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There is both clinical and neuroanatomical variability at the single-subject level in Alzheimer's disease, complicating our understanding of brain-behaviour relationships and making it challenging to develop neuroimaging biomarkers to track disease severity, progression, and response to treatment. Prior work has shown that both group-level atrophy in clinical dementia syndromes and complex neurological symptoms in patients with focal brain lesions localize to brain networks. Here, we use a new technique termed 'atrophy network mapping' to test the hypothesis that single-subject atrophy maps in patients with a clinical diagnosis of Alzheimer's disease will also localize to syndrome-specific and symptom-specific brain networks. First, we defined single-subject atrophy maps by comparing cortical thickness in each Alzheimer's disease patient versus a group of age-matched, cognitively normal subjects across two independent datasets (total Alzheimer's disease patients = 330). No more than 42% of Alzheimer's diseaseinto brain-behaviour relationships in patients with dementia. © The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email [email protected] To evaluate associations with neonatal hypothermia in a tertiary-level neonatal unit (NU) in Malawi. METHODS Neonates with a birth weight >1000 g were recruited and temperatures were recorded 5 min after birth, on admission and 4 h thereafter. Clinical course and outcome were reviewed. Data were analysed using Stata v.15 and p less then 0.05 was considered statistically significant. RESULTS Between August 2018 to March 2019, 120 neonates were enrolled, and 112 were included in the data analysis. Hypothermia at 5 min after birth was noted in 74%, 77% on admission to the NU and 38% at 24 h. Neonates who had hypothermia 5 min after birth were more likely to have hypothermia on admission to the NU compared with normothermic subjects (p less then 0.01). All neonates with hypothermia on admission to the NU died (100 vs.72%, p = 0.02), but hypothermia at 5 min nor at 24 h were not associated with mortality. After adjusting for potential confounders, the odds ratio of hypothermia at 5 min for hypothermia on admission to NU was 13.31 (95% CI 4.17-42.54). DISCUSSION A large proportion of hospitalized neonates is hypothermic on admission and has associated morbidity and mortality. Our findings suggest that a strong predictor of mortality is neonatal hypothermia on admission to the NU, and that early intervention in the immediate period after delivery could decrease the incidence of hypothermia and reduce associated morbidity and mortality. © The Author(s) [2020]. Published by Oxford University Press.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. 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. BIX 01294 solubility dmso 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 [email protected] 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 [email protected] 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.MOTIVATION Apparent time delays in partly observed, biochemical reaction networks can be modelled by lumping a more complex reaction into a series of linear reactions often referred to as the linear chain trick. Since most delays in biochemical reactions are no true, hard delays but a consequence of complex unobserved processes, this approach often more closely represents the true system compared with delay differential equations. In this paper, we address the question of how to select the optimal number of additional equations, i.e. the chain length (CL). RESULTS We derive a criterion based on parameter identifiability to infer CLs and compare this method to choosing the model with a CL that leads to the best fit in a maximum likelihood sense, which corresponds to optimizing the Bayesian information criterion. We evaluate performance with simulated data as well as with measured biological data for a model of JAK2/STAT5 signalling and access the influence of different model structures and data characteristics. Our analysis revealed that the proposed method features a superior performance when applied to biological models and data compared with choosing the model that maximizes the likelihood. AVAILABILITY AND IMPLEMENTATION Models and data used for simulations are available at https//github.com/Data2Dynamics/d2d and http//jeti.uni-freiburg.de/PNAS_Swameye_Data. 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 [email protected] Empirical Bayes techniques to genotype polyploid organisms usually either (i) assume technical artifacts are known a priori or (ii) estimate technical artifacts simultaneously with the prior genotype distribution. Case (i) is unappealing as it places the onus on the researcher to estimate these artifacts, or to ensure that there are no systematic biases in the data. However, as we demonstrate with a few empirical examples, case (ii) makes choosing the class of prior genotype distributions extremely important. Choosing a class is either too flexible or too restrictive results in poor genotyping performance. RESULTS We propose two classes of prior genotype distributions that are of intermediate levels of flexibility the class of proportional normal distributions and the class of unimodal distributions. We provide a complete characterization of and optimization details for the class of unimodal distributions. We demonstrate, using both simulated and real data that using these classes results in superior genotyping performance.
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