Search Results (28,502)

Background: Lameness in cattle negatively affects welfare and productivity. Early identification of lameness allows for prompt treatment, and mobility scoring allows for herd-level prevalence data to be monitored. The reliability of a four-point mobility scoring system was investigated when used by beef farmers and veterinary surgeons. Methods: An online questionnaire that contained forty video clips of beef cattle was created for mobility scoring performed by farmers and vets. Results: The Fleiss kappa coefficient for inter-observer agreement across all 81 respondents and all videos was 0.34, which showed fair agreement. Beef farmers generally had lower agreement than vets (0.29 vs. 0.38). Vets had significantly higher inter-observer reliability compared to beef farmers (p = 0.035). Overall, Cohen’s kappa coefficient for intra-observer agreement across all respondents varied from 0.085 (slight agreement) to 0.871 (almost perfect agreement). Limitations: The survey was only available online, which may have limited distribution and engagement. The recruitment of participants was not specific to differing levels of previous experience in mobility scoring. The mobility scoring was not performed in person, which could be more reflective of clinical application. Conclusions: The application of a four-point mobility scoring system for beef cattle had fair inter-observer reliability and a wide range of intra-observer reliability, but this is poorer than previously reported. This presents a challenge for the identification of lame beef cattle at both the individual and herd levels. Full article

Non-muscle-invasive bladder cancer (NMIBC) prognosis varies significantly due to the biological and clinical heterogeneity. High-risk stage T1-G3, comprising 15–20% of NMIBCs, involves the lamina propria and is associated with higher rates of recurrence, progression, and cancer-specific mortality. In the present study, we have evaluated the enumeration of tumour-derived extracellular vesicles (tdEVs) and circulating tumour cells (CTCs) in high-risk NMIBC patients and their correlation with survival outcomes such as time to progression (TTP), and cancer-specific survival (CSS). Eighty-three high-risk T1-G3 NMIBC patients treated between September 2010 and January 2013 were included. Blood samples were collected before a transurethral resection of the bladder (TURB) and analysed using the CellSearch^® system. The presence of at least one CTC was associated with a shorter TTP and CSS. Extending follow-up to 120 months and incorporating automated tdEV evaluation using ACCEPT software demonstrated that tdEV count may additionally stratify patient risk. Combining tdEVs and CTCs improves risk stratification for NMIBC progression, suggesting that tdEVs could be valuable biomarkers for prognosis and disease monitoring. Further research is needed to confirm these findings and establish the clinical significance of tdEVs in early-stage cancers. Full article

This paper describes the study of a floating offshore wind turbine (FOWT) blade in terms of its dynamic response due to structural damage and its repercussions on structural health monitoring (SHM) systems. Using a finite element model, natural frequencies and mode shapes were derived for both an undamaged and a damaged blade configuration. A 35% reduction in stiffness at node 1 was applied in order to simulate significant damage. Concretely, the results are that the intact blade has a fundamental frequency of 0.16 Hz, and this does not change when damaged, while higher modes exhibit frequency changes: mode 2 drops from 2.05 Hz to 2.00 Hz and mode 3 from 6.15 Hz to 6.01 Hz. The shifts show a critical loss in the capability of handling vibrational energy due to the damage; higher modes (4, 5, and 6) show larger frequency deviations going down to as low as 18.06 Hz in mode 6. The mode shape change is considerable for the edge-wise and flap-wise deflection of the 2D contour plots, indicating possible coupling effects between modes. These results indicate that lower modes are sensitive to stiffness reductions, and the continuous monitoring of the lower harmonic modes early is required to detect damages. These studies have helped to improve blade design, maintenance, and operational safety for FOWT systems. Full article

We investigate a quantum walk on a ring represented by a directed triangle graph with complex edge weights and monitored at a constant rate until the quantum walker is detected. To this end, the first hitting time statistics are recorded using unitary dynamics interspersed stroboscopically by measurements, which are implemented on IBM quantum computers with a midcircuit readout option. Unlike classical hitting times, the statistical aspect of the problem depends on the way we construct the measured path, an effect that we quantify experimentally. First, we experimentally verify the theoretical prediction that the mean return time to a target state is quantized, with abrupt discontinuities found for specific sampling times and other control parameters, which has a well-known topological interpretation. Second, depending on the initial state, system parameters, and measurement protocol, the detection probability can be less than one or even zero, which is related to dark-state physics. Both return-time quantization and the appearance of the dark states are related to degeneracies in the eigenvalues of the unitary time evolution operator. We conclude that, for the IBM quantum computer under study, the first hitting times of monitored quantum walks are resilient to noise. However, a finite number of measurements leads to broadening effects, which modify the topological quantization and chiral effects of the asymptotic theory with an infinite number of measurements. Full article

Malate is a key intermediate in the citric acid cycle, an enzymatic cascade that is central to cellular energy metabolism and that has been applied to make biofuel cells. To enable real-time sensing of malate levels, we have engineered a genetically encoded, protein-based fluorescent biosensor called Malon specifically responsive to malate by performing structure-based mutagenesis of the Cache-binding domain of the Citron GFP-based biosensor. Malon demonstrates high specificity and fluorescence activation in response to malate, and has been applied to monitor enzymatic reactions in vitro. Furthermore, we successfully incorporated Malon into redox polymer hydrogels and bacterial cells, enabling analysis of malate levels in these materials and living systems. These results show the potential for fluorescent biosensors in enzymatic cascade monitoring within biomaterials and present Malon as a novel tool for bioelectronic devices. Full article

Raman spectroscopy is a technique which involves quantitative and qualitative molecular analysis based on the interaction between incident light and isolation of scattered wavelengths in generating a molecular fingerprint. It has a broad array of potential scientific applications, encompassing areas as diverse as food science and forensics. However, it may also be highly useful in clinical oncology. A recent focus of research in oncology has been in achieving the individualisation of care. Two important strategies to achieve a so-called “precision oncology” approach may include the detection of circulating tumour DNA (ctDNA) in more objectively evaluating treatment response and guiding de-escalation and intensification approaches in systemic therapy and therapeutic drug monitoring (TDM). Therapeutic drug monitoring involves the quantitation of plasma drug levels in order to tailor medication dosing in optimizing outcomes. The existing approaches to characterize small molecules, such as fluorescence-based and chromatographic strategies, may be limited by high costs, long turnaround times, and bulky equipment. Surface-enhanced Raman spectroscopy (SERS) may be deployed by utilizing a handheld device, with the potential for point of care, rapid turnaround, low-cost assessment of clinically relevant parameters, and prompt implementation of attendant changes in treatment. Although there is a growing body of data supporting the implementation of TDM and evaluation of ctDNA in achieving precision medicine, the uptake of such approaches remains relatively limited outside of clinical trials. As stated, the nature of existing analytical methodologies may prove to be a significant barrier to the routine clinic-based implementation of such approaches. Therefore, we provide the existing evidence for SERS in alleviating these barriers. We also provide insights into how SERS could contribute to clinical oncology. Full article

Background: Recent years have seen a surge of interest in dual-modality imaging systems that integrate functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to probe brain function. This review aims to explore the advancements and clinical applications of this technology, emphasizing the synergistic integration of fNIRS and EEG. Methods: The review begins with a detailed examination of the fundamental principles and distinctive features of fNIRS and EEG techniques. It includes critical technical specifications, data-processing methodologies, and analysis techniques, alongside an exhaustive evaluation of 30 seminal studies that highlight the strengths and weaknesses of the fNIRS-EEG bimodal system. Results: The paper presents multiple case studies across various clinical domains—such as attention-deficit hyperactivity disorder, infantile spasms, depth of anesthesia, intelligence quotient estimation, and epilepsy—demonstrating the fNIRS-EEG system’s potential in uncovering disease mechanisms, evaluating treatment efficacy, and providing precise diagnostic options. Noteworthy research findings and pivotal breakthroughs further reinforce the developmental trajectory of this interdisciplinary field. Conclusions: The review addresses challenges and anticipates future directions for the fNIRS-EEG dual-modal imaging system, including improvements in hardware and software, enhanced system performance, cost reduction, real-time monitoring capabilities, and broader clinical applications. It offers researchers a comprehensive understanding of the field, highlighting the potential applications of fNIRS-EEG systems in neuroscience and clinical medicine. Full article

Using adaptive filtering to estimate the frequency of power systems has become a popular trend. In recent years, however, few studies have been performed on adaptive frequency estimations in non-stationary noise environments. In this paper, we propose the distributed complex inverse square root algorithm and distributed augmented complex inverse square root algorithm for the frequency estimation of power systems based on the widely linear model and the inverse square root cost function, where the function can restrain both positive and negative large errors, based on its symmetry. Moreover, the wireless sensor networks support monitoring and adaptation for the frequency estimation in the distributed networks, and the proposed approach can ensure good robustness of the balanced or unbalanced three-phase power system with the help of a local complex-value voltage signal generated by Clark’s transformation. In addition, the bound of step size is driven by the global vectors, and that low computation complexity do not hinder those performances. The results of several experiments demonstrate that our algorithms can effectively estimate the frequency in impulsive noise environments. Full article

The NOAA IMS (Interactive Multisensor Snow and Ice Mapping System) is a blended snow and ice product based on active and passive satellite sensors, ground observation, and other auxiliary information, providing the daily cloud-free snow cover extent in the Northern Hemisphere (NH) and having great application potential in snow cover monitoring and research in the Tibetan Plateau (TP). However, accuracy assessment of products is crucial for various aspects of applications. In this study, Landsat-8 OLI images were used to evaluate and validate the accuracy of IMS products in snow cover monitoring on the TP. The results show that (1) average overall accuracy of IMS 4 km products is 76.0% and average mapping accuracy is 88.3%, indicating that IMS 4 km products are appropriate for large-scale snow cover monitoring on the TP. (2) IMS 4 km products tend to overestimate actual snow cover on the TP, with an average commission rate of 45.4% and omission rate of 11.7%, and generally present that the higher the proportion of snow-covered area, the lower the probability of omission rate and the higher the probability of commission rate. (3) Mapping accuracy of IMS 4 km snow cover on the TP generally is higher at the high altitudes, and commission and omission errors increase with the decrease of elevation. (4) Compared with less regional representativeness of ground observations, the spatial characteristics of snow cover based on high-resolution remote sensing data are much more detailed, and more reliable verification results can be obtained. (5) In addition to commission and omission error metrics, the overall accuracy and mapping accuracy based on the reference image instead of classified image can better reveal the general monitoring accuracy of IMS 4 km products on the TP area. Full article

Truffles of the Tuber genus (Pezizales, Ascomycetes) are among the most valuable and expensive foods, but their shelf life is limited to 7–10 days when stored at 4 °C. Alternative preservation methods have been proposed to extend their shelf life, though they may alter certain quality parameters. Recently, a hypogeal display case equipped with an ultrasonic humidity system (HDC) was developed, extending the shelf life to 2–3 weeks, depending on the truffle species. This study assesses the efficacy of HDC in preserving Tuber melanosporum and Tuber borchii ascomata over 16 days, using quantitative magnetic resonance imaging (QMRI) to monitor water content and other parameters. Sixteen T. melanosporum and six T. borchii ascomata were stored at 4 °C in an HDC or a static fridge (SF) as controls. QMRI confirmed that T. borchii has a shorter shelf life than T. melanosporum under all conditions. HDC reduced the rate of shrinkage, water, and mass loss in both species. Additionally, the Apparent Diffusion Coefficient (ADC), longitudinal relaxation time (T1), and transverse relaxation time (T2), which reflect molecular changes, decreased more slowly in HDC than SF. QMRI proves useful for studying water-rich samples and assessing truffle preservation technologies. Further optimization of this method for industrial use is needed. Full article

Background/Objectives: Truncus arteriosus communis (TAC) is a rare congenital heart defect characterized by a single arterial trunk that supplies systemic, pulmonary, and coronary circulations. This defect, constituting approximately 1–4% of congenital heart diseases, poses significant challenges in prenatal diagnosis, management, and postnatal outcomes. Methods: A retrospective analysis was conducted at the local tertiary referral center on cases of TAC diagnosed prenatally between 2019 and 2024. Additionally, a systematic literature review was performed to evaluate the accuracy of prenatal diagnostics and the presence of associated anomalies in fetuses with TAC and compare already published data with the local results. The review included studies that especially described the use of fetal echocardiography, the course and outcome of affected pregnancies, and subsequent management strategies. Results: The analysis of local prenatal diagnoses revealed 14 cases. Of the 11 neonates who survived to birth, the TAC diagnosis was confirmed in 7 instances. With all seven neonates undergoing surgery, the intention-to-treat survival rate was 86%, and the overall survival rate was 55%. By reviewing published case series, a total of 823 TAC cases were included in the analysis, of which 576 were diagnosed prenatally and 247 postnatally. The presence of associated cardiac and extracardiac manifestations as well as genetic anomalies was common, with a 22q11 microdeletion identified in 27% of tested cases. Conclusions: Advances in prenatal imaging and early diagnosis have enhanced the management of TAC, allowing for the detailed planning of delivery and immediate postnatal care in specialized centers. The frequent association with genetic syndromes underscores the importance of genetic counseling in managing TAC. An early surgical intervention remains crucial for improving long-term outcomes, although the condition is still associated with significant risks. Long-term follow-up studies are essential to monitor potential complications and guide future management strategies. Overall, a coordinated multidisciplinary approach from prenatal diagnosis to postnatal care is essential for improving outcomes for individuals with TAC. Full article

Biotoxins are pervasive in food and the environment, posing significant risk to human health. The most effective strategy to mitigate the risk arising from biotoxin exposure is through their specific and sensitive detection. Aptasensors have emerged as pivotal tools, leveraging aptamers as biorecognition elements to transduce the specificity of aptamer-target interactions into quantifiable signals for analytical applications, thereby facilitating the meticulous detection of biotoxins. When integrated with readily portable devices such as lateral flow assays (LFAs), personal glucose meters (PGMs), smartphones, and various meters measuring parameters like pH and pressure, aptasensors have significantly advanced the field of biotoxin monitoring. These commercially available devices enable precise, in situ, and real-time analysis, offering great potential for portable biotoxin detection in food and environmental matrices. This review highlights the recent progress in biotoxin monitoring using portable aptasensors, discussing both their potential applications and the challenges encountered. By addressing these impediments, we anticipate that a portable aptasensor-based detection system will open new avenues in biotoxin monitoring in the future. Full article

The increasing complexity of modern mechanical systems, especially rotating machinery, demands effective condition monitoring techniques, particularly deep learning, to predict potential failures in a timely manner and enable preventative maintenance strategies. Health monitoring data analysis, a widely used approach, faces challenges due to data randomness and interpretation difficulties, highlighting the importance of robust data quality analysis for reliable monitoring. This paper presents a two-part approach to address these challenges. The first part focuses on comprehensive data preprocessing using only feature scaling and selection via random forest (RF) algorithm, streamlining the process by minimizing human intervention while managing data complexity. The second part introduces a Recurrent Expansion Network (RexNet) composed of multiple layers built on recursive expansion theories from multi-model deep learning. Unlike traditional Rex architectures, this unified framework allows fine tuning of RexNet hyperparameters, simplifying their application. By combining data quality analysis with RexNet, this methodology explores multi-model behaviors and deeper interactions between dependent (e.g., health and condition indicators) and independent variables (e.g., Remaining Useful Life (RUL)), offering richer insights than conventional methods. Both RF and RexNet undergo hyperparameter optimization using Bayesian methods under variability reduction (i.e., standard deviation) of residuals, allowing the algorithms to reach optimal solutions and enabling fair comparisons with state-of-the-art approaches. Applied to high-speed bearings using a large wind turbine dataset, this approach achieves a coefficient of determination of 0.9504, enhancing RUL prediction. This allows for more precise maintenance scheduling from imperfect predictions, reducing downtime and operational costs while improving system reliability under varying conditions. Full article

This work contributes to the improvement of novel medical technologies for the prevention and treatment of diseases. Electrical impedance tomography (EIT) has gained attention as a valuable tool for non-invasive monitoring providing real-time insights. The purpose of this work is to develop and validate a novel portable EIT system with a small form factor for respiratory monitoring. The device uses a 16-electrode architecture with adjacent stimulation and measurement patterns, an integrated circuit current source and a single high-speed ADC operating with multiplexers to stimulate and measure across all electrodes. Tests were conducted on 25 healthy subjects who performed a pulmonary function test with a flowmeter while using the EIT device. The results showed a good performance of the device, which was able to recognize all respirations correctly, and from the EIT signals and images, correlations of 96.7% were obtained for instantaneous respiratory rate and 96.1% for tidal volume prediction. These results validate the preliminary technical feasibility of the EIT system and demonstrates its potential as a reliable tool for non-invasive respiratory assessment. The significance of this work lies in its potential to democratize advanced respiratory monitoring technologies, making them accessible to a wider population, including those in remote or underserved areas. Full article

Background and Objectives: Modernization and population aging have increased the prevalence of systemic diseases, such as diabetes and hypertension, which are often accompanied by various dental diseases. Our aim was to investigate associations between common dental conditions and major systemic diseases in an elderly Korean population. Materials and Methods: Utilizing electronic medical record data from 43,525 elderly patients, we examined the prevalence of systemic diseases (diabetes, hypertension, rheumatoid arthritis, osteoporosis, dementia) and dental conditions (caries, periodontal disease, pulp necrosis, tooth loss). The analysis focused on the correlations between these diseases. Results: Significant associations were found between systemic diseases and an increased prevalence of dental conditions. Patients with systemic diseases, especially those with multiple conditions, had higher incidences of periodontal disease and tooth loss. The correlation was particularly strong in patients with diabetes and rheumatoid arthritis. Interestingly, temporomandibular joint disorder was less frequent in this cohort. Conclusions: The findings highlight the importance of integrated dental care in managing systemic diseases in elderly populations. Enhanced dental monitoring and proactive treatment are essential due to the strong association between systemic diseases and dental conditions. Collaboration between dental and medical professionals is crucial for comprehensive care that improves health outcomes and quality of life for elderly patients. Full article