Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Collaborating Filtering Community Image Recommendation System Based on Scene

With the advancement of smart city, the development of intelligent mobile terminal and wireless network, the traditional text information service no longer meet the needs of the community residents, community image service appeared as a new media service. “There are pictures of the truth” has become a community residents to understand and master the new dynamic community, image information service has become a new information service. However, there are two major problems in image information service. Firstly, the underlying eigenvalues extracted by current image feature extraction techniques are difficult for users to understand, and there is a semantic gap between the image content itself and the user’s understanding; secondly, in community life of the image data increasing quickly, it is difficult to find their own interested image data. Aiming at the two problems, this paper proposes a unified image semantic scene model to express the image content. On this basis, a collaborative filtering recommendation model of fusion scene semantics is proposed. In the recommendation model, a comprehensiveness and accuracy user interest model is proposed to improve the recommendation quality. The results of the present study have achieved good results in the pilot cities of Wenzhou and Yan'an, and it is applied normally

Collaborating Filtering Community Image Recommendation System Based on Scene

With the advancement of smart city, the development of intelligent mobile terminal and wireless network, the traditional text information service no longer meet the needs of the community residents, community image service appeared as a new media service. “There are pictures of the truth” has become a community residents to understand and master the new dynamic community, image information service has become a new information service. However, there are two major problems in image information service. Firstly, the underlying eigenvalues extracted by current image feature extraction techniques are difficult for users to understand, and there is a semantic gap between the image content itself and the user’s understanding; secondly, in community life of the image data increasing quickly, it is difficult to find their own interested image data. Aiming at the two problems, this paper proposes a unified image semantic scene model to express the image content. On this basis, a collaborative filtering recommendation model of fusion scene semantics is proposed. In the recommendation model, a comprehensiveness and accuracy user interest model is proposed to improve the recommendation quality. The results of the present study have achieved good results in the pilot cities of Wenzhou and Yan'an, and it is applied normally

Ningdong Granule Upregulates the Striatal DA Transporter and Attenuates Stereotyped Behavior of Tourette Syndrome in Rats

This study aimed to evaluate the possible mechanism of Ningdong granule (NDG) for the treatment of Tourette syndrome (TS). The rats with stereotyped behavior were established by microinjection with TS patients’ sera; then, the model rats were divided into NDG and haloperidol (Hal) group, and the nonmedication model rats were regarded as treatment control (TS group). The stereotyped behavior of the rats was recorded, the level of dopamine (DA) in striatum, and the content of homovanillic acid (HVA) in sera were tested, and dopamine transporter (DAT) expression was measured in the study. The experimental results showed that NDG effectively inhibited the stereotyped behavior (P<0.01), decreased the levels of DA in the striatum (P<0.05), increased the content of sera HVA (P<0.01), and enhanced the protein and mRNA expression of DAT in the striatum (P<0.01). Additionally, the results also revealed Hal could improve the stereotyped behavior as well but had no remarkable influence on DAT expression and DA metabolism. In conclusion, NDG attenuates stereotyped behavior, and its mechanism of action might be associated with the upregulation of DAT expression to regulate DA metabolism in the brain

Stress compensation based on interfacial nanostructures for stable perovskite solar cells

Abstract The long‐term stability issue of halide perovskite solar cells hinders their commercialization. The residual stress–strain affects device stability, which is derived from the mismatched thermophysical and mechanical properties between adjacent layers. In this work, we introduced the Rb2CO3 layer at the interface of SnO2/perovskite with the hierarchy morphology of snowflake‐like microislands and dendritic nanostructures. With a suitable thermal expansion coefficient, the Rb2CO3 layer benefits the interfacial stress relaxation and results in a compressive stress–strain in the perovskite layer. Moreover, reduced nonradiative recombination losses and optimized band alignment were achieved. An enhancement of open‐circuit voltage from 1.087 to 1.153 V in the resultant device was witnessed, which led to power conversion efficiency (PCE) of 22.7% (active area of 0.08313 cm2) and 20.6% (1 cm2). Moreover, these devices retained 95% of its initial PCE under the maximum power point tracking (MPPT) after 2700 h. It suggests inorganic materials with high thermal expansion coefficients and specific nanostructures are promising candidates to optimize interfacial mechanics, which improves the operational stability of perovskite cells