} ₁ ^. _{0 0 2} _{0 1 0 2 0 4 0 5 - 0 2 -} ^. _{0 1 -} ^. _{0 1} ^. _{0 3} 4₄ ⁵ ₄ ⁶ ₂ ⁵ ₉ 8 ⁸ ₉ ⁸ ₁ ⁰ ^{4 8 5 7 2} ₂ ^{2 7} ₅ _{3 1 5 1} ⁸ _{3 6} ⁰ ₈ ₃ ⁵ ₆ ^. _{5 6} ^. _{6 1} ^. _{7 4 4 9} ^. ₈ ^{. 1} ⁵ ₅ ₁ ^. ₇ ⁴ ₅ ⁸ ₂ ³ ₃ ⁴ ₂ ⁴ ₃ ³ ₁ ⁸ . 3 ⁵ ₄ ⁷ _{4 2 3 6} ₁ ⁶ ₁ ⁶ ₀ ⁰ ₃ ⁴ ₀ ^. ₆ ⁴ _{3 2}. 9₂ 39 09 65 8 _{4 3} 5^{6 7 0 5 8 2 4} 3 57 0 21 60 38 ₅ _{2 8 3 1 0 3 1} ⁸ ₇ ³ ₃ ³ ₇ ⁶ ₁ ³ ₁ ² ₇ ⁶ ₆ ⁴ ₈ ⁴ ₃ ⁴ ₄ ⁷ _{8 4} ⁴ ₁ ² ₁ ⁵ ₃ ⁵ ₁ ⁶ ₂ ³ ₃ ⁸ ₂ 1_/ _{. 2 2 . 2 2 . 2 2 . 2 2 . 2 2 2 2 2 2 2 2 2} ^S _T P _I ₅ ² ₃ ⁶ ₆ ⁴ ₅ ⁶ ₅ ₈ ⁴ ₅ ⁰ ₆ ⁵ ₅ ⁶ ₁ ^. ₀ ¹ ₅ ¹ ₆ ⁸ ₆ 5 ⁶ ₅ ⁶ _{6 3} 6 ⁸ ₄ ⁷ ₈ ⁰ ₀ ^. ₀ ⁴ ₇ ⁶ ₀- _E ⁸ ₀-^E ₁

⁶ ₇ ⁷ ₂ ⁷ ₇ ^{1 8 4 0 6} ^{0 0 0} ₇ 0 ₆ 0 _{3 8 6} _{0 0 0 0 0} ⁰ ₀ ^{0 3} ^{. . . . . .} ₀ ^. ₀ _{3 0 8 - 0 3 0 1 0 8 - 0 3 0 5 - 0 7 -} ^. _{0 3} ⁶ ₇ 0 7⁶ ^{3 2} ₀ ^. ₇ 0 7⁶ ³ ₇ 0 7⁶ ₇ 0 7⁶ ₇ 0 7⁶ ₇ 0 7⁶ ₇ 0 7⁶ ₇ 0 7⁶ ₇ ₁ ² ₀ ₅ ^. ₀ ³ ₁ ² ₀ ₅ ^. ₀ ³ _{0 0 0 0 0} _{1 5 0 1} ² ₅ ^. ₀ ³ ₁ ² ₅ ^. ₀ ³ ₁ ² ₅ ^. ₀ ³ ₁ ² ₅ ^. ₀ ³ ₁ ² ₅ ^. ₀ ³ ₁ ² ₅ ₆ ⁶ ₂ ⁸ ₅ ⁷ ₇ ¹ ₈ ^{4 7} ₆ ^{1 1} ² ₄ ₅ ⁰ ¹ ₀ ⁹ ₄ . ₈ ⁰ ¹ ₀ ⁴ ₃ . ₄ ^{0 3} ₅ 5 ₀ ^. ₆ ^{0 6} ₅ 4 ₀ ^. ₉ ^{0 7} ₅ 4 ₀ ^. ₈ ^{0 6} _{6 4} 4 ₀ ^. ₃ ⁰ ₀ ² ₄ ₁ ⁰ ₀ ³ ₀ _{7 0 4 0 5 0 9 0 2 0 8 0} ⁴ ₀ ^. ₀ ⁵ ₉ ^. ₀ ⁸ ₇ ⁶ ₈ ³ ₉ ⁴ ₆ ³ ₄ ⁵ ₈ ⁴ ₄ ^{0 6 5} ^{1 6 6 2 9 7} _{4 9 5} _{6 2 5 8 3 3} ⁰ ₀ ^{1 6} ^{7 9 4 2 2 6 0} _{6 8} _{5 6 5 6 5 9 1} ^{9 4} ^{0 1 7 9 9 8 8} _{2 8} _{1 1 0 0 0 0 0} ⁰ ₁ ⁸ ^{. . . .} _{0 3 -} ^. _{0 3} ^. _{0 4 -} ^. _{0 8} ^. ₂ _{0 5 - 0 9 0 6 - 0 5} ^. _{0 2} 4 7₇ ¹ ₅ 5 ⁶ 6 1⁸ _{6 4} ¹ ² ₈ ¹ ⁸ ₃ ⁹ ^{8 3} _{1 7} 8 ₁ ⁵ _{2 6 4} ^{0 3} ₇ _{9 2 9} ^. ₅ ^{. . .} ₄ ⁸ ⁵ . ₄ ² ₂ ⁹ _{4 4} . _{4 1 5 3 3 2 3} . _{3 9} ₇ ⁴ ₂ ¹ ₆ ⁶ ₈ ⁶ ₈ ⁵ ₁ ⁹ ₁ ² ₃ ⁹ ₈ ⁰ ₁ ⁰ ₃ ³ _{4 8} ^. ₄ ⁷ _{1 2}. 9₂ 36 ₃ 5^{6 9} 50 1 4 85 1 1 98 1 7 24 49 ₅ _{0 4} ⁰ ₃ ¹ ₀ ⁸ ₀ ³ ₃ ⁶ ₆ ⁵ ₃ ³ ₃ ¹ ₂ ² ₂ ⁴ ₃ ⁵ ₇ ⁷ ₆ ⁴ ₃ ⁷ ₄ ³ ₆ ⁵ ₃ ² ₆ ⁷ ₄ ⁹ ₃ ⁴ ₅ ⁴ ₂ ⁰ ₄ ¹ ₃ ⁴ ₅ ⁸ ₂ 1_/ _{2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2} ^S _T P _I ₁ ⁸ ₇ ⁰ ₅ ⁶ ₄ ² ₅ ⁸ ₅ ⁴ ₀ ⁴ ₃ 0 ⁰ ₂ ¹ _{5 8} ⁴ ₅ ⁷ ₃ ² ₁ ⁹ ₂ ₇ ⁰ ₁ ⁶ ₁ ² ₄ ⁸ ₉ ⁷

₀ ⁸ ₂ ⁵ ₁ ³ ₉ ³ ₀ ^{7 4 5 6} ^{1 0 0 0 1} _{7 6 0 6} _{0 0 0 0 0} ^{0 1 1 3} ^{. .} _{5 -} ^. _{0 7} ^. _{0 0 0 0} _{0 5 - 0 0 7} ^. _{0 3} ^. _{0 5 -} ^. _{0 4} ^. _{0 2 -} ^. _{0 8} ⁴ ₉ 1 7 ^{2 7} ₀ ⁸ ^. ₁ 2 7 ⁵ ₀ ³ ₁ 3 7⁹ ₈ 4 7⁹ ₇ 4 7⁹ ₇ 4 7⁹ ₇ 4 7⁹ ₇ 4 7⁹ ₇ ₅ ⁸ ₅ ^. ₀ ⁵ ₂ ⁴ ₀ ₃ ^. ₀ ¹ ₆ ⁹ _{0 0 0 0 0} _{2 4 0 3} ^. ₀ ⁰ ₇ ⁵ ₃ ^. ₀ ⁰ ₇ ⁵ ₃ ^. ₀ ⁰ ₇ ⁵ ₃ ^. ₀ ⁰ ₇ ⁵ ₃ ^. ₀ ⁰ ₇ ⁵ ₃ ₂ ⁵ ₄ ⁹ ₈ ³ ₂ ¹ ₂ ^{6 0 8 0} ¹ ₆ ₀ ⁰ ⁷ ₀ ⁵ ₀ . ₇ ^{0 6} ₅ 5 ₀ ^. ₉ ^{0 5} ₄ 3 ₀ ^. ₇ ^{0 9} ₃ 6 ₀ ^. ₆ ^{0 2} ₄ 8 ₀ ^. ₀ ^{0 3} _{3 3} 7 _{0 8} ⁰ ₀ ⁶ ₄ ₈ ⁰ ₀ ⁶ ₃ _{5 0 4 0 8 0 8 0 5 0 5} ^. ₀ ⁹ ₈ ^. ₀ ⁸ ₃ ^. ₀ ¹ ₅ 8₄ ⁵ ₇ ² ₇ ⁸ ₆ ^{8 0 1 6} ^{3 6 5 4} ₄ 5 _{7 9 9} _{2 1 3 4 2} ² ₄ ^{8 7} ^{4 2 6 9 3 5} ₃ 1 ₅ ₄ 9 _{5 6 0 7 2 9} ⁴ ₀ ₀ ^{3 8 9 9 8 5 9} ^. ₂ ^. _{0 0 0 2 1 2} _{1 0 8 0 3 -} ^. _{0 8 -} ^. _{0 1 -} ^. _{0 9} ^. _{0 8 -} ^. _{0 1} ^. _{0 6} 9₅ ^{5 9} 8 6 ⁴ 2 ⁵ ⁵ _{7 3} ₁ ⁶ ₈ ⁴ ₃ ⁰ ₉ ¹ _{8 2} 5⁰ _{9 1} 1 ^{. . 4} ₄ ⁸ _{2 0} ⁰ ₆ _{7 9} ⁷ _{4 8 3} ¹ ₉ ⁶ ⁴ ₃ ⁴ ₁ ⁸ ₅ ₈ ^. ₆ ¹ ₇ ⁶ . . _{1 6 3} . _{9 3 5} ₁ ¹ ₅ ⁹ ₅ ⁹ ₉ ³ ₈ ⁴ ₂ ^. ₂ ⁴ ₈ ⁰ ₁ ⁰ ₅ ^. ₄ ⁹ _{9 2}. 9₂ ₆ ³8 ₃ 5^{6 3} 6 85 98 46 09 73 1 4 49 ₅ _{2 3} ⁷ ₁ ⁶ ₅ ⁷ ₉ ¹ _{6 0} ⁰ ₂ ⁵ ₇ ⁷ ₉ ² ₁ ⁶ _{7 5} ⁰ ₈ ¹ _{8 2} ¹ ₁ ⁵ ₆ ³ ₉ ⁷ ₂ ⁷ ₇ ⁴ ₉ ⁰ ₄ ¹ ₃ ⁴ ₅ ⁸ ₂ 1_/ _{2 2 2 1 . 1 2 . 2 1 2 1 2 1 2 1 2 1 2} ^S _T P _I ₉ ³ ₇ ¹ ₈ ⁵ ₀- _E ⁷ ₃ ² _{1 8} 7 ⁹ ₀ ³ _{4 7} ⁴ ₁ ⁶ ₀ ⁴ ₈ ¹ ₄ ₅ ⁵ ₄ ¹ ₂

⁴ ₅ ⁸ ₃ ^{1 8 4 4 4 9} ^{0 0} ₆ 0 _{2 3 1 6 4} _{0 0 0} ⁰ ₀ ^{0 1 0 0} ^{. . . .} ₀ ^. _{0 0 0} _{9 - 0 1 - 0 7 - 0 1 0 6 - 0 -} ^. _{0 2 -} ^. _{0 -} ^. _{0 5} ² ₆ 6 7² ₆ 6 7² ₆ 7 7² ₁ 7 7² 8 7⁸ 0 8³ 0 8² 0 8² ^{8 2} ₀ ^{. 8 2} _{0 0 0 1 0 4 0 8 0 6 0 6} _{2 9 0 2 9} ^. ₀ ⁸ ₂ ² ₉ ^. ₀ ⁴ ₅ ⁸ ₄ ^. ₀ ⁴ ₅ ⁸ ₄ ^. ₀ ⁹ ₅ ¹ ₃ ^. ₀ ⁰ ₆ ⁷ ₄ ^. ₀ ⁹ ₉ ⁵ ₇ ^. ₀ ⁹ ₉ ⁵ ₇ ₈ ⁹ ⁰ _{5 6} ⁰ _{6 3} ⁵ _{7 8} ³ _{7 2} ³ _{6 1} ³ _{1 1} ¹ _{5 4} ⁸ ₉ ₃ ^{0 9 0 0 0 9 0 4 0 7 0 9 0 9} ₄ 0⁶ ⁷ _{0 8 0 3 0 8 0 0 0 6 0 5 0 6 0 9} ₅ ^. ₀ ⁶ ₈ ^. ₀ ¹ ₁ ^. ₀ ² ₈ ^. ₀ ⁶ ₉ ^. ₀ ⁰ ₄ ^. ₀ ⁴ ₆ ^. ₀ ³ ₈ ^. ₀ ⁷ ₄ ¹ ₄ ⁵ ₂ ⁴ ₃ ^{3 0 0 6 9} ^{6 2 4} _{5 9 6 7 7 7} _{8 7 3} ^{3 6 3 3 3 5} ^{1 3 8} _{2 2 9 2 7 1} _{1 0 2} ^{7 7 3 6 2 4} ^{7 2 9} _{7 0 4 3 4 6} _{1 1 0} ^{8 2 7 3 3 9} ^{. . .} _{0 1 0 3 1 0} _{0 0 1 0 7} ^. _{0 3 -} ^. _{0 3} ^. _{0 5} ^. _{0 2} ^. _{0 4} ^. ₀ 3 6 1 0¹ ₄ 5 ⁹ ₃ ¹ ₇ 0 5 1 8 ₅ 3⁸ ₉ ⁵ ₉ ⁶ ^{7 2} ₅ ⁶ _{2 4} ⁶ ⁰ ₂ _{3 7 7 0} ^. _{5 3 5 7} ^. _{9 6} ³ _{8 4 2} ¹ ₈ ⁸ . ₄ ⁶ ₁ ³ . 4 ⁸ ₃ ⁵ ₉ ⁴ ₃ ³ . 4 ⁵ ₁ ⁴ ₆ ⁴ ₃ . . ₄ ₃ ³ ₆ ^. ₂ ² ₁ ⁰ ₁ ⁶ ₉ ⁵ ₃ ¹ _{8 2}. 9₂ 33 97 52 8 ₃ 5^{7 9 2 3 3 4} 7 84 45 22 85 55 ₅ _{4 6 3 1 3 3} ² ₆ ⁶ ₅ ⁰ ₆ ³ _{0 2} ⁴ ₁ ⁹ ₅ ⁷ ₀ ³ ₂ ⁴ ₆ ¹ ₁ ⁰ ₃ ⁶ _{7 1} ⁷ ₂ ⁸ _{7 6} ¹ ₉ ⁸ _{0 6} ⁸ ₂ 1_/ _{. 2 2 . 2 2 . 2 2 2 2 2 2 . 2 1 . 2 1 2 1 2} ^S _T P _I ₅ ⁶ ₉ ² ₅ ⁸ ₅ ⁷ ₈ ₆ ¹ ₇ ⁷ ₉ ⁵ ₆ ⁵ ₁ ^. ₀ ¹ ₅ ² ₁ ⁹ ₉ 7 ⁵ ₀ ³ _{3 5} ⁶ ₅ ⁷ ₉ ⁵ ₄ ⁸ ₈ ₈ ⁴ ₀ ⁶ ₇ ³ ₁ ⁸ ₁ ⁰

₄ ⁵ ₀ ¹ ₄ ⁹ ₅ ^{5 9 2 8 3} ^{1 1 0 0} ₁ 0 _{7 3 3 5} _{0 0 0 0 0} ^{0 1 0 0} ^{. . . . .} _{0 0 0 0} _{0 5 0 0 6 - 0 2 - 0 8 -} ^. _{0 4 -} ^. _{0 2 -} ^. _{0 9 -} ^. _{0 7} ² ₆ 0 8² ₆ 0 8² ₆ 1 8⁹ ₈ 2 8⁶ 2 8¹ 3 8³ 3 8¹ 3 8⁴ ^{9 5} ₀ ^{. 9 5} ₀ ^{. 9 5} ₀ ^{. 3 9} ₀ ^. ₈ 5⁷ ₀ ^. ₄ 3⁵ ₀ ^. ₆ 9⁷ ₀ ^. ₆ 5⁶ _{0 8} _{9 7 0 9 7 0 9 7 0 6 2 0 2 9 0 3 5 0 1 8 0 6 2} ^. ₀ ⁶ ₆ ⁶ ₆ ₁ ^{8 0 7 3 5 6 0 8} ⁷ ₄ ³ _{5 3} 4 _{6 4 8 7 5 1 8 3 7 5 1} ₈ ⁰ ₀ ⁰ ₇ ^{0 2} ₄ ^{0 1} ₇ ^{0 3} ₃ ^{0 2 0 0 6} ³ ₀ ^{. 1} ₀ ^{. 5} ₀ ^{. 9} _{0 0 0 8 0 9 0 8} _{1 0 8 0 7 0 3} ^. ₀ ³ ₀ ^. ₀ ⁵ ₆ ^. ₀ ⁰ ₆ ^. ₀ ² ₆ ^. ₀ ¹ ₂ 8₈ ¹ ₅ ^{9 4 7 0 8 7} ^{5 3} ₄ 1 _{8 4 6 3 3} _{4 4 2} ² ₇ ^{3 7 8 2} ^{4 1 0 5} ₄ 3 _{4 1 8} _{7 3 5 8 3} ⁹ ₅ ^{2 4} ^{9 7 9 5 8 2} ₃ 3 ₀ _{3 1 0 0} ² ^. _{1 1 1 1} _{5 - 0 3 -} ^. _{0 6} ^. _{0 2} ^. _{0 2} ^. _{0 8} ^. _{0 1} ^. _{0 7} ^. _{0 3} 1₃ ⁴ ₀ ³ ₂ 7 ⁰ 6 1 3 7₆ ⁹ ₀ ⁹ ₂ ⁷ _{7 4} ⁸ ^{6 5} _{6 6} ⁶ ₈ ⁶ ₆ ₂ ⁶ ₉ ² ₈ ^. _{9 1 7 4} ⁴ ₂ ⁹ _{8 1} ⁸ ₁ ⁷ ₇ ⁷ ₇ ₂ ^. ₁ ² ₀ ₅ ^. ₇ ⁹ ₉ ⁸ ₂ ⁷ . 2 ⁷ ₄ . . ₈ . ₉ ₉ ⁰ ₅ ^. ₆ ⁷ ₃ ⁶ ₂ ⁸ ₆ ⁶ ₂ ⁹ ₆ ⁶ ₁ ⁵ _{0 2}. 9₂ 39 71 4 ₃ 5^{4 1 1 6 2} 3 06 07 25 94 20 89 ₅ _{9 8 2 3 2} ⁵ ₂ ⁷ ₄ ⁹ ₆ ⁸ ₃ ⁴ ₄ ⁸ ₄ ⁷ ₃ ² ₄ ² ₂ ⁹ ₃ ² ₇ ⁴ ₆ ⁵ ₂ ² ₆ ⁸ ₇ ⁰ ₄ ² ₃ ⁸ ₅ ⁸ ₂ ⁵ ₁ ⁴ ₄ ⁸ ₂ 1_/ _{2 1 2 1 2 2 . 2 2 2 1 2 2 2 2 . 2 1 2} ^S _T P _I ₈ ⁴ ₃ ⁹ ₉ ⁹ ₁ ⁷ ₄ ₅ ³ ₀ ⁷ ₀ ⁷ ₄ ¹ ₁ ^. ₀ ⁷ ₁ ⁰ ₉ ⁴ ₇ 6 ⁰ ₉ ⁵ _{6 2} 9 ⁹ ₇ ⁴ ₀ ¹ ₀ ^. ₀ ⁷ ₂ -_E ⁸ ₂ ⁸ ₀ ⁰ ₁-^E ₇

⁰ ₈ ^{3 6 7 9 6 9 0} ⁰ ₄ 0 _{4 9 6 7 6 2} _{0 0} ² ₀ ^{0 0 0 0 0} ^{. . .} ₀ ^. _{0 0 0 0} _{4 0 8 - 0 5 0 1 0 4} ^. ₀ ^. _{0 1} ^. _{0 1} ^. _{0 5} ⁸ ₀ 8 8 ^{8 3} ₀ ¹ ^. ₄ 9 8¹ ^{6 5} ₀ ^. ₀ 9 8¹ ⁰ ₀ ^. ₀ 9 8¹ ⁰ _{0 0} 1 9³ ₀ 2 9⁹ ₁ 2 9⁹ ₁ 2 9⁰ ₇ _{1 8} ^. ₀ ⁰ _{0 0 0 0} _{2 5 0 7 0 0 1 8 0 1 8} ^. ₀ ⁴ ₄ ³ ₄ ^. ₀ ⁷ _{0 6} ^. ₀ ⁷ _{0 6} ^. ₀ ⁶ ₄ ² ₅ ₁ ⁵ ⁸ ₉ ³ _{0 6} ³ _{0 6} ³ _{0 6} ⁵ ₈ ¹ ₇ ² ₉ ⁹ ₃ ₆ ⁰ ² ₀ ⁴ ₄ ¹ ₀ ⁰ ₃ ¹ ₀ ⁰ ₇ ¹ ₀ ⁴ ₆ ₃ ⁰ ₀ ³ ₈ ₄ ⁰ ₀ ⁷ ₈ ₄ ⁰ ₀ ⁵ ₆ ₆ ^{0 7} ₇ ₃ ^. ₀ ⁰ ₂ ^. ₀ ³ ₉ ^. ₀ ⁹ ₉ ^. ₀ ⁶ ₁ ^. ₀ ⁴ ₄ ^. ₀ ⁸ ₆ ^. ₀ ¹ ₀ ₉ ^. ₀ ⁰ ₄ ⁶ ₄ ⁹ ₈ ^{1 8 9 0 4 1 6} ^{0 1} _{6 6 4 8 5 7 3} _{7 5} ^{5 7 7 2 5 7 7} ^{2 4} ₀ 0 _{5 0 3 2 3 3} _{9 0 6} ^{3 7 1 4 8 1} ^{7 1 7} _{4 1 9 8 1 5} _{0 1 0} ^{2 9 0 1 8 1} ^{. . .} ₃ ^. _{0 1 1 0 1} _{0 6 - 0 6 0 - 0 -} ^. _{0 3 -} ^. _{0 9 -} ^. _{0 8 -} ^. _{0 8 -} ^. _{0 4} 1 3₅ ¹ ₀ ⁶ 1 0 8 ⁷ 4 1 ₄ ₂ ⁰ ₆ ⁰ ₃ ⁵ ₅ ² _{6 3} 8₂ ⁹ ₀ ₈ ⁸ _{3 1} ^. _{9 5} ³ ₁ ⁰ ₁ ⁶ _{4 7} ⁸ ₃ ^{. 1} ₁ ⁴ . ₃ ⁵ ₂ ⁷ ₉ ¹ ₁ ⁶ ₉ ₃ ^. ₁ ⁴ . . . _{8 3 3} . ₃ ₅ ⁵ ₅ ⁸ ₆ ⁹ ₇ ⁴ ₅ ⁸ ₁ ⁸ ₂ ¹ ₃ ⁴ ₂ ⁸ ₁ ⁵ _{7 2}. 9₂ 30 97 3 ₃ 5^{2 8 1 7 1} 6 85 29 95 06 03 30 ₅ _{9 6 3 8 4 1} ⁶ ₆ ⁷ ₃ ⁸ ₁ ¹ ₈ ⁴ ₁ ⁴ ₂ ⁸ ₉ ² ₆ ⁰ ₃ ⁴ ₂ ⁹ ₂ ¹ ₃ ⁴ ₆ ⁰ ₄ ² ₃ ² ₁ ⁹ ₈ ¹ ₃ ⁸ ₃ ¹ ₈ ⁸ ₂ 1_/ _{. 2 2 2 2 2 2 2 2 2 1 2 2 . 2 2 . 2 1 2} ^S _T P _I ₂ ⁷ ₇ ² ₅ ⁴ ₃ ⁷ ₃ ⁵ ₉ ⁵ ₁ ⁷ ₀ 5 ⁰ ₄ ¹ _{7 7} ⁸ ₂ ⁹ ₃

7 _E ² ₉ ⁷ ₈ ⁰ ₀ ⁰ ₆ ² _{4 y} ^d _{f 9} ^{0 8} ₉ ^{. 9 7} ₁ ^{. 1} _{0 1 0 0} ^_ 0_{9 0 7 5 0 3 0 0 9 3 0 2 8 e} ^. a_s ⁶ a₀ ¹- _{0 1 2 9 0 0} ^{4 . 7 . 7 4 . 3 1 . 7 7 g r .} ₂ ^E ₆ 7 ⁹ ₇ ⁸ _{5 3} ⁴ _{8 7} ⁶ _{9 4} ⁵ _{0 3} ⁸ _{0 4} ^r _a ₃ ^{5 9 4 6} ₄ ^{1 6} ₄ ^{3 7 0 8 0 3 e} _e _y u ^E ^l ₅ _a ⁶ ₁ ⁰ _{4 0 0 0 0 9 0 1 0 3} _{6 e} ^{v 4} ⁴ ₅ ³ ₃ ⁵ ₆ ⁰ ₀ ^. _{0 8}

⁰ ₇ 2 9 ^{6 2} ₀ ⁹ ^. ₈ 2 9⁸ ^{7 5} ₀ ^. ₀ 5 9⁰ ^{7 1} ₀ ^. ₈ 5 9⁵ 0 ³ ₄ ³ _{0 8} 6 9₀ ² ₇ 9 c 9₀ ⁶ ₂ n^a _{c r} ⁶ 2_{0 6 8 0 2 5 0 8 7} ^d _{f 1 5} _i . ⁸ e₆ ⁰- _{4 5 0 4 1 0 5 7} ^{. 3 6 . 8 9 . 1 2 _} _s ^{r .} ₁ ^E ₂ ₂ ^{0 3 e} ⁸ _{1 1 1 5} ³ _{7 7} ⁰ ₂ ⁶ _{7 8} ⁵ _{1 5 c e} -₅ ^{1 0} ₈ ^{1 4} ₈ ^{0 6} ₇ ^{1 6} ₉ ^{0 3} ₀ ^{0 0} n u^l _E _a ³ ₇ ⁵ ₀ ₈ ^. ₀ ⁵ ₀ ₈ ^. ₀ ⁸ ₀ ₈ ^. ₀ ⁹ ₀ ₉ ^. ₀ ⁷ _{0 0 5} ^a _c ₆ ^. ₀ ⁷ ₅ ^. ₀ ³ ₅ ^_ _{s i} ^v _p ³ ^. _{r 1} ^. ₆ ⁸ ₀ 7₉ ⁹ ⁵ ₆ ⁹ ₄ ⁷ ₃ ⁵ ₂ ⁹ ₇ ² _{g 1} ₆ ^{1 9} ³ ₄ ¹ ₄ ¹ ₉ ⁹ ₈ ¹ ₂ ^o _l ^. _r ^e _g ₉ ^{9 2 2 3 0 e} _c ⁿ ₉ _a ⁹ ₈ 7 _{3 6 9 3 9} ₀ ¹ n ^h _C ⁰ ₀ _{2 -} ^. ₁ ¹ 0₉ ^. ₁ ^{1 9 9} 0_{4 -} ^. _{1 0 2} ^a _c _{0 9} ^. _{0 3 -} ^. _{0 8} ^. _{0 6} ^_ _s ^d _l ⁴ i ^o ₁ _F ^. ₀ - 0 4 7 8 ² 4 3 7 7 0 3⁷ ₁ b^. _r 6₈ ¹ _{6 3 1 4} ₂ ^{3 5 2 1 2} ₇ ^e _c n ² n ^a _{e 2} 6² . ₉. _{7 5}. _{2 5}. _{9 5}. ₇ ² _{5 3} ^a ₄ ⁹ ₆ ⁶ ₉ ⁵ ₁ ⁶ ₂ ⁹ ₂ ⁶ ₁ ³ ₁ ⁷ ₉ ⁹ ₃ ⁹ ₇ ^. ₅ ⁴ _{5 c 0} ^. ^_ _s ^M _{e 3 2}. . _i ^s _a ⁷ ₃ ⁵ ₈ ⁹ ₂4 87 89 79 97 89 57 ^{3 3} ^r _t ^{3 0} ₃ 5^{4 4 8 0 9 8 5 4 3 6 0 6 0} ₁ ⁵ ₃ ⁹ _{2 5 7 3 2 0 1} _e n ^d _{4 7 5} _{5 2 2 3 2 2 1 4 1 3 4 2} ^{6 6 5 8 6 e} _i ^{6 4 8} 2_{2 2 2 1 2 2 2 1 2 1 . 1} ^l _{c 2} _b o _{4 3} ¹ _/ ₂ ^a _{T d _ c 2 2} ^S _T P _I 2 ¹ ₅ ⁸ ₀ ⁸ ^. ₀ ₀ ⁶ ₉ 8 ¹ ₅ ⁸ ₃ ² ₀ ^. ₀ ⁴ ₀ ₃ ⁷ ₇ ³ ₈ ⁶ ₁ ^. ₀ ¹ _{5 4} ¹ ₂ ⁰ ^. ₉ ₀ ⁰ ₂ 8 ⁷ ₉ ⁵ ₀ ^. ₃ ₀ ⁰ ₃ ₁ ⁸ ₉ ⁸ ₅ ⁴ ₅ ⁴ ₀ ^. ₀ ¹ ₇ ⁰ ₆ ⁰ ^. ₁ ₁ - _E ^E ₃ ⁷ ₄ ⁹ ₂ ^. ₃ ² ₁ -₈ ² ₉ ¹ ₀ ⁶ ₉ ⁰ ₀ ^. ₀

- - - -⁶ _{2 5} ⁶ _{2 5} ⁷ ₄ ¹ _{0 8} ² ₂ ² ₂ ⁵ ₉ ⁷ ₁ ⁷ ₁ ^{9 5} ₆ ^{5 6} ₅ ¹ ₅ ⁰ ^. - E¹ ₅ ⁰ ^. - ^{5 9} ₀ ⁸ ₀ ⁸ ₀ ⁸ ₀ ⁸ ₀ ⁸ ₀ ¹ ₁ ⁰ ₂ ² ₂ ⁸ ^{E 9} ₆ ^. ₀ ^{- 0} ₀ ^. ₂ 7⁰ ₀ ^. ₉ 7⁰ ₀ ^. ₉ 7⁰ ₀ ^. ₇ 9⁰ ₀ ^. ₅ 9⁰ ₀ ^. ₅ ⁰ _{0 9} ⁰ _{0 6} ⁰ _{0 5} ⁰ _{0 7} _{7 8 7 8 9 E 0 6 0 0 0 0 0 9 0 8 0} ⁹ ₈ ^. ₀ ¹ ₇ ^. ₀ ⁰ ₅ ^. ₀ ³ ₉ ^. ₀ ⁸ ₅ 3 -⁸ 3 6 9 4 4 3 1 5 5 5 5 6 _E ₁ ^{4 9 9 7 0 0 0 7 8 1 8} ² ₆ ⁷ _{2 3 5 7 0 2 2 5 1 2 6 1} ^. ₀ ^{2 7} ₀ ^{8 7 3 7 5 7 8 7 9 6 5 6 4 6 4 6 1 5 8 5 5} ₂ - _{E 4} ^. ₃ - _{E 1} ^. ₀ ₂ - _{E 5} ^. ₀ ₇ - _{E 1} ^. ₀ ₃ - _{E 1} ^. ₀ ₆ - _{E 4} ^. ₀ ₁ - _{E 4} ^. ₀ ₈ - _{E 6} ^. ₀ ₈ - _{E 6} ^. ₀ ₆ - _{E 4} ^. ₀ ₁ - _{E 7} ^. ₀ ₃ - _{E 8} ^. ₄ ⁵ ₀ ₄ ⁴ ₅ 1 ₁ 9 _{1 6 2 2 4 5 9 3 9} _{6 6 8} ⁵ ₂ ⁰ ₅ ³ ₃ ⁵ ₅ ^{4 9} ^{4 2 5} ₅ ⁷ _{4 6} ⁴ ₁ ³ _{2 1} _{5 8} ¹ ₁ ⁰ ₂ ² ₄ ³ ₃ ^{3 5 4} ^{1 4 8} ₈ ⁵ _{2 1 7} ⁸ ₆ ⁴ _{9 0} _{5 3} ⁶ ₅ ⁷ ₃ ⁰ ₄ ⁵ ₃ ^{2 7 0} ^{3 0} ₆ ¹ _{5 3 3} ¹ ₂ ² _{6 2} ₁ ^{. 4} _{0 2} - ^{. 3} 0₁ - ^{. 3} 0₂ - ^{. 5} 0₂ - ^{. 7 1 1 2} 0₂ - ^. ₀ ⁸ ₃ ¹ _{1 1} - ^. ₀ ^. ₀ ^. _{0 1} - ^. ₀ ¹ ₁ ⁷ _{1 1} - ^. ₀ ^. ₀ ^. ₀ -6 ⁰ 1 7 3 ₄ ⁰ ₈ ⁴ ₀ ⁴ ₇ ³ ₀ ⁰ 6 4 7 0 6 3 ⁴ ₆ ^{3 2 0 9 3 4} ₂ ⁴ ₉ ⁰ ₃ ² ₄ ⁸ ₆ ⁵ ₃ ⁶ _{1 8 9 3 1} ₉ ³ ₉ ^{8 4 6 8 7 3} ₉ ^. ₈ ⁸ ₆ ^. ₃ ⁶ ₁ ⁷ ₉ ^{1 6} ₁ ^. _{5 3} ^. _{2 6} ^. ₂ ^. ₁ 9⁴ ₄ ² ₃ ¹ . 4 ⁹ ₁ ⁹ ₁ ³ ₃ ² . 6 ⁰ ₂ ⁷ . 9 ² ₁ ⁹ _{4 8} ^. _{9 3 8} ^. _{4 7} ⁰ ₁ ⁸ _{6 1} ₁ ⁷ ₁ ⁹ ₀ ⁷ ₁ ⁶ ₂ ⁰ ₁ ⁹ _{9 5} ^. ₉ ⁵ . 1 ⁵ ₃ ⁴ ₂ ₉ . 9₂ 3³ ₄ ⁰ ⁶ ₇ ² ₆ ⁶ ₄ ⁴ _{2 7} ² ₅ ⁵ ₅ ⁵ _{5 0} ⁷ ₁ ⁸ ₉ ³ ₃ ₀ ^{4 6 9 7 7 9 1 1 0 8 2 3 5} ₅ ₄ ⁴ ₃ ⁰ ₅ ⁷ ₂ ³ ₁ ² ₂ ² ₂ ⁷ ₃ ² ₃ ⁶ ₈ ⁶ ₄ ² ₅ ⁸ ₃ ₆ ⁵ ₈ ₇ ⁰ ₇ ₅ ⁴ ₈ ₆ ² ₁ ₆ ⁶ ₁ ₁ ⁰ ₄ ₃ ² ₂ ₂ ⁴ ₃ ₅ ⁰ ₈ ₈ ⁷ ₆ ₄ ⁹ ₃ ₄ ⁵ ₅ ¹ ₅ ⁸ ₂ 1_/ _{1 2 1 2 2 . 2 2 . 2 1 . 2 1 . 2 1 . 1 2 2 2 2 1 2 1 2 2 2 2 2} ^S _T P _I 3 ⁶ ₅ ⁴ ₆ ¹ ^. ₉ ₀ ¹ ₁ 5 ² ₄ ⁸ ₅ ⁰ ₅ ^. _{0 5} ₃ ⁸ ₅ ³ ₃ ¹ ₀ ⁴ ₀ ^. ₀ -² _{1 3} ⁶ ₂ ² ^. ₈ ₀ ¹ ₀ 6 ³ ₂ ⁸ ₀ ⁵ ^. ₃ ₀ ⁹ ₀ ² ₃ ³ ₅ ⁴ ₂ ⁴ ₀ ^. ₀ ² ₄ ⁸ ₃ ₆ ¹ ^. - ₇ ^E ₈ ^E ₇ ⁷ ₂ ² ₉ ^. ₆ ⁵ ₁ -₄ ⁸ ₂ ⁴ ₄ ⁹ ₀ ¹ ₀ ^. ₀ ⁷ _{2 7}

⁰ ₀ ⁴ ^. _{7 2} 7⁰ ₀ ⁵ ^. _{4 2} 3⁰ ₀ ⁹ ^. _{2 2} 0 0_{2 0} ⁹ _{2 2} 0 0_{2 0} ⁹ _{2 3} 0 0_{2 0} ⁷ _{9 4 3} 0 0_{9 0} ¹ _{7 4} 0 0_{7 0} ³ _{6 4 3} 0 0_{8 0} ³ _{0 5 5} 0 0_{7 0} ⁸ _{7 5} 0 0_{4 0} ⁵ _{9 5 7} 0 0_{8 0} ⁵ _{8 5} 0 0_{2 0} ⁷ ₈ _{0 5 0 3} ^{6 . 6 . 6 . 8 . 1 . 3 . 6 . 9 . 8 . 7 .} ₀ ⁴ ₃ 4 ^{- - 0} ³ 6 6 3 5 _E 2 _E 1 8 7 8 2 ₀ ⁹ ¹ ₁ ⁷ ¹ ₄ 5 ¹ ₉ 4 _{3 2 3 0 1 0 5 0} ^{1 6 1 4} ⁶ ₈ ⁵ ^. ₀ ⁵ ₁ - _{E 5} ⁵ ^. ₀ ^{1 5} ₀ ^{7 1 4 5} ₀ ^{6 5} _{0 0 9} ₀ ⁰ ₁ ⁰ ₁ ⁰ ₉ ^{2 5} _{0 1} ₀ ⁰ ₆ ^{1 5} ₀ ₂ - _{E 6} ^. ₄ - _{E 6} ^. ₃ ⁵ _{0 5} ^. ₄ ⁵ _{0 7} ^. ₁ - _{E 8} ^. ₄ - _{E 0} ^. ₀ ⁷ _{3 0} ^. ₀ ⁶ _{4 0} ^. ₀ ³ _{4 5} ^. ₉ - _{E 0} ^. ₀ ² _{4 1} ^. ₈ - _E ₈ ⁴ _{8 2} ₇ ^{1 2} _{9 7 3 4 3 8 4 8 8} _{5 1} ⁶ ₅ ⁶ ₄ ³ ₀ ⁷ ₆ ⁰ ₂ ³ ₄ ⁸ ₀ ³ ₁ ² ₂ ³ ₅ ⁸ ₆ ^{0 1} _{4 3} ⁶ ₀ ¹ ₃ ⁵ ^{1 0} ₈ ⁰ ₃ ⁴ ₀ ⁹ ₄ ¹ ₅ ¹ ₇ ¹ ₂ ⁴ ₀ ⁹ _{1 0} ³ ₂ ⁹ ₀ ⁹ ₂ ⁴ ₉ ⁰ ₂ ⁹ ₈ ⁸ ₄ ^{4 0} ₉ ⁰ _{7 6} 2^. ₀ ¹ ₁ ² - ^. ₁ ² ₁ ¹ 0 ^. ₀ - ^. _{0 2} - ^{. 6} 0₂ ² ^. ₁ ₀ ^{. 1} 0₁ - ^. ₀ ⁴ ₃ ₁ - ^{. 7} 0₀ ¹ ^. ₁ ₀ ^{. 0} 0_{1 5} - ^. ₀ ² ₂ - ^. ₀ ³ ₉ 3 ^{4 1} 9 4 ^{4 8 9} 0 ^{5 3 0} ⁸ ₃ ³ _{2 2} 3 ₇ 1⁸ ₄ ¹ _{6 2} 3 ₆ 3 ₃ 9³ _{4 2} 5 ₄ 9 ₆ 5^{1 0} _{9 9} 4 ₆ 9⁸ ₁ ⁰ _{8 9} 4 ₆ 0 ₁ 6⁸ _{4 6} 0 ₀ 4 ₄ ₃. ^. ¹ _{1 4}. ₂. ^{7 5} ₄. ₉. ₇. ^. _{2 8}. ₄. ⁶ ₁. ₂ ₉ ⁷ ₀ ⁰ ₁ ² ₉ ⁵ ₃ ⁴ ₉ ⁰ ₅ ⁴ _{8 2} ^. ₅ ¹ _{1 4} ^. ₄ ⁷ ₇ ⁵ ₃ ⁴ ₉ ⁰ ₃ ¹ ₁ ⁸ ₂ ⁹ ₆ ² ₆ ⁷ ₈ ¹ ₃ ⁸ ₉ ³ _{6 6} ⁴ ₁ ³ ₃ . 9₂ 3¹ ₁ ⁷ ₃ ¹ ₂ ⁰ ₃ ⁵ ₀ ⁴ ₁ ² ₇ ⁶ ₁ ⁸ ₂ ⁸ ₁ ⁷ ₆ ⁴ ₆ ³ ₃ ⁶ _{8 3} ⁴ ₁ ² ₄ ^{8 5 0 2 3 4 6 1} ₃ 5₅ ₁ ⁴ ₅ ⁴ ₅ ⁰ ₈ ³ ₁ ³ ₁ ⁸ ₁ ⁵ ₀ ¹ ₂ ⁹ ₃ ⁴ ₄ ⁹ ₈ ⁵ ₅ ¹ ₅ ³ ₄ ⁷ ₄ ⁵ ₁ ¹ ₆ ₆ ¹ ₀ ₄ ³ ₇ ₆ ⁵ ₁ ₆ ³ ₂ ₇ ⁵ ₆ ₂ ⁸ ₉ ₀ ⁵ ₈ ₁ ⁴ ₃ ⁸ ₂ 1_/ _{2 . 2 2 . 2 2 . 2 2 . 2 2 . 2 1 . 1 1 2 2 2 2 2 2 2 2 . 2 2 2 1 2} ^S _T P _I ⁹ ₈ ⁵ ₈ ⁹ ₁ ₆ ² ₂ ² ₉ ⁴ ₇ ¹ ₂ ₈ ⁷ ₁ ¹ ₁ ₅ ⁸ ₇ ₃

^{8 4} _{7 6 3} 8⁸ _{6 4} ⁸ _{6 4} ⁸ _{6 3} ⁸ _{6 3} ⁸ _{6 3} ⁰ _{7 3} ⁰ _{7 3} ⁰ _{7 3} ⁰ _{7 3} ⁰ _{7 3} ^{2 0} ₀ ^. ₇ 2⁰ ₀ ¹ ^. ₁ 4⁰ ₀ ¹ ^. ₁ 4⁰ ₀ ¹ ^. ₇ 8⁰ ₀ ¹ ^. ₇ 8⁰ ₀ ¹ ^. ₇ 8⁰ ₀ ² ^. ₉ 2⁰ ₀ ² ^. ₉ 2⁰ ₀ ² ^. ₉ ⁰ ₀ ² ₉ ⁰ ₀ ² ₉ _{2 0 2 0 3 0 3 0 6 0 6 0 6 0 6 0 6 0} ² ₆ ^. ₀ ² ₆ ^. ₀ ² ₆ 1₄ -_E ⁵ ₅ ^{0 6 4 1 0 2 5} ^{2 7} 1 _{7 5 5 3 5 2 2} _{9 0} ^{1 1 1 1 2 2 2 2 2} ^{4 0} ₈ ₀ ^. ₈ 6⁹ ₀ ₇ ^{. 5 0} ₈ ⁵ ₀ . ₀ ^{- 0} ₀ ₀ ^. ₅ 4⁰ ₀ ₀ ^. ₁ 7⁰ ₀ ₀ ^. ₄ 6⁰ ₀ ₀ ^{. 2 0} ₀ ₀ ^. ₈ 0⁰ ₀ ₀ ^{. 7 0} ₀ ₀ ^. ₃ ⁰ _{0 6} _{8 0 3 6 0 6 E 0 5 0 1 0 4 0 5 0 3 0 2 0} ⁵ ₆ ^. ₀ ⁸ ₇ ₆ 5₅ ⁶ ₇ ₁ ¹ ₂ 7 _{5 8 3} 8 ₂ 9 _{5 3 8} 6₆ ⁷ ₄ ₁ ⁶ ₅ 7⁷ ₀ ⁶ _{0 2 1} ⁰ ₁ ⁸ ₃ ⁷ ₈ 4₆ ⁷ ₂ ₂ ³ ₄ ⁶ 1 ⁵ _{5 3} ¹ ⁵ ₆ ⁴ ₆ ³ ₆ ⁹ ₅ ⁷ ₇ ⁵ ₁ 0 ₁ ⁴ ₃ ² ₈ ⁸ ₀ ⁵ ₄ ⁹ ₈ ¹ ₃ ₁ ⁹ ^. ₁ ³ ₂ ¹ ₁ ² 0 ^. _{0 1} ¹ ₁ ² ₁ ⁰ ₁ ³ ₁ ^{7 4} - ^. ₀ - ^. ₀ - ^. ₀ ^. ₀ ^. ₀ - ^. ₀ - ^. _{0 1} - ^. _{0 1} ^. ₀ 4₉ 9 7 ^{2 6 3 5 3 9} 8 ⁹ ^{2 5} ₅ ⁹ _{2 1} 2 ₂ 2 ₅ 3 ₆ 3 ₈ 5 ₇ ⁹ _{7 4} ₀ 6⁹ ₅ ³ _{8 5} 9 ₂ 0 ₁ 6 ₄ 8 ₉ ⁸ ₅ ⁹ ₄ ⁵ ₉ ₁. ^{1 6} ₁. ₀. ₂. ₂. ¹ ₈. ⁶ ₈ ^{2 9} ₂ ₂ ⁰ ₅ ⁹ _{1 2} ^. ₉ ² _{7 9} ^. ₈ ⁵ ₇ ¹ ₃ ⁴ ₉ ⁸ ₁ ⁵ ₂ ⁴ _{2 9} ¹ ₂ ⁸ ₈ ² . 9 ³ ₄ ¹ ₈ ⁴ _{1 5} ^. . ₂ _{8 1} ⁸ ₁ ¹ ₉ . 9₂ 3₃ ^{6 4 4 2 4 6 6} ₃ 5¹ ₇ ₈ ³ ₉ ₃ ¹ ₂ ₈ ² _{3 1 6 8 3} ⁰ _{1 3} ⁵ ₉ ² ₈ ⁹ _{1 3} ⁹ ₃ ⁵ ₇ ⁸ ₅ ⁸ ₀ ⁸ ₀ ⁰ ₂ ⁹ ₁ ¹ _{2 5} ₁ ¹ ₉ ⁷ ₄ ⁰ ₂ ³ ₄ ⁷ ₄ ⁰ ₈ ¹ ₈ ¹ ₅ ⁷ ₂ ² ₆ ⁸ ₇ ⁶ ₂ ³ ₀ ⁶ ₂ ¹ ₃ ² ₄ ⁸ ₁ ⁷ ₄ ⁸ ₂ ⁸ ₂ 1_/ _{2 2 2 1 . 1 1 . 1 2 . 2 2 . 2 2 . 2 2 . 2 2 . 2 2 . 2 2 . 2 2 . 2} ^S _T P _I 0 ⁰ ₄ ⁹ ₀ ⁵ ^. ₅ ₀ ⁹ ₉ 5 ⁹ ₉ ⁶ ₃ ² ₀ ^. ₀ ⁶ ₉ ₁ ² ₈ ⁷ ₂ ¹ ₅ ⁵ ₂ ^. ₀ ¹ _{0 4} ⁴ ₀ ⁷ ^. ₁ ₀ ⁷ ₄ 2 ¹ ₂ ⁰ ₀ ⁸ ^. ₀ ₀ ⁹ ₉ ₆ ⁹ ₁ ⁵ ₇ ³ ₃ ⁹ ₀ ^. ₀ ⁹ ₅ ⁹ ₈ ₆ ⁰ ^. - ₁ ^E ₈ ^E ₃ ⁰ ₂ ⁷ ₇ ^. ₂ ⁰ ₁ -₆ ⁹ ₅ ² ₄ ⁹ ₀ ¹ ₀ ^. ₀

^. ₀ - ^. ₀ ^. ₀ ^. ₀ ^. ₇ - ^. ₀ - ^. ₀ ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ -⁷ _{7 3} ¹ ₄ ¹ ₅ ¹ ₅ ⁵ ₇ ⁵ ₇ ⁵ ₇ ^{8 0} ₂ ^{4 9 9} ⁰ ₀ ⁹ ^. _{3 8} 0² _{5 8} 0⁸ _{8 8} 0⁸ _{8 8} 0⁶ _{0 8} 0⁶ _{0 8} 0⁶ _{0 8} 0⁸ _{9 9} 0⁶ _{3 9 2} 0⁰ _{8 9} 0¹ _{9 9} 0¹ ₉ ₀ ⁹ _{6 0} ^. ₀ ⁹ _{1 0} ^. ₀ ⁶ _{2 0} ^. ₀ ⁶ _{2 0} ^. ₀ ¹ _{8 0} ^. ₀ ¹ _{8 0} ^. ₀ ¹ _{8 0} ^. ₀ ⁷ _{6 0} ^. ₀ ⁷ _{2 0} ^. ₀ ⁵ _{0 0} ^. ₀ ⁶ _{2 0} ^. ₀ ⁶ ₂ 3 ⁹ ³ ₂ ¹ ¹ ₂ ⁷ ¹ ₁ ² ¹ ₆ ⁵ ¹ ₉ ² ¹ ₉ ⁴ ₅ ⁵ ₄ ¹ ₈ 3 2 _{3 0 0 0 0 0} ¹ ₀ ^{1 3 3 2 1} ⁶ ₄ ⁵ ^. ₀ ₃ ^{- 0} E₀ ^. ₈ ₀ ^{6 0} 4₀ ^. ₇ ⁰ 0 ⁹ _{2 0} ^. ₃ ⁰ 0 ⁵ _{8 0} ^. ₉ ⁰ 0 ⁶ _{1 0} ^. ₈ ⁰ ₀ ₀ ⁶ _{4 0} ^. ₉ ⁰ ₀ ₀ ⁹ _{2 0} ^. ₈ ⁰ ₀ ₀ ¹ _{4 0} ^. ₂ ⁰ ₉ ₀ ⁴ _{6 0} ^. ₆ ⁵ 0 ¹ _{3 2} ⁵ ₅ . ₀ ₆ ^{- 7} E₃ ⁵ ^. ₀ ₇ - _E ₉ ⁰ _{4 3 8 4 4 3 8 2} ^{9 5} _{9 3} ⁵ ₈ ⁸ ₄ ⁵ ₈ ⁰ ^{9 2} ₉ ¹ ₅ ¹ ₁ ⁴ _{1 2 8} ₁ ^{4 9 1} _{5 1} ⁶ ₄ ⁰ ₃ ⁰ ₉ ⁶ ₄ ⁰ ₆ ⁶ ₁ ³ _{1 8 4} ₂ ² ₁ ^{1 1} ^{3 0} _{1 4} ³ ₃ ⁸ ₃ ¹ ₅ ⁷ ^. ₀ ^{2 0} ₃ ⁵ ₁ ¹ _{1 4} ⁵ ₂ ⁵ ₃ 0² _{8 1} _{2 2} ⁸ ₀ ² ₁ ⁰ ₃ ₁ ² ₂ ⁶ ₁ ⁹ ₀ ⁹ ₁ ² ₃ ⁸ ₂ - ^. ₀ ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ ^. ₀ ^. ₀ - ^. ₀ ^. ₀ ^. ₀ ^. ₀ ² ₈ ⁹ ₇ 7 0² ₆ ² ₁ 8 1 5 0⁷ ₂ 3 ⁵ ₀ 8 7 ⁶ ₃ ² _{0 9} 5⁴ ₂ ² _{5 4} 1 ₂ ³ ^{4 0} _{2 7} ³ ^{7 1} _{9 0} ⁰ ₆ ⁸ ₀ ⁹ _{8 0} 2⁷ ₁ ⁹ _{1 9} 1 ₇ 8¹ _{4 6} ^{. 4} ₀ ⁸ ₀ ⁸ ⁶ ₀ . ⁴ ₈ ⁴ . 5 ⁷ ₁ ² _{0 4} ^. ₃ ⁹ . 1 ⁷ ₂ ⁹ . 1 ¹ ₃ ⁴ _{9 7} ^. ₃ ² _{7 8} ^. ₈ ⁴ . 1 ⁴ ₁ ⁵ ₁ ₄ ⁰ . 2₅ ⁹ ₁ ⁷ _{1 5} ^. ₃ ^{4 6} 9₂ ^. ₃ ² ₂ ₀ . 9₂ 3² ₀ ⁴ ⁸ ₂ ⁰ _{1 3} ⁹ _{9 6} ⁶ ₂ ⁵ ₇ ⁶ ₃ _{8 3} ² ₃ ⁰ ₅ ^{8 2 2 8 5 6 1 6 4 9 5} ₅ ₆ ⁶ ₅ ⁰ ₈ ¹ ₈ ⁹ ₂ ⁵ ₀ ⁷ ₅ ⁴ ₁ ³ ₄ ⁷ ₄ ⁸ ₁ ⁶ _{8 6 1 7 5 7 9 8 5 1 4 2} ₆ ⁷ ₄ ⁹ ₄ ⁰ ₃ ¹ ₇ ⁸ ₅ ⁷ ₃ ⁵ ₁ ⁴ ₃ ⁸ ₂ ⁹ ₅ ⁰ ₅ ³ ₄ ⁸ ₂ 1_/ _{1 . 2 1 2 1 . 1 1 . 1 1 2 1 2 1 2 1 . 1 2 . 2 2 . 2 1 . 2 1 . 2} ^S _T P _I ³ ₈ ⁰ ₁ ⁹ ₇ ₉ ⁶ ₇ ² ₆ ⁸ ₂ ⁷ ₁ ₁ ⁷ ₃ ⁸ ₆ ₄ ¹ ₇

₀ ^. _{0 0} - ^. _{0 0} - ^. _{0 0} - ^. _{0 0} - ^. _{0 0} - ^. _{0 0} - ^. ₀ ¹ ₀ ⁰ _{0 0 0} ⁰ ₀ - ^. ₀ ^. ₀ ^. ₀ - ^. ₀ - ^. ₀ ₃ ^{0 2} _{0 3} 0³ ₀ ^{3 4} _{1 4} 8⁶ _{1 7} 0⁹ _{1 6} 8⁴ _{2 5} 3⁰ ₃ ^{5 0} _{3 6} 4¹ ₃ ¹ _{4 8} ¹ _{4 8} ¹ _{4 8} ₀ ¹ ₀ ¹ ₁ ¹ ₈ ¹ ₀ ¹ ₇ ¹ ₆ ^{1 1 1 5 1 3 1 3 1 3} ⁹ ₀ ^{. 9} ₀ ^{. 0} _{0 0} _{0 4 0} _{0 0 0} _{0 3 0} _{0 5 0 6} _{0 2 0 8} _{0 3 0 4} _{0 7 0 1} _{0 3 0 1} _{0 3 0 1} _{6 0 6 0} ^{. 1 . 3 . 2 . 6 . 6 . 0 . 8 . 7 . 7 .} ₀ ⁷ ₃ 7₈ ^{0 5 8 9 2 3 6 9 2 0 9} ⁴ _{4 1 4 0 1 1 1 6 4 8 1} _{9 0} ⁴ ^{6 0} _{5 0} ⁵ ⁰ _{1 0} ⁵ ⁰ ₀ ⁶ ⁰ _{1 0} ³ ⁰ _{4 0} ⁷ ⁰ _{5 0} ¹ ⁰ _{8 0} ⁷ ⁰ _{4 0} ⁵ ⁰ _{5 0} ⁴ ⁰ _{5 0} ⁴ ⁰ _{6 0} 0 ₈ _{8 0} ^. ₀ ¹ _{2 0} ^. ₀ ⁷ _{7 0} ^. _{0 4 0} ^. ₀ ⁹ _{7 0} ^. ₀ ⁰ _{7 0} ^. ₀ ² _{1 0} ^. ₀ ³ _{1 0} ^. ₀ ⁷ _{1 0} ^. ₀ ⁴ _{5 0} ^. ₀ ⁵ _{1 0} ^. ₀ ⁵ _{6 0} ^. ₀ ² ₆ _{9 6 6 1 9 1 2 7} 1₆ ¹ ₁ ⁷ ₅ ⁷ ₉ ⁰ ₅ ⁰ ₄ ⁶ ₁ ⁸ ₉ ⁹ _{3 3} 4₈ ^{3 1} ₁ 9₄ ¹ ₉ ² ₀ ² ₄ ² ₁ ³ ₂ ² ₈ ¹ ₇ ² ₂ ⁴ ₀ 0³ ₆ 8₃ ¹ ₂ ⁷ ₈ ⁷ ₅ ⁷ ₂ ⁴ ₆ ^{6 0} ₅ ¹ _{6 6} 0 ₅ 7⁶ ₁ 6₀ ^{. 1} ₇ _{0 1} ^{. 5} 0₁ ^{. 0} 0₁ ^{. 1} 0₁ ^{. 9} 0₀ ^{. 2} 0₁ ⁷ ^. ₁ ₀ ^{. 9} 0_{0 6} - ^. ₀ ⁸ ₀ ₀ - ^{. 3} 0₁ ⁰ ^. ₁ ₀ ^. ₀ - 0 7₇ ⁸ ₆ ⁴ ² ₄ ³ ₇ ⁷ ₀ 0 ^{0 1 5 4} 9 2 ⁹ 3 ⁸ ₉ ³ ₆ ⁰ _{3 8} 7⁷ ₀ ₅ ⁶ ₃ ₃ ⁷ ₁ ₂ ⁹ ₈ ² ₆ ⁰ ⁴ _{4 2} 8⁴ ₈ . ³ ₇ ³ ₇. ⁹ ₀. ⁸ _{0 4} . ^. ₅ ⁰ ₄. ⁵ ₀. ⁸ ₉. ⁵ _{8 6 3} . ^. ₉ ^. ₉ ⁸ ₃. ₂ ₉ ⁹ ₂ ⁷ ₀ ⁶ ₁ ⁷ ₀ ² ₁ ⁷ ₄ ⁴ ₆ ⁴ ₇ ⁵ ₃ ⁴ ₁ ⁴ ₆ ⁴ ₁ ⁷ ₁ ⁷ ₄ ³ ₃ ⁹ ₇ ⁵ ₅ ⁶ ₀ ³ ₃ ⁵ ₅ ⁵ ₂ ⁸ ₄ ⁸ _{4 3} . 9₂ 3⁵ ₃ ³ ⁶ ₆ ¹ ₀ ⁵ ₀ ¹ ₇ ³ ₄ ³ ₄ ⁴ _{2 9} ² ₁ ³ ₆ ⁶ ₇ ⁴ ₉ ⁹ ₈ ¹ ₃ ₅ ⁴ ₂ ⁹ ₂ ^{9 6 7 9 5} ₅ ₀ ⁴ ₆ ⁰ ₆ ¹ ₅ ³ ₉ ⁵ ₄ ³ ₄ ⁷ ₅ ⁷ ₄ ⁰ ₂ ¹ ₆ ³ ₃ ¹ ₈ ³ ₅ ⁹ ₅ ¹ ₁ ⁶ ₁ ₆ ² ₃ ₆ ⁸ ₉ ₇ ⁶ ₇ ² ₇ ₅ ⁹ ₇ ⁸ ₁ ₃ ² ₃ ₆ ⁸ ₇ ⁸ ₂ 1_/ _{2 2 2 2 . 2 2 . 2 2 . 2 2 2 1 2 2 2 1 . 2 2 2 1 2 1 2 1 2} ^S _T P _I ⁵ ₉ ³ ₆ ⁸ ₈ ₈ ⁹ ₇ ⁴ ₆ ¹ ₁ ⁷ ₄ ₅ ⁹ ₇ ⁹ ₁ ⁸ ₃

0₀ ¹ ^. ₀ ¹ 0 ^. _{0 0} ¹ ₀ ⁰ ₀ ¹ ₀ ⁰ ₀ ^{0 0} ₀ ² ₀ ⁰ ₀ - ^. ₀ ^. ₀ ^. ₀ - ^. ₀ ^. _{0 0} ^. ₀ ^. ₀ - ^. ₀ - ^. ₀ -₈ ^{3 1} _{4 8} 3¹ _{4 8} 3³ _{4 9} 7³ _{4 9} ³ _{4 9} ³ _{4 9} ³ _{4 9} ³ _{4 9} ¹ _{5 5} ³ _{5 4} ³ _{5 4} ₁ ¹ ₁ ¹ ₁ ^{1 1 7 1 7 1 7 1 7 1 7 1 5 1 9 1 9} ⁷ ₀ ^. _{3 0} _{0 3 0 5} _{0 1 0 5} _{0 1 0 5} _{0 1 0 5} _{0 1 0 5} _{0 1 0 5} _{0 1 0 6} _{0 9 0 4} _{0 4 0 4} _{3 0} ^{7 . 7 . 1 . 1 . 1 . 1 . 1 . 1 . 3 . 5 .} ₀ ⁵ ₄ 9₃ ^{3 5 3 0 1 6 9 2 3 5} ⁴ _{9 5 9 2 9 6 4 5 1 9} _{2 0} ^{4 2 1 2 1 1 2 7 3 1} ^{5 0} _{9 0} 0 _{3 0} 0 _{4 0} 0 _{5 0} 0 _{9 0} 0 _{1 0} 0 _{4 0} 0 _{1 0} 0 _{9 0} 0 _{3 0} 0 ₇ _{4 0} ^. ₀ ⁶ _{7 0} ^. ₀ ⁴ _{4 0} ^. ₀ ⁸ _{1 0} ^. ₀ ⁶ _{9 0} ^. ₀ ⁵ _{0 0} ^. ₀ ⁶ _{5 0} ^. ₀ ² _{9 0} ^. ₀ ⁰ _{4 0} ^. ₀ ³ _{8 0} ^. ₀ ⁷ _{1 0} ^. ₀ ⁸ ₅ ₂ 7_{8 8 8} 3 _{3 8 6 5} ₉ ⁴ ₂ ⁴ ₁ ^{7 1} _{2 3 5} 5⁴ 4₃ ⁰ _{6 3 3} ⁹ ₆ ⁶ ₆ ⁰ ₉ 5¹ 6_{0 2} ⁵ ³ ₁ ² ₂ ⁴ ₄ ⁶ ₇ ⁴ ₅ ¹ ₉ ⁶ ₃ ⁷ ₁ 1⁶ 1_{5 5} ¹ ₈ ⁹ ₆ ³ ₂ ³ ₀ ⁹ ₄ ¹ ₇ ⁷ ₉ ⁰ ₉ .₀ ³ ₁ ⁹ ₁ ³ ₁ ⁴ ₁ ⁹ ₁ ³ ₁ ⁵ ₁ ² ₁ ⁴ ₂ ⁵ ₂ - ^. ₀ ^. ₀ - ^. ₀ - ^. ₀ ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ ^. ₀ ^. ₀ 2₅ ⁵ ₉ ⁶ ₅ ³ ₆ ⁹ ₅ ⁷ ₈ ^{3 6 1} 6 4 3₄ ³ ₄ ⁴ ₉ ⁵ _{0 8 0} ₇ ⁹ ₃ ⁸ ₅ ^{7 4 6 7} ₄ ² ₉ 6₉ ¹ ₃ ⁷ ₆ ⁸ _{1 9 3} ₇ ⁵ ₉ ¹ ₅ ^{0 9 5 1} ₂ ⁸ ₃ ⁷ . ₉ ⁶ ₂ ⁵ . 5 ¹ ₃ ⁷ . . . . ₀. ₃. ₀. ⁶ 0 ² ₁ ⁷ ₉ ⁸ ₂ ² ₃ ⁶ ₁ ⁸ ₀ ⁴ ₁ ³ ₃ ⁴ ₆ ⁴ ₁ ⁵ ₁ ⁶ ₁ ⁷ ₁ ⁷ _{4 8} ^{. 1} 4 ⁹ _{3 0} ^. ₂ ₃ ⁵ ₁ . 9₂ 3⁸ ₉ ⁸ ² ₉ ⁸ ₇ ⁰ ₉ ⁶ ₉ ³ ₃ ⁵ ₆ ² ₇ ⁶ ₁ ⁰ ₂ ⁶ ₅ ⁵ ₁ ⁰ ₀ ² ₄ ⁹ ₇ ¹ ₅ ⁴ ₀ ³ ₇ ^{4 2 5 6 5} ₃ 5₅ ₁ ¹ ₄ ¹ ₄ ⁴ ₅ ⁶ ₂ ⁶ ₁ ² ₈ ⁸ ₁ ⁵ ₀ ⁶ ₂ ² ₁ ⁵ ₃ ⁹ ₄ ² ₅ ³ ₆ ⁸ ₆ ⁹ ₃ ⁰ ₇ ₆ ⁶ ₆ ₀ ⁶ ₉ ₁ ² ₉ ₀ ⁵ ₅ ₄ ⁰ ₆ ⁸ ₂ 1_/ _{2 1 2 1 2 1 . 2 1 . 2 1 . 2 1 . 2 1 . 2 1 . 2 2 . 2 1 1 1 1} ^S _T P _I 6 ⁸ ₄ ⁸ ₂ ^. ₄ ₀ ¹ ₂ 0 ² ₄ ⁴ ₄ ² ₁ ^. ₀ ⁵ ₀ ₆ ⁸ ₆ ¹ ₆ ⁹ ₁ ⁷ ₁ ^. ₀ ¹ _{1 1} ⁵ ₄ ⁹ ^. ₈ ₀ ⁵ ₀ 4 ⁵ ₈ ² ₁ ⁵ ^. ₄ ₀ ⁹ ₂ ₁ ¹ ₉ ⁶ ₈ ⁶ ₄ ⁹ ₀ ^. ₀ -⁷ ₃ ⁶ ₉ ₇ ⁰ ^. - ₂ ^E ₂ ^E ₆ ² ₅ ⁸ ₂ ^. ₃ ¹ ₁ -₈ ⁶ ₆ ⁵ ₆ ³ ₃ ² ₀ ^. ₀ -⁴ _{5 1}

₀ ² _{5 6 6 6 3 6 9 6 9 7 7 7 7 8 3 8 8 7 9 0 5} ¹ ^. ₂ 8¹ ₀ ¹ ^. ₀ 0¹ ₀ ⁵ ^. ₉ 8¹ ₀ ⁵ ^. ₆ 5¹ ₀ ³ ^. ₄ 1¹ ₀ ³ ^. ₄ 1¹ ₀ ⁷ ^. ₆ 0¹ ₀ ⁷ ^. ₆ 0¹ ₀ ² ^. ₅ 4¹ ₀ ¹ ^. ₈ 3¹ ₀ ³ ^. ₅ 3¹ ₀ ⁴ ^. ₄ 3² ₀ ⁷ ^. ₆ _{0 3 0 6 0 4 0 8 0 5 0 5 0 8 0 8 0 9 0 1 0 3 0 7 0} ³ ₃ ⁸ ₉ ⁵ ₆ ⁶ ₃ ⁵ ₈ ⁶ ₁ ⁹ ₇ ³ ₁ ³ ₂ ⁰ ₈ ⁷ ₅ ^{0 6 6} ^{2 9 8 8 7 6 0 0 3} _{0 6 7} _{0 0 0 0 0 0 1 1 0} ^{8 9 2 4} ⁰ ₀ ^. ₂ 1⁰ ₀ ^. ₂ 0⁰ ₀ ^. ₅ 4⁰ ₀ ^. ₁ 1⁰ ₀ ^. ₉ ⁰ ₆ ⁰ ₈ ⁰ ₃ ⁰ ₀ 0 _{1 0} 0 ₁ 0 ₀ 0 0_{3 0} _{0 6 0} _{0 8 0} _{0 6 0} _{0 5 0} _{0 1 0 1} _{0 9 0 9} _{0 1 0 4} _{0 2 0 4 0 5 0 7} ^{4 . 4 . 2 . 4 . 7 . 2 . 2 . 3 .} ₀ ⁸ ₂ _{5 9 5 3 4 5 6} ⁷ ₂ ² ₆ ¹ ₈ ₄ ^{8 7} _{8 8} 6 ₁ 9 ₂ 8 ₄ 7¹ ₅ ⁷ ₉ ⁴ _{4 7} 0⁷ ₅ ¹ ₆ ³ ₅ 6¹ _{1 6} 7 ₆ 7 ₆ 7 ₆ 3⁸ ₈ ¹ ₉ ¹ _{8 0} 2⁴ ₉ ² _{3 4} 4 ₅ 2⁰ _{7 3} 1 ₃ 7 ₇ 3 ₈ 3³ ₅ ⁶ ₃ ² _{2 3} 3^{4 9} _{3 9} 1 ₈ 2⁹ _{0 8} 2 ₆ 8 ₃ 5 ₂ 1⁹ ₃ ⁶ ₁ ¹ _{1 6} ₂ ^. ₀ ^. _{0 1} - ^. _{0 1} ^. ₀ ^. _{0 1} - ^. _{0 0} ^. _{0 1} ^. _{0 1} ^. ₀ ^. ₀ - ^. ₀ - ^. ₀ ⁶ ₂ - ^. ₀6⁷ 3₄ ⁶ ¹ ₁ ⁵ ₁ 8 ^{6 1 4} 4 ⁰ 7 7₂ ^{8 9 8} _{7 2} 4 ₀ 6 ₀ 8³ _{6 2} ³ ₂ ⁵ ₀ ⁴ ₂ ⁹ ₁ ⁶ _{2 1} ₈ ^{. 9} ₄ ⁹ _{3 9} ³ ₅ ³ _{8 3} . . ^. ₈ ⁶ ₃ ₀. ⁵ ₂ ₀ ⁷ ₀ ² ₇ ⁷ ₅ ⁶ ₅ ¹ ₃ ² ₃ . ⁰ ₃. ^{7 3} ₅. ^{4 7 2} ₈. ⁴ ₂ ₈ ⁷ ₁ ⁷ ₂ ⁹ ₉ ⁴ ₂ ² ₁ ¹ ₆ ³ ₄ ⁷ ₁ ⁷ ₄ ⁵ ₅ ⁷ _{4 4} ^. _{7 4} ³ ₆ ² _{2 0} ^. ₉ ⁸ _{8 7} ^. ₆ ⁵ ₁ ⁷ _{4 8} ^. ₅ ⁹ ₄ . 9₂ 3² ₆ ⁵ ⁶ ₉ ⁸ ₀ ⁴ ₂ ⁶ ₉ ⁰ ₇ ² _{0 6} ⁵ ₈ ³ ₅ ⁹ ₈ ¹ ₅ ³ ₀ ⁵ ₇ ⁴ ₀ ⁶ ₉ ⁵ ₃ _{5 7} ^{2 9 8 9 1 5 7 5} ₅ ₁ ² ₀ ⁵ ₃ ⁵ ₅ ⁵ ₄ ⁰ ₉ ⁴ ₄ ⁸ ₀ ⁵ ₄ ⁸ ₂ ³ ₅ ⁹ ₅ ⁵ ₁ ⁴ ₄ ⁵ ₄ ⁰ ₈ ⁸ ₅ ⁷ ₁ ₃ ⁶ ₇ ₂ ⁸ ₃ ₆ ⁵ ₃ ₃ ⁴ ₉ ₈ ⁹ ₁ ₁ ⁵ ₇ ₇ ⁹ ₉ ₃ ⁵ ₁ ⁸ ₂ 1_/ _{1 . 2 2 2 2 . 2 2 . 2 1 2 1 2 2 . 2 2 . 2 1 . 2 1 2 1 2 2 2 1 . 2} ^S _T P _I 0 ⁵ ₉ ⁷ ₀ ⁶ ^. ₈ ₀ ⁴ ₅ 9 ⁵ ₆ ³ ₇ ¹ ₀ ^. ₀ ³ ₈ ₂ ⁰ ₄ ³ ₇ ⁴ ₂ ^. ₀ -⁶ _{5 8} ⁸ ₅ ⁰ ^. ₃ ₀ ⁸ ₃ 3 ⁷ ₃ ⁴ ₂ ⁸ ^. ₆ ₀ ⁹ ₃ ₇ ⁴ ₀ ⁸ ₆ ⁰ ₆ ⁵ ₀ ^. ₀ -² ₃ ² ₆ ₁ ⁰ ^. - ₅ ^E ₅ ^E ₆ ⁷ ₅ ⁶ ₀ ^. ₁ ⁷ ₀ -₅ ⁵ ₈ ⁶ ₉ ⁴ ₄ ¹ ₀ ^. ₀ ⁰ _{0 5}

² ₀ ^{7 6} _{0 2} 6⁶ _{0 2} 6⁶ _{0 6} 1⁵ ₁ ^{2 5} ₁ ^{2 9} ₁ ¹ _{2 3} 0⁶ _{2 9} 3³ _{4 2} 0⁰ _{5 2} 8⁰ _{5 2} 8 ^. ₆ 3² ₀ ^. ₇ 5² ₀ ^. ₇ 5² ₀ ^. ₄ 3² ₀ ^. ₃ 6² ₀ ^. ₃ 6² ₀ ^. ₅ ² _{0 6} ² _{0 9} ² _{0 0} ² _{0 3} ² _{0 3} _{0 3 0 5 0 5 0 7 0 9 0 9 0} ⁵ ₀ ^. ₀ ¹ ₇ ^. ₀ ⁹ ₆ ^. ₀ ² ₇ ^. ₀ ⁹ ₆ ^. ₀ ⁹ ₆ ⁵ ₇ ^{4 2 0 7 8 8 0 2 1 5 4} ⁴ _{6 0 5 0 6 7 6 7 2 4 2} ₀ ^{2 3 3 5 4 5 5 5 2 7 7} ⁰ ₀ ^. _{4 1} 0 ₁ 0 ₁ 0 ₁ 0 ₁ 0 ₁ 0 ₀ 0 ₁ 0 ₁ 0 ₀ 0 ₀ 0 5_{0 3} _{0 0 0 6} _{0 6 0 1} _{0 2 0 1} _{0 8 0 6} _{0 4 0 5} _{0 7 0 5} _{0 0 0 3} _{0 0 0 6} _{0 3 0 4} _{0 0 0 4} ₀ ^{1 . 1 . 1 . 9 . 5 . 2 . 7 . 5 . 0 . 7 . 0 .} ₀ ² ₆ ₇ ⁷ _{2 5 9 7 2 9 8 3 9 2 4} ₄ ¹ ³ ₀ ^{6 6} ₄ ^{5 9} ₉ ⁴ ₃ ⁴ ₀ ₂ ⁹ ² _{2 4} 5⁶ _{4 9} ⁹ ⁸ ₆ ³ ₀ ^{3 1} 5₈ ^{0 5} 2₉ ⁴ ₃ ₅ ² ⁸ _{1 6} 4⁷ _{4 3} ³ ² ₆ ³ ₆ ^{1 1} 0₉ ^{0 5} 4₄ ¹ ₉ ₁ ^{. 7} _{0 3 7} - ^. ₀ ⁶ ₁ ² - ^. _{1 0} ⁵ 0 ^. ₀ ⁴ ₁ ⁹ ₅ ₁ ^{3 8} - ^{. 7} 0₀ ^{. 6} 0₀ ^. _{1 7} ₀ ^{. 8 9} 0 ⁰ _{2 0 0} - ^. ₀ ^{. 1} 0 ¹ _{6 1} - ^. ₀ ^. ₀ -⁵ ₉ 8 ^{1 4} 9 9 7 6^{2 4} _{7 3} 8 ₆ ⁰ ¹ ₉ ¹ ₃ ⁰ ₂ ⁸ ₉ ³ ₅ ⁴ ₃ ¹ ₄ ⁹ ₃ ₇ ^{8 1} _{4 5} 6 ₆ ⁸ ⁶ ₇ ¹ ₆ ⁸ ₇ ³ ₅ ¹ ₁ ⁶ ₀ ⁶ ₂ ⁵ ₅ ₂. ^{2 4} ₇. ₅. ⁰ ₃. ³ ₁. ^. ₅ ⁴ ₄. ^{5 .} ₂ ^{6 5} ₉. ₁ ⁴ _{3 2} ^. ₉ ⁹ ₁ ⁹ ₁ ³ ₄ ¹ ₁ ² ₅ ³ ₁ ¹ ₄ ⁴ ₈ ⁵ ₂ ¹ ₂ ⁶ ₃ ³ ₃ ² _{1 7} ^. ₈ ⁷ ₀ ⁶ ₁ ¹ _{1 1} ^. ₁ ¹ ₁ ² ₃ ⁶ ₂ ₅ . 9₂ 3² ₀ ⁷ ² ₀ ² ₁ ⁹ ₇ ³ ₃ ⁶ ₉ ⁷ ₂ ⁸ ₂ ⁵ _{9 3} ³ ₉ ³ ₈ ⁰ ₅ ¹ ₄ ⁹ ₃ ₄ ¹ ₅ ^{7 5 2 2 5 4 5} ₅ ₁ ⁸ ₀ ⁶ ₂ ⁸ ₆ ⁶ ₂ ⁶ ₇ ¹ ₂ ⁵ ₁ ⁵ ₄ ⁴ ₂ ² ₂ ² ₃ ³ ₃ ¹ ₂ ⁸ ₄ ² ₇ ₇ ⁹ ₉ ₃ ⁵ ₆ ₁ ² ₉ ₅ ⁶ ₇ ₅ ³ ₇ ⁶ ₀ ₈ ³ ₁ ₁ ³ ₁ ⁸ ₂ 1_/ _{1 . 2 2 . 2 2 . 2 2 . 2 2 . 2 2 . 2 2 . 2 1 . 2 2 2 1 2 1 . 2 1 . 2} ^S _T P _I 0 ⁸ ₄ ⁰ ₅ ⁵ ^. ₅ ₀ ⁶ ₀ 9 ⁹ ₂ ⁵ ₈ ⁹ ₂ ^. ₀ ⁶ ₅ ₆ ⁴ ₅ ⁸ ₇ ⁶ ₆ ⁴ ₀ ^. ₀ -¹ _{1 1} ⁵ ₄ ⁹ ^. ₈ ₀ ⁵ ₀ 5 ⁷ ₆ ² ₁ ⁹ ^. ₂ ₀ ⁷ ₈ ₅ ⁵ ₆ ⁰ ₂ ⁷ ₅ ⁴ ₀ ^. ₀ ⁴ ₀ ⁵ ₉ ₄ ⁰ ^. -₉ ^E ₅ ^E ₃ ² ₄ ⁰ ₄ ^. ₁ ⁰ ₁ -₃ ⁴ ₈ ⁰ ₁ ⁰ ₁ ¹ ₀

^. ₀ ^{0 0} _{0 0 0 0 0 0} ₀ ^. _{0 0} - ^. _{0 0} ^{. 0} 0₀ ^. ₀ ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ ⁰ ₀ ⁰ ₀ - ^. ₀ ^. ₀ ⁰ _{5 2} ₀ ^{8 0} ₅ ^{3 0} ₅ ^{3 0} ₅ ^{3 0} ₅ ^{3 0} ₅ ^{3 0} ₅ ^{3 0} ₅ ² _{5 1} ² _{5 1} ⁷ _{5 5} ⁷ _{5 5} ⁹ _{5 6} ² ^. ₃ 9² ₀ ^. ₃ 2² ₀ ^. ₃ 2² ₀ ^. ₃ 2² ₀ ^. ₃ ² _{0 3} ² ₃ ^{2 3} ₃ ^{2 0 2 0 2 0 2 0 2 2} 0_{7 0} _{0 7 0} _{0 7 0 6} _{0 9 0 6} _{0 9 0 6} _{0 9 0 6} _{0 9 0 9} _{0 6 0 7 0 7 0 7 0} ² ₇ ^{. 2 . 2 . 2 . 7 . 7 . 6 . 6 .} ₀ ⁴ ₄ ¹ ₄ ⁷ ₆ ⁵ ₂ ³ ₆ ⁰ ₄ ⁶ ₆ ⁹ ₇ ³ ₃ ⁰ ₈ ³ ₈ ⁵ ₇ ^{7 5} ^{7 1 0 3 3 2 3 8 3 3 9} ₆ 9 ₄ _{0 2 2 2 2 2 2 1 1 1 1 1} ⁰ ⁰ ₀ ^{. 5 0} _{0 1} ⁰ _{0 7} ⁰ _{0 9} ⁰ _{0 6} ⁰ _{0 5} ⁰ _{0 6} ⁰ _{0 2} ⁰ _{0 7} ⁰ _{0 2} ⁰ _{0 6} ⁰ ₂ _{0 4} ⁰ _{0 8} _{0 2} ^. ₀ ⁹ ₃ ^. ₀ ⁶ ₃ ^. ₀ ² ₁ ^. ₀ ⁰ ₄ ^. ₀ ⁹ ₅ ^. ₀ ² ₁ ^. ₀ ⁸ ₇ ^. ₀ ³ ₀ ^. ₀ ¹ ₀ ^. ₀ ¹ ₄ ^. ₀ ¹ ₁ ^. ₀ ⁰ ₂ _{2 8 7 5 5} ^{1 9} _{9 8 4 6 6} ₂ _{2 3} ⁵ ₂ ⁷ ₁ ⁷ ₁ ² ^{7 5} ₉ ¹ ₁ ^{9 7 2 0 8} ₀ ⁷ ₄ _{9 4} ⁸ ₆ ³ ₉ ³ ₃ ⁴ _{2 8 6 7} ₀ ² ₁ ¹ ₇ ^{0 5 5 5} ₉ ¹ ₉ ^{0 3} _{1 9} ² ₇ ¹ ₁ ⁴ ₂ ⁶ _{0 0 8} ^. ₀ ⁹ ₄ ³ ₃ ⁵ ₈ ⁷ ₉ ^{5 3 1} ₈ ² ₂ ₀ ⁹ ₀ ⁰ ₁ ⁹ ₀ ⁷ _{0 5} ₀ ¹ ₁ ⁶ ₁ ⁹ ₁ ² ₁ ^{9 7} ₀ ¹ ₁ - ^. ₀ ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. _{0 0} ^. ₀ ^. ₀ - ^. ₀ -⁵ ₇ ² ₃ ⁹ ₄ ⁴ ₉ ³ ₃ ⁴ ₅ ¹ ₁ ⁵ ₆ 5 ^{5 6} 0 ⁹ ^{8 0 6 5 1 3 9} _{3 1 1} ³ _{7 4} _{0 8 5 9 3 8} ⁷ ₀ ¹ ₂ ² ₅ ^{2 8} ⁰. ³. ³. ³. ⁴. ⁰. ^{3 3} _{4 4} ⁶ _{9 2} _{5 0 6 2 6 5 7}. ⁵ ₄. ^{5 5} ₄. ⁰ ₀ ^. ₆ ⁷ ₇ ³ ₇ ⁶ ₇ ⁹ ₁ ¹ ₂ ⁷ ₂ ⁸ ₁ ⁸ ₂ ⁹ ₄ ⁷ ₉ ⁵ ₄ ⁴ ₈ ⁹ ₀ ¹ ₂ ¹ ₄ ⁸ ₁ ⁵ _{6 4} ^. _{3 3} ⁸ ₂ ⁸ . 0 ⁹ ₂ ⁷ ₈ ⁸ ₁ ⁹ . 0 ³ ₁ ⁸ ₂ ₂ . 9₂ 3⁰ ₇ ² ⁵ ₁ ⁹ ₁ ⁵ ₅ ⁴ ₇ ⁹ ₀ ⁸ ₃ ₉ ⁸ ₇ ^{4 7 7 9 3 0 0 0 0 3 3 7 8 5 2 2 0 5} ₅ ₆ ³ ₇ ⁰ ₂ ⁰ ₆ ⁴ ₃ ⁶ ₀ ¹ ₄ ¹ _{1 8 7 7 6 7 3 7 8 0 4 4 3 2 6 9 0 4} ₄ ⁵ ₄ ³ ₅ ⁷ ₄ ⁹ ₃ ¹ ₆ ² ₀ ² ₆ ¹ ₃ ⁵ ₂ ⁷ ₂ ⁵ ₄ ³ ₄ ³ ₄ ⁹ ₂ ² ₅ ⁶ ₅ ⁹ ₆ ⁵ ₇ ⁸ ₂ 1_/ _{1 . 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 . 2 2 . 2 2 . 2} ^S _T P _I 5 ⁶ ₁ ⁰ ₀ ^. ₀ ⁷ ₃ 8 ⁸ ₃ ¹ ₀ ⁰ ₀ ^. ₀ ⁴ ₁ ₃ ⁴ ₄ ⁶ ₁ ⁶ ₁ ⁶ ₂ ^. ₀ ² _{3 1} ¹ ₇ ⁵ ^. ₃ ₀ ² ₆ 2 ⁴ ₄ ⁵ ₅ ⁵ ^. ₇ ₀ ¹ ₆ ₉ ⁸ ₂ ⁴ ₃ ⁴ ₃ ¹ ₀ ^. ₀ ⁵ ₄ ⁹ ₅ ₅ ⁰ ^. - ₃ ^E ₂ ^E ₆ ⁸ ₀ ¹ ₄ ^. ₃ ⁶ ₀ -⁴ ₁ ⁰ ₂ ⁰ ₆ ⁰ ₀ ^.

₀ ^. ₀ ^. ₀ - ^. ₀ ^. ₀ - ^. ₀ - ^. ₀ ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ - ^. ₀ ² _{6 8} ² _{6 8} ⁴ _{6 3} ⁷ _{8 2} ⁷ _{8 2} ⁸ _{8 3} ⁸ _{8 3} ⁹ _{8 3} ⁹ _{8 3} ² ₂ ⁴ ₁ ⁹ ₉ ⁹ ₉ ⁰ ₅ ² ₀ ⁵ ^. ₁ ² ₀ ⁷ _{5 0} ⁵ ^. ₁ ² 0 ⁷ _{5 0} ⁹ ^. ₂ ² 0 ¹ _{6 0} ⁶ ^. ₃ ² 0 ² _{9 0} ⁶ ^. ₃ ² 0 ² _{9 0} ⁵ ^. ₀ ² 0 ² _{4 0} ⁵ ^. ₀ ² 0 ² _{4 0} ² ^. ₈ ² 0 ⁰ _{5 0} ² ^. _{8 9} 2 0 ⁰ _{5 0} ⁷ ^. _{5 9} 2⁹ _{5 9} 2³ _{3 9} 2³ _{3 1} 3⁶ ₈ ₀ ⁶ _{2 0} ^. ₀ ⁶ _{1 0} ^. ₀ ⁰ _{7 0} ^. ₀ ⁰ _{7 0} ^. ₀ ⁰ ₆ ³ ₈ ¹ ₀ ⁶ ₀ ⁸ ₅ ⁵ ₇ ⁷ ₇ ⁷ ₅ ⁸ ₇ ⁰ ₈ ⁶ ₀ ^{5 6 9 8} ^{6 6 5 1 1 0 1 7 7 3} _{3 2 7 1} _{2 2 1 1 1 3 3 1 1 3} ⁴ ₃ ^{9 9 5} ⁰ ₀ ^. ₃ 1⁰ ₀ ^. ₁ 9⁰ ₀ ^. ₇ 0⁰ ₀ ^. ₇ 0⁰ ₀ ^. ₈ 9⁰ ₀ ^. ₉ 9⁰ _{0 9} ⁰ _{0 5} ⁰ _{0 5} ⁰ _{0 6} ⁰ _{0 4 1} 0 ₁ _{0 7} ⁰ ₂ _{0 7} ⁰ _{0 9} _{0 2 0 5 0 9 0 2 0 1 0 3} ^. ₀ ⁸ ₈ ^. ₀ ⁶ ₂ ^. ₀ ⁷ ₅ ^. ₀ ⁵ ₆ ^. ₀ ¹ ₅ ^. ₀ ² ₁ ^. ₀ ¹ ₈ ^. ₀ ⁴ ₄ ₆ ⁰ ₅ ₄ ¹ _{4 7 1 6 3 7 6 2 9 7 4} ⁹ ₃ ⁵ ₉ ⁹ ⁹ _{2 5} ⁰ ⁰ ₀ ⁴ ₂ ⁷ ₁ ⁷ ₁ ⁹ ₆ ₉ ² ₂ ⁸ ₂ ⁴ ₄ ⁷ ₆ ^{4 4} ₁ ₃ ⁹ ₄ ⁹ ₉ ³ ₃ ⁸ ₈ ² ₅ ⁷ ₅ ₉ ¹ ₅ ⁷ ₅ ¹ ₄ ⁷ ₄ ^{7 3} ₇ ⁷ ₂ ⁰ ₀ ^{. 9} _{0 0 0} ² ² ₇ ¹ ₅ ⁴ ₁ ¹ ₈ ⁵ ₈ ₅ ² ₁ ⁶ ₂ ⁷ ₈ ⁸ ₅ ^{4 5} ₀ ₁ ⁹ ₂ ² ₂ ⁶ ₁ ⁷ ₀ ⁶ ₈ ₀ ⁷ ₁ ⁹ ₀ ⁶ ₀ ⁷ ₀ ⁹ ₀ ⁸ ₀ - ^. ₀ - ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ - ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ -7⁵ ₂ ⁰ ₅ ³ ₉ 8 4 ⁹ ₄ ⁹ ₂ 7 ⁷ ₈ ² ₇ 5 ⁵ ₈ ¹ ₂ ⁰ ₅ ³ ₀ ⁹ ₄ ¹ ₂ ⁹ ₂ ⁸ ₉ ¹ ₀ ² ₁ ⁸ ₀ ¹ ₄ ³ ₅ ¹ ₄ ³ ₀ ⁹ ₂ ⁷ ₇ ^. ₃ ⁸ ₅. ⁴ ₆ ⁹ ₉ ⁴ ₁ . ^{7 8 9} ₃. ⁸ ₆ ⁶ ₂ . ^. ₄ ² ₁. ⁹ ₃ ⁸ ₅ . ^. ₉ ⁶ ₁. ³ ₂. ⁵ ₆ ² ₂ ⁹ ₁ ⁴ ₂ ⁴ ₁ ¹ ₂ ⁵ _{3 3} ^. ₂ ² _{4 3} ^. . ₂ ₃ ⁷ ₆ ⁰ ₁ ⁶ ₇ ⁶ ₆ ¹ ₂ ⁰ ₂ ⁵ ₆ ⁸ ₁ ⁹ ₆ ⁷ ₂ ⁵ ₇ ² ₁ ¹ ₀ ⁴ ₈ ⁹ ₆ ⁷ ₂ ⁷ ₃ ⁵ ₆ ⁹ ₈ . 9₂ 3⁵ ₆ ² ² ₉ ⁹ ₂ ⁴ ₃ ₄ ⁹ _{1 3} ⁵ ₇ ^{0 7 8 7 1 0 0 1 3 4 7 8 7 0 3 5 7 8 7 5} ₅ ₅ ⁶ ₅ ⁵ ₅ ⁷ ₈ ⁷ ₆ ² _{4 2 5 7 4 5 6 5 4 2 0 3 2 4 6 0 7 3 2 5 2} ₇ ³ ₃ ⁷ ₆ ⁴ ₃ ⁰ ₈ ⁷ ₉ ² ₃ ³ ₃ ⁸ ₂ ³ ₃ ¹ ₂ ⁶ ₆ ¹ ₅ ³ ₄ ⁹ ₂ ⁴ ₃ ⁹ ₁ ⁵ ₁ ⁴ ₄ ³ ₄ ⁹ ₂ ² ₄ ⁹ ₆ ⁸ ₂ 1_/ _{2 2 2 2 1 2 1 1 1 1 2 2 2 2 1 2 1 2 2 2 2 2 1 2 1 2 2 . 2} ^S _T P _I ² ₀ ² ₁ ⁴ ₂ ₆ ⁵ ₁ ⁷ ₈ ¹ ₃ ⁸ ₇ ₆ ⁶ ₂ ⁷ ₉ ₂ ⁸ ₉ ₆

³ _{3 3} ^{9 3} _{3 5 3 5 3 5 3 5 3 5 5 6 6 8 7 7} _{7 0} ⁶ ^. ₆ ³ 0 ⁷ _{2 0} ² ^. ₅ ³ 0 ³ _{0 0} ² ^. ₅ ^{3 2} ₅ ^{3 2} ₅ ^{3 2} ₅ ^{3 8} ₄ ^{3 6} ₉ ^{3 3} ₃ ^{3 1} _{1 8} 3⁷ _{9 1} 4³ ₂ ₀ ³ _{0 0} ^. ₀ ³ _{0 0} ^. ₀ ³ _{0 0} ^. ₀ ³ _{0 0} ^. ₀ ⁷ _{9 0} ^. ₀ ⁰ _{9 0} ^. ₀ ⁶ _{5 0} ^. ₀ ⁸ _{6 0} ^. ₀ ⁶ _{9 0} ^. ₀ ⁹ ₇ 5₉ ⁷ ₂ ⁷ ₂ ⁹ ₅ ^{4 4 7 6 3 9 4 7} ^{8 2 1} _{8 8 8 8 2 0 8 8} _{5 2} ^{4 0} ₁ ₀ ^{. 6 0} ₀ ^. _{4 1} ² ^{8 0} 7₀ ^. _{7 1} ¹ 0 ^{7 0} 9_{0 8 1} ¹ ⁰ 0_{4 0 4 1} ² ⁰ 0_{7 0 3 4} ⁴ ⁰ 0_{3 0 8 2} ⁶ ⁰ 0_{2 0 4 2} ⁷ ⁰ 0_{8 0 5 2} ⁹ ⁰ 0_{7 0 3 1} ⁰ ⁰ ₃ _{0 2 0 9} ⁰ 0_{5 0 1} _{9 0 1 0} ^{. 1 . 2 . 1 . 8 . 5 . 9 . 9 . 3 .} ₀ ⁶ ₉ ₆ 6_{9 3 1} 2 ₉ 4 _{1 5} 7³ _{7 8} ₃ ⁴ ₇ ⁶ _{4 7} 2 ₅ 9₅ ⁶ _{1 6} 3 ₃ 9 ₆ ² ₅ ⁴ ₃ ⁸ _{4 4 6} ⁰ ₈ ¹ ₅ 5₉ ⁸ _{3 8 0} ⁵ ₀ ^{8 5} 1 ⁸ ₇ ⁹ _{1 6} ⁰ ₅ ⁵ ₆ ⁴ ₃ 8⁷ 0 ⁹ 0_{1 2} ³ ₇ ³ ₆ ^{3 9 9 9} ₆ ⁶ ₇ ⁰ ₄ ⁵ ₅ . - ^. ₀ ¹ ₂ ⁴ ₂ ⁵ _{1 1} - ^. ₀ ^. ₀ ^. ₀ ^. _{0 0} - ^. _{0 0} - ^. ₀ ⁰ ₁ ⁶ ₀ ² ₃ ⁷ ₀ - ^. ₀ ^. ₀ ^. ₀ ^. ₀ 1₆ 4 9 7 9 9 ¹ ₁ ⁹ ₆ ⁹ ₅ ⁸ ₆ ⁸ ₃ 9 5 5_{5 9} ⁶ ₁ ⁴ ₉ ⁶ ₇ ⁴ ₀ ¹ ₉ ⁷ ₁ ^{7 9 2} ₈ ⁶ ₀ 4₅ ⁷ ₆ ¹ ₁ ¹ ₇ ¹ ₆ . ^{1 8 1} ₀ 7 ₈ 4⁴ ₃ ⁵ ₅ ^. ₇ ⁷ ₂ ^{2 6} 8₄ ^. ₅ ^{2 3} 6₇ ^. ₆ ^{8 9} 1₈ ^. ₃ ^{7 7} 6_{5 1} .₇ ⁵ . ₅ ₆ ⁴ ₁ ⁷ . ₆ ₃ ⁷ ₂ ⁸ . ₆ ₁ ⁷ ₉ ³ . ₀ ₁ ⁶ ₂ ³ . 2 ^{6 0} 8 ⁷ _{4 9} ^. _{4 2} ₁ ⁴ ₁ ² ₁ ⁵ ₄ . 9₂ 3⁵ ₀ ^{6 9 1 6 1 2 2} ₃ 5⁷ ₅ ₆ ⁷ ₉ ₄ ⁹ _{8 0 9 8 9 8} ³ ₃ ⁸ ₃ ² ₀ ⁰ ₄ ⁶ ₃ ³ ₈ ³ _{6 1} ⁰ _{6 1} ³ ₉ ³ ₈ ⁷ ₉ ⁶ ₉ ⁸ _{2 4 5} ₄ ⁸ ₃ ² ₁ ⁵ ₁ ⁴ ₃ ² ₂ ⁹ ₃ ⁶ ₃ ⁸ ₆ ⁹ ₆ ⁵ ₇ ⁰ ₅ ⁵ ₅ ⁰ ₃ ³ ₅ ⁵ ₅ ¹ ₀ ² ₂ ² ₃ ⁰ ₁ ⁵ ₇ ⁷ ₃ ¹ ₆ ⁸ ₂ 1_/ _{. 2 2 . 2 1 . 2 1 . 2 1 . 2 1 . 2 1 . 2 2 2 1 2 1 2 1 2 1 1 1 2} ^S _T P _I ⁶ ₀ ⁷ ₈ ⁴ ₅ ₄ ⁸ ₀ ⁸ ₈ ⁵ ₂ ⁶ ₁ ₉ ³ ₉ ⁷ ₇ ₁ ¹ ₅

₀ ^. ₀ ⁰ ₀ ⁰ _{0 0} - ^. ₀ ^. ₀ ^. _{0 0} - ^. _{0 0} - ^. _{0 0} - ^. _{0 0} - ^. ₀ ⁰ _{0 0} ⁰ ₀ ⁰ ₀ - ^. ₀ ^. ₀ - ^. ₀ ^. ₀ ₉ ^{7 7} ₉ ⁴ ₄ ^{8 0 0 1} ₂ ⁸ ₃ ³ ₇ ⁹ ₄ ⁹ ₄ ⁹ ₄ ⁹ ₄ _{5 1} 4⁷ _{5 4} 4⁷ _{6 4} 4⁶ _{6 5} 4⁵ _{1 5} 4⁵ _{1 5} 4⁸ _{8 5} 4⁶ _{2 6} 4⁷ _{7 6} 4⁸ _{1 6} 4⁸ ₆ 4⁸ ₆ 4⁸ ⁸ ₀ ^{. 8} ₀ ^{. 8} ₀ ^{. 4} ₀ ^. _{0 2 0} _{0 2 0} _{0 8 0} _{0 7 0} _{0 7 0} _{0 3 0 1} _{0 3 0 1} _{0 3 0 1} _{1 0 1 0 8 0 5} ^{7 . 7 . 1 . 9 . 4 . 1 . 1 . 1 .} ₀ ¹ ₃ 0₃ ³ ₂ ⁵ ₆ ⁴ ₃ ⁵ ₁ ^{9 5 2 3 0 3 5} ^{6 7 5 8 9} _{7 9 2 8 3 7 5} _{2 1 2 5 5} ^{8 1 9 8 8 7 9} ^{0 0} _{7 0} ^. ₅ ⁰ 0 ⁵ _{3 0} ^. ₆ ⁰ 0 ⁹ _{7 0} ^. ₇ ⁰ _{3 5} 0 _{6 1} 0 _{1 6} 0 _{5 0} 0 _{1 3} 0 _{1 3} 0 _{4 3} 0 _{7 3} 0 ₆ ₀ ⁰ _{9 0} ^. ₀ ³ _{9 0} ^. ₀ ⁷ _{9 0} ^. ₀ ⁸ _{7 0} ^. ₀ ⁷ _{3 0} ^. ₀ ⁸ _{8 0} ^. ₀ ⁴ _{3 0} ^. ₀ ⁵ _{6 0} ^. ₀ ⁰ _{2 0} ^. ₀ ⁷ ₄ _{2 1 5 9 4 4 8 7 5 1 2} 5₄ ⁹ ₄ ⁰ ₁ ⁵ ₀ ² ₄ ² ₆ ⁷ ₆ ⁹ ₅ ¹ ₈ ⁶ ₃ ⁷ ₅ ⁶ ₁ 8₅ ⁰ ₉ ⁰ ₇ ² ₁ ¹ ₀ ⁴ ₂ ⁴ ₆ ⁹ ₈ ⁷ ₈ ^{7 2} ₉ ⁵ ₃ 3₀ ³ ₁ ⁴ ₈ ⁰ ₃ ² ₆ ⁴ ₈ ⁷ ₈ ⁶ ₂ ² _{2 4} 6⁹ ₂ ¹ ₆ 4₁ ^{. 2} 0₄ ⁷ ^. ₀ ⁵ 0 ^. _{0 1} - ^{. 8} 0₀ ^{. 1} 0₂ ^{. 9} 0₁ ^{. 6 8} _{0 8} 8⁹ ₀ ⁷ ₀ _{0 2} ^. ₀ ^. _{0 1} - ^. ₀ ^. ₀ - ^. ₀ - 9₁ 6 0 ⁶ ⁸ ₂ ¹ ₆ 4 2 3 6 3 ⁹ ₂ ² ₆ ₆ ⁹ ₇ ⁸ ₂ ⁴ ⁹ ₂ ³ ₄ ⁹ ₁ ² ₃ ⁷ ₃ ⁰ ₂ ⁸ ₀ ⁶ ₉ ⁴ ₆ ₈ ⁴ ₉ ⁷ ₁ . ^{0 .} ₃ ⁰ ₄. ⁵ ₆ ² ₃ ⁴ ⁵ ₃ ⁴ ⁹ ₀ ¹ ⁷ ₀ ^{. 4} 4₈ 1⁸ ₃ ⁶ ₁ ⁶ ₉ ¹ ₁ ⁴ _{0 2} ^. . 1 ² ₈ ⁸ ₂ ² ₀ ⁶ ₁ ⁷ ₉ ⁴ ₄ ⁶ _{4 2} ^. ₆ ⁶ _{7 3} ^. ₄ ⁷ _{8 8} ^. ₅ ² ₈ ⁷ ₂ ⁸ _{0 8} ^. . . ₂ ₃ ⁸ ₃ ³ ₂ ⁴ ₃ ⁷ ₆ ¹ ₀ . 9₂ 3⁸ ₃ ³ ⁹ ₇ ⁴ ₇ ⁶ ₃ ² ₁ ⁵ ₈ ² ₉ ⁰ ₄ ⁵ ₄ ⁷ ₆ ⁹ ₄ ¹ _{3 9} ³ ₄ ⁷ ₈ ⁹ ₂ ¹ ₁ ^{3 2 8 0 7 4 3} ₃ 5₅ ₂ ⁰ ₆ ⁶ ₀ ¹ ₆ ⁸ ₀ ³ ₄ ¹ ₇ ⁷ ₁ ¹ ₁ ³ ₂ ¹ ₂ ⁴ ₇ ⁰ ₅ ⁵ ₂ ⁰ ₆ ³ ₇ ⁵ ₉ ⁵ ₅ ₁ ² ₂ ₅ ⁹ ₅ ₁ ⁶ ₁ ₄ ⁶ ₀ ₂ ² ₈ ₅ ⁴ ₃ ₅ ⁴ ₆ ⁸ ₂ 1_/ _{. 2 1 . 2 1 1 2 2 2 2 2 2 1 . 2 2 2 1 . 1 1 2 1 2 1 2 1 2} ^S _T P _I 9 ⁷ ₄ ² ₇ ³ ^. ₃ ₀ ⁰ ₂ 4 ⁸ ₄ ² ₃ ⁶ ₅ ^. _{0 9} ₉ ⁵ ₁ ³ ₅ ⁷ ₀ ⁶ ₀ ^. ₀ -⁹ _{6 8} ⁰ ₄ ⁸ ^. ₆ ₀ ⁶ ₉ 2 ⁰ ₄ ⁹ ₀ ³ ^. ₆ ₀ ⁸ ₇ ₇ ² ₅ ⁸ ₂ ¹ ₅ ⁵ ₀ ^. ₀ ⁰ ₄ ⁴ ₉ ₄ ⁰ ^. -₈ ^E ₅ ^E ₂ ² ₉ ² ₁ ^. ₁ ⁰ ₁ -₆ ⁰ ₀ ⁷ ₂ ¹ ₂ ¹ ₀ ^. ₀ ⁸ _{7 5}

⁴ ₀ ^{0 5} ₈ ^{7 5} ₈ ^{7 5} _{0 3} 2² _{1 3} 0⁸ _{3 9} 0⁸ _{3 9} 0⁸ _{3 9} 0⁸ _{4 8} 6⁵ _{7 8} 3⁴ _{8 7} ¹ ₀ ^. ₇ 2⁴ ₀ ^. ₁ 0⁴ ₀ ^. ₁ 0⁵ ₀ ^. ₉ 6⁵ ₀ ^. ₁ 0⁵ _{0 9} ⁵ _{0 9} ⁵ _{0 9} ⁵ _{0 8} ⁵ _{0 7} ⁵ ₀ ¹ ₁ ⁶ ₀ ⁴ ₀ _{0 2 0 0 0 0 0 0 0 8} ^. ₀ ⁶ ₇ ^. ₀ ⁶ ₇ ^. ₀ ⁶ ₇ ^. ₀ ³ ₅ ^. ₀ ³ ₀ ^. ₀ ⁸ ₀ ^. ₀ ¹ ₀ ⁰ ₆ ⁵ ₄ ⁶ ₀ ⁶ ₃ ⁸ ₃ ⁶ ₉ ⁹ ₆ ⁴ ₀ ⁰ ₀ ⁶ ₁ ^{8 9} ^{0 1 2 4 4 0 0 0 5 7} _{8 7} _{2 1 1 4 4 5 5 5 1 1} ^{1 6} ^{0 0 0 0 0 0 0 0 0 0} _{5 2} ₀ ^. ₁ 1 ₀ ^. ₂ 8 ₀ ^. ₂ 7 ₀ ^. ₉ 1 ₀ ^. ₄ 2 ₀ ^. ₄ 6 ₀ ^. ₃ 5 ₀ ^. ₉ 2 ₀ ^. ₆ 7 ₀ ^. ₉ 2⁰ ₀ ^. ₆ 7⁰ _{0 5} _{0 7 0 5 0 4 0 4 0 8 0 4 0 7 0 1 0 9 0 5 0 8} ^. ₀ ⁹ ₀ ₈ ⁶ _{2 5 3} ^{3 3 8} _{2 8} 6 ₁ 3² _{2 4} 4_{0 1 6 4} 2₅ _{4 8} ^{2 7 1} _{8 3} 3 ₄ 3 ₁ ² ³ ₀ ^{9 9} 8₂ ⁴ ₃ ³ ₇ ⁸ ₄ _{0 0} ^{1 3 9} _{5 4} 7 ₄ 7 ₅ ⁴ ³ ₀ ^{4 6} 6₇ ⁷ ₆ ⁵ ₅ ³ ₈ _{1 7} ^. ₀ ³ ₁ ⁵ - ^. _{1 1} ₀ ^. ₀ ⁸ ₁ ₁ - ^{. 8} ₈ ⁸ 0₁ ^{. 7} ₉ _{0 0} ^{8 4} ^{. 5} 0₁ ^. _{0 5} ³ ₉ ⁹ ₄ ₀ ^{. 2} 0 ⁷ ₁ ² ₃ ⁶ _{0 1} - ^. ₀ ^. ₀ ^. ₀ ^. ₀ -⁸ ₂ ² 0 0 7 7 7 9 ⁹ ₉ ³ ⁹ ₁ ⁸ ₂ ⁸ ₂ ⁷ ₅ ² ₆ ⁶ ₀ ⁹ ₉ ² ₈ ⁶ ₉ ⁸ ₄ ₇ ¹ ₁ ³ ⁰ ₂ ⁵ ₃ ⁵ ₃ ³ ₄ ⁸ ₇ ⁰ ₇ ³ ₃ ⁴ ₃ ⁵ ₅ ⁴ ₄ ₂. ₅ ³ . ⁰ ₅. ^{1 1 .} ₇ ^{0 0} ₃. ^{0 0 1} ₆. ⁵ ₄. ₂ _{3 8} ⁹ ₁ ⁵ ₂ ² ₁ ³ _{1 7} ^. ₃ ⁴ _{6 7} ^. ₃ ⁴ ₆ ⁰ ₁ ⁹ _{0 7} ^. ₆ ⁵ ₈ ⁵ ₇ ³ _{8 2} ^. ₇ ⁵ _{0 9} ^. ₁ ⁴ ₁ ⁹ ₇ ⁹ ₂ ¹ ₁ ⁴ ₀ . 9₂ 3¹ ₁ ⁷ ₃ ⁶ ₀ ² ₉ ⁰ ₁ ² ₅ ⁴ ₈ ⁰ ₅ ⁴ ₈ ⁰ ₅ ⁶ ₉ ⁵ _{9 2} ⁴ ₁ ⁷ ₈ ² ₃ ₁ ⁸ ₄ ^{7 6 3 9 5 5} ₅ ₁ ⁴ ₅ ⁵ ₂ ¹ ₀ ⁵ ₂ ⁸ ₁ ⁰ ₅ ⁰ ₈ ⁰ ₅ ⁰ ₈ ⁶ ₇ ² ₅ ² ₄ ⁹ ₁ ⁵ ₄ ³ ₅ ⁸ ₁ ⁸ ₉ ₆ ⁰ ₉ ₁ ⁵ ₃ ₇ ² ₀ ₂ ⁴ ₉ ₂ ⁵ ₈ ₆ ⁸ ₉ ⁸ ₂ 1_/ _{1 . 2 1 . 1 1 . 1 2 . 2 1 2 2 . 2 2 . 2 2 . 2 1 . 1 1 . 1 1 2 1 . 2} ^S _T P _I ⁵ ₁ ³ ₉ ⁵ ₀ ₅ ⁹ ₉ ⁵ ₆ ⁶ ₉ ¹ ₆ ¹ ₀ ⁵ ₂ ₆ ¹ ₅

₉ ^{5 5} _{3 9} 0⁷ _{4 3} 5⁷ _{4 3} 5⁶ _{5 5} 3⁵ _{8 2} ³ _{9 7} ⁹ _{9 3} ¹ ₂ ⁷ ₄ ⁷ _{6 1} ¹ _{7 3} ₆ ⁶ ₈ ⁶ ₆ ⁶ ₆ ^{6 6 3 6 6 6 8 7 7 8 7 8 7 6} ⁶ ₀ ^{. 0} ₀ ^{. 3} ₀ ^{. 3} ₀ ^. ₃ 6 ₀ ^. ₈ 4 ₀ ^. ₃ 6 ₀ ^. ₁ 5 ₀ ^. ₅ 5 ₀ ^. ₀ 9 ₀ ^. ₁ 2 _{0 4} _{8 0 1 0 4 0 4 0 6 0 6 0 8 0 8 0 2 0 8 0 2} ^. ₀ ⁶ ₃ 0₅ ⁶ ₅ ⁴ ₇ ⁷ ₀ ⁹ ₄ ^{2 5 2 2 0 4} ^{1 1 1 5} _{2 3 7 7 9 0} _{6 3 3} ^{9 3 5 4 3 9 2} ₁ ^{6 0} ₀ ^. ₂ 2⁰ ₀ ^{. 9 0} ₆ ₀ ^. ₂ 5⁰ ₆ ₀ ^. ₅ 7⁰ ₆ ₀ ^. ₈ 3⁰ ₃ ₀ ^{. 2 0} ₇ ₀ ^. ₆ 5⁰ ₀ ₀ ^. ₂ 6¹ ₃ ₀ ^. ₈ 0⁰ ₇ ₀ ^. ₉ 0⁰ _{0 9} _{8 0 8 0 9 0 6 0 4 0 1 0 2 0 6 0 4 0 2 0 0} ^. ₀ ⁸ ₀ _{4 9 6 6 3} 6⁷ _{3 4 4 4} ⁵ ₂ ⁸ _{3 6} ₇ 8₀ ^{5 1} 8₄ ⁴ ₀ ¹ ₁ ¹ ₄ ² ₄ ⁹ ₁ ^{9 6} ⁰ ₉ ^{0 2 5 7 9 8 0} ₉ ⁸ _{8 5} 8 6_{2 3 3 0 3 1} ^{1 5} ₁ 9₁ ^{9 3} ^. _{1 7} ⁸ ³ ₁ ⁹ ₉ ⁰ ₈ ⁷ ₃ ⁷ _{4 8} ₀ ^. _{0 1} ⁶ ₀ ⁵ ₁ ⁹ ₁ ² ₁ ⁶ ₁ ⁶ ₀ ² 0 ⁰ _{1 3} 6₀ - ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ ^. ₀ - ^. ₀ - ^. ₀ 2 2 0 ² ₄ 9 3 2 ² ₀ ³ ₁ 0 ⁹ ₃ 1 3 ⁰ ⁴ ₀ ₃ ² ⁹ ₀ ³ ₄ ⁸ ₈ ⁷ ₉ ² ₃ ⁹ _{8 3} ^{6 4} _{3 0} ⁴ ₇ ³ ₁ ^{1 1} ₁ .⁹ ₅ ⁴ ₃ ^{3 0 4} _{7 8} 5⁴ ₅ .₄ ^{7 5} 8_{4 1} .₅ ⁷ . 5 ³ ₁ ⁶ ₇ ₈ ⁶ ₁ ^{9 8} 1₄ ^. ₃ ^{9 9} 1_{3 8} .₄ ⁷ . ₂ ₈ ⁴ ₃ ⁷ . ^. 8 ² ₁ ⁹ _{4 7} ₆ ⁰ ₁ ⁹ . ^. _{7 2} ₁ ² ₄ ⁸ ₇ ² ₁ ⁹ ₅ . 9₂ 3⁹ ₇ ^{2 2 8 9 7 5 1} ₃ 5⁴ ₉ ₄ ² ₈ ₂ ⁹ _{6 8 2 1 9} ¹ ₆ ⁹ ₅ ⁹ ₅ ³ ₄ ⁷ ₈ ⁵ _{9 3} ⁹ _{7 3} ⁹ ₂ ² ₂ ⁹ ₄ ⁰ ₂ ² ₁ ⁵ _{5 5} ₃ ² ₅ ⁶ ₆ ⁵ ₆ ⁷ ₈ ² ₄ ⁸ ₄ ⁶ ₄ ⁰ ₅ ⁵ ₂ ⁰ ₆ ⁵ ₄ ³ ₃ ³ ₆ ⁰ ₉ ² ₃ ⁹ ₂ ⁸ ₄ ² ₇ ⁷ ₂ ⁹ ₅ ⁸ ₂ 1_/ _{. 2 2 . 2 1 . 2 1 . 2 2 . 2 2 . 2 1 2 1 . 2 2 . 2 2 2 1 . 2 1 2} ^S _T P _I ⁶ ₇ ⁴ ₃ ⁶ ₀ ₇ ⁵ ₇ ⁴ ₄ ¹ ₆ ² ₂ ₂ ⁶ ₀ ⁷ ₅ ₃ ⁵ ₃

¹ _{8 9 9} ^{3 7} _{9 9 0 1 2 5 2 3 7 5 6 6 7 9 9 9} _{1 0} ⁶ ^. ₂ ⁷ 0 ¹ _{4 0} ⁶ ^. ₂ ⁸ 0 ¹ _{4 0} ¹ ^. ₇ ⁸ 0 ⁸ _{6 0} ² ^. ₉ ⁸ 0 ⁰ _{0 0} ⁵ ^. ₇ ⁸ 0 ⁴ _{0 0} ⁷ ^. ₆ ^{8 0} ₀ ^{8 6} ₆ ^{8 1} ₁ ^{8 9} ₀ ^{8 9} ₀ ^{8 9} ₀ ₀ ⁷ _{0 0} ^. ₀ ³ _{5 0} ^. ₀ ² _{4 0} ^. ₀ ⁹ _{0 0} ^. ₀ ⁷ _{2 0} ^. ₀ ⁷ _{2 0} ^. ₀ ⁷ ₂ 7₄ ² ₇ ² ₉ ² ₃ ³ ₉ ³ ₀ ⁵ ₁ ^{9 5 1 7 2} ^{9 8 5 3 8 9} _{2 0 7 4 1} _{8 1 1 2 4} ^{6 3 0 5 8 8} ^{8 1} _{9 0} ^. ₁ ¹ 0 ⁵ _{0 0} ^. ₉ ⁰ _{5 7 4 8 3 9 9 9} ₀ ⁸ _{2 0} ^{. 0} ₇ ⁰ ₇ ^{0 0} ₄ ⁰ ₉ ^{0 0} ₈ ⁰ ₄ ⁰ ₇ ₀ ² _{5 0} ^. ₀ ⁸ _{8 0} ^. ₀ ⁹ _{1 0} ^. ₀ ⁴ _{2 0} ^. ₀ ⁴ _{5 0} ^. ₀ ¹ _{9 0} ^. ₀ ⁹ _{7 0} ^. ₀ ⁵ _{8 0} ^. ₀ ³ _{1 0} ^. ₀ ³ ₄ _{4 5} ₂ ⁹ ₉ 0 ₅ 1 _{7 1 9} 5 ₁ 2 _{5 4 2} 0 _{1 2 3} ^{7 2 7} _{4 7} ⁸ ₇ 9₉ ² ₄ ⁴ ⁷ ₈ ⁰ ₆ ⁸ _{7 2} 8 _{7 6} ^{0 7} 8 ³ _{5 3} ⁵ ₂ ³ ₈ ₈ ⁴ ₈ ⁷ _{7 9} ⁵ ₉ ⁷ ₃ ^{0 1} 4 ⁷ _{6 1} ⁴ ₈ ¹ ⁵ ₀ ^{6 2} ^. ₀ ₀ ^{. 3} 0₁ - ^{. 6} 0_{1 9} ⁷ ₇ ⁰ ₂ - ^. ₀ ¹ ₄ ³ ₁ ² ₁ ⁷ ₀ - ^. ₀ ^. ₀ ^. ₀ ^{. 8 6} 0_{1 1} ⁹ _{5 2} - ^. ₀ ⁰ ₁ ⁹ ₀ ⁷ ₀ - ^. ₀ ^. ₀ ^. ₀ - 2₃ 4 ⁵ ₇ 7 2 ⁶ ₃ ⁵ ₂ ⁵ ₇ 8 ⁸ ₂ ⁷ ₉ ⁸ ^{3 1} ₈ ⁶ ₅ ⁶ ₉ ⁰ _{3 0} ₉ ⁵ ₉ ⁰ ₄ ¹ ₅ ^{2 9 1 6} ⁷ ₉ ³ ₅ ^{. 7 7} ₂ ⁵ ₆ ^{1 1} ₄ 8⁷ _{5 4} 3 ₈ 2 ₆ 2 ⁹ . ₁ ⁹ ₉ ¹ _{1 5} ₂ ² ₃ ⁹ . 2 ² ₁ ^{3 0} 4₈ ^. ₄ ^{3 6} 6_{9 9} .₀ ⁵ . ₃ ₅ ³ ₁ ⁵ . ₄ ₇ ¹ ₁ ² . 1 ^{9 4} 7_{7 3 4} .₄ ⁵ . ₃ ₀ ¹ . ₂. ₂ ₁ ² ₁ ⁴ ₁ ² ₁ ⁰ ₈ ⁸ ₂ . 9₂ 3⁵ ₆ ^{4 5 0 4 6 5 6} ₃ 5¹ ₆ ₅ ⁵ _{5 4 5 0 0 0 7 2} ⁵ ₉ ⁹ ₂ ³ ₅ ⁷ _{5 9} ⁰ ₃ ⁷ ₃ ² ₉ ⁵ ₂ ⁴ ₇ ⁹ ₈ ¹ ₁ ⁸ ₆ ² ₁ ⁵ _{8 5} ₅ ⁴ ₆ ¹ ₆ ⁵ ₃ ⁰ ₂ ⁹ ₂ ⁹ ₂ ⁹ ₉ ³ ₆ ⁷ ₃ ³ ₅ ³ ₁ ⁶ ₆ ⁷ ₆ ³ ₁ ⁷ ₉ ⁷ ₃ ⁸ ₅ ⁷ ₁ ¹ ₇ ⁸ ₁ ⁸ ₀ ² ₃ ⁷ ₄ ⁸ ₂ 1_/ _{2 2 2 2 2 1 . 1 1 . 2 1 1 1 2 1 . 2 1 2 1 . 1 2 . 2 2 . 2 2 . 2} ^S _T P _I ⁵ ₇ ² ₂ ¹ ₉ ₆ ⁸ ₃ ⁰ ₂

3 3 7 6 3 _t s ^{3 4} _{4 8} ^{8 4} ₃ ⁹ ₁ ^{4 4} ₉ ⁵ ₁ n e _d u _lo ^{4 4} _{3 3} ^. ₃ ₀ ^{4 1} 4₀ ^. ₁ ₀ ^{9 7} 2₆ ^. ₆ ₀ ^{9 7} 3₉ ² ^. ₃ ₀ ^{7 0} 5₄ ³ ^. ₆ ₀ ⁸ _g o ^r 7 ^a _r ^e _t ^c _tn e_v ^s _uj ^Uo ₅ _c ^. ₉ ₉ ^. _{1 0}. 5_{6 1 9 3} A^d _{a F} Rⁿ _i ² ₂ ⁰ ₂ ³ ₁ ₄ ⁰ ₃ ² ₈ ₇ ³ ₁

₄ ^{1 3} _{8 2 8} 2⁴ _{4 9} 6⁸ _{5 3} 9³ ₆ ^{9 8} _{8 5} 6^{7 9} _F _{2 0} ^. ₈ ₀ ^{2 9} 3₀ ^. ₃ ₀ ^{0 9} 6₀ ^. ₂ ₀ ^{5 9} 8₀ ^. ₆ ₀ ^{4 9} 9₀ ^. ₈ ₀ ⁶ _{1 3} R ^F _Y)₂ ^F _Y)₄ )₅ D^{1 H} ^{Q Q2} _Q ⁰ ⁴ ₅ ^{2 4 6 0} _{C T 8} 3² ₈ 6 ₁ 4 ₉ 5 ₅ 3^d _i ^E _{Q: T 0} 3_: ^P ₁ _Q ³ _{4 9} 0 _{5 9} 0 _{6 5} 0 _{8 3} 0 _{7 1} 1 ₆ o O ^E _{Q :} O NO ⁸ _{4 0} ^. ₀ ¹ _{3 0} ^. ₀ ² _{7 0} ^. ₀ ² _{0 0} ^. ₀ ¹ _{8 0} ^. ₀ ⁸ ₅ ^r _t G ^N G ^N G ^N ⁿ _e ^{G G G} ^c _L ^D ^S _{I L} ^D ^S _{I L} ^D ^S _I _{7 S} ^Q _S ^Q _S 2 _{8 6 2} ^U ^{3 5} ₁ ⁷ _{0 F} ^A _E ^A _E ^Q ^A _E ₄ 8 ₄ 3⁷ _{6 6} ⁵ ₈ R _C ^S _{( C} ^S _{( C} ^S ₍ ₂ 6 ₉ ₃ ⁶ ⁸ ₆ ^{9 0} 4₃ ⁹ 0 ^{3 6} _{3 8} 7^{- - -} ^{. 2} _{1 1} ^. ₀ ⁹ _{1 0} ^. ₀ ^d _{i e 5 5 5} ₀ ^. ₀ - ^. ₀ - o V V V U^r _t ⁿ _e B B B 6₆ 3 3 2 _F n R^e _c ^g V ^R _{T 1} ^R _{T 1} ^R _{T 1} 5₉ ⁰ ₀ ⁵ ⁸ ₁ ⁵ ₇ ⁰ ₁ ⁸ ₁ ₀ ⁴ ₇ ⁰ ₃ ⁴ ₀ . 4⁶ 2 ⁶ _{3 0} ^. ₉ ^{1 2} 9₄ ^. ₈ ^{7 6} 1₈ ⁵ ₆ .₃ ⁶ . 3 ⁴ ₃ ² ₄ ^d _i 7 7 4 ₂ o ⁴ ₃ ⁴ ₃ ⁷ ₂ . . ^r _t ^{3 9} 4 ³ ₄ ² ₂ 3₉ ^{6 6 3 8 9 4 7 5 2 4} n e⁴ _{4 1 4 4 3 3 3 7 7 8 c} ^{6 6} ₆ 0 ₃ 5₅ ₅ ⁹ ₄ ⁵ ₄ ⁸ ₇ ⁵ ₁ ⁵ ₃ ⁴ ₈ ⁰ ₅ ⁴ ₂ ⁷ ₁ ⁶ _{5 1} _{e 4 0 4 0 5 6} ⁸ ₂ _{1 2 1 . 2 1 . 1 2 . 2} ^l _b ¹ _/ _{. 2 1 2} ^a _T ^_ _{d c} ² ₂ ² ₁ ² ₁ ^S _T P _I ⁵ ₀-_E ⁹ ₆ ^. ₉ ⁷ ₀-_E ⁸ ₁ ^. ₂ ⁴ ₁ ^. ₀ - ₃ ^. ₄ ⁰ ₂ ₂ ^. ₆ ⁸ ₁ ₈ 2₉, 4₁ ₅ 6₄ ^, ₅ 6₀ ^, ₁

^F _F Y ^H _Q ^Q ₎ ^{F F} _{F F} ^F _F _Y) ^H) ^Q _E ) _F ) ) ^F ) ^F ) ^T) ^L) ^F )^Q ₁ 4 _Q5 2 _{T )} 4^P ₎ _Q ^{9 F} ⁹ N_{9 F 4} 0^L ₉ 5^L _{E 7} 4 _{F 8} 8 _{F 8} 7 _{L 9} 6 _{E 6 F 2} _{T 1 6} D_{5 7} ² A_{9 E 0 8} A₁ ^L V ^G9 A¹ ^{E 1 P} _Q ^{1 T N Y} _{S :} ^{G E} _{E 8} N_{2 T 1} E ₁ _Q: ^N _{: S} ³ _{: S} ⁷ _: O ^{E 3} _: ^{G 4} _{: T} ² _{: T} ³ _: ³ _: ³ _: ¹ _{: T} ² _: G G G _{T T} G A NGO _S O O O ^N NO O NO NO ^TO O AO NOG ^N G ^N G ^N G ^N G N G ^N G G G _N ^G _S G G_L ^A _{L I} ^{G S} _S ^D I L_I D ^S _{L I} ^{S N} L_I ^{G N} L_I ^{G N} L_I ^{V N} I ^{G N N N} ^AD _I ^E _L ^D _I ^{D D D D D D} _Q ^D _L ^D _I ^G _L ^D _I ^G _L ^D _I ^S _I ^Q _E ^S _S ^Q _E ^S _S ^Q _E ^S _S ^Q _E ^S _S ^Q _E ^{S S S S S S S S} ^S _S ^Q _{E S} ^Q _{E S} ^Q _{E S} ^Q _{E S} ^Q _S ^Q _S ^Q _S ^Q ^{A A A A A} _{( E E E E} _C ^S _{( C} ^S _{( C} ^S _{( C} ^S _{( C F} ^A _C ^S ₍ ^A _C ^S ₍ ^A _C ^S ₍ ^A _C ^S ₍ ^A _C ^S ₍ ^A _C ^S ₍ ^A _C ^S ₍ ^A _C ^S ₍ -₅ -₅ -₅ -₅ -₆ ⁷ ₂ -₅ -₅ ⁷ ₂ ⁴ ₁ -₅ -₅ ⁷ ₂ V V V V V V V V V V V V VB B B B B B B B B B B B B^R _{T 1} ^R _{T 1} ^R _{T 1} ^R _{T 1} ^R _{T 4} ^R _T ^R _{T 1} ^R _{T 1} ^R _T ^R _T ^R _{T 1} ^R _{T 1} ^R _T ₇ ² 5 ₂ ⁷ ₀ 6 9 2 3 5 7 4 1 6 4 2 3 0 6 9¹ ₃ 4 3 0 ₂. ⁴ ₃ ₂ ² ₈ ₅ ⁵ ₅ ₅ ⁷ ₇ ₁ ³ _{6 1 2 8 4 5 5 8} ⁹ 0 ⁶ ₈ ⁷ ₈ ⁹ ₁ ¹ ₂ ⁸ ₈ ³ ₃ ^{1 1} ₂ 3^{3 2} ₅ ² ₃ ⁶ _{4 8} ⁸ _{6 4} ⁰ _{5 9} ² _{1 2 3} _{1 2 6 7} ⁰ _{3 1} ⁴ _{5 0} ⁷ _{4 2} ⁵ ₅ ⁷ ₁ ⁴ _{5 0} ⁵ ₅ 8₂ ₂ ^. ₂ ₂ ^. ₂ ₂ ^. ₂ ₁ ^. ₁ ₁ ^. ₁ ² ₂ ² ₂ ² _{2 2} ¹ _/ ₁ ² ₁ ² ₂ ² ₂ ^. ₂ ^. ₂ ^S _T P _I ³ ₀-_E ³ ₉ ^. ₂ ⁵ ₀-_E ¹ ₆ ^. ₄ ² ₁ ^. ₀ - ₃. ⁷ ₂ ₉. ⁴ ₂ ₃ 2₅ ^, ₁ ₄ 4₁, 7₈ _F

^{T F} _{L F} ^Q _{E F F F} ^F _F V)₁ ^L) ^S _F ^F _F ₍ Y ^T) ) ) ) ^L) ^F ) ^Q) _F ^Q _E ) ^SN⁷ _{E 0} ^I ⁸ _{T )} 9 _{L 4} ^H ² _{L 3} 9^F ₂ 7^F ₄ 3 _{E 0 0} G⁵ _L 0 _{E 0} Y 1^I _{6 (} 4 _FA₄ ^G ¹ _{T 5} ^F ¹ _F N₀ V 2 N₃ ^P ² _{S 2 F} 3 _{3 Y} 3 ₆ 3^T ₁ 2^K _{E 5} N Y_{7 T} N₃ ^Y ^G _{S :} N_: A) G_: A_: N_: ^Q _: ^Q _{: N:} ³ _: ^{2 3} _Q O GO ^E ₃ ^S _{E E} ^N _{S S :} ^G _: )₉ G ^N G ^N _T ¹ _{6 S} O G ^{N G} _S O ^GO N _Q ^{N Y} _S O ^Y _S O ^G _QO O GO G _S O ^E G _Y ⁶ ₃ ^G _L ^{D A} N D G ¹ _: ^{S D GD G} _P ^{D G N} ^{D G N} ^{D G N N G} ^{D GD} _S ^{N N} ^{D S D G} _Q ³ _: ^S _{I L I L I L I I L I L I L I L I} D _{I L I} _S ^{Q S} _S R Q O A ^S _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^{Q S} _S O^A _E ^A _E ^{A N} ^A _E ^A _E ^A _{E E E E E E} ₍ ^A _E ^N _C ^S _{( C} ^S _{( C} ^D _{I C} ^S _{( C} ^S _{( C} ^S ₍ ^A _C ^S ₍ ^D _C ^S ₍ ^A _C ^S ₍ ^A _C ^S ₍ ^A _C ^S _C ^S ₍ ^A _C ^D _I -₅ -₅ ⁷ ₂ ⁷ ₂ -₅ ⁸ ₁ ³ ₁ -₇ -₅ -₅ -₆ ⁷ ₂ ⁴ ₁ V V V V V V V V V V V V VB B B B B B B B B B B B B^R _{T 1} ^R _{T 1} ^R _T ^R _T ^R _{T 1} ^R _T ^R _T ^R _{T 9} ^R _{T 1} ^R _{T 1} ^R _{T 4} ^R _T ^R _T1 ³ 1 1 6 ₃ 4 0 ₃ ₁ ^{5 9 9 1 7 1 3} 1 3 6 8 8 4¹ 9 1 ₂. ⁵ ₀ ₀ ² ₃ ₇ ⁸ ₈ ₂ ⁷ ₅ ₆ ³ ₄ ₃ ⁶ _{6 6 7 0 3 8 8} ⁹ 8 ⁴ ₁ ⁸ ₆ ⁰ ₇ ³ ₂ ⁶ ₉ ⁰ ₁ ⁶ ₂ 3^{3 8} _{1 6} ¹ _{2 8} ⁴ _{4 4} ⁵ ₅ ³ ₄ ⁵ _{7 3} _{1 4 1 2} ¹ _{4 5} ⁵ _{6 2} ⁵ _{2 4} ⁵ _{1 1} ⁰ ₈ ³ _{3 4} ⁵ ₅ 8₂ ₂ ^. ₂ ₂ ^. ₂ ₂ ^. ₁ ₂ ^. ₁ ² ₁ ² ₂ ² ₂ ² ₂ ¹ 2 ^. _/ ₂ ² ₂ ² ₁ ² ₂ ² ₂ ^S _T P _I ³ ₀-_E ³ ₈ ^. ₆ ⁵ ₀-_E ⁰ ₈ ^. ₆ ⁹ ₁ ^. ₀ - ₆ ^. ₉ ₇ ^. ₈ ₃ 9₄ ₄ 4₃, 9₂ _F

Y^I _T ^Q _{T ) F} N ^{2 H}) _{F F L} ) ) ⁾ H) ^K ₁ ^Q) ¹ _{E 7} ^Q) ⁴ _{E 5} ^Q ⁵ _{E Y} Q) _F ) ^Q _T) ) D_{3 L 2} Y 5^I _{1 9 3 E} _T ^{3 F} _F ^{6 L} _P ⁰ N₇ N₇ N₈ ^S _{( T} E ₅ Y 2^I ₀ 1^E _Q0 ^G _{S 2} ^{T 2} ⁶ _{S :} ^P _{S 2} 1 N₄ 1 A₀ ^S ₄ ^{T 1} _: Y ^{1 Y 1} ^S _{: S : F} F _{Q0 T} N₆ G ⁴ _: ^G _: GO N_{: :} _Q ^{E 2} _{: N} ¹ _{: A}O _S O GO ) ¹ _: ² O O G ^{N G}O ^G _S O _T NO ^G _QO ^{G N} G ^N G ^N _Q T ₁ G _: 1 GO ^G _S O ^S _T ^N G ^N A^D _Q ^N G ^{N N N} _Q G A _{E 0} A ^N G ^N G ^P _P ^D _I ^G I L ^G _P ^D _I ^S G L ^D _I ^D _I ^{GD GD} I ^GD I ^{L D} Q I ^{G 2} _: ^{GD S D} ^{D A} I^S _S ^{Q S} _S ^Q _E ^R ^S _{S L I L} S _S ^{Q S} _S ^Q G^{Q S} _S ^{Q S} _S ^Q _L _E ^{S Q} ^S _{S E} ^{S Q} _L S _{S E} ^S _S O _L N^S _{I L} _S ^{Q S} _{I L} _S ^{Q S} _S ^Q _E ^A _E ^A ₍ ^A _E ^A _E ^G _{E E ( (} ^S _{( E} ^A _E ^S ₍ _C ^S _{( C F C} ^S _{( C} ^S _{( C} ^S ₍ ^A _C ^S ₍ ^A _{C F} ^A _{C F} ^A _{C F} ^A _C ^D _I ^A _C ^S _{( C} ^S ₍ ^A _{C F} ⁸ ₁ -₅ ⁸ ₁ ⁷ ₂ ⁰ ₂ -₅ -₅ -₅ ⁴ ₁ ⁷ ₂ -₅ ⁷ ₂ -₅ V V V V V V V V V V V V VB B B B B B B B B B B B B^R _T ^R _{T 1} ^R _T ^R _T ^R _{T 1} - ^R _{T 1} ^R _{T 1} ^R _{T 1} ^R _T ^R _T ^R _{T 1} ^R _T ^R _{T 1} 3 ¹ 6 _{3 3} 8 ₃ 7 0 2 2 2 _{3 6} ₉ ⁰ ₂ ⁷ ₄ ¹ ₈ ⁷ ₂ ^{8 3 1 8 8 6 1 5} ₂. ⁴ ₂ ⁴ ₁ ⁶ ₈ ⁰ ₁ ⁵ ₇ ₉ ⁹ ₀ ₁ ⁹ ₃ 8 ₁ 8 ₂ 9 ₅ 2 ₈ 0 ₀ 9⁹ ₂ 3^{2 7} _{3 5 0 1 0 1 9 3} _{1 2 4 6} ³ ₄ ⁰ ₈ ¹ _{5 2} ² ₆ ⁶ _{2 5} ⁶ _{2 8} ² _{4 0} ⁷ _{4 1} ⁸ _{6 4} ⁰ ₈ ⁹ ₂ ⁵ ₅ 8₂ ₁ ^. ₁ ¹ _/ ₁ ^. ₂ ₁ ^. ₂ ₂ ^. ₂ ₂ ^. ₂ ₂ ^. ₂ ₂ ^. ₂ ₂ ^. ₂ ₂ ^. _{2 2 1} ₂ ^. ₂ ^. ₁ ² ₁ ^. ₁ ^S _T P _I ³ ₀-_E ³ ₁ ^. ₈ ⁴ ₀-_E ⁸ ₁ ^. ₁ ⁵ ₁ ^. ₀ - ₄. ⁶ ₁ ₈. ⁴ ₁ ₂ 2₃ ^, ₁ ₂ 4₂, 9₇ F

^{L F} _{E ) F} ^Q ^G _T ⁰ ₇ ^H _L)₈ ^F _{) F} ^F _F ^F _{F Y F} ^F _L ⁾ ₄ 3 _F )₂ ^F ) _E ^Q) ^Q) ^{Q Q} ) ^F ) ^K ) 2 _{F 7} ¹ ⁷ N_{7 E 5} 6 _{E 2} 1 _{E )} 9 _{T )} 4^H _{L 1} 2 _{F 1} 9 _E ⁵ ₀ ^F _{F 0} 7 ^{N 2} _: ^P _{S 5} ^L _{E 7} ^L _{E 0} Y ¹ N S _: ^Y ₃ N Y₅ N Y₃ ^D _{T 5} ^P _{S 9} A E ₀ ^N _T ² _: ^Q ₉ _AO N ² _: ^G ^G _T ² _: G ³ _{: S} O _S ¹ _{: S} ¹ _: G ³ _: G ⁸ _: ³ _{: T} ⁴ _: O _E ¹ _: _Q ^{N G} G G N A Y QO O ^TO ^N O GO O ^AO O ^NO G ^N O N N ^N _N ^N G G ^{N N A} _L ^N _L ^Y _{S G} G G^GD I G ^{V AD} I ^{A G} _S ^G G ^N ^{S N N D} I ^N _L ^G _P ^D _I R^D _{I Q} ^{D D D D D D QD S GD} ^S _S ^Q _{I L} _E ^S _S D ^S _S ^S _S ^Q _{L I} D _{I L I} Y _{I E I L I L} _E ^{S S S S S S S Q} _{L I} _E ^S ^{S Q} Y^{Q Q} _S ^Q _S ^Q _S ^Q _S ^Q _S ^Q _S ^Q _{S S} ^Q ^A ₍ ^A _E ^S _E ^A _E ^S _{( E E} ^A _{E E E E} ^S _{( E} _{C F C} ^S _{( C} ^S _{( C} ^S ₍ ^A _{C F} ^A _C ^S ₍ ^A _C ^S _{( C} ^S ₍ ^A _C ^S ₍ ^A _C ^S ₍ ^V _C ^S ₍ ^A _{C F} ^A _C ^S ₍-₅ ⁸ ₁ ⁹ ₂ ⁴ ₁ -₅ -₇ -₆ ⁷ ₂ -_{6 2} -₇ ⁷ ₂ ³ ₁ V V V V V V V V V V V V VB B B B B B B B B B B B B^R _{T 1} ^R _T ^R _{T 1} - ^R _T ^R _{T 1} ^R _{T 9} ^R _{T 4} ^R _T ^R _{T 5} ^R _T ^R _{T 9} ^R _T ^R _T 7 ⁴ ₃ 5 6 7 ₇ 8 ₁ 2 3 0 ₁ ⁷ ₄ ⁶ ₆ ^{9 1 7 4 9 3 6 0} 7 3 4 3 ₂. ⁶ ₂ ⁶ ⁷ ₈ ² ₄ ₃ ⁸ ₇ ₈ ² ₃ ₁ ⁵ ₃ ₅ ⁶ ₅ ₉ ⁶ _{4 0 6 9 4} ⁹ 5 ⁴ ₄ ⁰ ₅ ³ ₆ ⁵ ₀ ³ _{4 2} 3₃ _{5 5} ³ ₄ ⁸ _{1 0} ⁷ _{4 2} ⁰ _{3 8} ⁸ ₅ ⁵ _{1 1} ⁸ ₂ ⁰ _{5 9} ¹ _{5 5} ⁴ _{6 1} ¹ _{5 1} ⁵ _{4 3} ⁵ ₅ 8₂ ₁ ^. ₁ ² ₁ ² ₁ ² ₁ ₁ ^. ₂ ₁ ^. ₂ ₂ ^. ₂ ₂ ^. ₂ ₁ ^. _{2 2} ¹ _/ ₁ ² ₂ ² ₂ ^. ₂ ^. ₂ ^S _T P _I ² ₀-_E ⁶ ₁ ^. ₁ ⁴ ₀-_E ⁹ ₄ ^. ₅ ¹ ₁ ^. ₀ ₂. ⁹ ₄ ₃. ³ ₅ ₁ 0₂ ^, ₃ ₀ 8₆ ^, ₇ 4₂

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_F ^A Y) _F ) ^F ) ) ) _F ^H _L ^T _L ^F _F ^Q _E _{E )} ^I ₉ Y_{4 T 6 2} ^F ₇ Y)₁ )₂ ) ) ^S ( ^T _N ⁶ _T ₆ N^{8 I} _T ¹ Y^{7 F} _F ^G _Q ⁷ _: ^G ₀ _S ¹ _: N₄ 3_: G₄ Y ² _Y ⁵ ₅ A^{4 Q} _T ^{8 V} _Y ^{0 P} _{S )} 3 V₎ ^Q N⁴ _{E 1} 8^F ₈ _Y ^{5 V} : ^{Q 2 E} _{7 1} _T ^{2 D 2 Q} ₅ _E ^{2 N} ₄ 6 A₂ 9 N₂ 2 ₇ Q³ _Y O O ^G _S O NO _{T : : : : Y: :} O NO _TO Y ^{G Y} _{S : E :} ^Q _E)₇ G ^GG ^N _Q ^N G ^N G ^{N D} O T ^N G ^{N G} _S ^G ^N G ^N _QO N _S O G ^{N G}O S ^{N Y}O S ^N H⁸ ₃ ^L _S ^D _I ^G _P ^D ^{Q S} _I ^E _L ^D ^S _I ^G _L ^D ^S _I ^S _L ^D ^S _I ^G _L ^D _I ^L _F ^D _I ^G _L ^D _I ^G _L ^D _I ^S _L ^D _I ^T _I ^D _I ^G _L ^D _I ^G _L ³ _: OG _{S S S S} ^S _S ^S _S ^S _S ^S _S ^S _S ^S _S ^{S S} _S ^A _E ^Q _C ^S ₍ ^A _E ^Q C ^S ₍ ^A _E ^Q C ^S ₍ ^A _E ^Q C ^S ₍ ^A _E ^{Q Q Q Q Q Q} C ^S ₍ ^A _E _C ^S ₍ ^A _E _C ^S ₍ ^A _E _C ^S ₍ ^A _E _C ^S ₍ ^V _E _C ^S ₍ ^A _{E S} ^{Q N} C ^S ₍ ^A _E _S ^S ₍ ^A _C ^D _I-₇ ⁸ ₁ -₅ -₅ ⁷ ₂ -₇ ⁸ ₂ -₇ -₇ ⁷ ₂ ⁹ ₁ -₅ ¹ ₁ V V V V V V V V V V V V VB B B B B B B B B B B B B^R _{T 9} ^R _T ^R _{T 1} ^R _{T 1} ^R _T ^R _{T 8} ^R _T ^R _{T 8} ^R _{T 9} ^R _T ^R _T ^R _{T 1} ^R _{T 3} -3 ⁸ 4 8 _{1 1} 8 9 ₄ 3 7 ₆ ⁵ ₇ ^{5 3 1 2 5 1 9 6} 1 8 9 1 4 1 ₂. ³ ₃ ² ₅ ₀ ⁶ ₅ ₃ ³ ₀ ₆ ⁰ ₃ ₆ ³ _{7 6 2 0 0 7 1} ⁹ 9 ⁷ ₉ ⁸ ₂ ⁵ ₃ ³ ₇ ⁶ ₃ ⁵ ₈ ⁰ ₂ 3^{6 9} _{6 0} ⁰ _{5 3} ⁰ ₃ ⁵ ₅ ² _{2 3} ⁰ _{4 3} _{3 8 1 6} ⁷ ₃ ² _{2 8} ⁰ _{6 4} ¹ _{6 2} ³ _{4 2} ⁷ _{1 5} ⁵ ₅ 8₂ ₂ ^. ₂ ₁ ^. ₁ ² ₂ ² ₁ ² ₁ ² ₁ ¹ ₁ ² ₂ ₁ ^. ₂ ¹ _/ ₁ ^. ₁ ¹ ₁ ² ₂ ² ₂ ^S _T P _I ² ₀-_E ⁰ ₅ ^. ₄ ³ ₀-_E ² ₉ ^. ₅ ⁹ ₀ ^. ₀ ₈. ⁶ ₅ ₃. ⁰ ₆ ₃ 6₄ ^, ₂ ₀ 2₇ ^, ₂ 4₂ ^Q

_E ^F ^S _{F F} F ^Q ₍ ^{Q S} ) ^Q _E ^Q _E ^F _F _E ) ^S _{( F} ) ^S ₍ ^{L Q} _{E )} ^Q _{T ) F} ) _F )^F _{E )} _F N⁹ _{L 8} V¹ N₎ ^F _Y4 6^F _{6 E )} _F ^{H7 F G3} N² ₀ ^{E 2} Q₄ ^H ₃ _Q ^{4 H} ₁ _Q ² ^L _E) Y₈ 4 N₅ 3^Y _S ¹ _: ^Q _{6 L 7 F 2} Y ¹ _: ¹ _{: 0 1} _E ^{2 L 2 L} _T ^{3 S} G ^{P 2 P 3} ^G ₆ G_{: :} O _{: E}) ^P _: ) N_{: S} O O _{Q: Q:} 7^S _TO A GO G S^N YO ^G _{4 S} _T ³ NO _E G₆ 1 AO G ^N G S_T ^N NO NO_T ^S ₄ ^{N S N} _T G ^N ₇ G ^N _{T 2} G ^N G G ^N G ^N _L ³ _: ^G _S ^D I L ^D _I ^D _I ^G _P ^G _L ^D N ² _: ^{GD N} _P ³ _: ^{GD AD} I ^GD I ^{GD GD} ^S _TO D N^S _S ^{Q S} _S ^{Q S} _S ^Q _I _E ^{S F} T ^Q _S O _I G ^{N S} _I _S ^{Q S} _S O _L N^S _{I L} _S ^{Q S} _S ^Q _L _E ^S _S ^Q _{L I L I} _E ^S _S ^{Q S} _S ^Q ^{A A} _E ^A _E ^AS ₍ ^A _E ^A _E ^A _E ^S ₍ ^S _{( E E} _C ^D _{I C} ^S _{( C} ^S _{( C F C} ^S ₍ ^G _S ^D _{I C} ^S _{( C} ^D _I ^A _C ^S ₍ ^A _{C F} ^A _{C F} ^A _C ^S ₍ ^A _C ^S ₍ ⁷ ₂ ⁷ ₂ -₆ ⁷ ₂ -₅ ⁷ ₂ ⁹ ₁ ⁸ ₁ -₅ -₇ -₅ ³ ₁ ³ ₁ V V V V V V V V V V V V VB B B B B B B B B B B B B^R _T ^R _T ^R _{T 4} ^R _T ^R _{T 1} ^R _T ^R _T ^R _T ^R _{T 1} ^R _{T 9} ^R _{T 1} ^R _T ^R _T 4 ¹ ₉ 8 5 5 0 3 ₃ 9 5 5 ₂ ⁰ ₅ ⁰ ₆ ⁵ ₉ ^{2 6 2 4 4 1 8 0} 5 0 ₂. ⁷ ₆ ¹ ³ ₃ ³ ₄ ⁹ ₅ ₂ ¹ ₄ ₁ ² ₅ ₂ ⁰ ₆ ₁ ⁴ _{5 0 1 8 8} ⁹ 8 ¹ ₁ ⁶ ₀ ⁰ ₁ ⁴ ₈ ⁴ _{8 2} 3₃ _{2 9} ⁴ ₇ ⁵ _{2 7} ³ ₇ ⁵ _{1 3} ⁹ _{1 8} ⁶ _{2 7} ⁴ _{5 3} ⁷ ₁ ⁵ _{2 2} ⁵ _{2 2} ⁰ _{5 0} ⁰ _{5 0} ⁵ ₅ 8₂ ² ₂ ₂ ^. ₁ ² ₁ ₂ ^. ₁ ² ₁ ² ₁ ² ₁ ² ₂ ₁ ^. _{1 1 2} ¹ _/ ₁ ^. ₁ ^. ₁ ^. ₂ ² ₁ ^S _T P _I ² ₀-_E ⁹ ₃ ^. ₅ ³ ₀-_E ⁰ ₁ ^. ₅ ⁸ ₀ ^. ₀ ₁ ^. ₀ ⁰ ₁ ₈ ^. ₅ ⁰ ₁ ₉ 3₆ ^, ₃ ₈ 0₆ ^, ₂ 0₂ ^Q

_E ^Q _E ⁾ _{0 L F} ^H _Y ^{T F} _F N⁴ _L Y Y ^S ( ^F _T) V) Y ^F ) _L) ^Q ) ^{Q Q L} _E) )^V _{2 1} N_{5 F 5 9 T )} V_{3 E ) E ) 9} ^F ₀ _Y Y Y⁴ A ^Q) A^{4 P} _S ^{0 9} N^{4 1 2} G ¹ _: G₉ ^{8 G6} _F ⁰ ^{1 G} _{: T 5} 5^E ₄ 2 N₀ ^E ¹ _Q1 7 A₆ 1^Y _{S 1} 9^Y _{S 4} 9 _{T 8} ^L _{E 0} ^Q _E)₀ GO Y_{: S} O ^D _{: T : : : : : :} N ¹ _: ² _: 1^A _L ^N NO G ^N _T D G GO NO ^GO GO ^G _S G G O ^G A TO ^GO ^G GO _TO Y₁ G ^{G N G} _S ^N G ^N _Q ^N R ^{N G N N} _T ^{N N N} _L ¹ _: ^GD I E ^E ^Q _L ^D _S ^D I I ^{L L} ^Q _F ^D _I ^G _L ^D _I ^G _P ^D _I ^Q _S _F ^D _I D^D _I ^G _L ^D _I ^G _L ^D _I G N V^{D I} ^S O N^{S S} _{S I Q} ^D _I _{S S E} S _T ^Q _E _E R ^S ^S _S ^Q _E ^S _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^Q ^{A A} ₍ ^{A A} ₍ ^{A V} _E ^A _E ^A _E ^V _E ^V _{E E E E} _C ^D _{I C F C} ^S _{( C F C} ^S _{( C} ^S _{( C} ^S _{( C} ^S _{( C} ^S _{( C} ^S ₍ ^A _C ^S ₍ ^V _C ^S ₍ ^A _C ^S ₍ -₇ -₆ -₅ ⁷ ₂ ⁸ ₂ -₅ ⁸ ₁ -₇ -₆ -₅ -_{7 9} ⁴ ₁ V V V V V V V V V V V V VB B B B B B B B B B B B B^R _{T 9} ^R _{T 1} ^R _{T 1} ^R _T ^R _T ^R _{T 6} ^R _T ^R _{T 2} ^R _{T 4} ^R _{T 1} ^R _{T 9} ^R _T ^R _T ² ₂ 8 4 9 ₀ 8 7 8 8 1 6 5 ₅ ⁹ ₁ ³ ₅ ⁸ ₆ ⁵ ₇ ⁴ ₂ ⁸ ₉ ^{4 9 6 7 8 0} ₂. ⁶ ₉ ⁵ ₉ ⁴ ₁ ² ₁ ⁷ ₉ ³ ₃ ⁹ ₄ ₉ ⁶ ₃ ₄ ² ₆ 8 ₈ 7 ₄ 1 ₅ 9⁹ ₂ 3^{6 2 5 8} _{9 6 2 9 5 3} _{7 4 4 7 1 8} ⁰ _{1 6} ² ₂ ⁵ _{6 5} ² _{3 9} ² _{2 2} ² _{5 9} ⁵ _{6 5} ² _{4 1} ⁶ _{4 9} ⁵ ₅ 8₂ ₂ ^. ₂ ¹ _/ ₂ ^. ₂ ₂ ^. ₁ ₂ ^. ₁ ₁ ^. ₁ ² ₂ ₁ ^. ₂ ₁ ^. ₂ ₁ ^. _{2 2 2 2} ₂ ^. ₁ ^. ₁ ^. ₂ ^. ₂ ^S _T P _I ² ₀-_E ⁴ ₉ ^. ₆ ³ ₀-_E ² ₃ ^. ₆ ⁰ ₂ ^. ₀ ₂. ² ₁ ₉. ³ ₁ ₄ 1₂ ₆ 4₄, 3₁

_F ^{F Q} ^S _L) _{F E} ₄ ^Q ₎ ^S ₍ ) ^{F Q} 0_{L E} ^L _F Y ) )₆ ^S ₍ ) ^F ) _E) ^H _L ) ^Q) E₃ 6V⁶ _E ³ N₇ ^{F F} _T ^{3 K} _E ^{4 F} _F ⁰ _F ^F _{F 9} 0 _{F 8} 8^G _{T 9} 1^P ₎ 2 N_{0 S} ^{3 F} ₆ _Y ⁰ N₁ 3 Y ⁴ _Y Y₅ 3^N _{9 8} _T ^{7 L} _E ^{2 Y} ₃ A_{9 : 0 3} _Q ^A _E ^{3 E 3 N} N ^{7 Q 2} Y_: A_{: :} ) G_{: : :} ) _{: T : A}O _{: E :} GOGO ^S _S O ^Q _{E 9} O AO O_S G¹ ₄ Y ^G _T ^T ₉ _E ⁹ ₃ ^T _NO NO ^{G N} G GO NO ^A _L ^N ^{S N} G ^N N ^N G ^{N N} G ^N G ^N _Q ^D _T ^N G ^{N D}D^{D S D G 1} _: ^{G G} G V ³ _: ^G _I ^Q _{L I} ^R I I_I ^R _I G I I_{L I L I} ^D _{I L} ^D _I ^D _I ^E _Q ^{D D} _P ^{GD D} V^S _S ^{Q S} _S ^S _S O ^S _S _T ^S _S ^S _S O ^Q _L ^Q G ^{G S S} _{S E} ^S _S ^{S S} _{S E} ^A _E ^Q ^A _E ^{A N Q} ^A _E ^{Q Q} ^A _E ^A _E V ^N _F ^Q ^S _{E S} ^Q _E ^AS ₍ ^Q ^A _{E S} ^Q ^T _E ^S ₍ _C ^S _{( C} ^S _{( C} ^D _{I C} ^S _{( C} ^S _{( C} ^S ₍ G^D _{I C} ^S ₍ ^A _F ^S _{( C F C} ^S _{( C} ^S ₍ ^A _{C F}-₆ ⁷ ₂ ¹ ₁ ⁹ ₁ -_{5 9} ⁵ ₂ ⁹ ₂ -₅ ⁸ ₁ ⁹ ₁ -_{5 9} V V V V V V V V V V V V VB B B B B B B B B B B B B^R _{T 4} ^R _T ^R _{T 3} - ^R _T ^R _{T 1} ^R _T ^R _{T 1} - ^R _{T 1} - ^R _{T 1} ^R _T ^R _T ^R _{T 6} ^R _T8 ⁰ _{3 3} 2 2 5 6 ₅ 5 ₀ 7₆ ³ ₃ ^{0 9 2 9 1 4 5 9 9} 9 7 5 3 ₂. ³ ₄ ⁵ ₉ ₉ ⁹ ₂ ₇ ⁹ ₇ ₂ ⁹ ₅ ₄ ² _{5 6 3 2 9 3 1} ⁹ 1 ³ ₈ ⁴ ₆ ⁵ ₄ ⁴ ₀ ⁶ ₀ ⁶ ₂ ⁹ ₂ 3^{5 5} ₄ ³ ₆ ² _{3 2} ⁸ _{4 0} ⁷ _{2 5} ⁸ _{2 3} _{2 7 1 5} ⁵ ₅ ¹ _{6 0} ⁰ ₂ ⁹ _{2 5} ³ _{6 5} ³ _{5 3} ⁵ ₅ 8₂ ² ₂ ₁ ^. ₂ ₁ ^. ₂ ² ₂ ₂ ^. ₁ ² ₁ ² ₂ ² ₂ ² _{1 2} ¹ _/ ₂ ^. ₁ ^. ₁ ¹ ₁ ² ₁ ^S _T P _I ² ₀-_E ⁰ ₄ ^. ₈ ³ ₀-_E ² ₆ ^. ₄ ² ₁ ^. ₀ ₁. ¹ ₁ ₆. ¹ ₁ ₂ 8₆ ₆ 9₅

, ^{, , , ,} ² _{7 0}, ₉, ₀, _{7 9 0}, ₄, ₃, ₄, ₃ ₅ ⁰ ₂ ¹ ₁ ⁰ ₆ ³ ₃ ¹ ₂ ¹ ₆ ³ ₆ ⁰ ₃ ⁹ ₅ ⁷ ₂ ⁴ ₁ _F F _QY^I _F ^L Y) _{E )} ^F ) ^E _{F F} F S Y^I ) ) ^F ) ^F ) ^H _L ^Q _{E )} ^H _L ⁾ ^P ₀ _{T ) 6} ⁷ _{9 F 6} ⁽ _{T 1 5 F 0 F 6 )} ⁴ _{5 S} ⁴ ^{9 I} _T ^{2 G} _T ^{L 2} _F ^{9 F} _T ^{4 9} A^{9 P 2} N ₅ N_{6 6} ¹ _{: E 5} N_{7 4} A_{0 5 S 5} Y ² N ¹ ^{G 4} N ² N ^{1 Y} G ¹ Y ^{1 E 3 E 2} N ⁷ _: Y_: _{S :} G ^G _: O ^G _{: Q}) _{: : T : T : :} ^S _S O O O _S O ^A _{T T}O G ^T ₀ ^S _S O GO NO N Y ^S _S G ^N N _E ⁶ ₅ G Y O G G ^S _S O G ^N G ^NG^{S N N D} I_L ^D ^S _I ^G _L ^D _I ^G _L ^{G N} Q G ^N G ^{N N N N S} ^D _T ^D I ^D I I ^{Q 1} S_: ^G _L ^D _I ^G _I ^D _I ^A _L ^D _I R D^D _I ^G _E ^D _I G ^G _L _S ^{Q S} _T ^{Q S} _S ^Q _E G O V^Q G ^{N S} _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^{Q S} _S ^Q _E ^S _S ^Q _E ^A _E ^A _E ^AS ₍ _F ^S _E _{C (} ^G _E _{C I C (} ^A _{E E E E} ^S ₍ ^S ₍ _C ^S _{( C} ^S _{( C} ^{S D AS} _C ^S ₍ ^A _C ^S ₍ ^A _C ^S ₍ ^A _C ^S ₍ ^A _{C F} ^A _{C F} ⁷ ₂ -₅ -₅ ⁹ ₂ ⁴ ₁ -₅ ⁹ ₁ -₅ -_{6 2 2} -₅ V V V V V V V V V V V VB B B B B B B B B B B B^R _T ^R _{T 1} ^R _{T 1} ^R _{T 1} - ^R _T ^R _{T 1} ^R _T ^R _{T 4} ^R _{T 4} ^R _T ^R _T ^R _{T 1} ₉ ⁷ 3 ₆ ⁷ 2 9 ⁸ 8 5 ¹ 6 7 ⁸ 8 0 ⁷ ₉ ₄ ⁴ 1 5 ₄ 5 3 4 7 4 8 6 ₂. ⁷ ₅ ⁰ _{5 4 1 8 2 5} ⁹ 3 ² ₉ ⁴ ₇ ¹ ₁ ² ₁ ⁹ ₁ ⁶ ₄ ³ ₄ ^{8 4 5} ₂ 3^{6 3} ₇ ⁷ _{3 5} ⁷ _{1 9} ⁸ _{1 8} ² _{3 3 7 3} _{6 1 3 5} ³ ₈ ⁹ _{4 6} ⁸ ₇ ⁵ _{3 9} ⁰ _{5 7} ⁷ _{1 2} ⁵ ₅ 8₂ ₂ ^. ₁ ² ₁ ₁ ^. ₂ ₁ ^. ₂ ₂ ^. ₂ ₂ ^. ₂ ₂ ^. _{2 1 2} ¹ _/ ₂ ² ₁ ² ₁ ^. ₁ ^. ₁ ^. ₂ ^S _T P _I

Claims

Attorney Docket No: SRU-004WO CLAIMS 1. A method for predicting presence, absence, or likelihood of cancer in a subject, the method comprising: obtaining or having obtained a dataset comprising identities of a plurality of T- cell receptors (TCRs) from the subject; generating a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs are encoded by at least: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, or TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, or TRBJ2-7; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. 2. The method of claim 1, wherein the identities of the plurality of TCRs from the subject comprise: a variable gene, wherein the variable gene is any one, or more, of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, or TRBV9; a joining gene, wherein the joining gene is any one, or more, of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, or TRBJ2-7; and/or a plurality of variable regions, wherein the variable regions are encoded for by at least: IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene of: TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29- 1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, or TRBV9; and a joining gene of: TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1- 5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, or TRBJ2-7. 3. The method of claim 1, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV13; and a joining gene selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV14; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV18; and a joining gene selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1-6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV19; and a joining gene selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1; a variable gene TRBV2; and a joining gene selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7; a variable gene TRBV20-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-5, TRBJ2-3, and TRBJ2-5; a variable gene TRBV25-1; and a joining gene selected from any one of TRBJ2- 1, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV27; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-4, and TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV5-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-5; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV5-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-8; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV6-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV6-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene selected from any one of TRBJ2-3, and TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-7; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-8; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2-7; a variable gene TRBV7-9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7; or a variable gene TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, and TRBJ2-7. 4. The method of claim 3, wherein the plurality of variable regions comprises variable regions encoded by any one set of: IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV13; and a joining gene TRBJ1-4; a variable gene TRBV13; and a joining gene TRBJ1-5; a variable gene TRBV13; and a joining gene TRBJ2-1; a variable gene TRBV13; and a joining gene TRBJ2-2; a variable gene TRBV13; and a joining gene TRBJ2-3; a variable gene TRBV13; and a joining gene TRBJ2-5; a variable gene TRBV13; and a joining gene TRBJ2-7; a variable gene TRBV11-3; and a joining gene TRBJ2-1; a variable gene TRBV11-3; and a joining gene TRBJ2-2; a variable gene TRBV11-3; and a joining gene TRBJ2-7; a variable gene TRBV14; and a joining gene TRBJ1-1; a variable gene TRBV14; and a joining gene TRBJ1-4; a variable gene TRBV14; and a joining gene TRBJ1-5; a variable gene TRBV14; and a joining gene TRBJ2-1; a variable gene TRBV14; and a joining gene TRBJ2-2; a variable gene TRBV14; and a joining gene TRBJ2-3; a variable gene TRBV14; and a joining gene TRBJ2-5; a variable gene TRBV14; and a joining gene TRBJ2-7; a variable gene TRBV18; and a joining gene TRBJ1-1; a variable gene TRBV18; and a joining gene TRBJ1-3; a variable gene TRBV18; and a joining gene TRBJ1-5; a variable gene TRBV18; and a joining gene TRBJ1-6; a variable gene TRBV18; and a joining gene TRBJ2-2; a variable gene TRBV18; and a joining gene TRBJ2-3; a variable gene TRBV18; and a joining gene TRBJ2-5; a variable gene TRBV18; and a joining gene TRBJ2-7; a variable gene TRBV19; and a joining gene TRBJ1-2; a variable gene TRBV19; and a joining gene TRBJ1-6; a variable gene TRBV19; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ1-6; a variable gene TRBV2; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ2-7; a variable gene TRBV20-1; and a joining gene TRBJ1-1; a variable gene TRBV20-1; and a joining gene TRBJ1-5; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV20-1; and a joining gene TRBJ2-3; a variable gene TRBV20-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-1; a variable gene TRBV25-1; and a joining gene TRBJ2-3; a variable gene TRBV25-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-7; a variable gene TRBV27; and a joining gene TRBJ1-1; a variable gene TRBV27; and a joining gene TRBJ1-2; a variable gene TRBV27; and a joining gene TRBJ1-3; a variable gene TRBV27; and a joining gene TRBJ1-4; a variable gene TRBV27; and a joining gene TRBJ2-1; a variable gene TRBV27; and a joining gene TRBJ2-2; a variable gene TRBV27; and a joining gene TRBJ2-3; a variable gene TRBV27; and a joining gene TRBJ2-5; a variable gene TRBV27; and a joining gene TRBJ2-6; a variable gene TRBV27; and a joining gene TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene TRBJ1-1; a variable gene TRBV29-1; and a joining gene TRBJ1-4; a variable gene TRBV29-1; and a joining gene TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene TRBJ1-1; a variable gene TRBV5-1; and a joining gene TRBJ1-2; a variable gene TRBV5-1; and a joining gene TRBJ1-3; a variable gene TRBV5-1; and a joining gene TRBJ1-4; a variable gene TRBV5-1; and a joining gene TRBJ1-5; a variable gene TRBV5-1; and a joining gene TRBJ1-6; a variable gene TRBV5-1; and a joining gene TRBJ2-1; a variable gene TRBV5-1; and a joining gene TRBJ2-2; a variable gene TRBV5-1; and a joining gene TRBJ2-3; a variable gene TRBV5-1; and a joining gene TRBJ2-4; a variable gene TRBV5-1; and a joining gene TRBJ2-5; a variable gene TRBV5-1; and a joining gene TRBJ2-6; a variable gene TRBV5-1; and a joining gene TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-4; and a joining gene TRBJ1-1; a variable gene TRBV5-4; and a joining gene TRBJ2-1; a variable gene TRBV5-4; and a joining gene TRBJ2-7; a variable gene TRBV5-5; and a joining gene TRBJ1-1; a variable gene TRBV5-5; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ1-1; a variable gene TRBV5-6; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ2-7; a variable gene TRBV5-8; and a joining gene TRBJ1-1; a variable gene TRBV5-8; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-2; a variable gene TRBV6-1; and a joining gene TRBJ2-7; a variable gene TRBV6-4; and a joining gene TRBJ1-1; a variable gene TRBV6-4; and a joining gene TRBJ2-1; a variable gene TRBV6-4; and a joining gene TRBJ2-2; a variable gene TRBV6-4; and a joining gene TRBJ2-6; a variable gene TRBV6-4; and a joining gene TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ1-1; a variable gene TRBV7-6; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ2-7; a variable gene TRBV7-7; and a joining gene TRBJ1-1; a variable gene TRBV7-7; and a joining gene TRBJ1-4; a variable gene TRBV7-7; and a joining gene TRBJ2-1; a variable gene TRBV7-7; and a joining gene TRBJ2-7; a variable gene TRBV7-8; and a joining gene TRBJ1-1; a variable gene TRBV7-8; and a joining gene TRBJ1-5; a variable gene TRBV7-8; and a joining gene TRBJ2-1; a variable gene TRBV7-8; and a joining gene TRBJ2-5; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-8; and a joining gene TRBJ2-7; a variable gene TRBV7-9; and a joining gene TRBJ1-1; a variable gene TRBV7-9; and a joining gene TRBJ1-4; a variable gene TRBV7-9; and a joining gene TRBJ1-5; a variable gene TRBV7-9; and a joining gene TRBJ1-6; a variable gene TRBV7-9; and a joining gene TRBJ2-1; a variable gene TRBV7-9; and a joining gene TRBJ2-2; a variable gene TRBV7-9; and a joining gene TRBJ2-3; a variable gene TRBV7-9; and a joining gene TRBJ2-4; a variable gene TRBV7-9; and a joining gene TRBJ2-5; a variable gene TRBV7-9; and a joining gene TRBJ2-7; a variable gene TRBV9; and a joining gene TRBJ1-1; a variable gene TRBV9; and a joining gene TRBJ1-4; a variable gene TRBV9; and a joining gene TRBJ2-1; a variable gene TRBV9; and a joining gene TRBJ2-2; a variable gene TRBV9; and a joining gene TRBJ2-3; or a variable gene TRBV9; and a joining gene TRBJ2-7. 5. The method of any one of claims 1-4, wherein the plurality of variable regions of the cancer-associated TCR RFUs comprises a CDR3 amino acid sequence comprising a formula of CAxxxxxxxx or CSxxxxxxxx, wherein a residue “C” is a cysteine, residue “A” is an alanine, residue “S” is a serine, and residue “x” is selected from any naturally occurring amino acid residue 6. The method of any one of claims 1-5, wherein the plurality of variable regions of the cancer-associated TCR RFUs comprises the CDR3 amiono acid sequence comprising the formula of CASxxxxx, and wherein a residue “C” is a cysteine, residue “A” is an alanine, residue “S” is a serine, and residue “x” is selected from any naturally occurring amino acid residue. 7. The method of any one of claims 1-6, wherein the plurality of variable regions of the cancer-associated TCR RFUs comprises the CDR3 amino acid sequence comprising the formula of CASSxxxx, CASTxxxx, or CASRxxxx, and wherein a residue “C” is a IPTS/128553107.1 Attorney Docket No: SRU-004WO cysteine, residue “A” is an alanine, residue “S” is a serine, and residue “x” is selected from any naturally occurring amino acid residue. 8. The method of any one of claims 1-7, wherein the plurality of variable regions of the cancer-associated TCR RFUs comprises at least one CDR3 amino acid sequence having at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identity to any one of CDR3 amino acid sequences as provided in Table 1. 9. The method of any one of claims 1-8, wherein the plurality of variable regions of the cancer-associated TCR RFUs comprise at least one CDR3 amino acid sequence having 100% identity to any one of CDR3 amino acid sequences as provided in Table 1. 10. The method of claim 1, wherein the cancer-associated TCR RFUs are determined by: obtaining or having obtained TCR sequencing data for a plurality of TCRs from a plurality of training samples; assigning TCRs of the plurality of TCRs into candidate RFUs by: grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores; clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc). 11. The method of claim 10, wherein the cancer-associated TCR RFUs are further determined by performing one or more of: filtering candidate RFUs to retain candidate RFUs that are observed in at least a first threshold number of training samples; filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a second threshold number of training samples; filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than a threshold value. IPTS/128553107.1 Attorney Docket No: SRU-004WO 12. The method of claim 11, wherein the cancer-associated TCR RFUs are further determined by: applying a gamma-Poisson generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. 13. The method of claim 12, wherein applying the gamma-Poisson generalized linear model further comprises incorporating demographic covariates. 14. The method of claim 13, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 15. The method of claim 10, wherein: the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. 16. The method of claim 1, wherein the predictive model is a logistic regression model. 17. The method of claim 1, wherein the cancer-associated TCR RFUs comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at IPTS/128553107.1 Attorney Docket No: SRU-004WO least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, or at least 197 RFUs. 18. The method of claim 1, wherein a performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 19. The method of claim 18, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.64. 20. The method of claim 18, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.70. 21. The method of claim 18, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.71. 22. The method of claim 18, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.83. 23. The method of claim 18, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.84. IPTS/128553107.1 Attorney Docket No: SRU-004WO 24. The method of claim 18, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.85. 25. The method of claim 1, wherein the method further comprises: obtaining or having obtained a second dataset comprising expression levels of a plurality of biomarkers from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a second predictive model to the expression levels of the plurality of biomarkers. 26. The method of claim 25, wherein the second predictive model is a support vector machine (SVM) model. 27. The method of claim 25, wherein the plurality of biomarkers comprises at two or more biomarkers selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. 28. The method of claim 25, wherein a performance of the second predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 29. The method of any one of claims 25-28, wherein a performance metric of the second predictive model is improved in comparison to a model solely incorporating CEACAM5. 30. The method of claim 1, wherein the method further comprises: obtaining or having obtained a third dataset comprising a mutational profile of a plurality of circulating tumor DNA (ctDNA) from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a third predictive model to the mutational profiles of ctDNA. 31. The method of claim 30, wherein the third predictive model is a logistic regression model. IPTS/128553107.1 Attorney Docket No: SRU-004WO 32. The method of claim 30, wherein the plurality of ctDNA comprises ctDNA selected from CDKN2A, MGAM, PIK3CA, EPHB1, PAK5, KEAP1, TP53, KRAS, KDM5A, ATM, and PTEN. 33. The method of claim 30, wherein the ctDNA comprises a mutation. 34. The method of claim 33, wherein the mutation is any one of combination of a frameshift mutation, a missense mutation, a synonymous mutation, a splice site mutation, or a nonsense mutation. 35. The method of any one of claims 30-34, wherein the mutation is a substitution, an insertion, a deletion, or any combination thereof. 36. The method of any one of claims 1-35, wherein the cancer is lung cancer. 37. The method of any one of claims 1-36, wherein the lung cancer is an adenocarcinoma, an adenosquamous cell cancer, a large cell cancer, a neuroendocrine cancer, a non-small cell lung cancer (NSCLC), a small cell cancer, or a squamous cell cancer. 38. The method of any one of claims 1-37, wherein the cancer is an early stage cancer. 39. The method of any one of claims 1-38, wherein the cancer is stage I, stage II, stage III, and/or stage IV lung cancer. 40. The method of any one of claims 1-39, wherein the identities of the plurality of TCRs are determined from a test sample obtained from the subject. 41. The method of claim 40, wherein the test sample is a blood or buffy coat or serum sample. 42. The method of claim 40 or claim 41, wherein the subject is suspected of having an early stage cancer. 43. The method of claim 40 or claim 41, wherein the subject is not suspected of having an early stage cancer. IPTS/128553107.1 Attorney Docket No: SRU-004WO 44. The method of any one of claims 1-43, wherein obtaining or having obtained the dataset comprising identities of the plurality of TCRs from the subject comprises performing an assay to determine the plurality of TCRs. 45. The method of claim 44, wherein the assay is an amplification-based assay. 46. The method of claim 45, wherein the amplification-based assay is a PCR assay, RT- PCR assay, qRT-PCR assay, or multiplex PCR assay. 47. The method of any one of claims 1-46, wherein the analyzing and generating the subject feature count comprises performing an assay to determine the plurality of TCRs, and performing a feature count to determine the subject feature count against the cancer- associated RFUs. 48. The method of claim 47, wherein the assay is a sequencing-based assay. 49. The method of claim 48, wherein the sequencing-based assay is an RNA-seq assay. 50. The method of any one of claims 44-49, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising primers. 51. The method of any one of claims 25-29, wherein obtaining or having obtained the second dataset comprises performing an assay to determine the expression levels of the plurality of biomarkers. 52. The method of claim 51, wherein the assay is a Proximity Extension Assay (PEA), a xMAP Multiplex Assay, a single molecule array (SIMOA) assay, mass spectrometry based protein or peptide assay, or an aptamer-based assay. 53. The method of claim 51 or claim 52, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising antibodies. 54. The method of claim 53, wherein the antibodies comprise one of monoclonal and polyclonal antibodies. 55. The method of claim 53, wherein the antibodies comprise both monoclonal and polyclonal antibodies. IPTS/128553107.1 Attorney Docket No: SRU-004WO 56. The method of any one of claims 30-35, wherein obtaining or having obtained the third dataset comprises performing an assay to determine the mutation profile of the plurality of ctDNA. 57. The method of claim 56, wherein the assay is an NGS-based hybrid capture method assay. 58. The method of claim 1, wherein the method further comprises administering a treatment to the subject. 59. The method of claim 58, wherein the treatment comprises a surgery, a chemotherapy, a radiation therapy, a targeted therapy, an immunotherapy, or any combination thereof. 60. The method of any one of claims 1-59, wherein the subject is an undiagnosed subject, at risk subject, or a subject previously diagnosed with cancer. 61. A method for predicting presence, absence, or likelihood of cancer in a subject, the method comprising: obtaining or having obtained a dataset comprising identities of a plurality of T- cell receptors (TCRs) from the subject; generating a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs comprise at least one CDR3 amino acid sequence having at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identity to any one of CDR3 amino acid sequences as provided in Table 1; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. 62. The method of claim 61, wherein the plurality of variable regions of the cancer- associated TCR RFUs comprise at least one CDR3 amino acid sequence having 100% identity to any one of CDR3 amino acid sequences as provided in Table 1. IPTS/128553107.1 Attorney Docket No: SRU-004WO 63. The method of claim 61, wherein the cancer-associated TCR RFUs are determined by: obtaining or having obtained TCR sequencing data for a plurality of TCRs from a plurality of training samples; assigning TCRs of the plurality of TCRs into candidate RFUs by: grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores; clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc). 64. The method of claim 63, wherein the cancer-associated TCR RFUs are further determined by performing one or more of: filtering candidate RFUs to retain candidate RFUs that are observed in at least a first threshold number of training samples; filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a second threshold number of training samples; filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than a threshold value. 65. The method of claim 64, wherein the cancer-associated TCR RFUs are further determined by: applying a gamma-Poisson generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. 66. The method of claim 65, wherein applying the gamma-Poisson generalized linear model further comprises incorporating demographic covariates. 67. The method of claim 66, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 68. The method of claim 63, wherein: IPTS/128553107.1 Attorney Docket No: SRU-004WO the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. 69. The method of claim 61, wherein the predictive model is a logistic regression model. 70. The method of claim 61, wherein the cancer-associated TCR RFUs comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at IPTS/128553107.1 Attorney Docket No: SRU-004WO least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, or at least 197 RFUs. 71. The method of claim 70, wherein a performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, at least 0.80, at least 0.81, at least 0.82, or at least 0.83. 72. The method of claim 71, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.64. 73. The method of claim 71, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.70. 74. The method of claim 71, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.71. 75. The method of claim 71, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.83. 76. The method of claim 71, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.84. 77. The method of claim 71, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.85. 78. The method of claim 61, wherein the method further comprises: obtaining or having obtained a second dataset comprising expression levels of a plurality of biomarkers from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a second predictive model to the expression levels of the plurality of biomarkers. IPTS/128553107.1 Attorney Docket No: SRU-004WO 79. The method of claim 78, wherein the second predictive model is a support vector machine (SVM) model. 80. The method of claim 78, wherein the plurality of biomarkers comprises at two or more biomarkers selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. 81. The method of claim 78, wherein a performance of the second predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 82. The method of any one of claims 78-81, wherein a performance metric of the second predictive model is improved in comparison to a model solely incorporating CEACAM5. 83. The method of claim 78, wherein the method further comprises: obtaining or having obtained a third dataset comprising a mutational profile of a plurality of circulating tumor DNA (ctDNA) from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a third predictive model to the mutational profiles of ctDNA. 84. The method of claim 83, wherein the third predictive model is a logistic regression model. 85. The method of claim 83, wherein the plurality of ctDNA comprises ctDNA selected from CDKN2A, MGAM, PIK3CA, EPHB1, PAK5, KEAP1, TP53, KRAS, KDM5A, ATM, and PTEN. 86. The method of claim 83, wherein the ctDNA comprises a mutation. 87. The method of claim 86, wherein the mutation is any one of combination of a frameshift mutation, a missense mutation, a synonymous mutation, a splice site mutation, or a nonsense mutation. IPTS/128553107.1 Attorney Docket No: SRU-004WO 88. The method of any one of claims 83-87, wherein the mutation is a substitution, an insertion, a deletion, or any combination thereof. 89. The method of any one of claims 61-88, wherein the cancer is lung cancer. 90. The method of any one of claims 61-89, wherein the lung cancer is an adenocarcinoma, an adenosquamous cell cancer, a large cell cancer, a neuroendocrine cancer, a non-small cell lung cancer (NSCLC), a small cell cancer, or a squamous cell cancer. 91. The method of any one of claims 61-90, wherein the cancer is an early stage cancer. 92. The method of any one of claims 61-91, wherein the cancer is stage I, stage II, stage III, and/or stage IV lung cancer. 93. The method of any one of claims 61-92, wherein the identities of the plurality of TCRs are determined from a test sample obtained from the subject. 94. The method of claim 93, wherein the test sample is a blood or serum sample. 95. The method of claim 93 or claim 94, wherein the subject is suspected of having an early stage cancer. 96. The method of claim 93 or claim 94, wherein the subject is not suspected of having an early stage cancer. 97. The method of any one of claims 61-96, wherein obtaining or having obtained the dataset comprising identities of the plurality of TCRs from the subject comprises performing an assay to determine the plurality of TCRs. 98. The method of claim 97, wherein the assay is an amplification-based assay. 99. The method of claim 98, wherein the amplification-based assay is a PCR assay, RT- PCR assay, qRT-PCR assay, or multiplex PCR assay. 100. The method of any one of claims 61-99, wherein the analyzing and generating the subject feature count comprises performing an assay to determine the plurality of TCRs, and performing a feature count to determine the subject feature count against the cancer- associated RFUs. IPTS/128553107.1 Attorney Docket No: SRU-004WO 101. The method of claim 100, wherein the assay is a sequencing-based assay. 102. The method of claim 101, wherein the sequencing-based assay is an RNA-seq assay. 103. The method of any one of claims 97-102, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising primers. 104. The method of any one of claims 78-82, wherein obtaining or having obtained the second dataset comprises performing an assay to determine the expression levels of the plurality of biomarkers. 105. The method of claim 104, wherein the assay is a Proximity Extension Assay (PEA), a xMAP Multiplex Assay, a single molecule array (SIMOA) assay, mass spectrometry based protein or peptide assay, or an aptamer-based assay. 106. The method of claim 104 or claim 105, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising antibodies. 107. The method of claim 106, wherein the antibodies comprise one of monoclonal and polyclonal antibodies. 108. The method of claim 106, wherein the antibodies comprise both monoclonal and polyclonal antibodies. 109. The method of any one of claims 83-88, wherein obtaining or having obtained the third dataset comprises performing an assay to determine the mutation profile of the plurality of ctDNA. 110. The method of claim 109, wherein the assay is an NGS-based hybrid capture method assay. 111. The method of claim 61, wherein the method further comprises administering a treatment to the subject. 112. The method of claim 111, wherein the treatment comprises a surgery, a chemotherapy, a radiation therapy, a targeted therapy, an immunotherapy, or any combination thereof. IPTS/128553107.1 Attorney Docket No: SRU-004WO 113. The method of any one of claims 61-112, wherein the subject is an undiagnosed subject, at risk subject, or a subject previously diagnosed with cancer. 114. A non-transitory computer-readable storage medium, the computer-readable storage medium comprising instructions that when executed by a processor, cause the processor to: obtain or having obtained a dataset comprising identities of a plurality of T-cell receptors (TCRs) from the subject; generate a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs are encoded by at least: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, or TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, or TRBJ2-7; and generate a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. 115. The non-transitory computer readable medium of claim 114, wherein the identities of the plurality of TCRs from the subject comprise: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, or TRBV9; IPTS/128553107.1 Attorney Docket No: SRU-004WO a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, or TRBJ2-7; and/or a plurality of variable regions, wherein the variable regions are encoded for by at least: a variable gene of: TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29- 1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, or TRBV9; and a joining gene of: TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1- 5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, or TRBJ2-7. 116. The non-transitory computer readable medium of claim 115, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV13; and a joining gene selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV14; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV18; and a joining gene selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1-6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV19; and a joining gene selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1; a variable gene TRBV2; and a joining gene selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7; a variable gene TRBV20-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-5, TRBJ2-3, and TRBJ2-5; a variable gene TRBV25-1; and a joining gene selected from any one of TRBJ2- 1, TRBJ2-3, TRBJ2-5, and TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV27; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-4, and TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV5-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-5; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV5-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-8; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV6-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV6-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene selected from any one of TRBJ2-3, and TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-7; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-8; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7; or a variable gene TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, and TRBJ2-7. 117. The non-transitory computer readable medium of claim 116, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV13; and a joining gene TRBJ1-4; a variable gene TRBV13; and a joining gene TRBJ1-5; a variable gene TRBV13; and a joining gene TRBJ2-1; a variable gene TRBV13; and a joining gene TRBJ2-2; a variable gene TRBV13; and a joining gene TRBJ2-3; a variable gene TRBV13; and a joining gene TRBJ2-5; a variable gene TRBV13; and a joining gene TRBJ2-7; a variable gene TRBV11-3; and a joining gene TRBJ2-1; a variable gene TRBV11-3; and a joining gene TRBJ2-2; a variable gene TRBV11-3; and a joining gene TRBJ2-7; a variable gene TRBV14; and a joining gene TRBJ1-1; a variable gene TRBV14; and a joining gene TRBJ1-4; a variable gene TRBV14; and a joining gene TRBJ1-5; a variable gene TRBV14; and a joining gene TRBJ2-1; a variable gene TRBV14; and a joining gene TRBJ2-2; a variable gene TRBV14; and a joining gene TRBJ2-3; a variable gene TRBV14; and a joining gene TRBJ2-5; a variable gene TRBV14; and a joining gene TRBJ2-7; a variable gene TRBV18; and a joining gene TRBJ1-1; a variable gene TRBV18; and a joining gene TRBJ1-3; a variable gene TRBV18; and a joining gene TRBJ1-5; a variable gene TRBV18; and a joining gene TRBJ1-6; a variable gene TRBV18; and a joining gene TRBJ2-2; a variable gene TRBV18; and a joining gene TRBJ2-3; a variable gene TRBV18; and a joining gene TRBJ2-5; a variable gene TRBV18; and a joining gene TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV19; and a joining gene TRBJ1-2; a variable gene TRBV19; and a joining gene TRBJ1-6; a variable gene TRBV19; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ1-6; a variable gene TRBV2; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ2-7; a variable gene TRBV20-1; and a joining gene TRBJ1-1; a variable gene TRBV20-1; and a joining gene TRBJ1-5; a variable gene TRBV20-1; and a joining gene TRBJ2-3; a variable gene TRBV20-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-1; a variable gene TRBV25-1; and a joining gene TRBJ2-3; a variable gene TRBV25-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-7; a variable gene TRBV27; and a joining gene TRBJ1-1; a variable gene TRBV27; and a joining gene TRBJ1-2; a variable gene TRBV27; and a joining gene TRBJ1-3; a variable gene TRBV27; and a joining gene TRBJ1-4; a variable gene TRBV27; and a joining gene TRBJ2-1; a variable gene TRBV27; and a joining gene TRBJ2-2; a variable gene TRBV27; and a joining gene TRBJ2-3; a variable gene TRBV27; and a joining gene TRBJ2-5; a variable gene TRBV27; and a joining gene TRBJ2-6; a variable gene TRBV27; and a joining gene TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene TRBJ1-1; a variable gene TRBV29-1; and a joining gene TRBJ1-4; a variable gene TRBV29-1; and a joining gene TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene TRBJ1-1; a variable gene TRBV5-1; and a joining gene TRBJ1-2; a variable gene TRBV5-1; and a joining gene TRBJ1-3; a variable gene TRBV5-1; and a joining gene TRBJ1-4; a variable gene TRBV5-1; and a joining gene TRBJ1-5; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-1; and a joining gene TRBJ1-6; a variable gene TRBV5-1; and a joining gene TRBJ2-1; a variable gene TRBV5-1; and a joining gene TRBJ2-2; a variable gene TRBV5-1; and a joining gene TRBJ2-3; a variable gene TRBV5-1; and a joining gene TRBJ2-4; a variable gene TRBV5-1; and a joining gene TRBJ2-5; a variable gene TRBV5-1; and a joining gene TRBJ2-6; a variable gene TRBV5-1; and a joining gene TRBJ2-7; a variable gene TRBV5-4; and a joining gene TRBJ1-1; a variable gene TRBV5-4; and a joining gene TRBJ2-1; a variable gene TRBV5-4; and a joining gene TRBJ2-7; a variable gene TRBV5-5; and a joining gene TRBJ1-1; a variable gene TRBV5-5; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ1-1; a variable gene TRBV5-6; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ2-7; a variable gene TRBV5-8; and a joining gene TRBJ1-1; a variable gene TRBV5-8; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-2; a variable gene TRBV6-1; and a joining gene TRBJ2-7; a variable gene TRBV6-4; and a joining gene TRBJ1-1; a variable gene TRBV6-4; and a joining gene TRBJ2-1; a variable gene TRBV6-4; and a joining gene TRBJ2-2; a variable gene TRBV6-4; and a joining gene TRBJ2-6; a variable gene TRBV6-4; and a joining gene TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ1-1; a variable gene TRBV7-6; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-7; and a joining gene TRBJ1-1; a variable gene TRBV7-7; and a joining gene TRBJ1-4; a variable gene TRBV7-7; and a joining gene TRBJ2-1; a variable gene TRBV7-7; and a joining gene TRBJ2-7; a variable gene TRBV7-8; and a joining gene TRBJ1-1; a variable gene TRBV7-8; and a joining gene TRBJ1-5; a variable gene TRBV7-8; and a joining gene TRBJ2-1; a variable gene TRBV7-8; and a joining gene TRBJ2-5; a variable gene TRBV7-8; and a joining gene TRBJ2-7; a variable gene TRBV7-9; and a joining gene TRBJ1-1; a variable gene TRBV7-9; and a joining gene TRBJ1-4; a variable gene TRBV7-9; and a joining gene TRBJ1-5; a variable gene TRBV7-9; and a joining gene TRBJ1-6; a variable gene TRBV7-9; and a joining gene TRBJ2-1; a variable gene TRBV7-9; and a joining gene TRBJ2-2; a variable gene TRBV7-9; and a joining gene TRBJ2-3; a variable gene TRBV7-9; and a joining gene TRBJ2-4; a variable gene TRBV7-9; and a joining gene TRBJ2-5; a variable gene TRBV7-9; and a joining gene TRBJ2-7; a variable gene TRBV9; and a joining gene TRBJ1-1; a variable gene TRBV9; and a joining gene TRBJ1-4; a variable gene TRBV9; and a joining gene TRBJ2-1; a variable gene TRBV9; and a joining gene TRBJ2-2; a variable gene TRBV9; and a joining gene TRBJ2-3; or a variable gene TRBV9; and a joining gene TRBJ2-7. 118. The non-transitory computer readable medium of claim 114, wherein the cancer- associated TCR RFUs are determined by: obtaining or having obtained TCR sequencing data for a plurality of TCRs from a plurality of training samples; assigning TCRs of the plurality of TCRs into candidate RFUs by: grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and IPTS/128553107.1 Attorney Docket No: SRU-004WO combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores; clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc). 119. The non-transitory computer readable medium of claim 118, wherein the cancer- associated TCR RFUs are further determined by performing one or more of: filtering candidate RFUs to retain candidate RFUs that are observed in at least a first threshold number of training samples; filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a second threshold number of training samples; filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than a threshold value. 120. The non-transitory computer readable medium of claim 118, wherein the cancer- associated TCR RFUs are further determined by: applying a gamma-Poisson generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. 121. The non-transitory computer readable medium of claim 120, wherein applying the gamma-Poisson generalized linear model further comprises incorporating demographic covariates. 122. The non-transitory computer readable medium of claim 121, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 123. The non-transitory computer readable medium of claim 118, wherein: the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. IPTS/128553107.1 Attorney Docket No: SRU-004WO 124. The non-transitory computer readable medium of claim 114, wherein the predictive model is a logistic regression model. 125. The non-transitory computer readable medium of claim 114, wherein the cancer- associated TCR RFUs comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, or at least 197 RFUs. IPTS/128553107.1 Attorney Docket No: SRU-004WO 126. The non-transitory computer readable medium of claim 114, wherein a performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 127. The non-transitory computer readable medium of claim 126, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.64. 128. The non-transitory computer readable medium of claim 126, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.70. 129. The non-transitory computer readable medium of claim 126, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.71. 130. The non-transitory computer readable medium of claim 126, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.83. 131. The non-transitory computer readable medium of claim 126, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.84. 132. The non-transitory computer readable medium of claim 126, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.85. 133. The non-transitory computer readable medium of claim 114, wherein the non- transitory computer readable medium further comprises: obtaining or having obtained a second dataset comprising expression levels of a plurality of biomarkers from the subject; and IPTS/128553107.1 Attorney Docket No: SRU-004WO generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a second predictive model to the expression levels of the plurality of biomarkers. 134. The non-transitory computer readable medium of claim 133, wherein the second predictive model is a support vector machine (SVM) model. 135. The non-transitory computer readable medium of claim 133, wherein the plurality of biomarkers comprises at two or more biomarkers selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. 136. The non-transitory computer readable medium of claim 133, wherein a performance of the second predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 137. The non-transitory computer readable medium of any one of claims 133-136, wherein a performance metric of the second predictive model is improved in comparison to a model solely incorporating CEACAM5. 138. The non-transitory computer readable medium of claim 114, wherein the non- transitory computer readable medium further comprises: obtaining or having obtained a third dataset comprising a mutational profile of a plurality of circulating tumor DNA (ctDNA) from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a third predictive model to the mutational profiles of ctDNA. 139. The non-transitory computer readable medium of claim 138, wherein the third predictive model is a logistic regression model. 140. The non-transitory computer readable medium of claim 138, wherein the plurality of ctDNA comprises ctDNA selected from CDKN2A, MGAM, PIK3CA, EPHB1, PAK5, KEAP1, TP53, KRAS, KDM5A, ATM, and PTEN. IPTS/128553107.1 Attorney Docket No: SRU-004WO 141. The non-transitory computer readable medium of claim 138, wherein the ctDNA comprises a mutation. 142. The non-transitory computer readable medium of claim 141, wherein the mutation is any one of combination of a frameshift mutation, a missense mutation, a synonymous mutation, a splice site mutation, or a nonsense mutation. 143. The non-transitory computer readable medium of any one of claims 138-142, wherein the mutation is a substitution, an insertion, a deletion, or any combination thereof. 144. The non-transitory computer readable medium of any one of claims 114-143, wherein the cancer is lung cancer. 145. The non-transitory computer readable medium of any one of claims 114-144, wherein the lung cancer is an adenocarcinoma, an adenosquamous cell cancer, a large cell cancer, a neuroendocrine cancer, a non-small cell lung cancer (NSCLC), a small cell cancer, or a squamous cell cancer. 146. The non-transitory computer readable medium of any one of claims 114-145, wherein the cancer is an early stage cancer. 147. The non-transitory computer readable medium of any one of claims 114-146, wherein the cancer is stage I, stage II, stage III, and/or stage IV lung cancer. 148. The non-transitory computer readable medium of any one of claims 114-147, wherein the identities of the plurality of TCRs are determined from a test sample obtained from the subject. 149. The non-transitory computer readable medium of claim 148, wherein the test sample is a blood or serum sample. 150. The non-transitory computer readable medium of claim 148 or claim 149, wherein the subject is suspected of having an early stage cancer. 151. The non-transitory computer readable medium of claim 148 or claim 149, wherein the subject is not suspected of having an early stage cancer. IPTS/128553107.1 Attorney Docket No: SRU-004WO 152. The non-transitory computer readable medium of any one of claims 114-151, wherein obtaining or having obtained the dataset comprising identities of the plurality of TCRs from the subject comprises performing an assay to determine the plurality of TCRs. 153. The non-transitory computer readable medium of claim 152, wherein the assay is an amplification-based assay. 154. The non-transitory computer readable medium of claim 153, wherein the amplification-based assay is a PCR assay, RT-PCR assay, qRT-PCR assay, or multiplex PCR assay. 155. The non-transitory computer readable medium of any one of claims 114-154, wherein the analyzing and generating the subject feature count comprises performing an assay to determine the plurality of TCRs, and performing a feature count to determine the subject feature count against the cancer-associated RFUs. 156. The non-transitory computer readable medium of claim 155, wherein the assay is a sequencing-based assay. 157. The non-transitory computer readable medium of claim 156, wherein the sequencing-based assay is an RNA-seq assay. 158. The non-transitory computer readable medium of any one of claims 152-157, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising primers. 159. The non-transitory computer readable medium of any one of claims 133-137, wherein obtaining or having obtained the second dataset comprises performing an assay to determine the expression levels of the plurality of biomarkers. 160. The non-transitory computer readable medium of claim 159, wherein the assay is a Proximity Extension Assay (PEA), a xMAP Multiplex Assay, a single molecule array (SIMOA) assay, mass spectrometry based protein or peptide assay, or an aptamer-based assay. IPTS/128553107.1 Attorney Docket No: SRU-004WO 161. The non-transitory computer readable medium of claim 159 or claim 160, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising antibodies. 162. The non-transitory computer readable medium of claim 161, wherein the antibodies comprise one of monoclonal and polyclonal antibodies. 163. The non-transitory computer readable medium of claim 161, wherein the antibodies comprise both monoclonal and polyclonal antibodies. 164. The non-transitory computer readable medium of any one of claims 138-193, wherein obtaining or having obtained the third dataset comprises performing an assay to determine the mutation profile of the plurality of ctDNA. 165. The non-transitory computer readable medium of claim 164, wherein the assay is an NGS-based hybrid capture non-transitory computer readable medium assay. 166. The non-transitory computer readable medium of any one of claims 114-165, wherein the subject is an undiagnosed subject, at risk subject, or a subject previously diagnosed with cancer. 167. A system comprising: a set of reagents used for determining identities of a plurality of T-cell receptors (TCRs) from a test sample from the subject; an apparatus configured to receive a mixture of one or more reagents in the set and the test sample and to measure the identities of a plurality of T-cell receptors (TCRs) from the test sample; and a computer system communicatively coupled to the apparatus to: obtain a dataset comprising identities of a plurality of T-cell receptors (TCRs) from the test sample; generate a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs are encoded by at least: IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29- 1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, or TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1- 5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, or TRBJ2-7; and generate a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. 168. The system of claim 167, wherein the identities of the plurality of TCRs from the subject comprise: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, or TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, or TRBJ2-7; and/or a plurality of variable regions, wherein the variable regions are encoded for by at least: a variable gene of: TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29- 1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, or TRBV9; and a joining gene of: TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1- 5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, or TRBJ2-7. IPTS/128553107.1 Attorney Docket No: SRU-004WO 169. The system of claim 168, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV13; and a joining gene selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV14; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV18; and a joining gene selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1-6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV19; and a joining gene selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1; a variable gene TRBV2; and a joining gene selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7; a variable gene TRBV20-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-5, TRBJ2-3, and TRBJ2-5; a variable gene TRBV25-1; and a joining gene selected from any one of TRBJ2- 1, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV27; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-4, and TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV5-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-5; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV5-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-8; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV6-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV6-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene selected from any one of TRBJ2-3, and TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-7; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-8; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2-7; a variable gene TRBV7-9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7; or a variable gene TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, and TRBJ2-7. 170. The system of claim 169, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV13; and a joining gene TRBJ1-4; a variable gene TRBV13; and a joining gene TRBJ1-5; a variable gene TRBV13; and a joining gene TRBJ2-1; a variable gene TRBV13; and a joining gene TRBJ2-2; a variable gene TRBV13; and a joining gene TRBJ2-3; a variable gene TRBV13; and a joining gene TRBJ2-5; a variable gene TRBV13; and a joining gene TRBJ2-7; a variable gene TRBV11-3; and a joining gene TRBJ2-1; a variable gene TRBV11-3; and a joining gene TRBJ2-2; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV11-3; and a joining gene TRBJ2-7; a variable gene TRBV14; and a joining gene TRBJ1-1; a variable gene TRBV14; and a joining gene TRBJ1-4; a variable gene TRBV14; and a joining gene TRBJ1-5; a variable gene TRBV14; and a joining gene TRBJ2-1; a variable gene TRBV14; and a joining gene TRBJ2-2; a variable gene TRBV14; and a joining gene TRBJ2-3; a variable gene TRBV14; and a joining gene TRBJ2-5; a variable gene TRBV14; and a joining gene TRBJ2-7; a variable gene TRBV18; and a joining gene TRBJ1-1; a variable gene TRBV18; and a joining gene TRBJ1-3; a variable gene TRBV18; and a joining gene TRBJ1-5; a variable gene TRBV18; and a joining gene TRBJ1-6; a variable gene TRBV18; and a joining gene TRBJ2-2; a variable gene TRBV18; and a joining gene TRBJ2-3; a variable gene TRBV18; and a joining gene TRBJ2-5; a variable gene TRBV18; and a joining gene TRBJ2-7; a variable gene TRBV19; and a joining gene TRBJ1-2; a variable gene TRBV19; and a joining gene TRBJ1-6; a variable gene TRBV19; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ1-6; a variable gene TRBV2; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ2-7; a variable gene TRBV20-1; and a joining gene TRBJ1-1; a variable gene TRBV20-1; and a joining gene TRBJ1-5; a variable gene TRBV20-1; and a joining gene TRBJ2-3; a variable gene TRBV20-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-1; a variable gene TRBV25-1; and a joining gene TRBJ2-3; a variable gene TRBV25-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-7; a variable gene TRBV27; and a joining gene TRBJ1-1; a variable gene TRBV27; and a joining gene TRBJ1-2; a variable gene TRBV27; and a joining gene TRBJ1-3; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV27; and a joining gene TRBJ1-4; a variable gene TRBV27; and a joining gene TRBJ2-1; a variable gene TRBV27; and a joining gene TRBJ2-2; a variable gene TRBV27; and a joining gene TRBJ2-3; a variable gene TRBV27; and a joining gene TRBJ2-5; a variable gene TRBV27; and a joining gene TRBJ2-6; a variable gene TRBV27; and a joining gene TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene TRBJ1-1; a variable gene TRBV29-1; and a joining gene TRBJ1-4; a variable gene TRBV29-1; and a joining gene TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene TRBJ1-1; a variable gene TRBV5-1; and a joining gene TRBJ1-2; a variable gene TRBV5-1; and a joining gene TRBJ1-3; a variable gene TRBV5-1; and a joining gene TRBJ1-4; a variable gene TRBV5-1; and a joining gene TRBJ1-5; a variable gene TRBV5-1; and a joining gene TRBJ1-6; a variable gene TRBV5-1; and a joining gene TRBJ2-1; a variable gene TRBV5-1; and a joining gene TRBJ2-2; a variable gene TRBV5-1; and a joining gene TRBJ2-3; a variable gene TRBV5-1; and a joining gene TRBJ2-4; a variable gene TRBV5-1; and a joining gene TRBJ2-5; a variable gene TRBV5-1; and a joining gene TRBJ2-6; a variable gene TRBV5-1; and a joining gene TRBJ2-7; a variable gene TRBV5-4; and a joining gene TRBJ1-1; a variable gene TRBV5-4; and a joining gene TRBJ2-1; a variable gene TRBV5-4; and a joining gene TRBJ2-7; a variable gene TRBV5-5; and a joining gene TRBJ1-1; a variable gene TRBV5-5; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ1-1; a variable gene TRBV5-6; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ2-7; a variable gene TRBV5-8; and a joining gene TRBJ1-1; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-8; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-2; a variable gene TRBV6-1; and a joining gene TRBJ2-7; a variable gene TRBV6-4; and a joining gene TRBJ1-1; a variable gene TRBV6-4; and a joining gene TRBJ2-1; a variable gene TRBV6-4; and a joining gene TRBJ2-2; a variable gene TRBV6-4; and a joining gene TRBJ2-6; a variable gene TRBV6-4; and a joining gene TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ1-1; a variable gene TRBV7-6; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ2-7; a variable gene TRBV7-7; and a joining gene TRBJ1-1; a variable gene TRBV7-7; and a joining gene TRBJ1-4; a variable gene TRBV7-7; and a joining gene TRBJ2-1; a variable gene TRBV7-7; and a joining gene TRBJ2-7; a variable gene TRBV7-8; and a joining gene TRBJ1-1; a variable gene TRBV7-8; and a joining gene TRBJ1-5; a variable gene TRBV7-8; and a joining gene TRBJ2-1; a variable gene TRBV7-8; and a joining gene TRBJ2-5; a variable gene TRBV7-8; and a joining gene TRBJ2-7; a variable gene TRBV7-9; and a joining gene TRBJ1-1; a variable gene TRBV7-9; and a joining gene TRBJ1-4; a variable gene TRBV7-9; and a joining gene TRBJ1-5; a variable gene TRBV7-9; and a joining gene TRBJ1-6; a variable gene TRBV7-9; and a joining gene TRBJ2-1; a variable gene TRBV7-9; and a joining gene TRBJ2-2; a variable gene TRBV7-9; and a joining gene TRBJ2-3; a variable gene TRBV7-9; and a joining gene TRBJ2-4; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-9; and a joining gene TRBJ2-5; a variable gene TRBV7-9; and a joining gene TRBJ2-7; a variable gene TRBV9; and a joining gene TRBJ1-1; a variable gene TRBV9; and a joining gene TRBJ1-4; a variable gene TRBV9; and a joining gene TRBJ2-1; a variable gene TRBV9; and a joining gene TRBJ2-2; a variable gene TRBV9; and a joining gene TRBJ2-3; or a variable gene TRBV9; and a joining gene TRBJ2-7. 171. The system of claim 167, wherein the cancer-associated TCR RFUs are determined by: obtaining or having obtained TCR sequencing data for a plurality of TCRs from a plurality of training samples; assigning TCRs of the plurality of TCRs into candidate RFUs by: grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores; clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc). 172. The system of claim 171, wherein the cancer-associated TCR RFUs are further determined by performing one or more of: filtering candidate RFUs to retain candidate RFUs that are observed in at least a first threshold number of training samples; filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a second threshold number of training samples; filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than a threshold value. 173. The system of claim 172, wherein the cancer-associated TCR RFUs are further determined by: IPTS/128553107.1 Attorney Docket No: SRU-004WO applying a gamma-Poisson generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. 174. The system of claim 173, wherein applying the gamma-Poisson generalized linear model further comprises incorporating demographic covariates. 175. The system of claim 174, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 176. The system of claim 171, wherein: the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. 177. The system of claim 167, wherein the predictive model is a logistic regression model. 178. The system of claim 167, wherein the cancer-associated TCR RFUs comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least IPTS/128553107.1 Attorney Docket No: SRU-004WO 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, or at least 197 RFUs. 179. The system of claim 167, wherein a performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 180. The system of claim 179, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.64. 181. The system of claim 179, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.70. 182. The system of claim 179, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.71. 183. The system of claim 179, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.83. 184. The system of claim 179, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.84. IPTS/128553107.1 Attorney Docket No: SRU-004WO 185. The system of claim 179, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.85. 186. The system of claim 167, wherein the system further comprises: obtaining or having obtained a second dataset comprising expression levels of a plurality of biomarkers from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a second predictive model to the expression levels of the plurality of biomarkers. 187. The system of claim 186, wherein the second predictive model is a support vector machine (SVM) model. 188. The system of claim 186, wherein the plurality of biomarkers comprises at two or more biomarkers selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. 189. The system of claim 186, wherein a performance of the second predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 190. The system of any one of claims 186-189, wherein a performance metric of the second predictive model is improved in comparison to a model solely incorporating CEACAM5. 191. The system of claim 167, wherein the system further comprises: obtaining or having obtained a third dataset comprising a mutational profile of a plurality of circulating tumor DNA (ctDNA) from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a third predictive model to the mutational profiles of ctDNA. 192. The system of claim 191, wherein the third predictive model is a logistic regression model. IPTS/128553107.1 Attorney Docket No: SRU-004WO 193. The system of claim 191, wherein the plurality of ctDNA comprises ctDNA selected from CDKN2A, MGAM, PIK3CA, EPHB1, PAK5, KEAP1, TP53, KRAS, KDM5A, ATM, and PTEN. 194. The system of claim 191, wherein the ctDNA comprises a mutation. 195. The system of claim 194, wherein the mutation is any one of combination of a frameshift mutation, a missense mutation, a synonymous mutation, a splice site mutation, or a nonsense mutation. 196. The system of any one of claims 191-195, wherein the mutation is a substitution, an insertion, a deletion, or any combination thereof. 197. The system of any one of claims 167-196, wherein the cancer is lung cancer. 198. The system of any one of claims 167-197, wherein the lung cancer is an adenocarcinoma, an adenosquamous cell cancer, a large cell cancer, a neuroendocrine cancer, a non-small cell lung cancer (NSCLC), a small cell cancer, or a squamous cell cancer. 199. The system of any one of claims 167-198, wherein the cancer is an early stage cancer. 200. The system of any one of claims 167-199, wherein the cancer is stage I, stage II, stage III, and/or stage IV lung cancer. 201. The system of any one of claims 167-200, wherein the identities of the plurality of TCRs are determined from a test sample obtained from the subject. 202. The system of claim 201, wherein the test sample is a blood or serum sample. 203. The system of claim 201 or claim 202, wherein the subject is suspected of having an early stage cancer. 204. The system of claim 201 or claim 202, wherein the subject is not suspected of having an early stage cancer. IPTS/128553107.1 Attorney Docket No: SRU-004WO 205. The system of any one of claims 167-204, wherein obtaining or having obtained the dataset comprising identities of the plurality of TCRs from the subject comprises performing an assay to determine the plurality of TCRs. 206. The system of claim 205, wherein the assay is an amplification-based assay. 207. The system of claim 206, wherein the amplification-based assay is a PCR assay, RT-PCR assay, qRT-PCR assay, or multiplex PCR assay. 208. The system of any one of claims 167-207, wherein the analyzing and generating the subject feature count comprises performing an assay to determine the plurality of TCRs, and performing a feature count to determine the subject feature count against the cancer- associated RFUs. 209. The system of claim 208, wherein the assay is a sequencing-based assay. 210. The system of claim 209, wherein the sequencing-based assay is an RNA-seq assay. 211. The system of any one of claims 205-210, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising primers. 212. The system of any one of claims 186-190, wherein obtaining or having obtained the second dataset comprises performing an assay to determine the expression levels of the plurality of biomarkers. 213. The system of claim 212, wherein the assay is a Proximity Extension Assay (PEA), a xMAP Multiplex Assay, a single molecule array (SIMOA) assay, mass spectrometry based protein or peptide assay, or an aptamer-based assay. 214. The system of claim 212 or claim 213, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising antibodies. 215. The system of claim 214, wherein the antibodies comprise one of monoclonal and polyclonal antibodies. 216. The system of claim 214, wherein the antibodies comprise both monoclonal and polyclonal antibodies. IPTS/128553107.1 Attorney Docket No: SRU-004WO 217. The system of any one of claims 191-196, wherein obtaining or having obtained the third dataset comprises performing an assay to determine the mutation profile of the plurality of ctDNA. 218. The system of claim 217, wherein the assay is an NGS-based hybrid capture system assay. 219. The system of any one of claims 167-218, wherein the subject is an undiagnosed subject, at risk subject, or a subject previously diagnosed with cancer. 220. A kit for predicting presence, absence, or likelihood of cancer in a subject, the kit comprising: a set of reagents for determining identities of a plurality of T-cell receptors (TCRs) from a test sample from the subject; and instructions for using the set of reagents to: generate a subject feature count across a plurality of cancer-associated TCR repertoire functional units (RFUs) by comparing the identities of the plurality of TCRs from the sample from the subject against a plurality of variable regions of the cancer-associated TCR repertoire functional units (RFUs), wherein the plurality of variable regions of the cancer-associated TCR RFUs are encoded by at least: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29- 1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, or TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1- 5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, or TRBJ2-7; and generate a prediction of presence, absence, or likelihood of the cancer in the subject by applying a predictive model to analyze the subject feature count across the plurality of cancer-associated TCR RFUs. 221. The kit of claim 220, wherein the identities of the plurality of TCRs from the subject comprise: IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, or TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, or TRBJ2-7; and/or a plurality of variable regions, wherein the variable regions are encoded for by at least: a variable gene of: TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29- 1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, or TRBV9; and a joining gene of: TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1- 5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2- 6, or TRBJ2-7. 222. The kit of claim 221, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV13; and a joining gene selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV14; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV18; and a joining gene selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1-6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV19; and a joining gene selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1; a variable gene TRBV2; and a joining gene selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV20-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-5, TRBJ2-3, and TRBJ2-5; a variable gene TRBV25-1; and a joining gene selected from any one of TRBJ2- 1, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV27; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-4, and TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV5-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-5; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV5-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-8; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV6-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV6-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene selected from any one of TRBJ2-3, and TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-7; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-8; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2-7; a variable gene TRBV7-9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7; or a variable gene TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, and TRBJ2-7. 223. The kit of claim 222, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV13; and a joining gene TRBJ1-4; a variable gene TRBV13; and a joining gene TRBJ1-5; a variable gene TRBV13; and a joining gene TRBJ2-1; a variable gene TRBV13; and a joining gene TRBJ2-2; a variable gene TRBV13; and a joining gene TRBJ2-3; a variable gene TRBV13; and a joining gene TRBJ2-5; a variable gene TRBV13; and a joining gene TRBJ2-7; a variable gene TRBV11-3; and a joining gene TRBJ2-1; a variable gene TRBV11-3; and a joining gene TRBJ2-2; a variable gene TRBV11-3; and a joining gene TRBJ2-7; a variable gene TRBV14; and a joining gene TRBJ1-1; a variable gene TRBV14; and a joining gene TRBJ1-4; a variable gene TRBV14; and a joining gene TRBJ1-5; a variable gene TRBV14; and a joining gene TRBJ2-1; a variable gene TRBV14; and a joining gene TRBJ2-2; a variable gene TRBV14; and a joining gene TRBJ2-3; a variable gene TRBV14; and a joining gene TRBJ2-5; a variable gene TRBV14; and a joining gene TRBJ2-7; a variable gene TRBV18; and a joining gene TRBJ1-1; a variable gene TRBV18; and a joining gene TRBJ1-3; a variable gene TRBV18; and a joining gene TRBJ1-5; a variable gene TRBV18; and a joining gene TRBJ1-6; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV18; and a joining gene TRBJ2-2; a variable gene TRBV18; and a joining gene TRBJ2-3; a variable gene TRBV18; and a joining gene TRBJ2-5; a variable gene TRBV18; and a joining gene TRBJ2-7; a variable gene TRBV19; and a joining gene TRBJ1-2; a variable gene TRBV19; and a joining gene TRBJ1-6; a variable gene TRBV19; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ1-6; a variable gene TRBV2; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ2-7; a variable gene TRBV20-1; and a joining gene TRBJ1-1; a variable gene TRBV20-1; and a joining gene TRBJ1-5; a variable gene TRBV20-1; and a joining gene TRBJ2-3; a variable gene TRBV20-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-1; a variable gene TRBV25-1; and a joining gene TRBJ2-3; a variable gene TRBV25-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-7; a variable gene TRBV27; and a joining gene TRBJ1-1; a variable gene TRBV27; and a joining gene TRBJ1-2; a variable gene TRBV27; and a joining gene TRBJ1-3; a variable gene TRBV27; and a joining gene TRBJ1-4; a variable gene TRBV27; and a joining gene TRBJ2-1; a variable gene TRBV27; and a joining gene TRBJ2-2; a variable gene TRBV27; and a joining gene TRBJ2-3; a variable gene TRBV27; and a joining gene TRBJ2-5; a variable gene TRBV27; and a joining gene TRBJ2-6; a variable gene TRBV27; and a joining gene TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene TRBJ1-1; a variable gene TRBV29-1; and a joining gene TRBJ1-4; a variable gene TRBV29-1; and a joining gene TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene TRBJ1-1; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-1; and a joining gene TRBJ1-2; a variable gene TRBV5-1; and a joining gene TRBJ1-3; a variable gene TRBV5-1; and a joining gene TRBJ1-4; a variable gene TRBV5-1; and a joining gene TRBJ1-5; a variable gene TRBV5-1; and a joining gene TRBJ1-6; a variable gene TRBV5-1; and a joining gene TRBJ2-1; a variable gene TRBV5-1; and a joining gene TRBJ2-2; a variable gene TRBV5-1; and a joining gene TRBJ2-3; a variable gene TRBV5-1; and a joining gene TRBJ2-4; a variable gene TRBV5-1; and a joining gene TRBJ2-5; a variable gene TRBV5-1; and a joining gene TRBJ2-6; a variable gene TRBV5-1; and a joining gene TRBJ2-7; a variable gene TRBV5-4; and a joining gene TRBJ1-1; a variable gene TRBV5-4; and a joining gene TRBJ2-1; a variable gene TRBV5-4; and a joining gene TRBJ2-7; a variable gene TRBV5-5; and a joining gene TRBJ1-1; a variable gene TRBV5-5; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ1-1; a variable gene TRBV5-6; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ2-7; a variable gene TRBV5-8; and a joining gene TRBJ1-1; a variable gene TRBV5-8; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-2; a variable gene TRBV6-1; and a joining gene TRBJ2-7; a variable gene TRBV6-4; and a joining gene TRBJ1-1; a variable gene TRBV6-4; and a joining gene TRBJ2-1; a variable gene TRBV6-4; and a joining gene TRBJ2-2; a variable gene TRBV6-4; and a joining gene TRBJ2-6; a variable gene TRBV6-4; and a joining gene TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-5; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ1-1; a variable gene TRBV7-6; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ2-7; a variable gene TRBV7-7; and a joining gene TRBJ1-1; a variable gene TRBV7-7; and a joining gene TRBJ1-4; a variable gene TRBV7-7; and a joining gene TRBJ2-1; a variable gene TRBV7-7; and a joining gene TRBJ2-7; a variable gene TRBV7-8; and a joining gene TRBJ1-1; a variable gene TRBV7-8; and a joining gene TRBJ1-5; a variable gene TRBV7-8; and a joining gene TRBJ2-1; a variable gene TRBV7-8; and a joining gene TRBJ2-5; a variable gene TRBV7-8; and a joining gene TRBJ2-7; a variable gene TRBV7-9; and a joining gene TRBJ1-1; a variable gene TRBV7-9; and a joining gene TRBJ1-4; a variable gene TRBV7-9; and a joining gene TRBJ1-5; a variable gene TRBV7-9; and a joining gene TRBJ1-6; a variable gene TRBV7-9; and a joining gene TRBJ2-1; a variable gene TRBV7-9; and a joining gene TRBJ2-2; a variable gene TRBV7-9; and a joining gene TRBJ2-3; a variable gene TRBV7-9; and a joining gene TRBJ2-4; a variable gene TRBV7-9; and a joining gene TRBJ2-5; a variable gene TRBV7-9; and a joining gene TRBJ2-7; a variable gene TRBV9; and a joining gene TRBJ1-1; a variable gene TRBV9; and a joining gene TRBJ1-4; a variable gene TRBV9; and a joining gene TRBJ2-1; a variable gene TRBV9; and a joining gene TRBJ2-2; a variable gene TRBV9; and a joining gene TRBJ2-3; or a variable gene TRBV9; and a joining gene TRBJ2-7. 224. The kit of claim 220, wherein the cancer-associated TCR RFUs are determined by: obtaining or having obtained TCR sequencing data for a plurality of TCRs from a plurality of training samples; assigning TCRs of the plurality of TCRs into candidate RFUs by: IPTS/128553107.1 Attorney Docket No: SRU-004WO grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores; clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc). 225. The kit of claim 224, wherein the cancer-associated TCR RFUs are further determined by performing one or more of: filtering candidate RFUs to retain candidate RFUs that are observed in at least a first threshold number of training samples; filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a second threshold number of training samples; filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than a threshold value. 226. The kit of claim 224, wherein the cancer-associated TCR RFUs are further determined by: applying a gamma-Poisson generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. 227. The kit of claim 226, wherein applying the gamma-Poisson generalized linear model further comprises incorporating demographic covariates. 228. The kit of claim 227, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 229. The kit of claim 224, wherein: the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. IPTS/128553107.1 Attorney Docket No: SRU-004WO 230. The kit of claim 220, wherein the predictive model is a logistic regression model. 231. The kit of claim 220, wherein the cancer-associated TCR RFUs comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, or at least 197 RFUs. 232. The kit of claim 220, wherein a performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at IPTS/128553107.1 Attorney Docket No: SRU-004WO least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 233. The kit of claim 232, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.64. 234. The kit of claim 232, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.70. 235. The kit of claim 232, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.71. 236. The kit of claim 232, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.83. 237. The kit of claim 232, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.84. 238. The kit of claim 232, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.85. 239. The kit of claim 220, wherein the kit further comprises: obtaining or having obtained a second dataset comprising expression levels of a plurality of biomarkers from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a second predictive model to the expression levels of the plurality of biomarkers. 240. The kit of claim 239, wherein the second predictive model is a support vector machine (SVM) model. 241. The kit of claim 239, wherein the plurality of biomarkers comprises at two or more biomarkers selected from IL6, TGFA, S100A12, OSM, TFPI2, LSP1, MDK, CXCL9, CLEC4D, HGF, VWA1, CEACAM5, MMP12, KRT19, CASP8, WFDC2, and PLAUR. 242. The kit of claim 239, wherein a performance of the second predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at IPTS/128553107.1 Attorney Docket No: SRU-004WO least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 243. The kit of any one of claims 239-242, wherein a performance metric of the second predictive model is improved in comparison to a model solely incorporating CEACAM5. 244. The kit of claim 220, wherein the kit further comprises: obtaining or having obtained a third dataset comprising a mutational profile of a plurality of circulating tumor DNA (ctDNA) from the subject; and generating a prediction of presence, absence, or likelihood of the cancer in the subject by applying a third predictive model to the mutational profiles of ctDNA. 245. The kit of claim 244, wherein the third predictive model is a logistic regression model. 246. The kit of claim 244, wherein the plurality of ctDNA comprises ctDNA selected from CDKN2A, MGAM, PIK3CA, EPHB1, PAK5, KEAP1, TP53, KRAS, KDM5A, ATM, and PTEN. 247. The kit of claim 244, wherein the ctDNA comprises a mutation. 248. The kit of claim 247, wherein the mutation is any one of combination of a frameshift mutation, a missense mutation, a synonymous mutation, a splice site mutation, or a nonsense mutation. 249. The kit of any one of claims 244-248, wherein the mutation is a substitution, an insertion, a deletion, or any combination thereof. 250. The kit of any one of claims 220-249, wherein the cancer is lung cancer. 251. The kit of any one of claims 220-250, wherein the lung cancer is an adenocarcinoma, an adenosquamous cell cancer, a large cell cancer, a neuroendocrine cancer, a non-small cell lung cancer (NSCLC), a small cell cancer, or a squamous cell cancer. 252. The kit of any one of claims 220-251, wherein the cancer is an early stage cancer. IPTS/128553107.1 Attorney Docket No: SRU-004WO 253. The kit of any one of claims 220-252, wherein the cancer is stage I, stage II, stage III, and/or stage IV lung cancer. 254. The kit of any one of claims 220-253, wherein the identities of the plurality of TCRs are determined from a test sample obtained from the subject. 255. The kit of claim 254, wherein the test sample is a blood or serum sample. 256. The kit of claim 254 or claim 255, wherein the subject is suspected of having an early stage cancer. 257. The kit of claim 254 or claim 255, wherein the subject is not suspected of having an early stage cancer. 258. The kit of any one of claims 220-257, wherein obtaining or having obtained the dataset comprising identities of the plurality of TCRs from the subject comprises performing an assay to determine the plurality of TCRs. 259. The kit of claim 258, wherein the assay is an amplification-based assay. 260. The kit of claim 259, wherein the amplification-based assay is a PCR assay, RT- PCR assay, qRT-PCR assay, or multiplex PCR assay. 261. The kit of any one of claims 220-260, wherein the analyzing and generating the subject feature count comprises performing an assay to determine the plurality of TCRs, and performing a feature count to determine the subject feature count against the cancer- associated RFUs. 262. The kit of claim 261, wherein the assay is a sequencing-based assay. 263. The kit of claim 262, wherein the sequencing-based assay is an RNA-seq assay. 264. The kit of any one of claims 258-263, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising primers. 265. The kit of any one of claims 239-243, wherein obtaining or having obtained the second dataset comprises performing an assay to determine the expression levels of the plurality of biomarkers. IPTS/128553107.1 Attorney Docket No: SRU-004WO 266. The kit of claim 265, wherein the assay is a Proximity Extension Assay (PEA), a xMAP Multiplex Assay, a single molecule array (SIMOA) assay, mass spectrometry based protein or peptide assay, or an aptamer-based assay. 267. The kit of claim 265 or claim 266, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising antibodies. 268. The kit of claim 267, wherein the antibodies comprise one of monoclonal and polyclonal antibodies. 269. The kit of claim 267, wherein the antibodies comprise both monoclonal and polyclonal antibodies. 270. The kit of any one of claims 244-249, wherein obtaining or having obtained the third dataset comprises performing an assay to determine the mutation profile of the plurality of ctDNA. 271. The kit of claim 270, wherein the assay is an NGS-based hybrid capture kit assay. 272. The kit of any one of claims 220-271, wherein the subject is an undiagnosed subject, at risk subject, or a subject previously diagnosed with cancer. 273. A method for developing cancer-associated TCR repertoire functional units (RFUs), the method comprising: obtaining or having obtained TCR sequencing data of a plurality of TCRs from a plurality of training samples; sorting the plurality of TCRs into candidate RFUs by: clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc); further processing candidate RFUs by performing one or more of: filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a first threshold number of training samples; and/or filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, or 32; and IPTS/128553107.1 Attorney Docket No: SRU-004WO analyzing, through a generalized linear model, the candidate RFUs to identify cancer- associated RFUs. 274. The method of claim 273, wherein the overall dissimilarity scores are generated by: grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores. 275. The method of claim 274, wherein further processing candidate RFUs further comprises: filtering candidate RFUs to retain candidate RFUs that are observed in at least a second threshold number of training samples. 276. The method of any one of claims 273-275, wherein analyzing, through the generalized linear model further comprises incorporating demographic covariates. 277. The method of claim 276, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 278. The method of claim 273, wherein the generalized linear model is a gamma-Poisson generalized linear model. 279. The method of claim 273, wherein the obtaining or having obtained the TCR sequencing data of a plurality of TCRs comprises performing an assay to determine TCR sequencing data of a plurality of TCRs. 280. The method of claim 279, wherein the assay is an amplification-based assay. 281. The method of claim 280, wherein the amplification-based assay is a PCR assay, RT-PCR assay, qRT-PCR assay, or multiplex PCR assay. 282. The method of claim 273, wherein: the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or IPTS/128553107.1 Attorney Docket No: SRU-004WO the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. 283. The method of claim 273, wherein the T-cell expansion is determined by estimating the number of T-cells carrying a TCR, wherein the TCR is any TCR provided herein. 284. The method of claim 283, wherein the T-cell expansion is present if more than 2, 4, 8, 16, 32, 64, 128, 256, or 512 clones carry TCRs, such as those provided herein. 285. The method of any one of claims 273-284, wherein the minimum amino acid-level recurrence is greater than 0, 1, 2, or 3. 286. The method of any one of claims 273-285, wherein the minimum amino acid-level recurrence is equal to 4. 287. The method of claim 273, wherein the first threshold number of training samples is at least 200, at least 210, at least 220, at least 230, at least 240, at least 250, at least 260, at least 270, at least 280, at least 290, or at least 300. 288. The method of claim 273, wherein the second threshold number of training samples is at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, or at least 50. 289. A method for developing a predictive model for predicting presence, absence, or likelihood of cancer, the model comprising: obtaining or having obtained feature counts across a plurality of cancer-associated TCR repertoire functional units (RFUs), wherein a plurality of variable regions of the cancer-associated TCR RFUs are encoded by at least: a variable gene of TRBV11-3, TRBV13, TRBV14, TRBV18, TRBV19, TRBV2, TRBV20-1, TRBV25-1, TRBV27, TRBV28, TRBV29-1, TRBV30, TRBV5-1, TRBV5-4, TRBV5-5, TRBV5-6, TRBV5-8, TRBV6-1, TRBV6-4, IPTS/128553107.1 Attorney Docket No: SRU-004WO TRBV6-5, TRBV6-6, TRBV7-2, TRBV7-4, TRBV7-6, TRBV7-7, TRBV7-8, TRBV7-9, or TRBV9; a joining gene of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, or TRBJ2-7; and analyzing, through a ML implemented method, the feature counts across the plurality of cancer-associated TCR RFUs to train the predictive model useful for predicting presence, absence, or likelihood of a cancer. 290. The method of claim 289, wherein the plurality of variable regions comprises variable regions encoded by any one set of: a variable gene TRBV11-3; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV13; and a joining gene selected from any one of TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV14; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV18; and a joining gene selected from any one of TRBJ1-1, TRBJ1-3, TRBJ1-5, TRBJ1-6, TRBJ2-2, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV19; and a joining gene selected from any one of TRBJ1-2, TRBJ1-6, and TRBJ2-1; a variable gene TRBV2; and a joining gene selected from any one of TRBJ1-6, TRBJ2-1, and TRBJ2-7; a variable gene TRBV20-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-5, TRBJ2-3, and TRBJ2-5; a variable gene TRBV25-1; and a joining gene selected from any one of TRBJ2- 1, TRBJ2-3, TRBJ2-5, and TRBJ2-7; a variable gene TRBV27; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene selected from any one of TRBJ1- 1, TRBJ1-4, and TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-1; and a joining gene selected from any one of TRBJ1-1, TRBJ1-2, TRBJ1-3, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, TRBJ2-6, and TRBJ2-7; a variable gene TRBV5-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-5; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV5-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV5-8; and a joining gene selected from any one of TRBJ1-1, and TRBJ2-1; a variable gene TRBV6-1; and a joining gene selected from any one of TRBJ2-1, TRBJ2-2, and TRBJ2-7; a variable gene TRBV6-4; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, TRBJ2-2, TRBJ2-6, and TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene selected from any one of TRBJ2-3, and TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene selected from any one of TRBJ1-1, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-7; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, and TRBJ2-7; a variable gene TRBV7-8; and a joining gene selected from any one of TRBJ1-1, TRBJ1-5, TRBJ2-1, TRBJ2-5, and TRBJ2-7; a variable gene TRBV7-9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ1-5, TRBJ1-6, TRBJ2-1, TRBJ2-2, TRBJ2-3, TRBJ2-4, TRBJ2-5, and TRBJ2-7; or a variable gene TRBV9; and a joining gene selected from any one of TRBJ1-1, TRBJ1-4, TRBJ2-1, TRBJ2-2, TRBJ2-3, and TRBJ2-7. 291. The method of claim 290, wherein the plurality of variable regions comprises variable regions encoded by any one set of: IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV13; and a joining gene TRBJ1-4; a variable gene TRBV13; and a joining gene TRBJ1-5; a variable gene TRBV13; and a joining gene TRBJ2-1; a variable gene TRBV13; and a joining gene TRBJ2-2; a variable gene TRBV13; and a joining gene TRBJ2-3; a variable gene TRBV13; and a joining gene TRBJ2-5; a variable gene TRBV13; and a joining gene TRBJ2-7; a variable gene TRBV11-3; and a joining gene TRBJ2-1; a variable gene TRBV11-3; and a joining gene TRBJ2-2; a variable gene TRBV11-3; and a joining gene TRBJ2-7; a variable gene TRBV14; and a joining gene TRBJ1-1; a variable gene TRBV14; and a joining gene TRBJ1-4; a variable gene TRBV14; and a joining gene TRBJ1-5; a variable gene TRBV14; and a joining gene TRBJ2-1; a variable gene TRBV14; and a joining gene TRBJ2-2; a variable gene TRBV14; and a joining gene TRBJ2-3; a variable gene TRBV14; and a joining gene TRBJ2-5; a variable gene TRBV14; and a joining gene TRBJ2-7; a variable gene TRBV18; and a joining gene TRBJ1-1; a variable gene TRBV18; and a joining gene TRBJ1-3; a variable gene TRBV18; and a joining gene TRBJ1-5; a variable gene TRBV18; and a joining gene TRBJ1-6; a variable gene TRBV18; and a joining gene TRBJ2-2; a variable gene TRBV18; and a joining gene TRBJ2-3; a variable gene TRBV18; and a joining gene TRBJ2-5; a variable gene TRBV18; and a joining gene TRBJ2-7; a variable gene TRBV19; and a joining gene TRBJ1-2; a variable gene TRBV19; and a joining gene TRBJ1-6; a variable gene TRBV19; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ1-6; a variable gene TRBV2; and a joining gene TRBJ2-1; a variable gene TRBV2; and a joining gene TRBJ2-7; a variable gene TRBV20-1; and a joining gene TRBJ1-1; a variable gene TRBV20-1; and a joining gene TRBJ1-5; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV20-1; and a joining gene TRBJ2-3; a variable gene TRBV20-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-1; a variable gene TRBV25-1; and a joining gene TRBJ2-3; a variable gene TRBV25-1; and a joining gene TRBJ2-5; a variable gene TRBV25-1; and a joining gene TRBJ2-7; a variable gene TRBV27; and a joining gene TRBJ1-1; a variable gene TRBV27; and a joining gene TRBJ1-2; a variable gene TRBV27; and a joining gene TRBJ1-3; a variable gene TRBV27; and a joining gene TRBJ1-4; a variable gene TRBV27; and a joining gene TRBJ2-1; a variable gene TRBV27; and a joining gene TRBJ2-2; a variable gene TRBV27; and a joining gene TRBJ2-3; a variable gene TRBV27; and a joining gene TRBJ2-5; a variable gene TRBV27; and a joining gene TRBJ2-6; a variable gene TRBV27; and a joining gene TRBJ2-7; a variable gene TRBV28; and a joining gene TRBJ2-3; a variable gene TRBV29-1; and a joining gene TRBJ1-1; a variable gene TRBV29-1; and a joining gene TRBJ1-4; a variable gene TRBV29-1; and a joining gene TRBJ2-2; a variable gene TRBV30; and a joining gene TRBJ2-7; a variable gene TRBV5-1; and a joining gene TRBJ1-1; a variable gene TRBV5-1; and a joining gene TRBJ1-2; a variable gene TRBV5-1; and a joining gene TRBJ1-3; a variable gene TRBV5-1; and a joining gene TRBJ1-4; a variable gene TRBV5-1; and a joining gene TRBJ1-5; a variable gene TRBV5-1; and a joining gene TRBJ1-6; a variable gene TRBV5-1; and a joining gene TRBJ2-1; a variable gene TRBV5-1; and a joining gene TRBJ2-2; a variable gene TRBV5-1; and a joining gene TRBJ2-3; a variable gene TRBV5-1; and a joining gene TRBJ2-4; a variable gene TRBV5-1; and a joining gene TRBJ2-5; a variable gene TRBV5-1; and a joining gene TRBJ2-6; a variable gene TRBV5-1; and a joining gene TRBJ2-7; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV5-4; and a joining gene TRBJ1-1; a variable gene TRBV5-4; and a joining gene TRBJ2-1; a variable gene TRBV5-4; and a joining gene TRBJ2-7; a variable gene TRBV5-5; and a joining gene TRBJ1-1; a variable gene TRBV5-5; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ1-1; a variable gene TRBV5-6; and a joining gene TRBJ2-1; a variable gene TRBV5-6; and a joining gene TRBJ2-7; a variable gene TRBV5-8; and a joining gene TRBJ1-1; a variable gene TRBV5-8; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-1; a variable gene TRBV6-1; and a joining gene TRBJ2-2; a variable gene TRBV6-1; and a joining gene TRBJ2-7; a variable gene TRBV6-4; and a joining gene TRBJ1-1; a variable gene TRBV6-4; and a joining gene TRBJ2-1; a variable gene TRBV6-4; and a joining gene TRBJ2-2; a variable gene TRBV6-4; and a joining gene TRBJ2-6; a variable gene TRBV6-4; and a joining gene TRBJ2-7; a variable gene TRBV6-5; and a joining gene TRBJ2-3; a variable gene TRBV6-6; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-3; a variable gene TRBV7-2; and a joining gene TRBJ2-5; a variable gene TRBV7-4; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ1-1; a variable gene TRBV7-6; and a joining gene TRBJ2-1; a variable gene TRBV7-6; and a joining gene TRBJ2-7; a variable gene TRBV7-7; and a joining gene TRBJ1-1; a variable gene TRBV7-7; and a joining gene TRBJ1-4; a variable gene TRBV7-7; and a joining gene TRBJ2-1; a variable gene TRBV7-7; and a joining gene TRBJ2-7; a variable gene TRBV7-8; and a joining gene TRBJ1-1; a variable gene TRBV7-8; and a joining gene TRBJ1-5; a variable gene TRBV7-8; and a joining gene TRBJ2-1; a variable gene TRBV7-8; and a joining gene TRBJ2-5; IPTS/128553107.1 Attorney Docket No: SRU-004WO a variable gene TRBV7-8; and a joining gene TRBJ2-7; a variable gene TRBV7-9; and a joining gene TRBJ1-1; a variable gene TRBV7-9; and a joining gene TRBJ1-4; a variable gene TRBV7-9; and a joining gene TRBJ1-5; a variable gene TRBV7-9; and a joining gene TRBJ1-6; a variable gene TRBV7-9; and a joining gene TRBJ2-1; a variable gene TRBV7-9; and a joining gene TRBJ2-2; a variable gene TRBV7-9; and a joining gene TRBJ2-3; a variable gene TRBV7-9; and a joining gene TRBJ2-4; a variable gene TRBV7-9; and a joining gene TRBJ2-5; a variable gene TRBV7-9; and a joining gene TRBJ2-7; a variable gene TRBV9; and a joining gene TRBJ1-1; a variable gene TRBV9; and a joining gene TRBJ1-4; a variable gene TRBV9; and a joining gene TRBJ2-1; a variable gene TRBV9; and a joining gene TRBJ2-2; a variable gene TRBV9; and a joining gene TRBJ2-3; or a variable gene TRBV9; and a joining gene TRBJ2-7. 292. The method of claim 289, wherein the cancer-associated TCR RFUs are determined by: obtaining or having obtained TCR sequencing data for a plurality of TCRs from a plurality of training samples; assigning TCRs of the plurality of TCRs into candidate RFUs by: grouping TCRs of the plurality of TCRs using a CDR3 dissimilarity metric; and combining V gene of each of the grouped TCRs with the CDR3 dissimilarity metric to generate overall dissimilarity scores; clustering TCRs into the candidate RFUs according to overall dissimilarity scores and a dissimilarity index (dc). 293. The method of claim 292, wherein the cancer-associated TCR RFUs are further determined by performing one or more of: filtering candidate RFUs to retain candidate RFUs that are observed in at least a first threshold number of training samples; IPTS/128553107.1 Attorney Docket No: SRU-004WO filtering candidate RFUs to retain candidate RFUs that exhibit evidence of T-cell expansion in at least a second threshold number of training samples; filtering candidate RFUs to retain candidate RFUs with a minimum amino acid-level recurrence greater than a threshold value. 294. The method of claim 292, wherein the cancer-associated TCR RFUs are further determined by: applying a gamma-Poisson generalized linear model to identify a subset of the candidate RFUs that exhibit association with cancer status of the plurality of samples. 295. The method of claim 294, wherein applying the gamma-Poisson generalized linear model further comprises incorporating demographic covariates. 296. The method of claim 295, wherein the demographic covariates comprise age, sex, race, or any combination thereof. 297. The method of claim 292, wherein: the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch; the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch or an insertion/deletion; or the dissimilarity index is established to cluster TCRs with one conservative amino acid mismatch, or an insertion/deletion, and an additional conservative mismatch. 298. The method of claim 289, wherein the predictive model is a logistic regression model. 299. The method of claim 289, wherein the cancer-associated TCR RFUs comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, IPTS/128553107.1 Attorney Docket No: SRU-004WO at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, or at least 197 RFUs. 300. The method of claim 289, wherein a performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73, at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least 0.78, at least 0.79, or at least 0.80. 301. The method of claim 300, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.64. 302. The method of claim 300, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.70. 303. The method of claim 300, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.71. IPTS/128553107.1 Attorney Docket No: SRU-004WO 304. The method of claim 300, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.83. 305. The method of claim 300, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.84. 306. The method of claim 300, wherein the performance of the predictive model is characterized by an area under the curve (AUC) of at least 0.85. 307. The method of claim 300, wherein the cancer is lung cancer. 308. The method of any one of claims 289-307, wherein the lung cancer is an adenocarcinoma, an adenosquamous cell cancer, a large cell cancer, a neuroendocrine cancer, a non-small cell lung cancer (NSCLC), a small cell cancer, or a squamous cell cancer. 309. The method of any one of claims 289-308, wherein the cancer is an early stage cancer. 310. The method of any one of claims 289-309, wherein the cancer is stage I, stage II, stage III, and/or stage IV lung cancer. 311. The method of any one of claims 289-310, wherein obtaining or having obtained the dataset comprising identities of the plurality of TCRs from the subject comprises performing an assay to determine the feature count against the cancer-associated RFUs. 312. The method of claim 311, wherein the assay is an amplification-based assay and/or a sequencing-based assay. 313. The method of claim 312, wherein the amplification-based assay is a PCR assay, RT-PCR assay, qRT-PCR assay, or multiplex PCR assay. 314. The method of claim 313, wherein the sequencing-based assay is an RNA-seq assay. 315. The method of any one of claims 289-314, wherein performing the assay comprises contacting a test sample with a plurality of reagents comprising primers. IPTS/128553107.1