Search Results (23,518)

Symmetry in gait coordination reflects the balanced timing and movement between lower limb joints, which are essential for efficient locomotion and functional independence in older adults. Although gait coordination is recognized as a key indicator of aging-related adaptations and functional decline, most studies rely on isolated measures without fully addressing symmetry in intra- and interlimb coordination. This study aimed to identify principal components of gait coordination symmetry and their association with functional disability in older adults. A cross-sectional study assessed 60 community-dwelling older adults (60+), stratified by functional disability (35 non-disabled; 25 disabled). The three-dimensional range of motion of lower limb joints was assessed during the gait cycle using an optoelectronic system. Intra- and intersegmental coordination was assessed by the continuous relative phase (CRP), a nonlinear measure that captures both timing and movement relationships between joint angles. Principal component analysis was applied to CRP means and coefficients-of-variation (CV) to identify key coordination principal components (PC). Of eight PC explaining 78.86% of variance, only the PC1 distinguished disability status (p = 0.007, d = 0.66). This component included sagittal-plane intrasegmental CRP mean and CV for the knee–ankle and hip–ankle. This study is novel in combining CRP-derived measures of intra- and interlimb symmetry with principal component analysis to distinguish functional disability in older adults. The findings indicate that sagittal-plane intrasegmental CRP symmetry may serve a relevant biomarker of gait impairment. By linking kinematic coordination features to functional disability, this approach complements clinical assessments and supports early identification of mobility decline in older adults. Full article

FPGA-based reconfigurable systems play a vital role in many critical domains by virtue of their unique advantages. They can effectively adapt to dynamically changing application scenarios, while featuring high parallelism and low power consumption. As a result, they have been widely adopted in key sectors such as aerospace, nuclear industry, and weapon equipment, where high performance and stability are of utmost importance. However, these systems face significant challenges. The continuous and drastic reduction in chip process size has led to increasingly complex and delicate internal circuit structures and physical characteristics. Meanwhile, the operating environments are often harsh and unpredictable. Additionally, the adoption of untrusted third-party foundries to reduce development costs further compounds these issues. Collectively, these factors make such systems highly susceptible to reliability threats, including environmental radiation, aging degradation, and malicious hardware attacks. These problems severely impact the stable operation and functionality of the systems. Therefore, ensuring the highly reliable operation of reconfigurable systems has become a critical issue that urgently needs to be addressed. There is a pressing need to summarize their technical characteristics, research status, and development trends comprehensively and in depth. In response, this paper conducts relevant research. By systematically reviewing 183 domestic and international research papers published between 2012 and 2024, it first provides a detailed analysis of the root causes of reliability issues in reconfigurable systems, thoroughly exploring their underlying mechanisms. Second, it focuses on the key technologies for achieving high reliability, encompassing four types of fault-tolerant design technologies, three types of aging mitigation technologies, and two types of hardware attack defense technologies. The paper comprehensively summarizes relevant research findings and the latest advancements in this field, offering a wealth of references for related research. Finally, it conducts a detailed comparative analysis and summary of the research hotspots in the field of high-reliability reconfigurable systems. It objectively evaluates the achievements and shortcomings of current research efforts and delves into the development trends of key technologies for high-reliability reconfigurable systems, providing clear directions for future research and practical applications. Full article

DEAD-box (DDX) RNA helicases are essential regulators of RNA metabolism and gene expression. Among them, DDX10 remains poorly characterized despite growing evidence supporting its involvement in human diseases. This review provides a comprehensive analysis of DDX10, from its structural and functional features to its emerging roles in solid tumors and hematologic malignancies. We discuss how DDX10, through its conserved domains, contributes to pre-rRNA processing, ribosome biogenesis, and cell proliferation, and explore potential links between DDX10 and processes such as liquid–liquid phase separation (LLPS) and epigenetic regulation, which may underlie its roles in cancer cell plasticity and stress response. We argue that the dysregulation of these fundamental cellular processes positions DDX10 as a focal point where aberrant RNA metabolism and altered molecular condensates converge to disrupt transcriptional homeostasis and drive oncogenic transformation. Aberrant DDX10 expression is a recurrent feature across multiple cancers, where it promotes tumor progression, therapy resistance, and poor prognosis. Moreover, DDX10 participates in oncogenic fusion events, most notably the NUP98::DDX10 fusion identified in a subset of acute myeloid leukemias, which drives leukemogenesis by disrupting transcriptional regulation and cellular differentiation. Given its tumor-associated expression and diverse biological functions, DDX10 is increasingly recognized as a potential diagnostic biomarker and a promising target for therapeutic strategies. By consolidating current knowledge under this unifying framework, this review highlights the multifaceted roles of DDX10 in cancer biology, advocating further research into its molecular functions and translational potential. Full article

Accurately assessing fracture complexity and parameter evolution after fracturing is crucial for optimizing stimulation effectiveness in tight gas reservoirs. In such reservoirs, volume fractures often interact with natural fractures, resulting in pressure-dependent changes in fracture compliance and effective fracture area during closure. Based on shut-in pressure analysis, percolation mechanics, and material balance theory, this study develops diagnostic models for naturally fractured, dynamically fractured, and multi-level closure fracture systems, together with corresponding G-function and double-logarithmic interpretations. The proposed framework characterizes fracture-closure behavior through identifiable closure stages, explicitly ordered closure-pressure intervals, and pressure-dependent evolution of fracture compliance and effective fracture area. Sensitivity analyses are conducted to evaluate the influence of key parameters on diagnostic curve responses. A field application using shut-in pressure data from a tight gas well demonstrates that variations in dominant fracture parameters produce distinct concavity or hump features in G-function superimposed pressure-derivative curves. These results indicate that the proposed method provides a structured quantitative diagnostic interpretation of shut-in pressure responses, enabling systematic identification of staged fracture-closure behavior without relying on fitting-based accuracy metrics. Full article

Against the backdrop of China’s “dual-carbon” goals, accurate analysis and prediction of subway passenger flows are crucial for optimizing operational efficiency and advancing low-carbon urban transportation. Beijing’s subway network exhibits pronounced spatiotemporal heterogeneity across workdays, weekends, and holidays, yet existing studies often rely on static networks or single-scale temporal analyses, failing to capture dynamic flow evolution. To address this gap, this study develops a dynamic time-varying network framework with a 15 min temporal granularity, integrating sliding time-window analysis, node strength evaluation, and betweenness centrality for bottleneck identification. A Temporal–Spatial Fusion Gated Recurrent Unit (TSF-GRU) model is proposed to fuse temporal dependencies, spatial correlations, and network topology for short-term passenger flow forecasting. Results show distinct flow patterns: workdays feature a “concentrated commuting” dual peak, holidays a “steady continuous” leisure pattern, and weekends an “extended flexible” hybrid pattern. Station functions and bottleneck evolution vary dynamically across date types, with transportation hubs central on holidays/weekends and business nodes dominating workday peaks. The TSF-GRU model achieves a test-set MAPE of 7.62% and bottleneck prediction accuracy of 92.3%, outperforming traditional methods. This study provides a feasible pathway for refined, low-carbon subway operations in megacities and methodological support for achieving dual-carbon goals. Full article

The development of Geospatial Artificial Intelligence (GeoAI), combining deep learning and remote sensing imagery, is of great interest for automated spatial inference and decision-making support. In this paper, a GeoAI-based efficient object detection framework named RS-YOLO is introduced by adopting the YOLOv11 architecture. The model integrates Dynamic Convolution for adaptive receptive field adjustment, Selective Kernel Attention for multi-path feature aggregation, and the MPDIoU loss function for geometry-aware localization. The proposed approach outperforms in experimental results on the TGRS-HRRSD dataset of 13 scenes from different geospatial scenarios, giving an 89.0% mAP and an 87 F1-score. Beyond algorithmic advancement, RS-YOLO provides a GeoAI-based analytical tool for applications such as urban infrastructure monitoring, land use management, and transportation facility recognition, enabling spatially informed and sustainable decision-making in complex remote sensing environments. Full article

The functional communication response (FCR) shares fundamental properties with the mand, with both responses linking the relevant motivating operation and the reinforcer maintaining a response. The FCR differs from the mand in that the communication response has the expressed intention of replacing challenging behavior by providing an outlet to access the same functional reinforcer. Research describes the development of mand and FCR repertoires; however, no research to date elucidates differences and similarities in how these repertoires are established. A scoping review was selected to systematically map and compare instructional procedures across FCT and mand training studies. In this scoping review, we analyzed 98 peer-reviewed empirical studies published between 2014 and 2024 that taught FCR or mand repertoires, identified through searches of PsychINFO and ERIC using predefined inclusion and exclusion criteria. We (a) reviewed teaching strategies for developing mand and FCR repertoires, (b) analyzed unique and shared teaching features, and (c) identified implications for future research on teaching FCRs to replace challenging behaviors. Results showed that mand training studies more often targeted multiple responses to expand communication repertoires, whereas FCT studies typically focused on teaching a single response to rapidly suppress problem behavior. Additional distinctions included strategies for contriving motivating operations, prompting procedures, and communication topographies. These findings highlight important procedural divergences and suggest the need for integrated instructional approaches that promote generalization and functional use of communication. Full article

Crowdsourced geospatial platforms constitute complex socio-technical systems in which data quality and reliability emerge from collective user behavior rather than centralized control. This study proposes a system-oriented, unsupervised machine learning framework to assess the reliability of crowdsourced building data using only intrinsic indicators. The framework is demonstrated through a large-scale analysis of OpenStreetMap building polygons in Tehran. Six intrinsic indicators—reflecting contributor activity, temporal dynamics, semantic instability, and geometric evolution—were normalized using fuzzy membership functions and objectively weighted based on their discriminative influence within a K-means clustering process. Five reliability classes were identified, ranging from very low to very high reliability. The resulting classification exhibited strong internal validity (average silhouette coefficient = 0.58) and pronounced spatial coherence (Global Moran’s I = 0.85, p < 0.001). This approach eliminates dependence on authoritative reference datasets, enabling scalable, reproducible, and feature-level reliability assessment in open geospatial systems. The framework provides a transferable methodological foundation for trust-aware analysis and decision-making in participatory and data-intensive systems. Full article

Neuromuscular junctions (NMJs) are highly specialized synapses that enable efficient communication between motor neurons and skeletal muscle fibers. Impaired formation or maintenance of NMJs is implicated in the pathogenesis of multiple neuromuscular disorders and contributes to age-related declines in skeletal muscle mass and strength. NMJ functionality is governed by complex regulatory crosstalk among different cells and is mediated by a diverse network of proteins. Moreover, immune cells often reside at NMJs and exhibit phenotypically different characteristics depending on the regenerative state of the muscle. These complex interfaces have posed a significant challenge for elucidating pathogenic mechanisms and developing biomarkers or effective targeted treatments. Many animal models have been developed to address this challenge by characterizing the fundamental structural features of neuromuscular junctions (NMJs) and their transmission capacity under both healthy and disease conditions. In contrast, studies of human NMJs remain limited, although emerging evidence is increasingly revealing substantial morphological and functional differences from animal NMJs. This review provides an overview of animal research on NMJs over the past decades, highlighting interspecies differences and key advances in our understanding of human NMJs. Full article

Salmonella Typhimurium is a major zoonotic pathogen, with pigs and chickens serving as key reservoirs for human infection, yet the genomic determinants of its host adaptation remain incompletely understood. This study integrated comparative genomics, genome-wide association studies (GWASs), and interpretable machine learning on 1654 high-quality genomes of swine- and chicken-origin S. Typhimurium isolates to identify host-associated genetic features. Phylogenetic analysis revealed host-preferred lineages and significantly lower genetic diversity within chicken-adapted subpopulations. Meta-analysis identified distinct host-associated profiles of antimicrobial resistance genes (e.g., higher prevalence of floR and bla_TEM-1 in swine) and virulence factors (e.g., enrichment of allB and the yersiniabactin system in chickens). GWASs pinpointed 1878 host-associated genes and multiple SNPs/indels, functionally enriched in metabolism, regulation, and cell processes. A two-stage Random Forest model, built using the most contributory features, accurately discriminated between swine and chicken origins (AUC = 0.974). These findings systematically revealed the genomic signatures of host adaptation in S. Typhimurium, providing a prioritized set of candidate markers for experimental validation. Full article

Soybean (Glycine max) is a globally important crop, as it has high protein and lipid content and plays a central role in sustainable agriculture. Recent advances in rhizosphere biology have highlighted the critical role of soybean root exudates, particularly isoflavones and other secondary metabolites, in shaping microbial community structure and function. These exudates mediate complex, bidirectional signalling with rhizosphere microorganisms, influencing nutrient acquisition, stress resilience, and disease suppression. This review describes current knowledge on soybean–microbe interactions, with a focus on the emerging concept of the rhizosphere as a dynamic communication network. Particular attention is given to Bacillus velezensis S141, a plant growth-promoting rhizobacterium (PGPR) with distinctive traits, including β-glucosidase-mediated isoflavone hydrolysis, phytohormone production, and drought resilience. Coinoculation studies with Bradyrhizobium spp. demonstrate enhanced nodulation, nitrogen fixation, and yield, supported by transcriptomic and ultrastructural evidence. Comparative genomic analyses further underscore host-adaptive features of S141, distinguishing it from other Bacillus strains. Despite promising findings, mechanistic gaps remain regarding metabolite-mediated signalling and environmental robustness. Future research integrating metabolomics, synthetic ecology, and microbial consortia design will be essential to harness rhizosphere signalling for climate-resilient, low-input soybean cultivation. Full article

Background: Melanoma outcomes have improved in recent years as a result of modern systemic therapies. A major molecular feature of melanoma is abnormal telomerase activation; this is most often caused by telomerase reverse transcriptase (TERT) promoter mutations, which occur in 50–82% of cases and are the most common noncoding alteration in this cancer. Telomerase maintains telomere length, allowing melanoma cells to avoid senescence and continue dividing. However, how telomerase activity influences melanoma cell doubling time remains unclear, and the pathways linking TERT expression to faster cell-cycle progression require further study. Although telomerase inhibitors show promise in preclinical models, their clinical use is limited by delayed cytotoxicity and resistance. Materials and Methods: A scoping review was conducted using Scopus, ScienceDirect, MEDLINE/PubMed, and CINAHL (Cumulative Index to Nursing and Allied Health Literature). Keywords included “telomerase,” “melanoma,” “cancer,” “cell proliferation,” and “doubling time,” using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: Telomerase-related biomarkers were found to correlate with disease stage and survival. Suggested therapeutic strategies include enzyme inhibitors, cytotoxic nucleotide incorporation, telomere destabilization, and immunotherapies such as peptide or dendritic cell vaccines, etc. Conclusions: Understanding both telomere-dependent and -independent TERT functions is essential for developing effective biomarkers and therapies that overcome resistance and slow melanoma progression. Full article

Drug resistance is an important challenge in medical research and clinical practice, posing a serious threat to the effectiveness of current therapeutic strategies. Transcriptomics has played a crucial role in analyzing resistance-related genes and pathways, while the application of machine learning in high-throughput data analysis and prediction has also opened up new avenues in this field. However, existing studies mostly focus on a single drug or specific categories, and their conclusions are limited in applicability across drug categories, while studies on drugs beyond antibacterial and antitumor categories remain limited. In this study, we systematically analyzed the transcriptomic data of resistant cell lines treated with 1738 drugs spanning 82 categories and identified core genes through an integrated analysis of three classical machine learning methods. Using the antibacterial drug salinomycin as an example, we established a resistance prediction model that demonstrated high predictive accuracy, indicating the significant value of the selected core genes in prediction. Meanwhile, some of the core genes identified through the protein–protein interaction (PPI) network overlapped with those derived from machine learning analysis, further supporting the reliability of these core genes. Pathway enrichment analysis of differential genes revealed potential resistance mechanisms. This study provides a new perspective for exploring resistance mechanisms across drug categories and highlights potential directions for resistance intervention strategies and novel drug development. Full article

The integration of artificial intelligence (AI) with ultrasonic biosensing presents a transformative opportunity for enhancing diagnostic accuracy in agricultural and biomedical applications. This study develops a data-driven deep learning model to address the challenge of acoustic artifacts in B-mode ultrasound imaging, specifically for sow pregnancy diagnosis. We designed a biosensing system centered on a mechanical sector-scanning ultrasound probe (5.0 MHz) as the core biosensor for data acquisition. To overcome the limitations of traditional filtering methods, we introduced a lightweight Deep Neural Network (DNN) based on the YOLOv8 architecture, which was data-driven and trained on a purpose-built dataset of sow pregnancy ultrasound images featuring typical artifacts like reverberation and acoustic shadowing. The AI model functions as an intelligent detection layer that identifies and masks artifact regions while simultaneously detecting and annotating key anatomical features. This combined detection–masking approach enables artifact-aware visualization enhancement, where artifact regions are suppressed and diagnostic structures are highlighted for improved clinical interpretation. Experimental results demonstrate the superiority of our AI-enhanced approach, achieving a mean Intersection over Union (IOU) of 0.89, a Peak Signal-to-Noise Ratio (PSNR) of 34.2 dB, a Structural Similarity Index (SSIM) of 0.92, and clinically tested early gestation accuracy of 98.1%, significantly outperforming traditional methods (IoU: 0.65, PSNR: 28.5 dB, SSIM: 0.72, accuracy: 76.4). Crucially, the system maintains a single-image processing time of 22 ms, fulfilling the requirement for real-time clinical diagnosis. This research not only validates a robust AI-powered ultrasonic biosensing system for improving reproductive management in livestock but also establishes a reproducible, scalable framework for intelligent signal enhancement in broader biosensor applications. Full article

Inland depression irrigation districts in the arid regions of Xinjiang, owing to the absence of natural drainage conditions, exhibit unique groundwater-salt dynamics and face prominent risks of soil salinization, thus necessitating clarification of their water-salt transport mechanisms to ensure sustainable agricultural development. This study takes the Karamay Agricultural Comprehensive Development Zone as the research subject. The study examines the distribution characteristics of soil salinity, groundwater depth, and Total Dissolved Solids (TDS) of groundwater across diverse soil textures, elucidates the correlative relationships between groundwater dynamics and soil salinity, and forecasts the evolutionary trajectory of groundwater levels within the irrigation district. The findings reveal that groundwater depth in silty soil regions (3.24–3.11 m) substantially exceeds that in silty clay regions (2.43–2.61 m), whereas TDS of groundwater demonstrates marginally elevated concentrations in silty clay areas (19.05–16.78 g L⁻¹) compared to silty soil zones (18.18–16.29 g L⁻¹). Soil salinity exhibits pronounced surface accumulation phenomena and considerable inter-annual seasonal variations: manifesting a “spring-peak, summer-trough” pattern in 2023, which inversely transitioned to a “summer-peak, spring-trough” configuration in 2024, with salinity hotspots predominantly concentrated in silty clay distribution zones. A significant sigmoid functional relationship emerges between soil salinity and groundwater depth (R² = 0.73–0.77), establishing critical depth thresholds of 2.44 m for silty soil and 2.72 m for silty clay, beneath which the risk of secondary salinization escalates dramatically. The XGBoost model demonstrates robust predictive capability for groundwater levels (R² = 0.8545, MAE = 0.4428, RMSE = 0.5174), with feature importance analysis identifying agricultural irrigation as the predominant influencing factor. Model projections indicate that mean groundwater depths across the irrigation district will decline to 2.91 m, 2.76 m, 2.62 m, and 2.36 m over the ensuing 1, 3, 5, and 10 years, respectively. Within a decade, 73.33% of silty soil regions and 92.31% of silty clay regions will experience groundwater levels below critical thresholds, subjecting the irrigation district to severe secondary salinization threats. Consequently, comprehensive mitigation strategies encompassing precision irrigation management and enhanced drainage infrastructure are imperative. Full article