Search Results (215,981)

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose clinical assessment relies mainly on behavioral and neuropsychological evaluation. This study evaluates a subject-wise machine learning framework for distinguishing children with ADHD from healthy controls using multichannel EEG-derived features. The public dataset comprised 121 participants (61 ADHD and 60 controls), with 19-channel EEG recordings sampled at 128 Hz. Signals were segmented into 4-s windows with 50% overlap, and statistical and spectral features were extracted, including mean, standard deviation, and theta-, alpha-, and beta-band power. Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), and Logistic Regression (LR) were evaluated using strict subject-wise separation. RF achieved the highest Accuracy (0.8099), F1-score (0.8160), Balanced Accuracy (0.8097), and MCC (0.6204), whereas SVM obtained the highest Sensitivity (0.8525) and ROC-AUC (0.8527). An additional subject-specific analysis based on individual alpha frequency (IAF) was performed to account for inter-individual spectral variability; mean IAF values were 8.8320 Hz for ADHD and 8.8833 Hz for controls, and the individualized-band analysis did not improve classification performance. Bootstrap confidence intervals and non-parametric tests indicated comparable performance among RF, SVM, and GB. Frontal and fronto-central channels, particularly Fz, showed the greatest model-derived contribution. Overall, the framework provides a reproducible subject-wise EEG classification approach, although external validation on independent cohorts remains necessary before clinical application. Full article

Background: Smartphone-based store-and-forward teledermatology (SAF-TD) has emerged as a potential approach to improving access to dermatological care. Patient-initiated SAF-TD systems, which enable direct image submission to dermatology specialists, represent an increasingly relevant but insufficiently studied model. However, diagnostic agreement remains insufficiently characterized in real-world, patient-initiated, direct-to-specialist systems in which adult patients independently submit conventional smartphone photographs and assessments are performed by dermatologists with different levels of clinical experience. Methods: A prospective cross-sectional, single-center study was conducted between February 2022 and September 2024, which included 83 patients. Patients independently submitted conventional smartphone photographs through a hospital patient portal. Diagnoses established remotely by an experienced and a beginner dermatologist were compared with face-to-face (FTF) diagnoses as the reference standard. Melanocytic lesions were defined as the positive category and non-melanocytic conditions as the negative category. Diagnostic agreement was assessed using Cohen’s kappa, and sensitivity and specificity were calculated with 95% confidence intervals (CIs). Results: Agreement between SAF-TD and FTF classification was 91.6% (95% CI, 83.6–95.9) for the experienced dermatologist (κ = 0.781; 95% CI, 0.628–0.934) and 90.4% (95% CI, 82.1–95.0) for the beginner dermatologist (κ = 0.760; 95% CI, 0.603–0.917), indicating substantial agreement. Sensitivity and specificity were 94.7% (95% CI, 75.4–99.1) and 90.6% (95% CI, 81.0–95.6), respectively, for the experienced dermatologist and 86.4% (95% CI, 66.7–95.3) and 91.8% (95% CI, 82.2–96.4), respectively, for the beginner dermatologist. Conclusions: Patient-initiated smartphone-based SAF-TD demonstrated substantial agreement with FTF binary classification by both dermatologists. These findings support its potential clinical use as a complementary method for initial remote dermatological assessment. However, the results require validation in larger, multicenter studies. Full article

Background/Objectives: Chronic lymphocytic leukemia (CLL) is characterized by marked clinical heterogeneity, underscoring the need for reliable biomarkers to improve diagnosis and risk stratification. Extracellular vesicles (EVs) represent promising liquid biopsy candidates because they reflect the molecular profile of their cells of origin. In CLL, CD200 is a well-established marker of tumor cells; its expression on EVs and its role as a CLL biomarker were poorly characterized. This study investigated the diagnostic and prognostic value of serum EVs in CLL. Methods: Serum EVs were isolated from 83 patients with CLL and 20 healthy subjects (HS). EV morphology, size, and concentration were assessed by transmission electron microscopy and nanoparticle tracking analysis. Flow cytometry was used to evaluate the expression of CD19, CD20, and CD200 on EVs, while digital PCR quantified EV-associated miR-93-5p, miR-125b-5p, miR-150-5p, and miR-484. Results: Compared with HS, CLL patients showed increased total EV counts and higher levels of CD19⁺, CD20⁺, CD200⁺, and CD19⁺/CD20⁺ EVs, together with enhanced CD20 and CD200 expression and reduced miR-93-5p, miR-125b-5p, and miR-484 levels. In contrast, EV-associated miR-150-5p levels were comparable between CLL patients and HS. EV features were correlated with clinical and biological characteristics, including disease stage, cytogenetic abnormalities, treatment, and time to treatment (TTT). Older age was associated with lower EV concentrations but higher CD20 and CD200 expression. Advanced-stage disease was characterized by smaller EVs with increased CD200 expression. Notably, elevated CD200⁺ EV levels and reduced CD19 expression were associated with shorter TTT and earlier treatment initiation. Conclusions: Multiparametric profiling identified distinct quantitative, phenotypic, and molecular features of CLL-derived EVs and revealed CD200 as a novel EV-associated biomarker. These findings support the clinical utility of serum EVs as a new valuable approach for CLL diagnosis, prognosis, and treatment prediction. Full article

Background: Psoralea corylifolia is a widely used traditional medicinal plant, with its dried mature fruits as the main medicinal part. Flavonoids and coumarins are the primary bioactive compounds of this species. However, the tissue-specific metabolic profiles, gene expression patterns, and potential regulatory factors underlying active compound biosynthesis remain largely uncharacterized across different tissues of P. corylifolia; Methods: In this study, five tissue types (roots, stems, leaves, flowers and fruits) of P. corylifolia were collected as experimental materials. We performed integrated widely targeted metabolomic and transcriptomic analysis, combined with weighted gene co-expression network analysis (WGCNA), to screen co-expression modules and candidate regulatory factors associated with flavonoid and coumarin accumulation; Results: Distinct tissue specificity was observed at both metabolomic and transcriptomic levels among different tissues, with the most remarkable difference between fruits and other tissues. Signature bioactive compounds including isobavachalcone, bavachin and corylin were specifically and highly accumulated in fruits. Differentially expressed genes were mainly enriched in phenylpropanoid biosynthesis, flavonoid biosynthesis and isoflavonoid biosynthesis pathways. WGCNA revealed that the magenta module was significantly positively correlated with fruit tissues and the contents of the above bioactive metabolites. Six candidate transcription factors were identified from this module and classified into three candidate-priority tiers based on a TF–pathway gene co-expression network (344 edges, |r| ≥ 0.8, p < 0.05) and connectivity metrics. The prioritized hubs were Cluster_22013.0 (C3H-type zinc finger transcription factor) and Cluster_21217.8 (NF-YA; Arabidopsis homolog NFYA9/AT3G20910), with Cluster_9299.0 (Rcd1-like) and Cluster_20910.0 (NAC; Arabidopsis homolog NAC002/AT5G04410) as highly connected positively correlated candidates, while Cluster_31326.1 (bZIP) and Cluster_11436.0 (C2H2) were identified as negatively correlated candidates, with all their significant edges representing negative correlations with pathway genes; Conclusions: This study characterizes tissue-specific metabolic and transcriptomic patterns in P. corylifolia, and identifies candidate co-expression modules and transcription factors associated with flavonoid and coumarin accumulation in fruits. The prioritized TF tiers, including candidate hub and negatively correlated TFs, provide a foundation for future functional studies on the regulation of active compound biosynthesis in P. corylifolia. Full article

Large deformation is one of the most critical hazards in tunnels excavated under complex geological conditions. It often causes significant economic losses and threatens construction safety. For layered soft rock tunnels subjected to high in situ stress, the deformation and failure mechanisms are largely governed by the bedding dip angle. To clarify these mechanisms and optimize the corresponding construction control measures, this study investigates a carbonaceous slate section of a railway tunnel in the Western Sichuan Plateau. Field monitoring and FLAC3D numerical modelling are coupled. The influence of the bedding dip angle on the plastic-zone evolution and the failure modes of the surrounding rock is analysed. The micro-bench, three-bench, and reserved core soil methods, together with the rock bolt length, are comparatively evaluated. On this basis, a differentiated reinforcement strategy is proposed for bedding-induced asymmetric loading. The results indicate that: (1) The bedding dip angle governs the failure mode of the surrounding rock. Under the micro-bench method, the plastic zone in subvertically bedded rock masses exhibits a quasi-symmetrical distribution along the normal direction of the bedding planes. The sidewalls predominantly undergo flexural failure. In contrast, under bedding-induced asymmetric loading, the plastic zone concentrates at the left springline and right shoulder. An asymmetric composite failure mode is formed, characterized by shallow flexural–tensile cracking and deep-seated interlayer shear. (2) Under the subvertical bedding condition (89°), the reserved core soil method mitigates the excavation-induced unloading disturbance most effectively. It achieves the lowest peak stress and the smallest tunnel convergence, which is 15.7% and 33.0% lower than those of the micro-bench and three-bench methods, respectively. Its plastic zone reaches full numerical convergence. The reserved core soil method is therefore identified as the optimal excavation Scenario under this condition. (3) The rock bolt length exhibits a threshold effect on deformation control. The most substantial improvement occurs when the bolt length is increased from 4 m to 6 m, beyond which the benefit tends to plateau. A bolt length of 6 m is therefore recommended as the best-performing Scenario among the tested values (4, 6, 8, and 10 m) for the investigated geological and support conditions. For surrounding rock subjected to bedding-induced asymmetric loading, a differentiated reinforcement strategy targeting the vulnerable zones reduces the maximum deformation by 18.8% and 28.3% compared with the uniform reinforcement Scenario and the baseline Scenario, respectively. These findings provide practical insights into excavation-method selection and support optimization for layered soft rock tunnels under similar conditions. Full article

Environmental DNA (eDNA) metabarcoding is an efficient, non-invasive approach for surveying fish diversity in complex habitats such as large rivers. This study conducted continuous eDNA monitoring along the Jiangsu section of the Yangtze River mainstem from 2022 to 2024 to characterize spatiotemporal variations in fish community composition and diversity during the fishing ban period. To improve species identification accuracy, a regional 12S rRNA barcode database was constructed, containing 141 fish species and 1204 sequences. Genetic distance analysis revealed a clear barcode gap, with inter-specific and intra-specific distances of 0.232 and 0.007, respectively, and 65.96% of species formed well-supported monophyletic clades in the phylogenetic tree, indicating useful taxonomic resolution for many of the included taxa. Using this regional reference database, eDNA-based monitoring detected 131 fish species over three years, 72.52% of which occurred in all three years. Cypriniformes consistently dominated (54.92–67.16%), showing a relative abundance trend that first increased and then declined, with dominant species changing annually. Alpha diversity analysis showed a significant decline in Chao1 richness from 83.33 to 36.75, while the Shannon index decreased slightly from 2.69 to 2.43. In contrast, Pielou_J evenness increased from 0.62 to 0.68, suggesting a shift toward lower richness but more even abundance distribution. These patterns may reflect the combined influences of multiple environmental and anthropogenic factors during the study period. Spatially, alpha diversity in the Tai–Tong reach was significantly lower than in the Ning–Zhen and Zhen–Tai reaches (p < 0.05), and community composition in 2022 and 2024 was clearly separated from upstream reaches. However, differences among reaches disappeared in 2023, suggesting interannual variation in spatial differentiation, likely associated with flow-driven mixing and transport. Overall, this study reveals multi-year fish community dynamics in the Jiangsu section of the Yangtze River mainstem and demonstrates that a regional barcode database combined with eDNA metabarcoding provides an effective approach for large-river fish monitoring and biodiversity assessment. Full article

Texas wines remain underrepresented in the scientific literature, and combined chemical and sensory datasets for key varieties are limited. This study characterized the physicochemical, phenolic, volatile, metabolomic, and sensory profiles of 26 Texas Cabernet Sauvignon and Tannat wines from the Texas High Plains and Texas Hill Country AVAs and compared them with benchmark wines from California, France, and Uruguay. Analyses included pH, titratable acidity, alcohol, color, antioxidant activity, targeted LC-MS/MS phenolic profiling, HS-SPME-GC-MS volatile analysis, untargeted metabolomics, and sensory profiling using Hierarchical Rate-All-That-Apply (HRATA). Multivariate analyses revealed distinct chemical and sensory patterns between Texas wines and benchmark regions, as well as notable differences between Texas subregions, as observed through PCA and PLS-DA score and loading plots. It is acknowledged that the wines in this study originated from different producers, vintages, and winemaking protocols; therefore, observed regional differences may reflect a combination of environmental conditions and winemaking practices rather than terroir alone. Texas High Plains wines were generally associated with more favorable aromatic and sensory attributes, while Texas Hill Country wines showed distinct chemical and sensory patterns. Interpretations of relationships between chemical compounds and sensory attributes are based on multivariate biplot associations and should be understood as observed co-occurrences rather than direct causal relationships. These results provide the first combined reference dataset for Texas Cabernet Sauvignon and Tannat wines and support future research on regional identity, varietal performance, and wine quality in Texas. Full article

The rapid development of multimodal foundation models has shifted video understanding from perception-centered recognition toward more general semantic interpretation, reasoning, and decision-making over dynamic visual content. As video understanding tasks increasingly require fine-grained perception, long-range temporal modeling, multimodal grounding, and adaptive reasoning, collaboration among heterogeneous functional units, including specialized models, modules, agents, memory systems, and external tools, has emerged as an important system-level paradigm. However, existing surveys mainly organize video understanding methods by architectures, learning strategies, or task categories, leaving the collaborative structure of modern systems insufficiently examined. This survey provides a structured narrative review of multi-model collaboration in video understanding, which we formulate as collaborative video understanding. We introduce a unified analytical framework that characterizes collaborative systems through functional units, inter-unit communication mechanisms, and collaborative state representations, and organize existing methods according to their coordination dynamics into static collaboration and dynamic collaboration, with the latter further distinguished into controller-based and agent-based collaboration. We further review representative benchmarks, evaluation metrics, and empirical analysis, showing that current evaluation protocols mainly capture task-level performance but provide limited insight into collaborative organization, memory use, adaptive execution, and system-level collaborative capability. Finally, we discuss key challenges and future directions, including adaptive task decomposition, semantically aligned inter-unit communication, persistent shared memory, uncertainty-aware error containment, evidence-grounded reasoning, and collaboration-centric evaluation. By reinterpreting video understanding from a collaborative systems perspective, this survey aims to provide a structured foundation for developing more adaptive, reliable, and scalable video understanding systems. Full article

Background and Objectives: Although herbal medicine-based obesity treatment programs are used in obesity management, empirical research on patient acceptability remains limited. This study examined acceptability of a Korean medicine-based obesity treatment program (KM-OBP) among patients who achieved predefined weight-loss targets in routine clinical care. Materials and Methods: Medical records and satisfaction survey data routinely collected during clinical practice at a single Korean medicine clinic were retrospectively analyzed. Patients had baseline body mass index (BMI) ≥ 30 kg/m² and achieved both ≥10% weight loss and BMI ≤ 23 kg/m² during follow-up. BMI trajectories and adverse-event occurrence were summarized descriptively. Satisfaction ratings were reported on a 5-point Likert scale, and free-text responses were retrospectively analyzed using the Theoretical Framework of Acceptability. Results: Of 3798 patients with a baseline BMI ≥ 30 kg/m² and at least two BMI measurements, 37 (0.97%) met the predefined targets; 16 completed the satisfaction survey. Mean BMI change from the first to last measurement was −8.9 kg/m², and median change was −8.8 kg/m² (interquartile range, −9.6 to −7.7). Common documented adverse events were constipation, nausea, and dizziness. Satisfaction ratings were descriptively high for the overall program, perceived helpfulness for obesity treatment, and herbal medicine. Among the 16 survey respondents, perceived weight-loss benefits, program structure, safety, and health improvement were described as facilitators of acceptability, whereas post-treatment weight regain, dietary burden, and financial concerns were described as barriers. Conclusions: The findings characterize satisfaction and reported acceptability experiences among survey respondents within a highly selected target-achieving cohort. Interpretation is limited by outcome-based selection, potential non-response bias, the small survey sample, and the single-center design. Full article

Legal systems evolve continuously in response to legislative reforms, emerging judicial interpretations, and shifting societal expectations, making it increasingly difficult to anticipate changes in legal precedent using conventional analytical methods. This study introduces an explainable artificial intelligence framework for predictive jurisprudence that captures the temporal evolution of legal reasoning by jointly modeling semantic, structural, and causal relationships within judicial decisions. The proposed framework integrates neural temporal graph networks to learn evolving citation dependencies, dynamic topic modeling to characterize changes in legal doctrines over time, and causal-inference techniques to distinguish genuine jurisprudential influence from spurious associations. To enhance transparency, the predictive process is complemented by GNNExplainer, enabling the identification of the legal principles, precedents, and citation patterns that most strongly influence model predictions. The framework is evaluated using the Free Law Project and LePaRD benchmark datasets and demonstrates superior performance over existing approaches in detecting causal judicial influences and accurately quantifying precedent evolution. Its practical applicability and interpretability are further validated through expert legal assessment and historical backtesting against documented jurisprudential shifts. The experimental findings demonstrate that integrating explainable machine learning with causal legal analytics provides reliable early indicators of doctrinal change, offering valuable decision-support capabilities in legal environments. Full article

Cement production is associated with substantial carbon dioxide (CO₂) emissions due to the high material and energy intensity of Portland clinker manufacture. Partial clinker replacement with supplementary cementitious materials is one of the most promising strategies for reducing the carbon footprint of cement; however, the thermal activation of aluminosilicate raw materials does not always yield highly reactive products. In this study, a pillaring approach is proposed as a controlled method for modifying the structure of clays and unlocking their latent reactivity. Different clay types—namely, kaolinitic, montmorillonitic, and illite–chlorite clays—were sequentially treated with an aluminum sulfate solution and calcined at 650 °C. Their phase composition and microstructure were characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM), while specific surface area was determined by BET analysis and pozzolanic activity. The results showed that pillaring doubled the specific surface area of montmorillonitic (2:1) and illite–chlorite (2:1:1) clays. Replacing 30% of clinker with pillared clays and limestone increased the compressive strength to 86.5 MPa and the flexural strength to 34.6 MPa. The developed low-carbon composite cements also exhibited high durability: the density of the hardened cement mortar increased to 2.410 g/cm³, the strength loss after 200 freeze–thaw cycles decreased to ≤5.5%, and the sulfate resistance coefficient (Ks) increased to 0.98 (with minimal expansion of the samples <0.02%). The proposed approach makes it possible to reduce the carbon footprint of cement by 25–30% while enabling the use of locally available raw materials for the production of competitive low-carbon green cements. Reported reductions of this order are broadly consistent with the known effect of lowering clinker content through supplementary cementitious materials in blended cement systems. Full article

Current studies have elucidated the mechanisms of radiation-induced skin injury (RISI) and identified several medical countermeasures to reduce its severity. However, no treatment has yet proven effective in preventing or reversing radiation-induced skin fibrosis. Here, we show that radiation upregulates lipoprotein-associated phospholipase A2 (Lp-PLA₂) expression and induces endothelial cell dysfunction, characterized by an increased DNA damage response and reduced tube-forming capacity and mitochondrial function. To define the role of Lp-PLA₂ in the progression of RISI, we treated human dermal microvascular endothelial cells and the skin of SKH1 hairless mice with darapladib, a selective Lp-PLA₂ inhibitor. In endothelial cells, darapladib attenuated radiation-induced cellular damage and suppressed endothelial-to-mesenchymal transition (EndoMT). In irradiated mouse skin, darapladib reduced radiation-induced inflammation, adipose tissue disruption, dermal thickness, and skin fibrosis. Notably, darapladib modulated macrophage polarization and inhibited radiation-induced macrophage infiltration in irradiated skin. These findings suggest that Lp-PLA₂ inhibition may reveal potential targets for the treatment of RISI and other fibrotic skin diseases. Full article

Accurate characterization of pore-fluid mobility is essential for evaluating tight sandstone reservoirs. This study investigates ten tight sandstone samples from the Sangonghe Formation in the Junggar Basin using petrophysical measurements, X-ray diffraction, scanning electron microscopy, low-field nuclear magnetic resonance (NMR), and multifractal analysis. Saturated–centrifugation NMR results show that the conventional single-T₂-cutoff model cannot fully separate bound and movable fluids. A dual-cutoff framework was therefore used to classify pore fluids into totally bound, partially movable, and totally movable states. The experimentally determined T_2C1 and T_2C2 values range from 0.127 to 0.582 ms and 155.340 to 265.210 ms, respectively. An adaptive second-order difference method was further applied to the fully saturated T₂ spectrum to estimate the dual cutoffs. Within the investigated dataset, the model-derived values show strong agreement with the centrifugation-derived results, with MAPE values of 3.040% for T_2C1 and 3.820% for T_2C2. Correlation, multifractal, and grey relational analyses indicate that T_2C1 is more strongly associated with clay-mineral-related fluid retention and pore heterogeneity, whereas T_2C2 is more closely associated with porosity and permeability. These results demonstrate the potential of the proposed approach for NMR-based evaluation of fluid mobility in tight sandstone, although further validation using larger and more diverse datasets is required. Full article

Field-measured structural responses are often noisy, multicomponent, nonstationary, and finite in length, complicating dominant-frequency identification, component extraction, and time-frequency characterization. This study develops an MTM-assisted Normal Time-Frequency Transform procedure with multiscale permutation entropy (MPE)-guided endpoint extension, termed MTM-eNTFT, to improve target-frequency-band determination and mitigate boundary-related reconstruction errors. Multitaper spectral estimation is used to determine stable target-frequency regions, while MPE-guided endpoint extension is used before band-limited NTFT reconstruction. Under the investigated simulation conditions, MTM-eNTFT provides more accurate component reconstruction, better noise suppression, and smaller boundary-related reconstruction errors than conventional NTFT, CEEMDAN, VMD, and SET. The reconstructed signal yields an RMSE below 0.08, a Pearson correlation coefficient over 0.98, and an SNR improvement of about 19 dB relative to the noisy input. The method is also applied to a selected continuous 15-min X-direction acceleration record acquired at a roof-corner sensor of a 68-storey building in Hong Kong during a high-wind event. Two dominant frequency components centered at approximately 0.208 and 0.965 Hz are extracted. Their energy increases between approximately 130 and 460 s, possibly indicating a temporary increase in the measured dynamic response. The results indicate the applicability of MTM-eNTFT to component extraction and time-frequency characterization of noisy finite-length structural-response records. Full article