Pearl millet is an important crop in arid regions, but its yield is reduced by foliar diseases like Downy Mildew and Rust. Traditional and deep learning methods struggle with accurate lesion detection, severity estimation, and robustness under complex field conditions, and often lack interpretability for practical agricultural deployment. To address these challenges, this study proposes the Adaptive Severity-Aware Swin Attention Network (ASA-SAN), an integrated framework designed for disease segmentation, classification, and severity estimation in pearl millet leaves. The proposed architecture combines a Swin Transformer encoder for hierarchical feature extraction with a ResUNet++ decoder for accurate lesion segmentation. This is further enhanced using Adaptive Channel Attention to improve feature discrimination and a dual-stream classification network to jointly capture local lesion characteristics and global contextual information. Additionally, an Adaptive Disease Severity Index (ADSI) is introduced to quantitatively assess disease progression based on lesion area ratio, color degradation, edge irregularity, and texture variations. Experimental evaluations conducted on a pearl millet leaf dataset demonstrate that the proposed method achieves a Dice score of 97.8%, IoU of 95.6%, classification accuracy of 98.3%, and F1-score of 98.2%, outperforming several state-of-the-art methods. Furthermore, Grad-CAM visualizations enhance model interpretability by highlighting disease-relevant regions. Overall, the ASA-SAN framework provides a robust, interpretable, and severity-aware solution for automated pearl millet disease analysis, enabling early detection and supporting precision agriculture practices for improved crop protection and yield optimization.

Random polygonal aggregates represent the most realistic aggregate morphology in the mesostructure of concrete. Their angular characteristics and spatial distribution have a decisive influence on the stress concentration, crack initiation, and ultimate failure mode of the material under uniaxial compression. Therefore, in-depth research in this area is essential for revealing the essence of the macroscopic mechanical behavior of concrete. To this end, this paper presents an SPH-based simulation framework for uniaxial compression failure of concrete by integrating the Mohr-Coulomb criterion with tensile cutoff and an improved smoothing kernel function. A key component is the development of an in-house particle generation program for random polygonal aggregates, which achieves automatic generation satisfying geometric constraints and precise particle attribute classification based on the separating axis theorem and the ray casting method. Through three sets of comparative numerical examples, the effects of the number of aggregates (30, 40, 50), aggregate size range (1-8 mm, 1-10 mm, 1-12 mm), and shape complexity (number of edges: 8-15, 8-20, 8-25) on the failure mode of concrete are systematically investigated. The findings reveal that an increase in aggregate density or expansion of the size range promotes a transition from localized shear failure to more distributed damage patterns, accompanied by enhanced material brittleness. In contrast, increasing the shape complexity of aggregates (i.e., a larger number of edges) shifts the failure mode from "angularity-dominated concentrated shear failure" to "interface-dominated fine diffuse failure," significantly improving material ductility. Comparison and validation with existing results show that the proposed method achieves good agreement in terms of failure morphology and crack evolution. This study provides an efficient modeling approach and theoretical support for the mesomechanical analysis of concrete with irregular aggregates.

Transition metals in atmospheric particulates pose a major health risk and impact secondary aerosol formation via liquid-phase and interfacial reactions. In this study, an integrated approach combining X-ray fluorescence (XRF) and single-particle imaging using Synchrotron Radiation was adopted. The concentration properties of iron (Fe) and manganese (Mn) and their depth-resolved distributions within PM2.5 were characterized. The mass concentrations of Fe in air (0.337 μg/m³) exhibited a 16-fold higher value than Mn (0.021 μg/m³). Higher concentrations were found in industrial and urban areas compared to rural sites. The averaged mass fractions of Fe and Mn in PM2.5 were 9.31 and 0.58 μg/mg, respectively. The irregular morphology of PM2.5 increased the particle surface area compared to standard spherical particles, with an averaged PM2.5 surface area to volume ratio of 4.78 (μm2/μm3). Both transition metals exhibited enrichment on the particle surface, with 50.9 % of Fe and 60.5 % of Mn concentrated within the surface 22 nm. Fe in PM2.5 particles exhibited a Fe²⁺ and Fe³⁺ mass percentage of 22.5 % and 77.5 %, respectively. Hydroxyl radical (•OH) production in human lungs induced by Fe²⁺ Fenton reactions is simulated. The production reaches 0.67 μmol/day when all Fe²⁺ participates, and 0.34 μmol/day if only the very surface (< 22 nm) Fe²⁺ is involved. This study emphasizes the surface-specific behavior of transition metals on PM2.5, underscoring their oxidative potential and associated health risks.

Distal and perihilar biliary strictures and intraductal lesions frequently trigger immediate concern for cholangiocarcinoma (CCA). However, a broad spectrum of benign, inflammatory, infectious, vascular, and extrinsic processes can produce overlapping imaging appearances. Typical CCA of the common bile duct (CBD) commonly manifests as a long, irregular, asymmetric stricture with conspicuous mural thickening and progressive enhancement. These lesions may be accompanied by periductal soft tissue and vascular encasement. In contrast, mimickers more often demonstrate short, smooth, symmetric narrowing; obstructive causes such as stones or periampullary diverticula; extrinsic compression such as lymphadenopathy or portal cavernoma; inflammatory patterns with interval improvement; congenital ductal ectasia; or characteristic systemic/infectious findings. Careful pattern recognition on MRCP and contrast-enhanced MRI and CT integrating stricture morphology, enhancement behavior, vascular relationships, and temporal evolution provide a reliable framework to distinguish malignant CBD cholangiocarcinoma from benign mimics. This review presents a structured approach to differentiate CBD cholangiocarcinoma from key mimickers using MRI/MRCP and CT.

This cross-sectional study examined whether cumulative adverse social determinants of health (SDOH) were associated with accelerometer-derived rest-activity rhythms and multidimensional sleep metrics, and whether depression and overactive bladder statistically accounted for part of the SDOH-relative amplitude association in exploratory pathway analyses. We analyzed 6,130 U.S. adults aged 20-80 y (mean age 49.5 y; 51.7% female) from NHANES 2011-2014. From wrist-worn 7-d accelerometer recordings, nonparametric rest-activity indices (interdaily stability, intradaily variability, and relative amplitude) and sleep parameters (duration, efficiency, timing irregularity, social jetlag, and catch-up sleep) were derived. A composite SDOH score (0-8) was constructed across economic, educational, healthcare, housing, and social domains. In fully adjusted survey-weighted linear regression, each 1-unit higher SDOH score was associated with lower relative amplitude (beta = -0.007; 95% CI, -0.008 to -0.005), greater rhythm fragmentation, shorter sleep duration, lower sleep efficiency, and more irregular sleep timing. Graded associations were observed across SDOH quartiles. At the component level, unpartnered status, unemployment, and food insecurity showed the most consistent inverse associations with rhythm robustness. Exploratory pathway analyses were consistent with small indirect associations through depression severity and overactive bladder. Overall, greater cumulative adverse SDOH burden was associated with less robust, more fragmented rest-activity rhythms and poorer multidimensional sleep health.

Introduction The impact of COVID-19 vaccination on menstrual cycle regularity has raised public health concerns, particularly regarding vaccine hesitancy. Studies report mixed findings, ranging from minor, transient changes to more significant menstrual alterations, highlighting the need for post-vaccination monitoring. This retrospective observational study aimed to describe self-reported menstrual changes before and after COVID-19 vaccination among women aged 18-40 years in Dubai, UAE, including changes in menstrual flow, cycle interval, and associated symptoms, and to compare findings between recipients of the Pfizer and Sinopharm vaccines. Materials and methods This multicenter, retrospective observational study included 398 women who received either the Sinopharm or Pfizer vaccine over a six-month period and were subsequently asked about the regularity of their menstrual cycles before and after vaccination. Results Among 398 adult women (mean age 28.7 ± 5.0 years) vaccinated with Pfizer (73.1%) or Sinopharm (26.9%), 91.7% had regular menstrual cycles before vaccination. Post-vaccination, 78% of those with regular cycles maintained regularity, while 14% developed irregularities, more frequently among Pfizer recipients and women on hormonal therapy. Reported menstrual changes included prolonged cycles (5%), dysmenorrhea (3.8%), heavier bleeding (2.8%), shortened cycles (2.5%), spotting (1.3%), and amenorrhea (0.8%). Conclusions Our study demonstrated that some participants reported menstrual irregularities following receipt of either the Pfizer or Sinopharm vaccines. However, due to the retrospective observational design and limited follow-up period, the findings demonstrate only a temporal association, and it remains unclear whether these reported changes were temporary or directly related to vaccination.

Ultrahigh-pressure (UHP) treatment can reconfigure polysaccharide structures to enhance their immunomodulatory activity. This study compared the structural and immunomodulatory properties of Auricularia auricula polysaccharides extracted by hot water (AAP) and ultrahigh-pressure-assisted (UAAP) methods. Structural analysis revealed that UAAP exhibited a lower molecular weight (Mw (15,333 Da), Mn (11,167 Da)) with a narrower polydispersity index, indicating superior homogeneity. Although both shared similar monosaccharide compositions and thermal stability below 200°C, UAAP displayed a more porous and irregular morphology. In a cyclophosphamide-induced immunosuppressed mouse model, both polysaccharides promoted recovery of immune organ indices and phagocytic function. They also upregulated key serum cytokines (TNF-α, IFN-γ, IL-1β, IL-2, IL-4) and immunoglobulins (IgA, IgG, IgM) in a concentration-dependent manner. Mechanistically, UAAP/AAP activated the MAPK/NF-κB pathway by enhancing the phosphorylation of p38, JNK, and NF-κB p65, and promoting nuclear translocation of NF-κB p65. Overall, UAAP demonstrated superior immunomodulatory activity. Correlation analysis further revealed distinct association patterns between individual monosaccharides and MAPK/NF-κB signaling indicators, with weighted prediction suggesting stronger pathway activation potential for UAAP than AAP. This study provides an experimental basis for the efficient extraction and immunomodulatory application of Auricularia auricula polysaccharides.

To address the challenges of insufficient detection accuracy in UAV aerial imagery, this paper proposes EDC-Net, a small object detection method based on an Edge-Aware Dynamic Context Network. First, an Edge-Aware Enhancement Module (EAEM) is designed to compensate for contour degradation in deep features. By embedding gradient cues into intermediate feature learning and combining them with spatial-context information, EAEM enhances the network's sensitivity to tiny-object boundaries. Second, we propose a Multi-scale Adaptive Context Module (MACM), which coordinates dynamic non-linear activation, statistical self-attention, and multi-receptive-field refinement to suppress background interference, aggregate global context with linear complexity, and strengthen multi-scale feature representation. Finally, a Dynamic Structural Alignment Head (DSAH) is constructed by introducing Dynamic Snake Convolution into the regression branch, enabling adaptive geometric alignment for irregular targets and improving localization accuracy. Across the VisDrone2019 dataset, experimental results indicate that the proposed approach outperforms the baseline YOLO11s, with gains of 4.5% and 3.0% in mAP@50 and mAP@50:95, respectively, and a 29.8% reduction in the total number of parameters. Additionally, generalization tests on the TinyPerson dataset confirm the capability of the presented method in small object detection tasks.

Myopia is a global public health concern, especially among children and adolescents. This exploratory study aimed to develop a myopia risk prediction model for Tibetan adolescents using interpretable machine learning, while explicitly acknowledging the methodological limitations of non-cycloplegic refraction and self-reported data. We included 735 Tibetan adolescents. An XGBoost algorithm was used to build a binary classification model, with the SHAP method applied for feature interpretation. Data were split into 80% training and 20% test sets to analyze associations between behavioral, demographic factors, and myopia risk. Myopia was defined using non-cycloplegic autorefraction (uncorrected visual acuity < 5.0 [logMAR 0.0] and spherical equivalent ≤ - 0.50 D). In children aged 9-15 years, non-cycloplegic autorefraction is known to overestimate myopia due to accommodative tone, leading to outcome misclassification. Moreover, behavioral exposures were self-reported by children, introducing recall and desirability bias. Consequently, all findings are purely hypothesis-generating and require validation using cycloplegic refraction and objective exposure measures. Statistical analyses included independent samples t-tests, Mann-Whitney Utests, chi-square tests, and binary logistic regression. Older age was significantly associated with higher myopia risk (OR = 1.200, 95% CI: 1.069-1.347, P = 0.002). Male sex (OR = 0.583, 95% CI:0.427-0.796) and boarding at school (OR = 0.419, 95% CI: 0.250-0.702) were associated with lower myopia prevalence. Behavioral associated factors included irregular breakfast habits (OR = 2.882), reduced frequency of classroom seat rotation, and longer daily after-school study time. Machine learning analysis identified learning behaviors (seat rotation, study time, grade) as the most important predictors, followed by lifestyle factors (breakfast habits, screen time). However, due to the absence of cycloplegia and the cross-sectional design, these associations may be biased and should not be interpreted causally. In this exploratory cross-sectional study with major methodological limitations, several behavioral and demographic factors were associated with myopia status among Tibetan adolescents. The findings do not establish causality and are not suitable for clinical or public health recommendations. Future research must employ cycloplegic refraction, objective exposure monitoring (e.g., wearable light sensors, school records), and longitudinal designs before any reliable conclusions can be drawn.

Urban cooling demand (UCD) is shaped by the three-dimensional evolution of cities, where climate, morphology, and demographic pressures interact to determine thermal energy needs. However, urbanization is driving heterogeneous volumetric growth, spanning horizontal expansion, vertical densification, and infilling, whose combined effects on UCD remain poorly quantified. Here, we analyse 88 Indian and 52 global cities from 2003 to 2023 and reveal a divergence in UCD trajectories driven by growth typologies. Cities undergoing extensive, irregular expansion exhibit the fastest relative increases in UCD despite lower initial demand, whereas medium-sized cities sustain the highest absolute demand. Compact urban forms show moderated trends, indicating structural thermal advantages. Across regions, volumetric growth, particularly vertical development and volumetric densification, is strongly associated with UCD and exhibits substantial explanatory power after climate, suggesting that urban form is  an important consideration in climate-responsive and energy-efficient urban transitions.

Friedreich ataxia (FRDA) is a progressive neuromuscular degenerative disorder caused by GAA repeat expansions in the FXN gene, leading to frataxin deficiency and multisystem pathology. Cardiomyopathy is the leading cause of mortality in individuals with FRDA. To investigate the cellular and molecular mechanisms underlying FRDA-associated cardiac dysfunction, we employed induced pluripotent stem cell (iPSC) lines derived from three individuals with FRDA, each paired with an isogenic control line generated through CRISPR/Cas9-mediated excision of the pathogenic GAA repeat expansion. Correction of the mutation restored FXN expression to levels comparable to healthy donor iPSCs, and all lines differentiated efficiently into cardiomyocytes. Functional analysis revealed significant contractile abnormalities in FRDA cardiomyocytes and multicellular cardiac microtissues, including prolonged contraction and relaxation times and faster beating rates, consistent with clinical observations of cardiac contractile dysfunction. FRDA cardiomyocytes also exhibited pathological features such as increased cell size, irregular calcium transients, elevated mitochondrial reactive oxygen species levels, increased mitochondrial fission and increased cell death. These phenotypes were exacerbated by pathological levels of iron supplementation in culture media, highlighting the heightened sensitivity of frataxin-deficient cardiomyocytes to iron-induced metabolic stress. RNA sequencing revealed a distinct transcriptional profile associated with frataxin deficiency. MEG3 and PCDHGA10 were consistently dysregulated across all three FRDA-iPSC lines and may represent early molecular markers of FRDA cardiomyopathy. Functional interrogation of these candidates demonstrated that targeted silencing of MEG3 or PCDHGA10 in FRDA cardiomyocytes significantly reduced disease‑associated cell death without affecting FXN expression. Notably, PCDHGA10 silencing also normalized elevated mitochondrial reactive oxygen species, whereas MEG3 silencing did not, highlighting gene‑specific contributions to FRDA cardiomyocyte survival. Collectively, these findings identify MEG3 and PCDHGA10 as functionally relevant regulators of FRDA cardiomyocyte pathology.