Browse Articles

Comprehensive genetic analysis defines the genetic architecture and phenotypic spectrum of Korean patients with congenital hypogonadotropic hypogonadism

Silicon nanoparticle morphology and aggregation state modulate plant antioxidant responses under abiotic stress: a meta-analysis

Abstract Drought, salinity, and heavy metal toxicity collectively reduce global crop yields by more than 50% and share a common biochemical outcome: excessive reactive oxygen species (ROS) accumulation. Silicon dioxide nanoparticles (SiO 2 NPs) have attracted growing interest as a means of bolstering plant antioxidant defences against these stresses, yet the factors governing their efficacy remain poorly defined. Following PRISMA 2020 guidelines, we synthesised data from 31 studies encompassing 313 effect sizes across 11 plant species and three stress categories. Random-effects meta-analysis (restricted maximum likelihood, REML; Paule-Mandel I 2 correction) revealed significant enhancements of catalase (CAT; + 49%), glutathione reductase (GR; + 53%), ascorbate peroxidase (APX; + 29%), and superoxide dismutase (SOD; + 39%), with concomitant reductions in malondialdehyde (MDA; − 32.3%) and hydrogen peroxide (H 2 O 2 ; − 32.2%). Effects were strongest under drought (CAT + 77%) and heavy metal stress (GR + 70%). High heterogeneity (I 2 † ≥ 89% for all variables) was substantially explained by nanoparticle morphology: hollow and porous SiO 2 NPs release up to 14-fold more silicic acid than solid particles, associated with correspondingly larger antioxidant responses. The DLS/TEM hydrodynamic ratio served as a significant negative moderator of efficacy (β = − 0.087; p = 0.042), suggesting that aggregation in suspension reduces nanoparticle bioavailability. Critically, 76% of included studies did not report dynamic light scattering characterization, leaving aggregation state unknown for the majority of the literature. P-curve analysis confirmed genuine biological effects rather than selective-reporting artefacts. What emerges from this synthesis is that NP architecture and dispersibility, rather than dose or species identity, are the variables most worth engineering in next-generation nano-Si crop protection formulations.

Frailty and its associated factors among community-dwelling late-middle-aged adults in a rural county in Taiwan: a cross-sectional study

Sensitivity of measured external magnetic field on cross-section of toroidal current column in ADITYA-U tokamak

Major intensification of fire weather across southern Europe in recent decades

Material requirements for adapting to climate change in the Maldives

How standard DFT reveals the microscopic orbital picture of strongly correlated cuprate superconductors

High DDIT4 expression in colorectal cancer is associated with an immunosuppressive tumour microenvironment and poor prognosis

Randomized trial of dexmedetomidine and remimazolam with remifentanil for sedation during hepatic radiofrequency ablation

Hybrid spectral learning framework for real-time material classification of end-of-life photovoltaic panels

Biodegradation potential of bacterial isolates Enterococcus faecalis RBRJ026 and Klebsiella pneumoniae RBRJ024 against benzyl butyl phthalate

Manual federated simulation for multiple sclerosis integrating XGBoost algorithm with SHAP explanation

Abstract Multiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system, underscoring the importance of early and accurate diagnosis. In this study investigates the predictive modelling of MS progression in patients with Clinically Isolated Syndrome (CIS), privacy-preserving for a federated and explainable Machine Learning (ML) framework. To address missing data while preserving inter-feature dependencies, Multivariate Imputation by Chained Equations (MICE) with iterative imputers was employed. Classification was performed using the Extreme Gradient Boosting (XGBoost) algorithm. Model interpretability was developed through Explainable Artificial Intelligence (XAI) techniques, specifically Shapley Additive Explanations (SHAP). To ensure data confidentiality and simulate decentralized clinical environments, an in silico federated learning framework was applied. Experimental results demonstrated strong predictive performance, achieving 96.7% accuracy and 99% ROC–AUC during training, 92.5% accuracy in validation, and 81.8% accuracy with an AUC of 88% on the test set. For the Federated Learning (FL) simulation, the model maintained competitive performance, yielding an accuracy of 76.3% and an AUC of 83.9%. The proposed approach supports early diagnosis, enhances clinical trust through interpretability, and promotes secure data collaboration, thereby contributing to more informed and transparent clinical decision-making and improved patient care.

AI-powered talent chain management with multi-agent systems for industry and innovation growth

ANN-powered design equations and graphical user interface for capacity prediction of short concrete-filled double skin steel tubular columns

Abstract Concrete-filled double steel tubular (CFDST) and concrete-filled double-skin steel tube (CFDSST) columns have emerged as an efficient steel–concrete composite system, offering improved axial capacity than conventional columns. However, traditional methods, such as experimental and numerical studies, for assessing their capacity can be both complex and time-consuming, highlighting the need for accurate and efficient data-driven predictive tools. Hence, this paper aims at developing an ANN-based model for predicting the axial capacity of short circular CFDSST columns, incorporating both normal and high-strength materials. A comprehensive database comprising 296 CFDSST specimens, consisting of 138 validated FEA results and 158 experimental data collected from the literature, was used for model development. The ANN model was trained using eight input parameters representing the geometric and material properties of the column. The developed ANN model achieved an overall correlation coefficient of 0.9978, demonstrating excellent predictive capability. Furthermore, the proposed model exhibited improved prediction accuracy when compared with existing design provisions and previously published theoretical models, using various performance indices, achieving RMSE of 207.74, MAPE of 4.62%, R 2 of 0.99, VAF of 99.38% and a20 index of 0.97, demonstrating improved prediction accuracy and reliability over existing design approaches. In addition, the Shapley additive interpretation (SHAP) technique is also adopted to examine the contribution of input design parameters for predicting axial capacity. To further strengthen the explainable AI framework, Partial Dependence Plots (PDPs) were also incorporated alongside SHAP to illustrate the nonlinear effect of the governing input variables on the predicted axial capacity. Lastly, an explicit ANN-based equation is proposed and based on these, a Graphical User Interface (GUI) is also developed to facilitate user-friendly approach for the prediction of the capacity of stub CFDSST columns. Overall, the proposed ANN framework, coupled with explainable AI and a user-friendly Excel-based design tool, proves an accurate, efficient and practical approach for predicting the axial capacity of the CFDSST columns, thereby supporting reliable design and optimization in engineering practice.

Characteristics and distribution of sweet spots in ultra-deep basement reservoirs in compressional tectonic settings: insights from the Northern Qaidam Basin

Single-cell pancancer analysis reveals epithelial–immune crosstalk in shaping the premetastatic niche of liver metastasis

An interpretable machine learning model for preoperative risk stratification of testicular viability in pediatric testicular torsion: A multicenter study

An intelligent decision framework for personalized sports training plan optimization using fermatean fuzzy CURLI based MCDM

Modelling farmer’s intention-behavior to participate in ecological governance in the Northwest China arid region

Chemical signatures, source characteristics, and transboundary transport of PM0.49 in Thailand–Myanmar border region

Abstract Air pollution, particularly biomass burning (BB)–derived particulate matter (PM), remains a major challenge in Southeast Asia, affecting atmospheric processes, climate, human health, and socio-economic systems. This study presents the chemical characterization and source contributions of PM 0.49 at a rural site along the Thailand–Myanmar border in 2023. PM 0.49 concentrations showed pronounced temporal variability, with smoke-haze (SH) concentrations (67.9 ± 46.0 µg/m 3 ) nearly six times higher than those during non-haze periods (non-SH; 10.9 ± 4.4 µg/m 3 ). Organic carbon (OC) dominated during SH, accounting for 46% of PM 0.49 mass, followed by secondary inorganic aerosols (SO 4 2− , NO 3 − , NH 4 + ; 13%) and BB tracers such as K + (1.4%) and levoglucosan (2.7%), indicating strong BB influence. K⁺ exhibited strong correlations with levoglucosan ( r  = 0.95) and major anions ( r  = 0.52–0.94), highlighting its dual role as both a neutralizing cation and a BB tracer in both fresh and aged BB aerosols. Elevated OC to elemental carbon ratios (OC/EC; 13.4 ± 4.6) and char-EC/soot-EC ratios (9.68 ± 4.82) indicate smoldering BB as the principal source. Positive matrix factorization attributed 57% of PM 0.49 to BB during SH (up to 69% during peak phase), whereas soil dust dominated during non-SH conditions (70%). Backward trajectories and fire hotspot analyses suggest substantial cross-border transport from Myanmar. Furthermore, the seasonal shift in the effective carbon ratio (ECR), from 0.63 (SH) to 1.13 (non-SH), indicates a transition from light-absorbing to light-scattering aerosol dominance, underscoring the role of PM 0.49 in aerosol transformation under BB influence. These findings suggest air quality and potential climate implications, supporting coordinated mitigation.