Rosendal Farmer (pumpcereal25)

We analyzed age- and sex-specific morbidity and mortality data from SARS-COV-2 pandemic in China and Republic of Korea (ROK). Data from China exhibit a Gaussian distribution with peak morbidity in the 50-59 years cohort, while the ROK data have a bimodal distribution with highest morbidity in the 20-29 years cohort. © The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.Levels of social capital can change after a natural disaster; thus far, no study has examined how changes in social capital affect the mental health of disaster victims. This study examined how pre-disaster social capital and its changes after a disaster were associated with the onset of mental disorders. In October 2013, we mailed a questionnaire to participants of the Japan Gerontological Evaluation Study living in Mifune town (Kumamoto, Japan) and measured pre-disaster social capital. In April 2016, the Kumamoto earthquake struck the region. Three years after the baseline survey, post-disaster social capital and symptoms of mental disorders were measured using the Screening Questionnaire for Disaster Mental Health (SQD) (n = 828). A multiple Poisson regression indicated that a standard deviation of 1 in pre-disaster social cohesion at community-level reduced the risk of depression (relative risk [RR] = 0.44); a decline in social capital after the disaster elevated the risk among women (RR = 2.44). In contrast to social cohesion, high levels of social participation at community-level were positively associated with the risk of depression among women. Policymakers should pay attention to gender differences and the types of social capital when leveraging social capital for recovery from disasters. © The Author(s) 2020. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.Effectively representing Medical Subject Headings (MeSH) headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify. In this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships), which can be constructed by the tree num. Then, five graph embedding algorithms including DeepWalk, LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed methods, we carried out the node classification and relationship prediction tasks. The results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the representation ability of vectors. Thus, it can serve as an input and continue to play a significant role in any computational models related to disease, drug, microbe, etc. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network perspective. © The Author(s) 2020. Published by Oxford University Press.OBJECTIVE Vocational rehabilitation for people with severe mental illness (SMI) has many benefits. Among the existing models, supported employment has consistently shown to have better impact on vocational outcomes while the findings on non-vocational outcomes are inconsistent. One source of variation with regard to non-vocational outcomes could be related to differences between consumers' self-reports and the providers' point of view. DESIGN A cross-sectional study of people with SMI consuming three different vocational services and their service providers. SETTING Data were co