BACKGROUND We aimed to assess the adherence of short-term medical missions (STMMs) operating in Latin America and the Caribbean (LAC) to key best practices using the Service Trip Audit Tool (STAT) and to calculate the inter-rater reliability of the data points. This tool was based on a previously published inventory of 18 STMM best practices. METHODS Programme administrators and recent volunteers from 335 North American organizations offering STMMs in LAC were invited to complete the STAT anonymously online. Adherence to each of 18 best practices was reported as either ‘yes’, ‘no’ or ‘not sure’. Fleiss’ κ was used to assess inter-rater agreement of the responses. IACS-010759 in vitro RESULTS A total of 194 individuals from 102 organizations completed the STAT (response rate 30.4%; 102/335 organizations) between 12 July and 7 August 2017. Reported adherence was >80% for 9 of 18 best practices. For 37 non-governmental organizations (NGOs) with multiple raters, inter-rater agreement was moderate to substantial (κ>0.4) for 12 of 18 best practices. CONCLUSIONS This is the first study to evaluate adherence to STMM best practices. Such an objective evaluation will be valuable to governments, volunteers and NGO donors who have an interest in identifying high-quality partners. Assessment and monitoring of STMMs through self-audit may be foundational steps towards quality improvement. © The Author(s) 2020. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene.BACKGROUND From December 2019 to February 2020, 2019 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a serious outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China. Related clinical features are needed. METHODS We reviewed 69 patients who were hospitalized in Union hospital in Wuhan between January 16 to January 29, 2020. All patients were confirmed to be infected with SARS-CoV-2 and the final date of follow-up was February 4, 2020. RESULTS The median age of 69 enrolled patients was 42.0 years (IQR 35.0-62.0), and 32 patients (46%) were men. The most common symptoms were fever (60[87%]), cough (38[55%]), and fatigue (29[42%]). Most patients received antiviral therapy (66 [98.5%] of 67 patients) and antibiotic therapy (66 [98.5%] of 67 patients). As of February 4, 2020, 18 (26.9%) of 67 patients had been discharged, and five patients had died, with a mortality rate of 7.5%. According to the lowest SpO2 during admission, cases were divided into the SpO2≥90% group (n=55) and the SpO2 less then 90% group (n=14). All 5 deaths occurred in the SpO2 less then 90% group. Compared with SpO2≥90% group, patients of the SpO2 less then 90% group were older, and showed more comorbidities and higher plasma levels of IL6, IL10, lactate dehydrogenase, and c reactive protein. Arbidol treatment showed tendency to improve the discharging rate and decrease the mortality rate. CONCLUSIONS COVID-19 appears to show frequent fever, dry cough, and increase of inflammatory cytokines, and induced a mortality rate of 7.5%. Older patients or those with underlying comorbidities are at higher risk of death. © 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.MOTIVATION Isoforms are alternatively spliced mRNAs of genes. They can be translated into different functional proteoforms, and thus greatly increase the functional diversity of protein variants (or proteoforms). Differentiating the functions of isoforms (or proteoforms) helps understanding the underlying pathology of various complex diseases at a deeper granularity. Since existing functional genomic databases uniformly record the annotations at the gene-level, and rarely record the annotations at the isoform-level, differentiating isoform functions is more challenging than the traditional gene-level function prediction. RESULTS Several approaches have been proposed to differentiate the functions of isoforms. They generally follow the multi-instance learning paradigm by viewing each gene as a bag and the spliced isoforms as its instances, and push functions of bags onto instances. These approaches implicitly assume the collected annotations of genes are complete and only integrate multiple RNA-seq datasets. A, and observed that DisoFun can differentiate functions of their isoforms with 90.5% accuracy. AVAILABILITY AND IMPLEMENTATION The code of DisoFun is available at mlda.swu.edu.cn/codes.php?name=DisoFun. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.MOTIVATION Functions of cancer driver genes vary substantially across tissues and organs. Distinguishing passenger genes, oncogenes (OGs) and tumor-suppressor genes (TSGs) for each cancer type is critical for understanding tumor biology and identifying clinically actionable targets. Although many computational tools are available to predict putative cancer driver genes, resources for context-aware classifications of OGs and TSGs are limited. RESULTS We show that the direction and magnitude of somatic selection of protein-coding mutations are significantly different for passenger genes, OGs and TSGs. Based on these patterns, we develop a new method (genes under selection in tumors) to discover OGs and TSGs in a cancer-type specific manner. Genes under selection in tumors shows a high accuracy (92%) when evaluated via strict cross-validations. Its application to 10 172 tumor exomes found known and novel cancer drivers with high tissue-specificities. In 11 out of 13 OGs shared among multiple cancer types, we found functional domains selectively engaged in different cancers, suggesting differences in disease mechanisms. AVAILABILITY AND IMPLEMENTATION An R implementation of the GUST algorithm is available at https//github.com/liliulab/gust. A database with pre-computed results is available at https//liliulab.shinyapps.io/gust. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) 2019. Published by Oxford University Press.