Browse Articles

Analysis of healthcare workers’ disaster preparedness status from the perspective of awareness and attitude

Grassland ecological security assessment in Northwest Sichuan using the driver-pressure-state-impact-response framework and optimized projection pursuit model

Environmental radiofrequency exposure and genotoxic biomarkers in schoolchildren: a cross-sectional analysis

A coupled LSTM model for predicting blue carbon and fishery dynamics in tropical coastal wetlands under climate change

Perceptions on continuing medical education credit system: a cross-sectional study in Sichuan Province, China

Predicting the Young’s Modulus of biomedical titanium alloys using machine learning: a data-driven approach

A multi-reader multi-case framework for evaluating the decision impact of emerging diagnostic tests for lung cancer

Optimal scheduling of intergroup pumping of PV complementary wells based on improved NSGA-III algorithm

Correction: Dream content and slow waves benefit prey against predator in a video game confrontation

Rock-avalanche-driven glacier advances in a volcanic mountain revealed by cosmogenic exposure dating

Author Correction: Autophagic cell death restricts chromosomal instability during replicative crisis

Computational evaluation of the interactions of polycyclic aromatic hydrocarbons with a human receptor via DFT and molecular docking

Multimodal temporal feature fusion for teacher competency assessment and precision training resource recommendation

Abstract Assessing teacher competency in a reliable and multidimensional manner remains an open problem, largely because conventional evaluation instruments capture only a fraction of the behavioral repertoire that defines effective instruction. We tackle this challenge by developing an integrated framework that fuses heterogeneous classroom signals—video, audio, transcribed text, and physiological recordings—through modality-specific encoders coupled with a cross-modal attention mechanism. The attention module adaptively re-weights each data stream according to its diagnostic relevance for a given competency dimension, while a hierarchical temporal component jointly models short-term pedagogical adjustments and long-term professional growth trajectories. Competency scores are formulated as a continuous regression task (evaluated via RMSE and MAE) and simultaneously discretized into ordinal proficiency levels for classification-based evaluation (accuracy and F1-score), thereby addressing both assessment perspectives within a unified multi-task objective. A knowledge graph–enhanced recommendation engine then maps diagnosed competency gaps onto targeted training resources. Experiments conducted on multimodal recordings from 856 teachers across 15 schools demonstrate that our model reaches 0.834 classification accuracy and 0.312 RMSE, outperforming all baselines on each of the seven evaluation dimensions. The recommendation module attains 0.478 Precision@5, a 13.0% relative gain over the strongest knowledge-graph baseline. Ablation analyses confirm that every architectural component contributes measurably; removing temporal modeling alone reduces accuracy by 7.1 percentage points. Taken together, these results establish a closed-loop, interpretable pipeline from diagnostic assessment to actionable professional development pathways.

Distinct and combined effects of mental and physical fatigue on prospective time perception in basketball players

Public sentiments toward artificial intelligence in agriculture across the United States and United Kingdom

Abstract Artificial intelligence (AI) is rapidly expanding within agricultural systems, yet public attitudes toward its use remain poorly understood. We analyse single-word responses from public surveys in the United Kingdom ( N  = 1054) and the United States ( N  = 998) to quantify both sentiment polarity and emotional responses to agricultural AI. Using lexicon-based methods, we find that sentiment is relatively more negative in the United States, although the overall emotional structure is similar across both countries. Fear and anticipation emerge as the dominant emotions, indicating persistent ambivalence toward AI in agriculture. Cross-country differences are evident, with statistically significant higher levels of anger in the United States compared to the United Kingdom. Regression and marginal effect estimates show that attitudes toward science and technology are the most consistent predictors of both sentiment and emotional responses, significantly reducing negative emotions while increasing positive ones. Demographic factors play a secondary role, although age accounts for systematic variation, particularly in the United Kingdom. These findings suggest that public sentiments toward agricultural AI are primarily shaped by underlying orientations toward science and technology rather than socioeconomic characteristics. Understanding these dynamics in affective association is critical for designing governance and communication strategies that foster trust and support the responsible adoption of AI in agricultural systems.

Knowledge and attitudes of the community toward human mpox virus infection in, Oromia, Ethiopia: a community-based cross-sectional study

A multinational randomized clinical trial of an eye-tracking-based binocular amblyopia treatment in children aged 4–9 years

Ubiquitination of glycogen and metabolites in cells and tissues

Human haematopoietic stem cells remember inflammatory stress

Abstract Inflammation activates blood cells, contributing to ageing and malignancy 1–3 . Haematopoietic stem cells (HSCs) survive a lifetime of infection to sustain life-long haematopoiesis 1–9 , but how human HSCs respond and adapt to inflammatory stress is largely unknown. Here, to empirically understand this adaptation, we developed xenograft inflammation–recovery models and performed single-cell multiomics on xenografted human HSCs. Two transcriptionally and epigenetically distinct HSC subsets were identified with one, termed HSC inflammatory memory (HSC-iM), retaining a molecular memory of previous inflammatory treatments. The HSC-iM subset exhibited quiescence and restrained haematopoietic output. Molecularly, the HSC-iM program was enriched in HSCs from adult and paediatric samples across conditions ranging from COVID-19 recovery, sickle cell disease, ageing and clonal haematopoiesis, establishing both the validity of our xenograft models and the physiological relevance of HSC-iM. Clonal haematopoiesis mutations in HSC-iM attenuated the effects of inflammatory stress by promoting HSC activation and differentiation. Moreover, transmission of the pro-inflammatory HSC-iM transcriptional program to differentiated immune progeny was demonstrated in xenograft and physiological settings. Finally, HSC-iM program enrichment in circulating blood cells was associated with a heightened risk score for all-cause mortality in population cohort analyses, underscoring the clinical relevance of this newly identified HSC subset in characterizing heterogeneous health outcomes across a lifetime.

β-Arrestin condensates regulate G-protein-coupled receptor function