Open University of China

The differentiation of macrophages into foam cells is a hallmark of atherosclerotic plaques, and macrophage polarization is a key driver of disease progression, yet the underlying genetic mechanisms remain poorly understood. We employed summary data-based Mendelian randomization (SMR) combined with transcriptomic validation to explore causal relationships and validate findings at the tissue level. We integrated multi-omics data, including DNA methylation (mQTLs), gene expression (eQTLs), and protein expression (pQTLs), from GeneCards and GWAS summary statistics from FinnGen and UK Biobank. SMR and colocalization analyses were used to identify causal associations. Tissue-specific validation was conducted using eQTL data from artery tissues, and transcriptome analysis was performed using GEO datasets to confirm gene expression patterns in atherosclerotic tissues, followed by confirmation in clinical samples. The function of key genes were explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. SMR and colocalization analyses identified 41 methylation sites, 2 genes (DNMT3A and DLGAP5), and 1 protein associated with atherosclerosis. Specifically, methylation site cg10130446 in DLGAP5 was negatively correlated with atherosclerosis, while sites cg08485187 and cg17742416 in DNMT3A were positively associated. Tissue-specific validation revealed that SIRT6 gene expression was linked to atherosclerosis risk in aortic and tibial artery tissues. Transcriptome analysis from GEO database and blood samples confirmed elevated DLGAP5 and SIRT6 expressions. This study integrates SMR with transcriptomic validation to reveal causal links between macrophage polarization-related genes and atherosclerosis. Key genes DNMT3A, DLGAP5, and SIRT6 were identified as significant candidate regulatory factors. Tissue-specific validation and GEO-based transcriptome analysis provided robust evidence supporting these findings, highlighting the importance of macrophage polarization in atherosclerosis pathogenesis and offering new insights for therapeutic intervention.

fx1 Irisin, a myokine generated through the proteolytic cleavage of fibronectin type III domain-containing protein 5 (FNDC5), has been increasingly recognized as a critical mediator of the salutary effects of physical exercise on metabolic homeostasis, energy metabolism, and the regulation of inflammatory, oxidative, and fibrotic pathways. Recent investigations have delineated a significant association between irisin levels and renal pathophysiology, particularly within the context of chronic kidney disease. Emerging evidence suggests that irisin may play a modulatory role in renal function, with particular relevance to diabetic nephropathy and acute kidney injury. Therefore, this mini-review consolidates current findings on the mechanistic underpinnings of irisin's involvement in renal pathophysiology, elucidating its intricate interplay with kidney disease. Additionally, we highlight the potential of irisin as a promising biomarker and therapeutic target in nephrology.

Abstract Urban inundation prediction enables the timely implementation of emergency measures and mitigates casualties and property losses caused by disasters. Physical models, hindered by their complex computational requirements, can no longer satisfy the demands for high-efficiency applications. In recent years, machine learning algorithms—particularly convolutional neural networks and recurrent neural networks have demonstrated substantial advancements in time series prediction tasks. This paper employs the Seasonal Autoregressive Integrated Moving Average Model (SARIMA) to forecast inundation scenarios under a 100-year extreme rainstorm event in Jinan City, Shandong Province, China. Four water inundation points within the jurisdiction of the Yuxiu River Basin were selected as the prediction targets, and the inundation depth, unit flow rate, and flow velocity were predicted. The prediction accuracy for both long and short durations was analyzed. Additionally, it was discussed that variations in data volumes, rainfall durations, and rainfall peak coefficients had minimal impact on the prediction results. The results show that, the more available the data and the smaller the variation within the data during the time period, the higher the prediction accuracy. The RMSE values for inundation depth, unit flow rate, and node flow velocity predictions at the water accumulation points under the 100-year scenario were 0.016 m, 0.051 (m 3 /s), and 0.084 (m/s) respectively. These efforts are expected to contribute to the advancement of inundation prediction, offering a new perspective on rainfall events preprocessing to achieve more accurate predictions.

The rapid evolution of artificial intelligence (AI) technologies has brought about transformative changes across various industries, with the financial sector standing as a prominent beneficiary. This study focuses on exploring the integration and application of AI in the financial industry, aiming to uncover the underlying mechanisms, current status, and future trends of this technological fusion. This paper delves into the deep integration mechanism of interlinked learning environments and social learning platforms, along with their ecological evolution path, from the perspective of socio-technical-evolution. By analyzing the transformative role of big data, artificial intelligence, and other technologies in reshaping the learning environment, and incorporating the co-evolutionary mechanism of natural technology and social technology from socio-technical theory, a dual-wheel driving integration model of “technological empowerment-social fitness” is constructed. It is revealed that deep integration necessitates overcoming challenges such as data silos and scene fragmentation. Through the application of key technologies like multi-modal perception, cross-domain learning scene modeling, and human-machine collaborative community construction, combined with the synergistic innovation of socio-technical elements such as policy and regulations, industry models, and social culture, personalized learning support and sustainable ecological development can be achieved. This research proposes a triad ecological evolution path of “environment-platform-community,” offering theoretical references and practical guidance for the construction of smart education ecosystems.

Teaching is a complex job, and the interaction between teachers and students also has complex characteristics. This study explored the latent characteristics and evolution patterns of teaching interaction in cMOOC, a Chinese online learning community course designed based on Connectionism. Based on the eigen microstate method from complex system phase transition theory, we found that the top four eigen microstates (EMs) are highly correlated with the student types artificially judged by teachers. The study also identified significant fluctuations in the evolution of teaching interactions during the transition from the course warm-up period to the start of Theme 1, which later stabilized, indicating a critical tipping point in the evolution.

ABSTRACT Generative Artificial Intelligence (GenAI) is increasingly deployed across both utilitarian and hedonic contexts. While technologically beneficial, its hedonic applications raise ethical concerns, as emotion-driven decision-making may impair users’ ethical concerns. Using functional Magnetic Resonance Imaging (fMRI), this study investigated the neural mechanisms underlying ethical decision making in hedonic versus utilitarian GenAI interactions. Results revealed that hedonic GenAI activates brain regions associated with emotional integration and moral conflict (e.g. ventromedial prefrontal cortex and orbitofrontal cortex), whereas utilitarian GenAI engages regions linked to cognitive control and moral reasoning (e.g. dorsolateral prefrontal cortex and precuneus). Emotion-related brain activity positively predicted ethical acceptability, whereas cognition-related activity negatively predicts it. As the first neuroimaging study to compare hedonic and utilitarian GenAI interactions, this study provides a neurophysiological foundation for ethical governance of GenAI.

With industrial transformation and the rise in the 15 min community life circle, optimizing walkability and preserving industrial heritage are key to revitalizing former industrial areas. This study, focusing on Shijingshan District in Beijing, proposes a walkability evaluation framework integrating multi-source big data and street-level perception. Using Points of Interest (POI) classification, which refers to the categorization of key urban amenities, pedestrian network modeling, and street view image data, a Walkability Friendliness Index is developed across four dimensions: accessibility, convenience, diversity, and safety. POI data provide insights into the spatial distribution of essential services, while pedestrian network data, derived from OpenStreetMap, model the walkable road network. Street view image data, processed through semantic segmentation, are used to assess the quality and safety of pedestrian pathways. Results indicate that core communities exhibit higher Walkability Friendliness Index scores due to better connectivity and land use diversity, while older and newly developed areas face challenges such as street discontinuity and service gaps. Accordingly, targeted optimization strategies are proposed: enhancing accessibility by repairing fragmented alleys and improving network connectivity; promoting functional diversity through infill commercial and service facilities; upgrading lighting, greenery, and barrier-free infrastructure to ensure safety; and delineating priority zones and balanced enhancement zones for differentiated improvement. This study presents a replicable technical framework encompassing data acquisition, model evaluation, and strategy development for enhancing walkability, providing valuable insights for the revitalization of industrial districts worldwide. Future research will incorporate virtual reality and subjective user feedback to further enhance the adaptability of the model to dynamic spatiotemporal changes.