Delays in appropriate antibiotic therapy are a key determinant for deleterious outcomes among patients with vancomycin-resistant Enterococcus (VRE) bloodstream infections (BSIs). This was a multi-center pre/post-implementation study,... more
Delays in appropriate antibiotic therapy are a key determinant for deleterious outcomes among patients with vancomycin-resistant Enterococcus (VRE) bloodstream infections (BSIs). This was a multi-center pre/post-implementation study, assessing the impact of a molecular rapid diagnostic test (Verigene® GP-BC, Luminex Corporation, Northbrook, IL, USA) on outcomes of adult patients with VRE BSIs. The primary outcome was time to optimal therapy (TOT). Multivariable logistic and Cox proportional hazard regression models were used to determine the independent associations of post-implementation, TOT, early vs. delayed therapy, and mortality. A total of 104 patients with VRE BSIs were included: 50 and 54 in the pre- and post-implementation periods, respectively. The post- vs. pre-implementation group was associated with a 1.8-fold faster rate to optimized therapy (adjusted risk ratio, 1.841 [95% CI 1.234–2.746]), 6-fold higher likelihood to receive early effective therapy (<24 h, adjust...
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The importance of automatic particle tracking for analyzing microscopy image data to discover hidden knowledge of complex biological systems has motivated the development of many tracking approaches. We have developed a new tracking... more
The importance of automatic particle tracking for analyzing microscopy image data to discover hidden knowledge of complex biological systems has motivated the development of many tracking approaches. We have developed a new tracking approach that exploits information from multiple image scales and multiple time points, and directly combines the information in the optimization procedure. Our approach allows selecting an appropriate scale of a particle by using temporal information. Many-to-one and one-to-many associations are supported to deal with occlusion and deocclusion of particles. We have successfully applied our approach to real fluorescence microscopy image sequences displaying avian leukosis virus particles and quantified the performance.
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Designing novel molecules with targeted biological activities and optimized physicochemical properties is a challenging endeavor in drug discovery. Recent developments in artificial intelligence have enhanced the early steps of de novo... more
Designing novel molecules with targeted biological activities and optimized physicochemical properties is a challenging endeavor in drug discovery. Recent developments in artificial intelligence have enhanced the early steps of de novo drug design and compound optimization. Herein, we present a generative adversarial network trained to design new chemical matter that satisfies a given biological signature. Our model, called pqsar2cpd, is based on the activity of compounds across multiple assays obtained via pQSAR (profile-quantitative structure–activity relationships). We applied pqsar2cpd to Chagas disease and designed a novel molecule that was experimentally confirmed to inhibit growth of parasites in vitro at low micromolar concentrations. Altogether, this approach bridges chemistry and biology into one single framework for the design of novel molecules with promising biological activity.
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Recent advances in generative modelling allow designing novel compounds through deep neural networks. One such neural network model, JT-VAE (the Junction Tree Variational Auto-Encoder), excels at proposing chemically valid structures.... more
Recent advances in generative modelling allow designing novel compounds through deep neural networks. One such neural network model, JT-VAE (the Junction Tree Variational Auto-Encoder), excels at proposing chemically valid structures. Here, on the basis of JT-VAE, we built a generative modelling approach, JAEGER, for finding novel chemical matter with desired bioactivity. Using JAEGER, we designed compounds to inhibit malaria. To prioritize the compounds for synthesis, we used the in-house pQSAR (Profile-QSAR) program, a massively multitask bioactivity model based on 12,000 Novartis assays. On the basis of pQSAR activity predictions, we selected, synthesized and experimentally profiled two compounds. Both compounds exhibited low nanomolar activity in a malaria proliferation assay as well as a biochemical assay measuring activity against PI(4)K, which is an essential kinase that regulates intracellular development in malaria. The compounds also showed low activity in a cytotoxicity assay. Our findings show that JAEGER is a viable approach for finding novel active compounds for drug discovery. Tropical diseases, such as malaria, can develop resistance to specific drugs. Godinez and colleagues present here a generative design approach to find new anti-malarial drugs to circumvent this resistance.
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Open AcceResearch Human endogenous retrovirus HERV-K(HML-2) encodes a stable signal peptide with biological properties distinct from Rec
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Combination therapies are common in many therapeutic contexts, including infectious diseases and cancer. A common approach for evaluating combinations in vitro is to assess effects on cell growth as synergistic, antagonistic, or neutral... more
Combination therapies are common in many therapeutic contexts, including infectious diseases and cancer. A common approach for evaluating combinations in vitro is to assess effects on cell growth as synergistic, antagonistic, or neutral using "checkerboard" experiments to systematically sample combinations of agents in multiple doses. To further understand the effects of antibiotic combinations, we employed high-content imaging to study the morphological changes caused by combination treatments in checkerboard experiments. Using an automated, unsupervised image analysis approach to group morphologies, and an "expert-in-the-loop" to annotate them, we attributed the heterogeneous morphological changes ofEscherichia coli cells to varying doses of both single-agent and combination treatments. We identified patterns of morphological change, including morphological potentiation, competition, and the emergence of unexpected morphologies. We found these frequently did not correlate with synergistic or antagonistic effects on viability, suggesting morphological approaches may provide a distinctive signature of the biological interaction between compounds over a range of conditions. Among the unexpected morphologies we observed, there were transitional changes associated with intermediate doses of compounds and uncharacterized phenotypes associated with well-studied antibiotics. Our approach exemplifies how unsupervised image analysis and expert knowledge can be combined to reckon with complex phenotypic changes arising from combination screening, dose titrations, or polypharmacology. In this way, quantification of morphological diversity across treatment conditions could aid in analysis and prioritization of complementary pairings of antibiotic agents by more closely capturing the true signature of the integrated cellular response.
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ABSTRACT We are investigating the dynamical relationships exhibited by virus particles via fluorescence time-lapse microscopy. To obtain a quantitative description of each particle over time, these objects are tracked. To derive an... more
ABSTRACT We are investigating the dynamical relationships exhibited by virus particles via fluorescence time-lapse microscopy. To obtain a quantitative description of each particle over time, these objects are tracked. To derive an explicit characterization of each particle as well as to identify interesting transient behaviors, the intensity over time of each particle needs to be analyzed. We have developed an approach based on hybrid stochastic systems for identifying behaviors of interest. We employ a hybrid particle filter for estimating the behavior of individual particles. The approach has been successfully applied to particles tracked in synthetic image sequences as well as in real image sequences displaying HIV-1 particles.
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Recent advances in generative modeling allow designing novel compounds through deep neural networks. One such neural network model, the Junction Tree Variational Auto- Encoder (JT-VAE), excels at proposing chemically valid structures.... more
Recent advances in generative modeling allow designing novel compounds through deep neural networks. One such neural network model, the Junction Tree Variational Auto- Encoder (JT-VAE), excels at proposing chemically valid structures. Based on JT-VAE, we built a generative modeling approach (JAEGER) for finding novel chemical matter with desired bioactivity. Using JAEGER, we designed compounds to inhibit malaria. To prioritize the compounds for synthesis, we used the in-house Profile-QSAR (pQSAR) program, a massively-multitask bioactivity model based on 12,000 Novartis assays. Based on the pQSAR activity predictions, we selected, synthesized, and experimentally profiled two compounds. Both compounds exhibited low nanomolar activity in a malaria proliferation assay as well as a biochemical assay measuring activity against PI(4)K, which is an essential kinase that regulates intracellular development in malaria. The compounds also showed low activity in a cytotoxicity assay. Our findin...
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Explicit and tractable characterizations of the dynamical behavior of virus particles are pivotal for a thorough understanding of the infection mechanisms of viruses. This thesis deals with the problem of extracting symbolic... more
Explicit and tractable characterizations of the dynamical behavior of virus particles are pivotal for a thorough understanding of the infection mechanisms of viruses. This thesis deals with the problem of extracting symbolic representations of the dynamical behavior of fluorescent particles from fluorescence microscopy image sequences. The focus is on the behavior of virus particles such as fusion with the cell membrane. A numerical representation is obtained by tracking the particles in the image sequences. We have investigated probabilistic tracking approaches, including approaches based on the Kalman filter as well as based on particle filters. For reasons of efficiency and robustness, we developed a tracking approach based on the probabilistic data association (PDA) algorithm in combination with an ellipsoidal sampling scheme that exploits effectively the image data via parametric appearance models. To track objects in close proximity, we compute the support that each image posi...
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Tracking subcellular structures displayed as ‘particles’ in fluorescence microscopy images yields quantitative descriptions of the underlying dynamical processes. We have developed an approach for tracking multiple fluorescent particles.... more
Tracking subcellular structures displayed as ‘particles’ in fluorescence microscopy images yields quantitative descriptions of the underlying dynamical processes. We have developed an approach for tracking multiple fluorescent particles. Our approach includes a localization scheme using probabilistic data association that combines a top-down strategy driven by the Kalman filter and a bottom-up strategy using standard localization algorithms for fluorescent particles. The combined scheme yields multiple positions that are incorporated to the filter via a combined innovation. To track objects in close proximity, we introduce a support map that adjusts the association probabilities. By using the combined localization scheme in conjunction with the Kalman filter we integrate localization and position estimation. The approach has been successfully applied to synthetic images as well as to real microscopy image sequences and the performance has been quantified.
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Large-scale cellular imaging and phenotyping is a widely adopted strategy for understanding biological systems and chemical perturbations. Quantitative analysis of cellular images for identifying phenotypic changes is a key challenge... more
Large-scale cellular imaging and phenotyping is a widely adopted strategy for understanding biological systems and chemical perturbations. Quantitative analysis of cellular images for identifying phenotypic changes is a key challenge within this strategy, and has recently seen promising progress with approaches based on deep neural networks. However, studies so far require either pre-segmented images as input or manual phenotype annotations for training, or both. To address these limitations, we have developed an unsupervised approach that exploits the inherent groupings within cellular imaging datasets to define surrogate classes that are used to train a multi-scale convolutional neural network. The trained network takes as input full-resolution microscopy images, and, without the need for segmentation, yields as output feature vectors that support phenotypic profiling. Benchmarked on two diverse benchmark datasets, the proposed approach yields accurate phenotypic predictions as we...
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Beta-lactam antibiotics comprise one of the earliest known classes of antibiotic therapies. These molecules covalently inhibit enzymes from the family of penicillin-binding proteins, which are essential to the construction of the... more
Beta-lactam antibiotics comprise one of the earliest known classes of antibiotic therapies. These molecules covalently inhibit enzymes from the family of penicillin-binding proteins, which are essential to the construction of the bacterial cell wall. As a result, beta-lactams have long been known to cause striking changes to cellular morphology. The exact nature of the changes tend to vary by the precise PBPs engaged in the cell since beta-lactams exhibit a range of PBP enzyme specificity. The traditional method for exploring beta-lactam polyspecificity is a gel-based binding assay which is low-throughput and typically run ex situ in cell extracts. Here, we describe a medium-throughput, image-based assay combined with machine learning methods to automatically profile the activity of beta-lactams in E. coli cells. By testing for morphological change across a panel of strains with perturbations to individual PBP enzymes, our approach automatically and quantifiably relates different be...
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Significance Vessel cooption is a strategy that many tumors employ to progress without creating new blood vessels but by exploiting preexisting vessels of the host tissue. In addition to promoting tumor growth, cooption is also associated... more
Significance Vessel cooption is a strategy that many tumors employ to progress without creating new blood vessels but by exploiting preexisting vessels of the host tissue. In addition to promoting tumor growth, cooption is also associated with tumor resistance to antiangiogenic therapy. Despite the importance of this mode of tumor progression, the molecular and cellular mechanisms are not fully understood. Here, we combine intravital microscopy imaging and mathematical modeling to explore the dynamics of individual cancer cell cooption and collective response of the coopted cancer cells during antiangiogenic treatment. We also provide guidelines for effective therapeutic strategies that combine inhibition of both angiogenesis and cooption.
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Although essential for many cellular processes, the sequence of structural and molecular events during clathrin-mediated endocytosis remains elusive. While it was long believed that clathrin-coated pits grow with a constant curvature, it... more
Although essential for many cellular processes, the sequence of structural and molecular events during clathrin-mediated endocytosis remains elusive. While it was long believed that clathrin-coated pits grow with a constant curvature, it was recently suggested that clathrin first assembles to form flat structures that then bend while maintaining a constant surface area. Here, we combine correlative electron and light microscopy and mathematical growth laws to study the ultrastructural rearrangements of the clathrin coat during endocytosis in BSC-1 mammalian cells. We confirm that clathrin coats initially grow flat and demonstrate that curvature begins when around 70% of the final clathrin content is acquired. We find that this transition is marked by a change in the clathrin to clathrin-adaptor protein AP2 ratio and that membrane tension suppresses this transition. Our results support the notion that BSC-1 mammalian cells dynamically regulate the flat-to-curved transition in clathri...
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Although essential for many cellular processes, the sequence of structural and molecular events during clathrin-mediated endocytosis remains elusive. While it was believed that clathrin-coated pits grow with a constant curvature, it was... more
Although essential for many cellular processes, the sequence of structural and molecular events during clathrin-mediated endocytosis remains elusive. While it was believed that clathrin-coated pits grow with a constant curvature, it was recently suggested that clathrin first assembles to form a flat structure and then bends while maintaining a constant surface area. Here, we combine correlative electron and light microscopy and mathematical modelling to quantify the sequence of ultrastructural rearrangements of the clathrin coat during endocytosis in mammalian cells. We confirm that clathrin-coated structures can initially grow flat and that lattice curvature does not show a direct correlation with clathrin coat assembly. We demonstrate that curvature begins when 70% of the final clathrin content is acquired. We find that this transition is marked by a change in the clathrin to clathrin-adaptor protein AP2 ratio and that membrane tension suppresses this transition. Our results suppo...
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Identifying phenotypes based on high-content cellular images is challenging. Conventional image analysis pipelines for phenotype identification comprise multiple independent steps, with each step requiring method customization and... more
Identifying phenotypes based on high-content cellular images is challenging. Conventional image analysis pipelines for phenotype identification comprise multiple independent steps, with each step requiring method customization and adjustment of multiple parameters. Here, we present an approach based on a multi-scale convolutional neural network (M-CNN) that classifies, in a single cohesive step, cellular images into phenotypes by using directly and solely the images' pixel intensity values. The only parameters in the approach are the weights of the neural network, which are automatically optimized based on training images. The approach requires no a priori knowledge or manual customization, and is applicable to single- or multi-channel images displaying single or multiple cells. We evaluated the classification performance of the approach on eight diverse benchmark datasets. The approach yielded overall a higher classification accuracy compared with state-of-the-art results, incl...
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Dynamic microtubules (MTs) are required for neuronal guidance, in which axons extend directionally toward their target tissues. We found that depletion of the MT-binding protein Xenopus cytoplasmic linker-associated protein 1 (XCLASP1) or... more
Dynamic microtubules (MTs) are required for neuronal guidance, in which axons extend directionally toward their target tissues. We found that depletion of the MT-binding protein Xenopus cytoplasmic linker-associated protein 1 (XCLASP1) or treatment with the MT drug Taxol reduced axon outgrowth in spinal cord neurons. To quantify the dynamic distribution of MTs in axons, we developed an automated algorithm to detect and track MT plus ends that have been fluorescently labeled by end-binding protein 3 (EB3). XCLASP1 depletion reduced MT advance rates in neuronal growth cones, very much like treatment with Taxol, demonstrating a potential link between MT dynamics in the growth cone and axon extension. Automatic tracking of EB3 comets in different compartments revealed that MTs increasingly slowed as they passed from the axon shaft into the growth cone and filopodia. We used speckle microscopy to demonstrate that MTs experience retrograde flow at the leading edge. Microtubule advance in ...
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Understanding complex cellular processes requires investigating the underlying mechanisms within a spatiotemporal context. Although cellular processes are dynamic in nature, most studies in molecular cell biology are based on fixed... more
Understanding complex cellular processes requires investigating the underlying mechanisms within a spatiotemporal context. Although cellular processes are dynamic in nature, most studies in molecular cell biology are based on fixed specimens, for example, using immunocytochemistry or fluorescence in situ hybridization (FISH). However, breakthroughs in fluorescence microscopy imaging techniques, in particular, the discovery of green fluorescent protein (GFP) and its spectral variants, have facilitated the study of a wide range of dynamic processes by allowing nondestructive labeling of target structures in living cells. In addition, the tremendous improvements in spatial and temporal resolution of light microscopes now allow cellular processes to be analyzed in unprecedented detail. These state-of-the-art imaging technologies, however, provide a huge amount of digital image data. To cope with the enormous amount of image data and to extract reproducible as well as quantitative inform...
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ABSTRACT We introduce a new approach for tracking-based segmentation of 3D tubular structures. The approach is based on a novel combination of a 3D cylindrical intensity model and particle filter tracking. In comparison to earlier work we... more
ABSTRACT We introduce a new approach for tracking-based segmentation of 3D tubular structures. The approach is based on a novel combination of a 3D cylindrical intensity model and particle filter tracking. In comparison to earlier work we utilize a 3D intensity model as the measurement model of the particle filter, thus a more realistic 3D appearance model is used that directly represents the image intensities of 3D tubular structures within semiglobal regions-of-interest. We have successfully applied our approach using 3D synthetic images and real 3D MRA image data of the human pelvis.
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Ordered movement of virus particles along cell filopodia before cell entry known as viral surfing is an important pathway for cell infection. To quantitatively analyze the process of viral surfing, tracking of viruses over time in... more
Ordered movement of virus particles along cell filopodia before cell entry known as viral surfing is an important pathway for cell infection. To quantitatively analyze the process of viral surfing, tracking of viruses over time in fluorescent time-lapse microscopy images is required. We have developed an automatic approach for tracking single surfing virus particles. The approach combines probabilistic tracking of virus particles with a novel approach for segmentation of cell filopodia which is used to discriminate surfing virus particles. The approach was successfully applied to synthetic images as well as live cell microscopy images.
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Automatic tracking of fluorescent particles is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a probabilistic tracking approach based on multi-frame... more
Automatic tracking of fluorescent particles is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a probabilistic tracking approach based on multi-frame association finding and the Kalman filter. We have successfully applied the approach to synthetic as well as real microscopy image sequences of ALV virus particles and have performed a quantitative comparison with previous approaches.
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A bottleneck for high-throughput screening of live cells is the automated analysis of the generated image data. An important application in this context is the evaluation of the duration of cell cycle phases from confocal time-lapse... more
A bottleneck for high-throughput screening of live cells is the automated analysis of the generated image data. An important application in this context is the evaluation of the duration of cell cycle phases from confocal time-lapse microscopy image sequences, which typically involves a tracking step. The tracking step is an important part since it relates segmented cells from one time
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Research Interests: Computer Science, Algorithms, Artificial Intelligence, Computer Vision, Microscopy, and 15 moreFluorescence Microscopy, Image Analysis, Medicine, Cell Culture, Mitosis, Humans, Life Cycle, Cell nucleus, Image Enhancement, HeLa cells, Reproducibility of Results, Large Scale, Bottleneck, Image Sequence, and Automatic segmentation
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Background Most retroviruses enter their host cells by fusing the viral envelope with the plasma membrane. Although the protein machinery promoting fusion has been characterized extensively, the dynamics of the process are largely... more
Background Most retroviruses enter their host cells by fusing the viral envelope with the plasma membrane. Although the protein machinery promoting fusion has been characterized extensively, the dynamics of the process are largely unknown. Results We generated human immunodeficiency virus-1 (HIV-1) particles pseudotyped with the envelope (Env) protein of ecotropic murine leukemia virus eMLV to study retrovirus entry at the plasma membrane using live-cell microscopy. This Env protein mediates highly efficient pH independent fusion at the cell surface and can be functionally tagged with a fluorescent protein. To detect fusion events, double labeled particles carrying one fluorophor in Env and the other in the matrix (MA) domain of Gag were generated and characterized. Fusion events were defined as loss of Env signal after virus-cell contact. Single particle tracking of >20,000 individual traces in two color channels recorded 28 events of color separation, where particles lost the E...
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The role of the intranuclear movement of chromatin in gene expression is not well-understood. Herpes simplex virus forms replication compartments (RCs) in infected cell nuclei as sites of viral DNA replication and late gene transcription.... more
The role of the intranuclear movement of chromatin in gene expression is not well-understood. Herpes simplex virus forms replication compartments (RCs) in infected cell nuclei as sites of viral DNA replication and late gene transcription. These structures develop from small compartments that grow in size, move, and coalesce. Quantitative analysis of RC trajectories, derived from 4D images, shows that most RCs move by directed motion. Directed movement is impaired in the presence of actin and myosin inhibitors as well as a transcription inhibitor. In addition, RCs coalesce at and reorganize nuclear speckles. Lastly, distinct effects of actin and myosin inhibitors on viral gene expression suggest that RC movement is not required for transcription, but rather, movement results in the bridging of transcriptionally active RCs with nuclear speckles to form structures that enhance export of viral late mRNAs.
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Modern developments in time-lapse fluorescence microscopy enable the observation of a variety of processes exhibited by viruses. The dynamic nature of these processes requires the tracking of viruses over time to explore spatial-temporal... more
Modern developments in time-lapse fluorescence microscopy enable the observation of a variety of processes exhibited by viruses. The dynamic nature of these processes requires the tracking of viruses over time to explore spatial-temporal relationships. In this work, we developed deterministic and probabilistic approaches for multiple virus tracking in multi-channel fluorescence microscopy images. The deterministic approaches follow a traditional two-step paradigm comprising particle localization based on either the spot-enhancing filter or 2D Gaussian fitting, as well as motion correspondence based on a global nearest neighbor scheme. Our probabilistic approaches are based on particle filters. We describe approaches based on a mixture of particle filters and based on independent particle filters. For the latter, we have developed a penalization strategy that prevents the problem of filter coalescence (merging) in cases where objects lie in close proximity. A quantitative comparison based on synthetic image sequences is carried out to evaluate the performance of our approaches. In total, eight different tracking approaches have been evaluated. We have also applied these approaches to real microscopy images of HIV-1 particles and have compared the tracking results with ground truth obtained from manual tracking. It turns out that the probabilistic approaches based on independent particle filters are superior to the deterministic schemes as well as to the approaches based on a mixture of particle filters.
Research Interests: Engineering, Mathematics, Computer Science, Algorithms, Artificial Intelligence, and 15 moreComputer Vision, Fluorescence Microscopy, Medical Image Analysis, Medicine, Probabilistic Logic, Biomedical Imaging, Computer Simulation, Nearest Neighbor, Particle Filter, Medical Image, Image Enhancement, Ground Truth, Image Sequence, Probabilistic Approach, and Medical and Health Sciences
Human immunodeficiency virus type 1 (HIV-1) particles assemble at the plasma membrane, which is lined by a dense network of filamentous actin (F-actin). Large amounts of actin have been detected in HIV-1 virions, proposed to be... more
Human immunodeficiency virus type 1 (HIV-1) particles assemble at the plasma membrane, which is lined by a dense network of filamentous actin (F-actin). Large amounts of actin have been detected in HIV-1 virions, proposed to be incorporated by interactions with the nucleocapsid domain of the viral polyprotein Gag. Previous studies addressing the role of F-actin in HIV-1 particle formation using F-actin-interfering drugs did not yield consistent results. Filamentous structures pointing toward nascent HIV-1 budding sites, detected by cryo-electron tomography and atomic force microscopy, prompted us to revisit the role of F-actin in HIV-1 assembly by live-cell microscopy. HeLa cells coexpressing HIV-1 carrying fluorescently labeled Gag and a labeled F-actin-binding peptide were imaged by live-cell total internal reflection fluorescence microscopy (TIR-FM). Computational analysis of image series did not reveal characteristic patterns of F-actin in the vicinity of viral budding sites. Fu...
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To evaluate whether quantitative characterization of aortic arch geometry including its branches is feasible based on in vivo computed tomography (CT) angiography and magnetic resonance (MR) angiography data in healthy and diseased aortic... more
To evaluate whether quantitative characterization of aortic arch geometry including its branches is feasible based on in vivo computed tomography (CT) angiography and magnetic resonance (MR) angiography data in healthy and diseased aortic arches. Ten healthy volunteers, 10 patients with abdominal aortic disease, and 10 patients with aortic arch disease underwent MR angiography (10 volunteers) or CT angiography (20 patients). Commercial software was used for individual segmentation of supraaortic arteries. In-house software was developed for segmentation of aortic arch landmarks based on standardized multiplanar reformations (MPRs) and for subsequent aortic arch mapping. Supraaortic arteries and aortic arch landmarks were successfully segmented in all 30 subjects for CT angiography and MR angiography data. Significant tapering within the first centimeter was observed in all supraaortic arteries (P &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; .001). The three supraaortic arteries showed significantly different vessel diameters and areas (P &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; .001). The software developed in-house allowed detailed aortic arch mapping with quantitative definitions of the positional relationships between each supraaortic artery and the aorta. Distances between supraaortic arteries were less than 5 mm in 77.6% (mean 4.1 mm ± 3.8). The brachiocephalic trunk tended to be positioned on the right side of the aortic arch, and the left subclavian and left common carotid arteries tended to be positioned on the left side of the aortic arch. The feasibility and application of a postprocessing method allowing quantification of geometry of supraaortic arteries and aortic arch mapping were successfully demonstrated. Validation and evaluation of clinical implications are warranted.
Research Interests: Computed Tomography, Germany, Humans, Female, Feasibility Studies, and 15 moreMale, Magnetic Resonance, Immunobiology, DICOM, Clinical Sciences, Angiography, Aged, Adult, CT angiography, Case Control Studies, Endovascular Procedures, Magnetic resonance angiography, Aortic arch, Aorta Abdominal, and Computed tomography angiography
The entry process of virus particles into cells is decisive for infection. In this work, we investigate fusion of virus particles with the cell membrane via time-lapse fluorescence microscopy. To automatically identify fusion for single... more
The entry process of virus particles into cells is decisive for infection. In this work, we investigate fusion of virus particles with the cell membrane via time-lapse fluorescence microscopy. To automatically identify fusion for single particles based on their intensity over time, we have developed a layered probabilistic approach. The approach decomposes the action of a single particle into three abstractions: the intensity over time, the underlying temporal intensity model, as well as a high level behavior. Each abstraction corresponds to a layer and these layers are represented via stochastic hybrid systems and hidden Markov models. We use a maxbelief strategy to efficiently combine both representations. To compute estimates for the abstractions we use a hybrid particle filter and the Viterbi algorithm. Based on synthetic image sequences, we characterize the performance of the approach as a function of the image noise. We also characterize the performance as a function of the tracking error. We have also successfully applied the approach to real image sequences displaying pseudotyped HIV-1 particles in contact with host cells and compared the experimental results with ground truth obtained by manual analysis.
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Live-cell imaging allows detailed dynamic cellular phenotyping for cell biology and, in combination with small molecule or drug libraries, for high-content screening. Fully automated analysis of live cell movies has been hampered by the... more
Live-cell imaging allows detailed dynamic cellular phenotyping for cell biology and, in combination with small molecule or drug libraries, for high-content screening. Fully automated analysis of live cell movies has been hampered by the lack of computational approaches that allow tracking and recognition of individual cell fates over time in a precise manner. Here, we present a fully automated approach to analyze time-lapse movies of dividing cells. Our method dynamically categorizes cells into seven phases of the cell cycle and five aberrant morphological phenotypes over time. It reliably tracks cells and their progeny and can thus measure the length of mitotic phases and detect cause and effect if mitosis goes awry. We applied our computational scheme to annotate mitotic phenotypes induced by RNAi gene knockdown of CKAP5 (also known as ch-TOG) or by treatment with the drug nocodazole. Our approach can be readily applied to comparable assays aiming at uncovering the dynamic cause o...