Search Results (14,637)

This study investigated single-nucleotide polymorphisms (SNPs) within the CAPN1 316, CAPN1 4751, and CAST 2959 markers using high-resolution melting (HRM) analysis to predict meat tenderness in crossbred beef cattle. Tenderness was assessed using the Warner–Bratzler shear force (WBSF) test, with results expressed in grams (g), representing the force required to shear muscle fibers. Significant differences in phenotypic data were observed among the genotypic groups. The finding showed that polymorphisms at CAPN1 316, CAPN1 4751, and CAST 2959 exert interactive effects on meat quality traits. Notably, the TT genotype at CAPN1 4751 increased the adjusted WBSF (aWBSF) by approximately 792 g, indicating that TT was an unfavorable variant for tenderness. These results support the use of marker-assisted selection strategies in which the TT genotype is managed to minimize its frequency while other relevant markers are concurrently monitored, thereby enhancing genetic progress in meat tenderness across commercial cattle populations. This study demonstrated that CAPN1 4751 could serve as an effective marker for genetic selection in crossbred beef cattle and confirmed the efficiency of HRM analysis as a molecular tool for SNP genotyping. In conclusion, the findings provided an alternative approach for SNP detection in livestock breeding programs and represented an important step toward improving meat quality, meeting consumer expectations, and supporting the long-term sustainability of Thailand’s beef industry. The results highlighted the polygenic nature of meat tenderness and emphasized the importance of integrating multiple SNP markers to accurately assess the genetic potential for meat quality traits in cattle. Full article

Cholesterol plays a fundamental role in the human body—it stabilizes cell membranes, modulates gene expression, and is a precursor to steroid hormones, vitamin D, and bile salts. Its correct level is crucial for homeostasis, while both excess and deficiency are associated with serious metabolic and health consequences. Excessive accumulation of cholesterol leads to the development of atherosclerosis, while its deficiency disrupts the transport of fat-soluble vitamins. Magnetic resonance spectroscopy (MRS) enables the detection of cholesterol esters and the differentiation between their liquid and crystalline phases, but the technical limitations of clinical MRI systems require the use of dedicated coils and sequence modifications. This study demonstrates the feasibility of using MRS to identify cholesterol-specific spectral signatures in atherosclerotic plaque through ex vivo analysis. Using a custom-designed experimental coil adapted for small-volume samples, we successfully detected characteristic cholesterol peaks from plaque material dissolved in chloroform, with spectral signatures corresponding to established NMR databases. To further enhance spectral quality, a deep-learning denoising framework based on a 1D U-Net architecture was implemented, enabling the recovery of low-intensity cholesterol peaks that would otherwise be obscured by noise. The trained U-Net was applied to experimental MRS data from atherosclerotic plaques, where it significantly outperformed traditional denoising methods (Gaussian, Savitzky–Golay, wavelet, median) across six quantitative metrics (SNR, PSNR, SSIM, RMSE, MAE, correlation), enhancing low-amplitude cholesteryl ester detection. This approach substantially improved signal clarity and the interpretability of cholesterol-related resonances, supporting more accurate downstream spectral assessment. The integration of MRS with NMR-based lipidomic analysis, which allows the identification of lipid signatures associated with plaque progression and destabilization, is becoming increasingly important. At the same time, the development of high-resolution techniques such as μOCT provides evidence for the presence of cholesterol crystals and their potential involvement in the destabilization of atherosclerotic lesions. In summary, nanotechnology-assisted MRI has the potential to become an advanced tool in the proof-of-concept of atherosclerosis, enabling not only the identification of cholesterol and its derivatives, but also the monitoring of treatment efficacy. However, further clinical studies are necessary to confirm the practical usefulness of these solutions and their prognostic value in assessing cardiovascular risk. Full article

With the ongoing miniaturization of smart devices, fine-grained hand gesture recognition using millimeter-wave radar has attracted increasing attention, yet practical deployment remains challenging in continuous-gesture segmentation, robust feature extraction, and reliable classification. This paper presents an end-to-end fine-grained gesture recognition framework based on frequency modulated continuous wave(FMCW) millimeter-wave radar, including gesture design, data acquisition, feature construction, and neural network-based classification. Ten gesture types are recorded (eight valid gestures and two return-to-neutral gestures); for classification, the two return-to-neutral gesture types are merged into a single invalid class, yielding a nine-class task. A sliding-window segmentation method is developed using short-time Fourier transformation(STFT)-based Doppler-time representations, and a dataset of 4050 labeled samples is collected. Multiple signal classification(MUSIC)-based super-resolution estimation is adopted to construct range–time and angle–time representations, and instance-wise normalization is applied to Doppler and range features to mitigate inter-individual variability without test leakage. For recognition, a variable-channel deep residual shrinkage network (DRSN) is employed to improve robustness to noise, supporting single-, dual-, and triple-channel feature inputs. Results under both subject-dependent evaluation with repeated random splits and subject-independent leave one subject out(LOSO) cross-validation show that DRSN architecture consistently outperforms the RefineNet-based baseline, and the triple-channel configuration achieves the best performance (98.88% accuracy). Overall, the variable-channel design enables flexible feature selection to meet diverse application requirements. Full article

Occupational exposure to sharps waste represents a critical challenge for public health systems, directly affecting healthcare workers’ safety, institutional costs, and environmental sustainability. This study aimed to analyze sharps waste management practices and to structure improvement actions for biosafety governance in Brazilian Emergency Care Units (ECUs) through the application of the Lean Six Sigma (LSS) and DMAIC method (Define, Measure, Analyze, Improve, and Control). A single multiple-case study was conducted across three public units in different regions of Brazil, combining direct observation, regulatory checklists based on ANVISA Resolution No. 222/2018 (RDC), and cause–and–effect (5M) analysis. The diagnostic phase identified recurrent nonconformities in labeling, documentation, and internal transport routes, primarily due to managerial and behavioral gaps. Based on these findings, the DMAIC framework supported the development of a low-cost, evidence-based action plan that outlined proposed interventions, including visual checklists, standardized internal routes, and key performance indicators (KPIs), intended to strengthen biosafety traceability and occupational safety. The se proposed actions are expected to support continuous learning, staff engagement, and a culture of shared responsibility for safe practices. Overall, the study provides a structured basis for future implementation and empirical validation of continuous improvement initiatives, aimed at enhancing public health governance and occupational safety in resource-constrained healthcare environments. Full article

Whole-genome bisulfite sequencing (WGBS) was employed in this article to map blood DNA methylation profiles at single-base resolution in Yili horses before a 5000 m speed race, with comparative analysis of epigenetic differences between the ‘elite group’ and ‘ordinary group’ across six four-year-old stallions. The overall methylation level in the elite group was generally higher than that in the ordinary groups, with a minority of regions showing hypomethylation. For instance, the promoter regions of key metabolic and neuro-related genes exhibited significant hypomethylation. The article identified over 10,000 CG differential methylation regions (DMRs), predominantly enriched in promoter and CpG island regions, anchoring 7221 differentially methylated genes (DMGs). These DMGs were significantly enriched in key biological processes including oxidative phosphorylation, protein binding, axon guidance, glutamatergic synapses, and the Hedgehog signalling pathway. Among these, six genes—ACTN3, MSTN, FOXO1, PPARGC1A, ND1, and ND2—were selected as core candidate genes closely associated with muscle strength, energy metabolism, and stress adaptation. The study confirms that the differences in athletic ability among Yili horses have a significant epigenetic basis, with DNA methylation participating in the epigenetic regulation of athletic traits by modulating the expression of genes related to energy metabolism and neuroplasticity. The constructed “promoter hypomethylated DMR panel” holds promise for translation into non-invasive blood-based epigenetic markers for early performance evaluation and targeted breeding in racehorses. This provides a theoretical basis and molecular targets for improving equine athletic phenotypes and optimising training strategies. Full article

High-precision land cover classification is fundamental to environmental monitoring, urban planning, and sustainable land-use management. With the growing availability of multimodal remote sensing data, combining spectral and structural information has become an effective strategy for improving classification performance in complex high-resolution scenes. However, most existing methods predominantly rely on shallow feature concatenation, which fails to capture long-range dependencies and cross-modal interactions that are critical for distinguishing fine-grained land cover categories. This study proposes a multi-level cross-modal attention fusion network, Cross-Modal Cross-Attention UNet (CMCAUNet), which integrates a Cross-Modal Cross-Attention Fusion (CMCA) module and a Skip-Connection Attention Gate (SCAG) module. The CMCA module progressively enhances multimodal feature representations throughout the encoder, while the SCAG module leverages high-level semantics to refine spatial details during decoding and improve boundary delineation. Together, these modules enable more effective integration of spectral–textural and structural information. Experiments conducted on the ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness of the proposed approach. CMCAUNet achieves an mean Intersection over Union (mIoU) ratio of 81.49% and 84.76%, with Overall Accuracy (OA) of 90.74% and 90.28%, respectively. The model also shows superior performance in small object classification, with targets like “Car,” achieving 90.85% and 96.98% OA for the “Car” category. Ablation studies further confirm that the combination of CMCA and SCAG modules significantly improves feature discriminability and leads to more accurate and detailed land cover maps. Full article

Generative Artificial Intelligence (GenAI) systems have introduced new classes of security incidents that traditional response frameworks were not designed to manage, ranging from model manipulation and data exfiltration to misinformation cascades and prompt-based privilege escalation. This study proposes a Practical Incident-Response Framework for Generative AI Systems (GenAI-IRF) that bridges established cybersecurity standards with emerging AI assurance principles. Using a Design Science Research (DSR) approach, this study identifies six recurrent incident archetypes and formalises a structured playbook aligned with NIST SP 800-61r3, NIST AI 600-1, MITRE ATLAS, and OWASP LLM Top-10. The artefact was evaluated in controlled scenarios using scenario-based simulations and expert reviews involving AI-security practitioners from academia, finance, and technology sectors. The results suggest high inter-rater reliability (κ = 0.88), strong usability (SUS = 86.4), and improved incident resolution times compared to baseline procedures. The findings demonstrate how traditional response models can be adapted to GenAI contexts using taxonomy-driven analysis, artefact-centred validation, and practitioner feedback. This framework provides a practical foundation for security teams seeking to operationalise AI incident response and contributes to the emerging body of work on trustworthy and resilient AI systems. Full article

The operational reliability of wind turbines is critical for sustainable energy production in smart grids. This study proposes a remote monitoring approach using perceptually enhanced satellite imagery. Sentinel-2 multispectral data (10 m resolution) has been processed with a Super-Resolution Generative Adversarial Network (SRGAN) to improve visual quality to a perceptual resolution of 30 cm. Although true spatial refinement is not achieved, the sharper structural details enhance classification accuracy. The data set comprises 15,000 images—10,000 SRGAN-enhanced and 5000 augmented through rotation, zoom in, increasing brightness, noise addition, and blurring. A custom Convolutional Neural Network (CNN) has been trained to classify turbines as damaged or intact, achieving 95% accuracy, a 0.99 ROC-AUC, and a 0.95 F1 score. These results demonstrate that perceptually sharpened satellite data can effectively support automated wind turbine damage detection and predictive maintenance. The proposed framework also lays the groundwork for broader real-time and multimodal monitoring and cost-efficient applications in renewable energy systems. Full article

Problem Statement: Upper crossed syndrome (UCS), as first described by Janda, refers to a group of muscle imbalances in which tightness in the upper trapezius and levator scapulae dorsally cross with tightness in the pectoralis major and minor muscles, and weakness of deep cervical flexors cross ventrally with weakness of the middle and lower trapezius. Postural alterations from this dysfunction, including forward head, rounded shoulders, and scapular dyskinesis, contribute to upper-back and shoulder pain, particularly among office workers who spend long periods of the workday on a computer. Upper crossed syndrome is a significant contributor to both neck pain and shoulder pain among computer users, which have been rated at 55–69%, and 15–52%, respectively. Despite its prevalence, knowledge about UCS and its treatment remains spotty among primary care physicians. In addition, improvements in workstation ergonomics along with hourly work breaks may be considered as primary prevention strategies for UCS. Objectives: This narrative review examines and synthesizes evidence about the epidemiology and diagnosis of UCS, along with clinical recommendations for physiotherapeutic approaches to treatment. Ergonomic measures in the workplace, including changes in the design of computer workstations so that both the keyboard and monitor are at the proper heights to minimize the risk of long-term musculoskeletal disorders, are also critical. Methods: The first author, a Doctor of Behavioral Health, performed the initial literature search, which was reviewed by the second author, a PhD in sports injury epidemiology. The third author, a chiropractor and practice owner, provided clinical recommendations for stretching and strengthening exercises, which were also described in the literature. Discussion: While easily treatable when caught early, UCS may become resistant to noninvasive approaches over time, and more severe pathologies of the neck and shoulder, including impingement, thoracic outlet syndrome, and cervicogenic headaches may result. Because there is no specific ICD code for UCS, it is important for physicians to recognize the early signs, consider them in the context of workplace-related injuries, and understand physiotherapeutic strategies for symptom resolution. Full article

Background: Fungal infection of pancreatic fluid collections (PFCs) in severe acute pancreatitis (SAP) is under-recognized and associated with poor outcomes. Overlap with bacterial infections and the need for invasive sampling often delay diagnosis, leading to prolonged antibiotic use without the use of antifungal agents. Methods: We report three cases of SAP complicated by fungal infection of PFCs. Two patients, one with alcohol-related pancreatitis and the other with biliary pancreatitis, developed symptomatic encapsulated necrosis. Both were successfully managed with endoscopic drainage and targeted antifungal therapy against Candida albicans, achieving full resolution. The third patient, with necrotizing biliary pancreatitis, underwent multiple surgical and endoscopic interventions and developed an infection with a non-albicans Candida species. Reduced susceptibility requires individualized antifungal adjustment guided by susceptibility testing. Despite aggressive multimodal therapy, the patient progressed to multiorgan failure and died subsequently. Results: These cases emphasize the clinical impact of fungal infections in patients with SAP, particularly their association with severe disease, prolonged hospitalization, and prior antibiotic exposure. These findings highlight the prognostic value of early microbiological sampling, species-level identification, and prompt initiation of antifungal therapy. Infections caused by non-albicans species pose additional challenges due to their reduced sensitivity to standard antifungal agents. Conclusions: Fungal infection of PFCs is a clinically significant and frequently underestimated complication of SAP. Early recognition and species-directed antifungal therapy are critical for improving outcomes in high-risk patients. Full article

Mapping coastal landforms is critical for the sustainable management of ecosystems influenced by both natural dynamics and human activity. This study investigates the application of Transformer-based semantic segmentation models for pixel-level classification of key surface types such as water, sandy shores, rocky areas, vegetation, and built structures. We utilize a diverse, multi-resolution dataset that includes NAIP (1 m), Quadrangle (6 m), Sentinel-2 (10 m), and Landsat-8 (15 m) imagery from U.S. coastlines, along with high-resolution aerial images of the Greek coastline provided by the Hellenic Land Registry. Due to the lack of labeled Greek data, models were pre-trained on U.S. datasets and fine-tuned using a manually annotated subset of Greek images. We evaluate the performance of three advanced Transformer architectures, with Mask2Former achieving the most robust results, further improved 11 through a coastal-class weighted focal loss to enhance boundary precision. The findings demonstrate that Transformer-based models offer an effective, scalable, and cost-efficient solution for automated coastal monitoring. This work highlights the potential of AI-driven remote sensing to replace or complement traditional in-situ surveys, and lays the foundation for future research in multimodal data integration and regional adaptation for environmental analysis. Full article

‘Candidatus Phytoplasma solani’ of the 16SrXII group is an emerging vector-borne pathogen in European crop production. The cixiid planthopper Hyalesthes obsoletus transmits 16SrXII-A stolbur phytoplasmas that are associated with diseases in grapevine, potato, and various weeds. While 16SrXII-P genomes transmitted by Pentastiridius leporinus are available, no genome of an H. obsoletus-transmissible 16SrXII-A phytoplasma has been reported from Germany. Here, we present insights into the phylogenetic position and pathogen–host interactions through the functional reconstruction of the complete 832,614 bp genome of the H. obsoletus transmissible ‘Ca. P. solani’ 16SrXII-A strain POT from a potato field. Phylogenetic analyses highlight the heterogeneity within the stolbur group using whole-genome alignment and a BUSCO-based core gene analysis approach. The POT chromosome shares highest average nucleotide identity with Italian bindweed-associated genomes and displays strong synteny with the c5 strain. Consistent with the typical phytoplasma architecture, the POT genome combines mobile-element-driven instability with a conserved core metabolism. Virulence factors include transposon-linked effectors but lack pathogenicity island organisation. POT further differs from other 16SrXII-group phytoplasmas through unique collagen-like proteins that could contribute to virulence. These findings provide a robust genomic framework that improves diagnostics, enables strain-level resolution and supports the assessment of breeding materials under stolbur phytoplasma pressure, thereby refining our understanding of stolbur phytoplasma diversity and highlighting the evolutionary divergence within the 16SrXII subgroup. Full article

The growing depletion of fossil fuel resources and rising energy costs underscore the need for efficient renewable energy technologies, such as unglazed transpired solar collectors (UTSCs). UTSCs harness solar energy to preheat outdoor air, thereby improving building energy efficiency and reducing reliance on conventional heating systems. This study presents a computational fluid dynamics (CFD) analysis of UTSC performance under Lithuanian winter conditions (ambient air temperature −2.64 °C, solar irradiance 733.45 W/m², wind speed 1.93 m/s) using two- and three-dimensional models developed in ANSYS FLUENT. The 3D model simulates a realistic wall fragment with multiple repeating sheet metal profiles and an air gap, while the 2D model represents a longitudinal section applicable to generic UTSC configurations. Both models were validated against experimental data and used to evaluate airflow velocity, pressure distribution, and air temperature rise. The results indicate overall thermal efficiencies of 54.32% for the 3D model and 54.07% for the 2D model, demonstrating that simplified 2D models can achieve comparable accuracy while significantly reducing computational cost. These findings highlight the potential of high-resolution CFD modelling for optimizing UTSC design and enabling faster, more reliable assessments for integration in industrial and commercial building applications. Full article

Automated optical inspection (AOI) technologies are widely used in PCB and semiconductor manufacturing to improve accuracy and reduce human error during quality inspection. While existing AOI systems can perform defect detection, they often rely on pre-defined camera positions and lack flexibility for interactive inspection, especially when the operator needs to visually verify solder pad conditions or examine specific layout regions. This study focuses on the front-end optical positioning and inspection stage of the AOI workflow, providing an automated mechanism to link digitally generated layout reports from EDA layout tools with real PCB inspection tasks. The proposed system operates on component-placement reports exported by EDA layout environments and uses them to automatically guide the camera to the corresponding PCB coordinates. Since PCB design reports may vary in format and structure across EDA tools, this study proposes a vision-based extraction approach that employs Hough transform-based region detection and a CNN-based digit recognizer to recover component coordinates from visually rendered design data. A dual-axis sliding platform is driven through a hierarchical control architecture, where coarse positioning is performed via TB6600 stepper control and Bluetooth-based communication, while fine alignment is achieved through a non-contact, gesture-based interface designed for clean-room operation. A high-resolution autofocus camera subsequently displays the magnified solder pads on a large screen for operator verification. Experimental results show that the proposed platform provides accurate, repeatable, and intuitive optical positioning, improving inspection efficiency while maintaining operator ergonomics and system modularity. Rather than replacing defect-classification AOI systems, this work complements them by serving as a positioning-assisted inspection module for interactive and semi-automated PCB quality evaluation. Full article

With the rapid development of high-resolution satellite remote sensing technology, wind turbine detection based on remote sensing imagery has emerged as a crucial research area in renewable energy. However, accurate identification of wind turbines remains challenging due to complex geographical backgrounds and their typical appearance as small objects in images, where limited features and background interference hinder detection performance. To address these issues, this paper proposes CGA-YOLO, a specialized network for detecting small targets in high-resolution remote sensing images, and constructs the SDWT dataset, containing Gaofen-2 imagery covering various terrains in Shandong Province, China. The network incorporates three key enhancements: dynamic convolution improves multi-scale feature representation for precise localization; the Convolutional Block Attention Module (CBAM) enhances feature convergence through channel and spatial attention mechanisms; and GhostBottleneck maintains high-resolution details while strengthening feature channels for small targets. Experimental results demonstrate that CGA-YOLO achieves an F1-score of 0.93 and an mAP50 of 0.938 on the SDWT dataset, and obtains an mAP50 of 0.9033 on both RSOD and VEDAI public datasets. CGA-YOLO establishes its superior accuracy over multiple mainstream detection models under identical experimental conditions, confirming its potential as a reliable technical solution for accurate wind turbine identification in complex environments. Full article