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Background Socioeconomic development, lifestyle changes and the COVID-19 pandemic all have an impact on people's mental and physical health, which may affect the prevalence of mental disorders. Currently, there is still no sufficient epidemiological information of mental disorders in Xinjiang. Objective To investigate the prevalence and influencing factors of common mental disorders among people aged 15 and above in northern Xinjiang, then compare the data with those of their counterparts in southern Xinjiang, and summarize the overall prevalence of common mental disorders in Xinjiang, providing a scientific basis for the formulation of corresponding mental health plans. Methods From November 2021 to July 2022, a multistage, stratified, random sampling method was used to select 3 853 residents from northern Xinjiang to attend a survey. General Demographic Questionnaire, and self-assessment scales (the 12-Item General Health Questionnaire, Mood Disorder Questionnaire, Symptom Checklist-90, etc.) and other assessment scales (Hamilton Depression Inventory, Bech-Rafaelsen Mania Rating Scale, Brief Psychiatric Rating Scale, etc.) were used as survey instruments. Mental disorders were diagnosed by the ICD-10 classification of mental and behavioral disorders by two psychiatrists with at least five years' working experience, or by a chief or associate chief psychiatrist when there is an inconsistency between the diagnoses made by the two psychiatrists. Results The point prevalence rate and age-adjusted rate of common mental disorders in northern Xinjiang were 9.71% (374/3 853) and 10.07%, respectively. The point prevalence rate and age-adjusted rate of common mental disorders in the whole Xinjiang were 9.69% (750/7 736) and 9.90%, respectively. The point prevalence rates of mood disorders, anxiety disorders, schizophrenia, organic mental disorders, and mental retardation in northern Xinjiang were 4.83% (374/7 736), 3.63% (281/7 736), 0.63% (49/7 736), 0.23% (18/7 736), and 0.36% (28/7 736), respectively. Multivariate Logistic regression analysis for northern Xinjiang showed that: the risk of mood disorders in females was 1.854 times higher than that in males〔95%CI (1.325, 2.593) 〕; The risk of mood disorders increased by 5.210 times in 25-34-year-olds〔95%CI (1.348, 20.143) 〕 and 3.863 times in 35-44-year-olds 〔95%CI (1.030, 14.485) 〕 compared with that in those aged ≥65 years; The risk of mood disorders increased by 0.199 times in those with high school or technical secondary school education〔95%CI (0.078, 0.509) 〕 and 0.147 times in those with two- or three-year college and above education〔95%CI (0.056, 0.388) 〕 compared with that in illiteracies. The risk of anxiety disorder in females was 1.627 times higher than that in males〔95%CI (1.144, 2.315) 〕; The risk of anxiety disorder increased by 0.257 times in 15-24-year-olds〔95%CI (0.091, 0.729) 〕, 0.243 times in 45-54-year-olds〔95%CI (0.101, 0.583) 〕, and 0.210 times in 55-64-year-olds〔95%CI (0.067, 0.661) 〕 compared to that of those aged ≥65 years old. The risk of schizophrenia among people living in villages or towns was 4.762 times higher than that of those living in cities〔95%CI (1.705, 13.300) 〕; The risk of schizophrenia among people with high school or technical secondary school education was 0.079 times higher than that of illiteracies〔95%CI (0.015, 0.405) 〕. Conclusion The prevalence of mood disorders and anxiety disorders is high among all types of mental disorders in Xinjiang. Females, rural people, or low educated people in northern Xinjiang are more prone to various types of mental disorders.

Background: With the accelerated development of the aging trend in Chinese society, the aging problem has become one of the key factors affecting sustainable economic and social development. Given the importance of controlling carbon emissions for achieving global climate goals and China's economic transformation, studying the spatial and temporal effects of population aging on carbon emissions and their pathways of action is of great significance for formulating low-carbon development strategies adapted to an aging society. Objective: This paper aims to explore the spatial-temporal effects of population aging on carbon emissions, identify the key pathways through which aging affects carbon emissions, and further explore the variability of these effects across different regions. The findings will provide theoretical support and empirical evidence for government departments to formulate policies to promote the coordinated development of a low-carbon society and an aging society. Methods: Based on the panel data of 30 provinces in China from 2004 to 2022, this paper systematically investigates the impact of population aging on carbon emission intensity from both spatial and temporal dimensions by using the spatial Durbin model and the mediating effect model. The direct effect of aging on carbon emission intensity, the spatial spillover effect, and the indirect effect through mediating variables such as residents' consumption, environmental regulation, and new urbanization are analyzed in depth. Results: The study found that population aging in China has significant spatial and temporal effects on carbon emissions. From the spatial dimension, there is a significant spatial spillover effect of the effect of aging on carbon emissions, and aging reduces local carbon emissions but increases carbon emissions in adjacent regions. From the time dimension, the effect of aging on carbon emissions shows a stage characteristic, initially it will reduce carbon emissions, but with the deepening of aging, its effect may tend to weaken. In addition, this study identifies a number of key pathways through which aging affects carbon emissions, including reducing residential consumption, promoting new urbanization, and increasing the intensity of environmental regulations. Finally, this study explores the regional heterogeneity of the impact of aging on carbon emissions and its mechanism of action. Conclusion: This study is instructive: first, the complex impact of population aging on carbon emissions should be fully recognized to formulate a comprehensive low-carbon development strategy; second, attention should be paid to the spatial spillover effect of aging on carbon emissions to strengthen inter-regional cooperation and coordination; and lastly, differentiated low-carbon policies should be formulated to address the characteristics of aging in different regions and stages in order to promote the synergistic development of a low-carbon society and an aging society. [ABSTRACT FROM AUTHOR]

Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point cloud data, are extracted using SMILES. Secondly, for different modal data, a Bi-LSTM, gMLP, multi-head attention mechanism, and multi-scale GCNs are comprehensively applied to extract the drug feature. Then, it selects appropriate omics features from gene expression and mutation omics data of cancer cell lines to construct cancer cell line features. Finally, these features are combined to predict the synergistic anti-cancer drug combination effect. The experimental results verify that MMFSyn has significant advantages in performance compared to other popular methods, with a root mean square error of 13.33 and a Pearson correlation coefficient of 0.81, which indicates that MMFSyn can better capture the complex relationship between multimodal drug combinations and omics data, thereby improving the synergistic drug combination prediction. [ABSTRACT FROM AUTHOR]

Incorporating renewable energy sources into the grid poses challenges due to their volatility and uncertainty in optimizing dispatch strategies. In response, this article proposes a cloud–edge collaborative scheduling strategy for distributed multi-energy systems, operating across various time scales. The strategy integrates day-ahead dispatch, intra-day optimization, and real-time adjustments to minimize operational costs, reduce the wastage of renewable energy, and enhance overall system reliability. Furthermore, the cloud–edge collaborative framework helps mitigate scalability challenges. Crucially, the strategy considers the multi-timescale characteristics of two types of energy storage systems (ESSs) and three types of demand response (DR), aimed at optimizing resource allocation efficiently. Comparative simulation results evaluate the strategy, providing insights into the significant impacts of different ESS and DR types on system performance. By offering a comprehensive approach, this strategy aims to address operational complexities. It aims to contribute to the seamless integration of renewable energy into distributed systems, potentially enhancing sustainability and resilience in energy management. [ABSTRACT FROM AUTHOR]

In the last decade, graph embedding-based dimensionality reduction for multi-view data has been extensively studied. However, constructing a high-quality graph for dimensionality reduction is still a significant challenge. Herein, we propose a new algorithm, named multi-view low-rank graph optimization for dimensionality reduction (MvLRGO), which integrates graph optimization with dimensionality reduction into one objective function in order to simultaneously determine the optimal subspace and graph. The subspace learning of each view is conducted independently by the general graph embedding framework. For graph construction, we exploit low-rank representation (LRR) to obtain reconstruction relationships as the affinity weight of the graph. Subsequently, the learned graph of each view is further optimized throughout the learning process to obtain the ideal assignment of relations. Moreover, to integrate information from multiple views, MvLRGO regularizes each of the view-specific optimal graphs such that they align with one another. Benefiting from this term, MvLRGO can achieve flexible multi-view communication without constraining the subspaces of all views to be the same. Various experimental results obtained with different datasets show that the proposed method outperforms many state-of-the-art multi-view and single-view dimensionality reduction algorithms. [ABSTRACT FROM AUTHOR]

Immune system imbalances contribute to the pathogenesis of several different diseases, and immunotherapy shows great therapeutic efficacy against tumours and infectious diseases with immune‐mediated derivations. In recent years, molecules targeting the programmed cell death protein 1 (PD‐1) immune checkpoint have attracted much attention, and related signalling pathways have been studied clearly. At present, several inhibitors and antibodies targeting PD‐1 have been utilized as anti‐tumour therapies. However, increasing evidence indicates that PD‐1 blockade also has different degrees of adverse side effects, and these new explorations into the therapeutic safety of PD‐1 inhibitors contribute to the emerging concept that immune normalization, rather than immune enhancement, is the ultimate goal of disease treatment. In this review, we summarize recent advancements in PD‐1 research with regard to immune normalization and targeted therapy. [ABSTRACT FROM AUTHOR]