Search Results (24,133)

Bovine viral diarrhea (BVD) is a major cause of economic losses in the global cattle industry, particularly in countries characterized by intensive livestock production systems. Pestivirus tauri, formerly known as Bovine viral diarrhea virus type 2 (BVDV-2), is the current taxonomic designation according to the International Committee on Taxonomy of Viruses (ICTV). Between 2005 and 2018, Pestivirus tauri was detected in cattle herds in mainland Italy, particularly in the Lombardy region. Four viral strains were successfully isolated in cell cultures and subjected to whole-genome sequencing. Phylogenetic reconstruction placed all Italian isolates within the Pestivirus tauri subgenotype c, a lineage encompassing strains reported in Asia, Europe and the United States. Consistently, comparative sequence identity analyses indicated the highest similarity with the Parker strain (USA, 1991) and the Potsdam 1600 strain (Germany, 2000). These results contribute to a more detailed understanding of Pestivirus tauri genomic architecture and evolutionary dynamics, providing a valuable resource for comparative genomic studies. Such data are crucial for exploring viral diversity and evolution, optimizing the design of diagnostic primers and probes, and advancing insights into the molecular epidemiology of Pestivirus. Full article

Crohn’s disease (CD) is a complex inflammatory bowel disease, defined by chronic transmural inflammation and marked heterogeneity in both anatomical distribution and disease behavior, with potential involvement of any segment of the gastrointestinal tract and multiple phenotypes. Advanced cross-sectional imaging nowadays plays a central role in CD management, reliably assessing both luminal and extraluminal inflammatory manifestations, supporting initial diagnosis, phenotypic characterization, and longitudinal monitoring of disease activity, complications and treatment response. Over the last two decades, Intestinal Ultrasound (IUS), MR Enterography (MRE), and Computed Tomography Enterography (CTE) have become central components of the diagnostic pathway. MRE has emerged as the most comprehensive, radiation-free modality for evaluating intestinal extent, inflammatory activity, and complications in Crohn’s disease. Multiparametric MRE, combining T2-weighted imaging, contrast-enhanced sequences, diffusion-weighted imaging, and cine acquisitions, enables a real “Crohn’s disease staging”, namely a thorough evaluation of the transmural inflammation, of fibrotic and fistulizing lesions in the small and large bowel, as well as in the perianal region. IUS provides a dynamic, widely accessible, safe and repeatable imaging technique that is particularly well suited for tight-monitoring strategies, early assessment of therapeutic response, and routine follow-up, especially in experienced centers. Notably CTE, despite concerns related to cumulative ionizing radiation exposure, remains indispensable in acute clinical settings owing to its rapid acquisition, broad availability, and high diagnostic accuracy for detecting abscesses, perforation, and bowel obstruction. Combined, these three modalities offer a complementary and patient-tailored framework for optimal CD management. This review outlines the pathological complexity of Crohn’s disease, traces the evolution of imaging approaches, and provides a comparative overview highlighting the specific strengths and limitations of each modality. Full article

Broccoli is rich in glucosinolates (GSLs), secondary metabolites that contribute to both plant defense and human health. Optimizing the composition of major aliphatic GSLs is an important breeding objective, yet robust molecular markers for marker-assisted selection (MAS) remain limited. In this study, candidate gene-based kompetitive allele-specific PCR (KASP) markers were developed from conserved GSL biosynthesis genes, focusing on AOP2 and GSL-OH selected from 19 GSL-related genes. Marker–trait associations were evaluated in a natural broccoli population and further validated in an independent F₂ population. Among the tested markers, S101, located in AOP2, exhibited consistent genotype-dependent effects on GNA and PRO across both populations, supporting its stable predictive value. Receiver operating characteristic (ROC) analysis further confirmed strong classification performance of S101 for distinguishing high- and low-content genotypes of these traits in the F₂ population. In contrast, S074 and S035 showed population-dependent effects, with significant associations detected only in the natural population. Although association signals were reduced under mixed linear model (MLM) analysis with false discovery rate (FDR) correction, major loci identified under the general linear model (GLM) framework remained detectable. Overall, these results demonstrate the potential of candidate gene-based KASP markers for improving aliphatic GSL composition in broccoli through marker-assisted selection. Full article

Monitoring peptide and protein self-association is essential for understanding biological function, formulation stability, and aggregation mechanisms. While size-exclusion chromatography (SEC) is routinely used to quantify protein-size variants under native conditions, its hyphenation to high-resolution mass spectrometry (HRMS) for simultaneous structural characterization remains limited. Here, we report the development and validation of a robust SEC-UV/HRMS method optimized for native-like analysis of bovine serum albumin (BSA) monomers and higher-order oligomers using standard-flow electrospray ionization. Systematic evaluation of source parameters, mobile-phase composition, and chromatographic conditions enabled retention of native BSA structure, minimized in-source unfolding, and enhanced MS sensitivity, allowing detection of oligomers up to the heptamer. A short, narrow-bore 200 Å UHPLC SEC separation column was used. Low-flow separations (~0.05 mL/min) enabled efficient ionization and 10 min run times. An accelerated 60 °C stress-testing protocol demonstrated that SEC-MS can semi-quantitatively monitor oligomerization dynamics, complementing UV-based quantification and revealing transient species not resolved by UV alone. The method showed acceptable linearity, precision, and sample stability, and comparison with SEC-RALS/LALS confirmed molecular-weight trends across aggregation states. Overall, the developed SEC-UV/HRMS workflow provides a rapid, sensitive, and widely accessible approach for UV-based quantification of monomer- and HRMS-based characterizing protein aggregation in research and quality control in pharmaceutical laboratories. Full article

Accurate identification of the cotton top bud is a prerequisite for automated cotton topping. However, the detection of the cotton top bud is low due to the small target size and a similar background. Therefore, a method named CFSS-YOLO was proposed to detect the cotton top bud based on YOLOv12 with an attention mechanism. Firstly, a Convolutional Block Attention Module (CBAM) was introduced into the neck structure of YOLOv12 to suppress background interference and improve target recognition accuracy. Secondly, a new loss function, FSSLoss, was designed where the Shape-IoU (Intersection over Union) optimized by Focaler-IoU was used for the part of localization loss, and Slideloss was integrated to improve the classification loss. The improvement of the loss function aimed to balance the relationship between classification loss and localization loss and accelerate the convergence speed of the model. The experimental results show that the precision, recall and mAP50 of the proposed CFSS-YOLO are 87.6%, 75.3% and 84.8%, respectively. The detection performance of the proposed method is superior to mainstream object detection models such as YOLOv12s, YOLOv5s, SSD, RT-DETR, and DEIM-R18 in outer farmland. These results demonstrate that the proposed CFSS-YOLO has high potential for application and promotion in the cotton top bud recognition task. Full article

Accurate delineation of stream networks in low-gradient wetlands remains challenging due to subtle topographic variation and dense vegetation cover. This study systematically evaluated 48 Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) processing workflows through 1128 pairwise comparisons to identify optimal approaches for mapping fine-scale channels in Japan’s Kushiro Wetland, a Ramsar-designated ecosystem. The workflows combined three ground filtering methods (Progressive Morphological Filter, Cloth Simulation Filter, Multiscale Curvature Classification), four interpolation techniques (Inverse Distance Weighting, Triangulated Irregular Network, Kriging, Multilevel B-spline Approximation), two sink-filling algorithms (Planchon & Darboux; Wang & Liu), and two flow direction models (D8, D-infinity). Performance was first assessed using pixel-based Intersection over Union (IoU) metrics to quantify inter-method consensus. Independent plausibility-based validation was then conducted using near-contemporaneous Sentinel-2 imagery. Although pairwise statistical analysis identified workflows that achieved high inter-method consensus (median IoU = 0.90), external validation demonstrated that the CSF-MBA-Planchon-D8 workflow provided the most realistic presentation of optically observable channel corridors (validation IoU ≈ 0.85). These findings reveal that high inter-method agreement does not necessarily imply accurate landscape representation; multiple workflows may converge on systematically biased solutions. Ground filtering exerted the strongest influence on pairwise consensus, whereas plausibility-based validation highlighted the importance of selecting workflow combinations that preserve subtle channel morphology. Sink-filling and flow direction choices exerted comparatively minor effects in this low-gradient setting. The proposed dual-validation framework provides methodological guidance for wetland restoration planning and highlights the necessity of external validation in LiDAR-derived hydrological feature extraction. Full article

To address the critical challenge of drilling fluid invasion in deep coalbed methane (CBM) reservoirs, this study provides novel insight into the micro-scale sealing mechanism and pore structure evolution by leveraging Low-Field Nuclear Magnetic Resonance (LF-NMR) as a quantitative probe. Unlike traditional macroscopic evaluations, we utilized dynamic NMR T₂ spectral analysis to decipher the synergistic behavior of a proposed “Bridging–Filling–Densifying” ternary sealing system, which integrates a nano-plugging agent, micro-fillers, and size-matched skeletal agents. The results demonstrate a significant improvement in sealing efficiency. The optimized hierarchical architecture reduced the NMR signal intensity of the invaded cores by over 99.8% under a differential pressure of 10 MPa, effectively eliminating fluid invasion channels. Crucially, the study reveals that while multi-scale particle size matching is the precondition for sealing, the mechanical rigidity of the skeletal particles is the determinant for maintaining filter cake integrity against high-pressure deformation. These findings elucidate the transition from a “macropore-dominated” structure to a “zero-detectable” sealed state, establishing a robust theoretical framework for designing non-damaging drilling fluids tailored to the complex geomechanics of deep CBM exploration. Full article

At present, the global logistics sector faces critical challenges, including rising energy costs and pressure to reduce CO₂ emissions. Traditional linear supply chains are becoming inefficient, necessitating a transition toward shared logistics based on the principles of the sharing economy. This paper presents a progressive three-layer architecture that transforms conventional reactive data collection into an autonomous, proactive management system for the distribution of consumable materials. While previous research established foundations in IoT connectivity for smart vending machines, this study advances the process by integrating an intelligent layer of artificial intelligence (AI) algorithms. The framework utilizes Long Short-Term Memory (LSTM) neural networks for demand forecasting, dynamic route optimization (VRP/ACO) for replenishment, and Isolation Forest/DBSCAN algorithms for real-time anomaly detection. To evaluate the framework, a numerical simulation was conducted using representative pilot scenarios. The results indicate that within the simulated environment, the system achieves over 95% accuracy in inventory depletion prediction (MAPE = 4.02%). In these analyzed instances, this leads to a 25–30% reduction in stock-out risks and a 25% reduction in replenishment distance. These findings demonstrate the significant potential for reducing operational costs and carbon footprints in green logistics. The study confirms that the synergy between IoT infrastructure and AI-driven analysis provides a robust foundation for transitioning from static methodologies to resilient, collaborative logistics ecosystems. Full article

Background: Breast cancer is the most common malignancy in women, and sentinel lymph node (SLN) biopsy is essential for accurate nodal staging while avoiding unnecessary axillary dissection. Aim: This study aimed to compare SLN detection rates between two dual-tracer techniques: indocyanine green plus patent blue (ICG + PB) and technetium-99m plus patent blue (99mTc + PB), and to identify factors associated with detection failure for each tracer. Methods: All clinically node-negative breast cancer patients undergoing SLN biopsy between January 2014 and December 2024 were retrospectively evaluated. SLN detection was considered successful when at least one node was identified intraoperatively and confirmed histologically. Multivariate analysis assessed clinical and tumor-related predictors of failure. Results: A total of 269 procedures (258 patients) were analyzed, including 152 ICG + PB and 117 99mTc + PB procedures. Detection rates were comparable between groups (95.4% vs. 94.9%, p = 0.96), with no significant differences in the number of SLNs retrieved or nodal positivity. Multivariate analysis identified increasing patient age as the only independent predictor of PB failure, while no variables were associated with ICG failure. Tumor location in the upper-inner quadrant was the sole predictor of 99mTc failure. Conclusions: ICG + PB and 99mTc + PB provide equivalent and high SLN detection rates. ICG appears to be a robust, radiation-free alternative with no identifiable predictors of failure, supporting its role as an effective mapping strategy, particularly in centers aiming to optimize workflow and patient safety, despite the limited available data on its efficacy. Full article

Renal cell carcinomas (RCCs) driven by TFE3 rearrangement or TFEB alteration (MiT-RCC) account for up to 40% of pediatric RCCs but are rare in adults. MiT-RCC includes fusion-driven tumors with TFE3 or TFEB rearrangements (translocation RCC, tRCC) and TFEB-amplified RCC. Morphologic heterogeneity and historical exclusion from trials have limited evidence-based management. We reviewed the literature through January 2026 to summarize molecular biology, pathology, clinical behavior, and systemic therapy. MiT-RCC comprises biologically distinct entities: TFEB-rearranged tumors are often indolent in younger patients, whereas TFEB-amplified RCC, frequently co-amplifying VEGFA, behaves aggressively in older adults. In TFE3-rearranged RCC, fusion partner influences prognosis. Paradoxically, ASPSCR1::TFE3 fusions have the poorest natural history, yet fusion-annotated cohorts suggest these tumors may derive particular benefit from immune checkpoint inhibitor (ICI) plus VEGF receptor tyrosine kinase inhibitor (VEGFR-TKI) combinations. Diagnostic advances including GPNMB immunohistochemistry, TRIM63 RNA in situ hybridization, and sequencing-based fusion panels improve detection of cryptic alterations. First-line ICI + VEGFR-TKI combinations are increasingly favored for metastatic tRCC in eligible patients, while optimal management of TFEB-amplified RCC remains uncertain. Full article

The Nd:GYSAG crystal enables multi-wavelength near-infrared laser output, with adjustable wavelengths tailored for specific application requirements, making it highly valuable for space-borne water vapor detection. This study reports, for the first time, the side-pumping characteristics and electro-optical Q-switching performance of this crystal. Using Ø3 × 73 mm and Ø4 × 73 mm crystal rods doped with 1.21 at.% Nd:GYSAG (chemical formula Nd_0.033Gd_0.93Y_1.79Sc_0.70Al_4.54O_11.99), 1060.4 nm laser output was achieved under 808 nm laser diode (LD) side-pumping at a repetition rate of 100 Hz and a pump pulse width of 250 μs. The experimental results show that the Ø4 × 73 mm rod had a higher laser threshold but exhibited significantly superior slope efficiency and maximum output power compared to the Ø3 × 73 mm rod. Using a flat–flat resonator, optimal laser performance was obtained with an output coupler transmission of 35%, yielding a slope efficiency of 37.2%. A maximum output energy of 179.4 mJ was achieved at a pump energy of 646 mJ. Thermal lensing effects were compensated using a flat–convex cavity, leading to improved laser performance and beam quality. Electro-optical Q-switching experiments were conducted using a KD*P crystal. A comparison between voltage-applied and voltage-removed Q-switching techniques revealed superior performance for the voltage-applied method. High-performance laser output was realized, achieving a maximum pulse energy of 59.6 mJ, a pulse width of 14.93 ns, and a peak power of 3.99 MW. This study provides an important foundation for the development of near-infrared laser devices based on Nd:GYSAG. Full article

Straw burning is a major source of regional air pollution. However, its reliable remote sensing detection faces problems in distinguishing agricultural fires from non-agricultural thermal anomalies, adequately leveraging burning seasonality, and overcoming the scarcity of pixel-level annotations. To comprehensively address these issues, this study proposes an end-to-end framework for straw burning identification that integrates spatio-temporal weighting and semi-supervised learning. The framework introduces a data-driven spatial weight optimization method to automatically learn discriminative weights for diverse land cover types (e.g., farmland, industry), replacing subjective empirical settings. Furthermore, a temporal weighting model, developed using Kernel Density Estimation, dynamically adjusts classification confidence according to historical burning seasonality, enhancing recall during peak seasons while suppressing off-season false positives. Finally, an adapted Dual-Backbone Dynamic Mutual Training (DB-DMT) strategy collaboratively leverages both limited labeled (24.5%) and abundant unlabeled (75.5%) high-resolution imagery, significantly improving model generalization in label-scarce scenarios. Validation across five representative regions of China demonstrated the framework’s superior performance, achieving a semantic segmentation mean Intersection over Union (mIoU) improvement of 3.33% (to 71.92%) and increasing precision in Henan from 95.21% to 97.71%. Crucially, the framework effectively reduced the off-season false positive rate (FPR) from 5.14% to a mere 0.23% in highly industrialized regions like Tianjin. By systematically mitigating both spatial geolocation bias and seasonal phenology confusion, our approach offers a robust and scalable solution for straw burning monitoring and a transferable paradigm for other environmental remote sensing applications. Full article

A hybrid metaheuristic methodology that combines the Red-billed Blue Magpie Optimization (RBMO) algorithm with the Aquila Optimizer (AO) is introduced in this work as the RBMOAO method. The novel algorithm addresses a critical shortcoming of the standard AO: its exploration-to-exploitation ratio across different optimization stages is inefficient, yielding premature convergence and low diversity within the population. This is achieved by using RBMO’s Group-Based Directional Perturbation (GDP) and its dynamic convergence factor (CF) as part of the methodology. The early stages of the optimization process are characterized by a grouping methodology to maintain population diversity through coordinated exploration across subgroups of varying sizes using GDP. Later iterations are characterized by a CF-guided updating process that increases the resolution of the search for the best areas, thereby improving convergence precision without sacrificing solution quality. Empirical testing of the proposed methodology using the CEC 2015 and CEC 2020 test sets demonstrated RBMOAO’s superior performance compared to other metaheuristics, outperforming other optimizers in 73.33% of CEC 2015 functions and 80% of CEC 2020 functions, with statistical significance in the increased precision and robustness of solutions across all problem types. Additionally, the RBMOAO methodology demonstrated outstanding performance in constrained engineering design problems. In addition to optimization, an RBMOAO-optimized ensemble architecture was implemented to predict cybersecurity intrusion threats, achieving an accuracy of 89.6%. Through the dynamic calibration of the base learner weights via metaheuristic search, the RBMOAO ensemble achieved the top ranking. These results illustrate the wide range of applications of the RBMOAO methodology and provide support for its deployment in the context of high-stakes predictive analytics. Full article

Side-scan sonar is essential to underwater target detection, yet its effectiveness is hindered by scarce annotated data and complex acoustic artifacts. This study systematically evaluates four YOLO variants, YOLOv8n, YOLOv10n, YOLOv11n, and the newly released YOLOv13n, on two public side-scan sonar datasets with limited samples and severe class imbalance. We assess detection accuracy, computational efficiency, inference speed, and transfer learning using COCO pre-trained weights, as well as the impact of optimizer choice between SGD and AdamW. The results reveal distinct strengths: YOLOv8n achieves the fastest inference at 60.98 FPS, with a competitive mAP50 of 0.906, ideal for real-time applications. YOLOv11n offers the best accuracy–efficiency balance, attaining the highest recall of 0.859 and mAP50 of 0.917. YOLOv13n demonstrates exceptional precision of 0.993 and high-IoU localization, with an mAP75 of 0.760. Transfer learning consistently boosts performance, with average mAP50:95 gains exceeding 54% on the more challenging dataset, highlighting its critical role in overcoming data scarcity. SGD generally outperforms AdamW, confirming its suitability as the default optimizer. These findings provide practical guidelines: YOLOv8 for real-time needs, YOLOv11 for balanced performance, and YOLOv13 for precision-critical tasks with ample resources. This work also establishes a benchmark for future underwater autonomous system research. Full article

Background/Objectives: The global increase in metabolic dysfunction-associated steatotic liver disease underscores the need for accessible and reliable markers to assess hepatic steatosis. The triglyceride–glucose (TyG) index, derived from fasting plasma glucose and triglyceride levels, has emerged as a practical surrogate marker of insulin resistance and has been increasingly associated with metabolic liver involvement. This study aimed to evaluate the relationship between the TyG index and the severity of hepatic steatosis assessed by ultrasonography. Methods: This retrospective cross-sectional study included 480 adult patients without a prior diagnosis of diabetes mellitus or hypertension who underwent fasting laboratory testing and abdominal ultrasonography between January 2024 and May 2025. Fasting plasma glucose and triglyceride levels were obtained on the same day as ultrasonographic evaluation. Hepatic steatosis was assessed by a single experienced radiologist using standardized ultrasonographic criteria, and patients were categorized into three groups according to steatosis grade (grade 0, grade 1, and grade 2–3; n = 160 for each group). Demographic data and laboratory parameters, including glucose, triglycerides, HbA1c, platelet count, neutrophils, lymphocytes, monocytes, ALT, AST, and total cholesterol levels, were recorded. The TyG index was calculated using the formula: TyG = ln[(fasting triglycerides × fasting glucose)/2]. Results: A total of 480 patients (30.6% male) were included in the analysis. Mean fasting glucose, triglyceride, and TyG index values were 94.20 ± 11.15 mg/dL, 146.91 ± 83.94 mg/dL, and 8.70 ± 0.55, respectively. Metabolic and inflammatory parameters increased significantly with advancing steatosis grades (all p < 0.05). The TyG index demonstrated a clear stepwise increase from grade 0 (8.29 ± 0.42) to grade 1 (8.74 ± 0.42) and grade 2–3 steatosis (9.07 ± 0.49) (p < 0.001), with all pairwise comparisons remaining statistically significant. Receiver operating characteristic (ROC) analysis showed good discriminative performance of the TyG index for hepatic steatosis (AUC = 0.829), and an optimal cutoff value of 7.90 was identified using the Youden index, yielding high sensitivity for detection. In multivariable logistic regression analysis, the TyG index remained the strongest independent predictor of hepatic steatosis (adjusted OR 11.41, 95% CI 6.10–21.34; p < 0.001). Conclusions: The TyG index increased progressively with the severity of hepatic steatosis and showed strong associations with metabolic and inflammatory parameters. These findings support the TyG index as a simple and accessible marker reflecting metabolic dysfunction and hepatic steatosis, with potential value for early risk stratification in clinical practice. Full article