Search Results (1,598)

The human microbiome is often presented as “the next genetics,” with the expectation that microbial profiles will explain complex diseases and yield new therapies. Yet for most conditions, it remains unclear whether microbiome changes act as causal drivers or primarily mirror underlying host biology and pathology. In this narrative review, we argue that microbiome causality is frequently overstated relative to the roles of host genetics and the environment, and we explore the implications for molecular pathology. We outline a simple framework in which the microbiome can act as (i) a primary driver, (ii) a conditional mediator or effect modifier or (iii) an association biomarker that mainly reflects upstream processes. We then use marital and household studies as natural experiments to test whether chronic diseases track more strongly with a shared microbiome or with a shared lifestyle and host susceptibility. Across metabolic, inflammatory, neurodegenerative and ageing-related outcomes, spouses show only low to modest disease concordance, which is difficult to reconcile with a universally strong, transmissible microbiome causality. Adult microbiomes instead appear mostly host-constrained and context-dependent, acting more as destabilisers of homeostasis and amplifiers of allostatic load than as independent disease-causing factors. For molecular pathology, this suggests that microbiome features are often most informative as biomarkers integrated alongside host genomics, immune context and histopathology, rather than as standalone targets. Study designs and diagnostic workflows should therefore jointly model the host genome, environment, behaviour and microbiome within broader systems medicine frameworks. Full article

Introduction. Although the aortic valve and mitral valve differ significantly in structure, function, and location, they both play a significant role in left ventricular (LV) function. The aim of the current study was to analyze the relationship between the mitral valve annulus (MVA) and the aortic valve annulus (AVA), as measured by three-dimensional speckle-tracking echocardiography (3DSTE) in the same healthy individuals with average or smaller/larger annular diameters (Ds), areas (As), and perimeters (Ps) in end-diastole (D) and end-systole (S). Methods. This study comprised 134 healthy adult participants with a mean age of 31.0 (16.0) years (73 males). A complete medical investigation included physical examination, laboratory tests, standard 12-lead electrocardiography, and two-dimensional Doppler echocardiography supplemented with 3DSTE. Results. Almost all end-diastolic and end-systolic MVA dimensions increased significantly with enlarging MVA. Similarly, as MVA-D-D and MVA-P-D increased, nearly all end-diastolic and end-systolic AVA dimensions exhibited a positive trend. Lower-than-average MVA-A-D was associated with a trend toward higher AVA dimensions (excluding AVA-P-D) compared to the mean MVA-A-D; conversely, higher-than-average MVA-A-D was also associated with increased AVA dimensions. AVA perimeter values were notably higher than those recorded in the lower-than-average MVA-A-D subgroup. In subjects with lower-than-average end-diastolic MVA dimensions, a non-significantly higher proportion of larger end-systolic AVA was observed relative to end-diastolic AVA. While AVA dimensions remained unchanged despite increasing MVA-D-S, a positive trend in AVA dimensions—reaching statistical significance for certain parameters—was observed alongside increasing MVA-A-S and MVA-P-S. In subjects with lower-than-average end-systolic MVA dimensions, there was a non-significantly higher prevalence of larger end-systolic AVA compared to end-diastolic AVA. Furthermore, nearly all end-diastolic and end-systolic AVA dimensions increased significantly with increasing AVA. Increases in AVA-D-D, AVA-A-D, and AVA-P-D were generally accompanied by a trend toward higher end-diastolic and end-systolic MVA dimensions; however, MVA-D-S peaked in the presence of lower-than-average end-diastolic AVA dimensions. In subjects with lower-than-average end-diastolic AVA, a non-significantly higher proportion of larger end-systolic AVA was noted compared to end-diastolic AVA. Notably higher MVA parameters were observed in the presence of mean AVA-D-S and AVA-A-S compared to their lower-than-average counterparts. Finally, end-diastolic MVA parameters showed a positive trend with increasing AVA-P-S, and subjects with higher-than-average end-systolic AVA dimensions demonstrated a significantly higher proportion of larger end-systolic AVA compared to end-diastolic AVA. Conclusions. There is a strong and complex association between the dimensions of the MVA and AVA, as assessed by 3DSTE, when measured simultaneously in the same healthy adults. Full article

Severe COVID-19 and bacterial sepsis share clinical manifestations of systemic inflammation and organ dysfunction. Yet, early differentiation between these conditions and timely identification of patients at risk of deterioration remain major clinical challenges. Extracellular vesicle (EV)-associated microRNAs (miRNAs) have emerged as promising biomarkers of host immune dysregulation. In our study, we have characterized circulating EV-miRNAs in patients with COVID-19, bacterial sepsis, localized bacterial infections, and healthy subjects to assess their diagnostic and prognostic utility. After EV isolation from plasma and characterization by nanoparticle tracking analysis and flow cytometry, a panel of 12 inflammation-related miRNAs were individually quantified by qRT-PCR. Four EV-miRNAs—miR-28-5p, miR-199a-5p, miR-200a-3p, and miR-369-3p—were significantly elevated in COVID-19 patients, with higher levels in those with poor prognosis. miR-199a-5p and miR-200a-3p were increased in bacterial sepsis compared with COVID-19, enabling discrimination between viral and bacterial sepsis. Three EV-miRNAs—miR-28-5p, miR-199a-5p, and miR-200a-3p—were markedly higher in bacterial sepsis than localized infections, and ROC analysis showed a strong diagnostic performance, particularly for miR-199a-5p, alone or in combination with other EV-miRNAs. The increased expression of selected EV-miRNAs was associated with higher SOFA scores and in-hospital mortality. These findings indicate that EV-miRNAs reflect pathogen-specific and severity-related immune responses, supporting their potential as minimally invasive biomarkers for early diagnosis and risk stratification in severe infections. Full article

For marine development in harsh sea states, floating-body salvage equipment serves as critical support infrastructure. Aiming at the challenges of nonlinear dead-zone, model uncertainty, and actuator failures in the wave compensation systems of such equipment, this paper proposes an intelligent fault-tolerant control method based on neural networks. First, the dead-zone nonlinearity of the hydraulic system is compensated using an inverse model approach. Then, neural networks are employed to online learn unmodeled dynamics, while adaptive laws are designed to handle partial actuator failures and Lyapunov theory is used to prove the global stability of the closed-loop system, effectively enhancing the robustness and fault-tolerance of the wave compensation system under complex sea conditions. Unlike existing studies that rely on accurate system models, the proposed method integrates data-driven learning with model-based compensation. This integration enables adaptive handling of wave disturbances, model uncertainties, and actuator faults, thereby overcoming the strong model dependence and complex observer design inherent in traditional sliding-mode fault-tolerant control. Simulation and experiment results show that the method ensures high-precision dynamic tracking and compensation performance under various sea conditions. Full article

A hierarchical cooperative control strategy guided by a virtual leader is proposed to enhance the speed regulation and robustness of dual permanent magnet synchronous motor (PMSM) systems. The upper layer employs a virtual leader with model predictive speed control (MPSC) to achieve coordinated tracking, while the lower layer utilizes model predictive current control (MPCC) for regulation. A theoretical complexity analysis demonstrates that this decoupled architecture reduces the computational burden by approximately 75% compared to centralized MPC. Furthermore, a load disturbance observer is designed to estimate and compensate for external torques. Simulation and experimental results, covering both forward and reverse rotations, validate the effectiveness of the proposed strategy. Comparative results show that, compared with a conventional PI controller, the proposed method reduces speed overshoot by approximately 20% under sudden load changes, exhibiting superior steady-state performance and strong robustness against load variations. Full article

Maritime surveillance is critical for ensuring the safety and continuity of sea logistics, port operations, and coastal activities in the presence of anomalies such as unlawful maritime activities, security-related incidents, and anomalous events (e.g., tsunamis or aggressive marine wildlife). Recent advances in unmanned aerial vehicles (UAVs)/drones and computer vision enable automated, wide-area monitoring that can reduce dependence on continuous human observation and mitigate the limitations of traditional methods in complex maritime environments (e.g., waves, ship clutter, and marine animal movement). This study proposes a hybrid anomaly detection and tracking pipeline that integrates YOLOv12, as the primary object detector, with two auxiliary modules: (i) motion assistance for tracking moving anomalies and (ii) stillness (appearance) assistance for tracking slow-moving or stationary anomalies. The system is trained and evaluated on a custom maritime dataset captured using a DJI Mini 2 drone operating around a port area near Bayshore MRT Station (TE29), Singapore. Windsurfers are used as proxy (dummy) anomalies because real anomaly footage is restricted for security reasons. On the held-out test set, the trained model achieves over 90% on Precision, Recall, and mAP50 across all classes. When deployed on real maritime video sequences, the pipeline attains a mean Precision of 92.89% (SD 13.31), a mean Recall of 90.44% (SD 15.24), and a mean Accuracy of 98.50% (SD 2.00%), indicating strong potential for real-world maritime anomaly detection. This proof of concept provides a basis for future deployment and retraining on genuine anomaly footage obtained from relevant authorities to further enhance operational readiness for maritime and coastal security. Full article

In recognising both the emerging industrial applications of multi-link robotic manipulators and the inherent challenges of modelling and controlling their highly complex nonlinear dynamics, this work proposes a completely model-free terminal sliding mode control (MFTSMC) design approach to reduce the sensitivity and complexity often associated with model-based routines. Consequently, the proposed design achieves strong robustness, simplicity, and good operation tuning by eliminating the need for system modelling and enabling direct operator–machine interaction. Simulink simulations on a 3-link case subjected to different disturbance conditions (free, low-frequency, high-frequency, and mixed) show rapid dynamic convergence, good tracking precision, and strong disturbance rejection. The system reaches the sliding surface within 0.07 s, maintains steady-state errors around ${10}^{-2}$, and achieves a smooth torque response with low energy costs. The benchmark results confirm the finite-time convergence and demonstrate that the proposed framework is practical and scalable for multi-DOF systems and has potential for underactuated manipulators. It should be noted that a generalised dynamic model for a planar n-link manipulator is presented in the study for (1) the ground truth of the manipulator in simulation (not for the MFTSMC design), (2) the model-based controller designs in comparison to the MFTSMC, and (3) understanding the dynamic characteristics. Full article

Background: Patterns of dysregulated eating, including overeating, frequent snacking, and heightened food cravings, are associated with an increased risk of obesity and metabolic disease. Eating behaviors are multidimensional and can influence many factors, including social, cultural, and biological processes. Emerging evidence suggests that genetic variation may contribute to inter-individual differences in appetite regulation and reward-related eating, potentially influencing susceptibility to dysregulated eating patterns and behaviors. Objectives: This exploratory, secondary analysis investigated possible relationships between the genetic variants FTO rs9939609, CLOCK rs1801260, MC4R rs17782313, and CD36 rs1761667 and eating behavior traits and postprandial appetite regulation in healthy young males. Methods: Thirty healthy males (27.7 ± 3.6 y; BMI 24.5 ± 2.7 kg/m²) completed the Three-Factor Eating Questionnaire (TFEQ-R18) and consumed a standardized burrito-style meal, with appetite tracked over four hours using visual analogue scales (VAS). VAS data were baseline-adjusted and summarized as incremental area under the curve (AUC) to generate two derived exploratory composites of appetite suppression and cravings suppression. Genotyping was performed using iPLEX MassARRAY, and associations were tested with ANOVA and linear regression models. Results: FTO rs9939609 was significantly associated with higher emotional eating scores (β = 11.67; 95% CI 3.50, 19.83; p = 0.007, unadjusted), and this association remained significant after false discovery rate (FDR) correction. CLOCK rs1801260 showed a nominal association with reduced postprandial cravings suppression (β = −59.17; 95% CI −104.98, −13.35; p = 0.013, unadjusted). No associations were observed for MC4R or CD36. Conclusions: This exploratory analysis found a strong association between FTO rs9939609 and emotional eating, as well as a nominal relationship between CLOCK rs1801260 and craving regulation. These findings should be interpreted as hypothesis-generating and require confirmation in larger cohorts. Full article

The rapid development of autonomous driving systems has increased the demand for scalable frameworks capable of modeling vehicle motion patterns in complex traffic environments. This paper proposes a big data spatio-temporal modeling architecture that integrates Apache Spark version 4.0.1 (SparkML) with Long Short-Term Memory (LSTM) networks to analyze and classify vehicle trajectory patterns. The proposed SparkML–LSTM framework exploits Spark’s distributed processing capabilities and LSTM’s strength in sequential learning to handle large-scale traffic trajectory data efficiently. Experiments were conducted using the DETRAC dataset, which is a large-scale benchmark for vehicle detection and multi-object tracking consisting of more than 10 h of video captured at 24 different locations. The videos were recorded at 25 frames per second with a resolution of 960 × 540 pixels and annotated across more than 140,000 frames, covering 8.250 vehicles and approximately 1.21 million bounding box annotations. The dataset provides detailed annotations, including vehicle categories (Car, Bus, Van, Others), weather conditions (Sunny, Cloudy, Rainy, Night), occlusion ratio, truncation ratio, and vehicle scale. Based on the extracted trajectory features, vehicle motion patterns were categorized into predefined movement classes derived from trajectory dynamics. The experimental results demonstrate strong classification performance. These findings suggest that the proposed SparkML–LSTM architecture is effective for large-scale spatio-temporal trajectory modeling and traffic behavior analysis, and can serve as a foundation for higher-level decision-making modules in intelligent transportation system. Full article

Public schools often operate with shared devices, unstable connectivity, and limited support for digital tools, which can make feature-heavy platforms difficult to adopt and sustain. This study reports the first formal design iteration and formative evaluation of VLEPIC, a school-centred virtual learning environment (VLE) developed to support secondary English as a Foreign Language in a low-resource Ecuadorian public school. Using a design-based research approach with a convergent mixed-methods design, one Grade 10 cohort (n = 42; two intact classes) used VLEPIC for one month as a complement to regular lessons. Data were collected through questionnaires on perceived usability and motivation, platform usage logs, and open-ended feedback from students and the teacher; results were analysed descriptively and thematically and then integrated to inform design decisions. Students reported high perceived usability and strong motivational responses in attention, relevance, and satisfaction, while confidence was more heterogeneous. Usage logs indicated recurrent but uneven engagement, with distinct low-, medium-, and high-activity profiles. Qualitative feedback highlighted enjoyment and clarity alongside issues with progress tracking between missions, navigation on mobile devices, and task submission reliability. The main contribution is a set of empirically grounded, context-sensitive design principles linking concrete interface and task-design decisions to perceived usability, motivation, and real-world usage patterns in constrained school settings. Full article

Prolonged violation behavior is characterized by sustained temporal presence, slow action changes, and similarity to normal behavior. Due to the complex construction environment, intelligent recognition algorithms face significant challenges. This paper proposes an improved YOLOv8-based model, DGEA-YOLOv8, to address these issues, using “playing with mobile phones” as a case study. The model integrates the DCNv3 module in the backbone to enhance behavior deformation adaptability and the GELAN module to improve lightweight performance and global perception in resource-limited environments. An ECA attention mechanism is added to enhance small target detection, while the ASPP module boosts multi-scale perception. ByteTrack is incorporated for continuous tracking of prolonged violation behavior in construction scenarios. Experimental results show that DGEA-YOLOv8 achieves 94.5% mAP50, a 2.95% improvement over the YOLOv8s baseline, with better data capture rates and lower ID change rates compared to algorithms like Deepsort and Strongsort. A construction-specific dataset of over 3000 images verifies the model’s effectiveness. From the perspective of data symmetry, the proposed model demonstrates strong capability in addressing asymmetric feature distributions and behavioral imbalance inherent in prolonged violations, restoring spatiotemporal consistency in detection. In conclusion, DGEA-YOLOv8 provides a precise, efficient, and adaptive solution for recognizing prolonged violation behaviors in construction sites. Full article

The Global Navigation Satellite System-Scattering (GNSS-S) radar has a wide coverage and strong concealment, enabling large-scale and long-term monitoring of sea surface targets. However, its signal power is extremely low and susceptible to sea clutter interference. To address the challenge of detecting and tracking moving targets in complex maritime environments using low-resolution radar, this paper proposes a method for extracting moving target trajectories from GNSS-S radar under low signal-to-noise ratio (SNR) conditions. The method constructs a feature plane consisting of statistical and polarization characteristics, based on the unique distribution of different motion targets in this plane, the distinction between sea clutter and multi-motion targets is carried out using machine learning algorithms, and finally the trajectory association of the targets is achieved by the Kalman filter, and the tracking correctness can reach more than 93.89%. Compared with the tracking method based on high-resolution imaging targets, this technique does not require complex imaging operations, and only requires certain processing on the radar echo, which has the advantages of easy operation and high reliability. Full article

This study presents a four-year (2021–2024) radar-based analysis of warm cloud (non-glaciated) dynamics across northern Thailand, specifically characterizing their properties, kinematics, and occurrence. Utilizing high-resolution S-band dual-polarization weather radar data, a total of 20,493 warm cloud events were tracked and analyzed, with identification based on a maximum reflectivity (≥35 dBZ) and a cloud top height below the seasonal 0 °C isotherm. Occurrence exhibited a profound seasonal disparity, with the rainy season (82.68% of events) dominating due to the influence of the moist Southwest Monsoon (SWM), while the spatial distribution confirmed that convective initiation is exclusively concentrated over mountainous terrain, underscoring orographic lifting as the essential mechanical trigger. Regarding properties, while vertical development and mass are greater in the warm seasons, microphysical intensity and Duration (mean ~26 min) remain highly uniform, suggesting a constrained, efficient warm rain process. In kinematics, clouds move fastest in winter (mean WSPD ~18.38 km/h), yet pervasive directional chaos (SD > 112°) highlights the strong influence of terrain-induced local circulations. In conclusion, while topography dictates where warm clouds form, the monsoon dictates when and how robustly they develop, creating intense, short-lived events that pose significant operational constraints for localized precipitation enhancement strategies. Full article

Effectively fusing the rich semantic information from camera images with the high-precision geometric measurements provided by LiDAR point clouds is a key challenge in mobile robot environmental perception. To address this problem, this paper proposes a highly extensible instance-aware fusion framework designed to achieve efficient alignment and unified modeling of heterogeneous sensory data. The proposed approach adopts a modular processing pipeline. First, semantic instance masks are extracted from RGB images using an instance segmentation network, and a projection mechanism is employed to establish spatial correspondences between image pixels and LiDAR point cloud measurements. Subsequently, three-dimensional bounding boxes are reconstructed through point cloud clustering and geometric fitting, and a reprojection-based validation mechanism is introduced to ensure consistency across modalities. Building upon this representation, the system integrates a data association module with a Kalman filter-based state estimator to form a closed-loop multi-object tracking framework. Experimental results on the KITTI dataset demonstrate that the proposed system achieves strong 2D and 3D detection performance across different difficulty levels. In multi-object tracking evaluation, the method attains a MOTA score of 47.8 and an IDF1 score of 71.93, validating the stability of the association strategy and the continuity of object trajectories in complex scenes. Furthermore, real-world experiments on a mobile computing platform show an average end-to-end latency of only 173.9 ms, while ablation studies further confirm the effectiveness of individual system components. Overall, the proposed framework exhibits strong performance in terms of geometric reconstruction accuracy and tracking robustness, and its lightweight design and low latency satisfy the stringent requirements of practical robotic deployment. Full article

The rapid adoption of generative artificial intelligence (AI) in higher education has intensified a pedagogical dilemma: while AI tools can increase immediate classroom engagement, they do not necessarily foster the self-regulated learning (SRL) capacities required for ethical and reflective professional practice, particularly in human-service fields. In this two-time-point, pre-post cohort-level (repeated cross-sectional) evaluation, we examined a six-week AI-integrated curriculum incorporating explicit SRL scaffolding among social work undergraduates at a Taiwanese university (pre-test N = 37; post-test N = 35). Because the surveys were administered anonymously and individual responses could not be linked across time, pre-post comparisons were conducted at the cohort level using independent samples. The participating students completed the AI-Enhanced Learning Attitude Scale (AILAS); this is a 30-item instrument grounded in the Technology Acceptance Model, Attitude Theory and SRL frameworks, assessing six dimensions of AI-related learning attitudes. Prior pilot evidence suggested an engagement regulation gap, characterized by relatively strong learning process engagement but weaker learning planning and learning habits. Accordingly, the curriculum incorporated weekly goal-setting activities, structured reflection tasks, peer accountability mechanisms, explicit instructor modeling of SRL strategies and simple progress tracking tools. The conducted psychometric analyses demonstrated excellent internal consistency for the total scale at the post-test stage (Cronbach’s α = 0.95). The independent-samples t-tests indicated that, at the post-test stage, the cohorts reported higher mean scores across most dimensions, with the largest cohort-level differences in Learning Habits (Cohen’s d = 0.75, p = 0.003) and Learning Process (Cohen’s d = 0.79, p = 0.002). After Bonferroni adjustment, improvements in the Learning Desire, Learning Habits and Learning Process dimensions and the Overall Attitude scores remained statistically robust. In contrast, the Learning Planning dimension demonstrated only marginal improvement (d = 0.46, p = 0.064), suggesting that higher-order planning skills may require longer or more sustained instructional support. No statistically significant gender differences were identified at the post-test stage. Taken together, the findings presented in this study offer preliminary, design-consistent evidence that SRL-oriented pedagogical scaffolding, rather than AI technology itself, may help narrow the engagement regulation gap, while the consolidation of autonomous planning capacities remains an ongoing instructional challenge. Full article