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The interplay between happiness motivations (hedonic and eudaimonic orientations) and general self-efficacy determining life satisfaction remain a central focus in positive psychology. However, existing studies still have controversies in cross-cultural applicability, mediating pathways, and verification of long-term effects. This study synthesizes cross-cultural data in Eastern and Western populations and systematically examines the mechanisms by which the dual pathways of happiness motives (hedonic pleasure and self-actualization) and general self-efficacy influence life satisfaction through results of structural equation modeling (SEM) and cross-lagged analysis. The study finds that eudaimonia enduring positive influence on life satisfaction, mediated by two channels of meaning experience and emotional experience; hedonism is only related to short-term pleasure and the positive effect weakened by internal motivational conflicts. General self-efficacy not only directly predicts life satisfaction but also indirectly bolsters adaptive capacity via mediating resources (e.g., hope). The results provide theoretical basis for well-being intervention strategies in cross-cultural contexts.

With the widespread adoption of artificial intelligence across diverse fields, issues related to data privacy and heterogeneity have become increasingly prominent. Federated learning emerges as a distributed learning framework specifically designed to address data privacy concerns. By enabling multiple clients to collaboratively train a global model without sharing their raw data, federated learning effectively safeguards privacy while managing data heterogeneity. However, due to the highly heterogeneous model and data distributions of each client, traditional aggregation methods (e.g., FedAvg) tend to face challenges of insufficient personalization, high communication cost, and unstable model convergence. In this study, a fresh framework for personalized federated learning is put forward, named DynKT-pFL. This framework is founded on knowledge transfer and contains a dynamic temperature regulation strategy based on communication rounds and real-time client performance, as well as an adaptive knowledge coefficient fusion mechanism using an MLP combined with client performance feedback. The knowledge distillation process is achieved with accurate control and fast convergence of the model. Experiments in non-iid and client heterogeneous environments demonstrate that DynKT-pFL exhibits considerable advancement in model consistency, stability, communication efficiency, and accuracy, providing an effective solution for the practical implementation of federated learning in complex heterogeneous environments.

It is well known that periodontitis has an important impact on systemic diseases. The aim of this study was to investigate potential crosstalk genes, pathways and immune cells between periodontitis and IgA nephropathy (IgAN). We downloaded periodontitis and IgAN data from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify shared genes. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the shared genes. Hub genes were further screened using least absolute shrinkage and selection operator (LASSO) regression, and a receiver operating characteristic (ROC) curve was drawn according to the screening results. Finally, single-sample GSEA (ssGSEA) was used to analyze the infiltration level of 28 immune cells in the expression profile and its relationship with shared hub genes. By taking the intersection of WGCNA important module genes and DEGs, we found that the and genes were the most important cross-talk genes between periodontitis and IgAN. GO analysis showed that the shard genes were most significantly enriched in kinase regulator activity. The LASSO analysis results showed that two overlapping genes ( and ) were the optimal shared diagnostic biomarkers for periodontitis and IgAN. The immune infiltration results revealed that T cells and B cells play an important role in the pathogenesis of periodontitis and IgAN. This study is the first to use bioinformatics tools to explore the close genetic relationship between periodontitis and IgAN. The and genes were the most important cross-talk genes between periodontitis and IgAN. T-cell and B-cell-driven immune responses may play an important role in the association between periodontitis and IgAN.

This study investigates the longitudinal flight control problem of air-breathing hypersonic vehicles subject to the asymmetric angle of attack (AoA) constraint. With the help of introduced tangent errors, the proposed control becomes low complexity in both structure and expression, especially for the non-adaptive control algorithm in the altitude loop. The asymmetric AoA constraint, which is more practical in comparison with the previously considered symmetric AoA constraint, is well accommodated. Output tracking errors are regulated into small residual sets within the designated convergence time. Uncertain aerodynamic coefficients, structural flexibilities and scramjet input saturation are synthetically handled, making the proposed control competent for a real hypersonic flight mission.

NLRP3 inflammasome activation, which can be triggered by reactive oxygen species (ROS), contributes to nonalcoholic steatohepatitis (NASH) progression. Exercise is an effective therapeutic strategy for NASH. However, whether exercise prevents NLRP3 activation in NASH has not been investigated. Here, we investigated the effect of exercise on NLRP3 inflammasome in mice with high-fat diet (HFD)-induced or methionine and choine-deficient (MCD) diet-induced NASH and explored whether adropin, a metabolic peptide hormone shown to inhibit inflammation, mediates an exercise-induced benefit against NLRP3 inflammasome activation. Exercise alleviated diet-induced hepatic steatosis, inflammation, and fibrosis. Importantly, exercise significantly reduced the expression of NLRP3 inflammasome components, decreased Caspase-1 enzymatic activity, normalized IL-1β production, and suppressed ROS overproduction in HFD-fed and MCD diet-fed mice. The exercise-elicited NLRP3 inflammasome inhibition was accompanied by increased adropin levels. Moreover, serum adropin levels were negatively correlated with serum IL-1β levels. We further explored the effect of adropin on the NLRP3 inflammasome in palmitic acid (PA)-treated hepatocytes and Kupffer cells. Although adropin treatment did not significantly decrease the levels of all inflammasome components, it reduced the active Caspase-1 level, decreased Caspase-1 activity and downregulated IL-1β expression in hepatocytes and Kupffer cells (KCs) treated with PA. Moreover, ROS levels in PA-stimulated hepatocytes and Kupffer cells were reduced upon adropin treatment. In summary, we demonstrated that the inhibitory effect of exercise on NLRP3 inflammasome activation was associated with adropin induction, resulting in NASH improvement. This study shows that exercise increases adropin levels and inhibits NLRP3 inflammasome activation in mice with diet-induced nonalcoholic steatohepatitis (NASH). Furthermore, adropin suppresses palmitic acid-induced NLRP3 inflammasome activation in hepatocytes and Kupffer cells. These results indicate that exercise may inhibit NLRP3 inflammasome activation via adropin induction, resulting in NASH improvement.

Social infrastructure plays a critical role in shaping neighborhood well-being by fostering social and cultural interaction, enabling service provision, and encouraging exposure to diverse environments. Despite the growing knowledge of its spatial accessibility, time use at social-infrastructure places is underexplored due to the lack of a spatially resolved national dataset. We address this gap by developing scalable Social-infrastructure Time Use (STU) measures that capture length and depth of activity engagement, diversity, and spatial inequality, supported by the first-of-their-kind datasets spanning multiple geographic scales—from census tracts to metropolitan areas. Our datasets leverage anonymized and aggregated foot traffic data collected between 2019 and 2024 across 49 continental U.S. states. The data description reveals variances in STU across time, space, and differing neighborhood socio-demographic characteristics. Validation demonstrates generally robust population representation, consistent with established national survey findings while revealing more nuanced patterns. Future analyses could link STU with public health outcomes and environmental factors to inform targeted interventions aimed at enhancing population well-being and guiding social infrastructure planning and usage.

The gut microbiota offers numerous benefits to the human body, including the promotion of nutrient absorption, participation in metabolic processes, and enhancement of immune function. Recent studies have introduced the concept of the gut-organ axis, which encompasses interactions such as the gut-brain axis, gut-liver axis, and gut-lung axis. This concept underscores the complex interplay between gut microbiota and various organs and tissues, including the brain, heart, lungs, liver, kidneys, muscles, and bones. Growing evidence indicates that gut microbiota can influence the onset and progression of multi-organ system diseases through their effects on the gut-organ axis. Traditional Chinese medicine has demonstrated significant efficacy in regulating the gastrointestinal system, leveraging its unique advantages. Considerable advancements have been made in understanding the role of gut microbiota and the gut-organ axis within the mechanisms of action of traditional Chinese medicine. This review aims to elucidate the roles of gut microbiota and the gut-organ axis in human health, explore the potential connections between traditional Chinese medicine and gut microbiota, and examine the therapeutic effects of traditional Chinese medicine on the microbiota-gut-organ axis. Furthermore, the review addresses the limitations and challenges present in current research while proposing potential directions for future investigations in this area.

A new biodegradable, renewable, and environmentally friendly starch-based wood adhesive that can be used at room temperature was synthesized by grafting of itaconic acid (IA) monomer onto cassava starch. When the addition of IA was 5% (starch basis), we can obtain the optimal mechanical properties of 15.38 MPa in dry state and 4.56 MPa in wet state. Compared with unmodified starch adhesive, the shear strength of grafted starch adhesive improved by 43.6% in dry state and 93.2% in wet state, respectively. The improved performance of the grafted starch-based wood adhesive was characterized by the measurement of viscosity and solid content, Fourier-transform infrared (FTIR), Thermogravimetry (TGA), X-ray diffraction (XRD) and scanning electron microscopy analyses (SEM). The results showed that there was a new characteristic peak of ester at 1700 cm−1 in the infrared spectrogram, which indicated the successful grafting of IA onto starch. After grafting modification, the storage stability and thermal stability of starch adhesive were improved. From SEM images and XRD patterns, we can witness that the morphology and crystallinity of starch particles were changed, which as a supplement of FTIR, confirmed the improvement of bonding strength and water resistance of the adhesive.Graphic Abstract

Photoelectric synaptic device is a promising candidate component in brain‐inspired high‐efficiency neuromorphic computing systems. Implementing neuromorphic computing with broad bandwidth is, however, challenging owing to the difficulty in realizing broadband characteristics with available photoelectric synaptic devices. Herein, taking advantage of the type‐II heterostructure formed between environmentally friendly CuInSe2 quantum dots and organic semiconductor, broadband photoelectric synaptic transistors (BPSTs) that can convert light signals ranging from ultraviolet (UV) to near‐infrared (NIR) into post‐synaptic currents are demonstrated. Essential synaptic functions, such as pair‐pulse facilitation, the modulation of memory level, long‐term potentiation/depression transition, dynamic filtering, and learning‐experience behavior, are well emulated. More significantly, benefitting from broadband responses, information processing functions, including arithmetic computing and pattern recognition can also be simulated in a broadband spectral range from UV to NIR. Furthermore, the BPSTs exhibit obvious synaptic responses even at an ultralow operating voltage of −0.1 mV with an ultralow energy consumption of 75 aJ per event, and show their potential in flexible electronics. This study presents a pathway toward the future construction of brain‐inspired neural networks for high‐bandwidth neuromorphic computing utilizing energy‐efficient broadband photoelectric devices. Broadband photoelectric synaptic transistors that can convert light signals ranging from ultraviolet to near‐infrared into postsynaptic currents are demonstrated, utilizing the heterostructure formed between environmentally friendly CuInSe2 quantum dots and organic semiconductor. Besides essential synaptic functions, arithmetic computing and pattern recognition are simulated in a broadband spectral range. Furthermore, an ultralow energy consumption of 75 aJ is obtained.