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Pipe for Once-a-year Averaged Wind flow Power Output Generation Conjecture regarding Wind generators Based on Large Blowing wind Speed Info Sets along with Energy Curve Data.
MOTIVATION Single cell RNA sequencing (scRNA-seq) allows us to dissect transcriptional heterogeneity arising from cellular types, spatio-temporal contexts, and environmental stimuli. Transcriptional heterogeneity may reflect phenotypes and molecular signatures that are often unmeasured or unknown a priori. Cell identities of samples derived from heterogeneous subpopulations are then determined by clustering of scRNA-seq data. These cell identities are used in downstream analyses. How can we examine if cell identities are accurately inferred? Unlike external measurements or labels for single cells, using clustering-based cell identities result in spurious signals and false discoveries. RESULTS We introduce non-parametric methods to evaluate cell identities by testing cluster memberships in an unsupervised manner. Diverse simulation studies demonstrate accuracy of the jackstraw test for cluster membership. We propose a posterior probability that a cell should be included in that clustering-based subpopulation. Posterior inclusion probabilities (PIPs) for cluster memberships can be used to select and visualize samples relevant to subpopulations. The proposed methods are applied on three scRNA-seq datasets. First, a mixture of Jurkat and 293T cell lines provides two distinct cellular populations. Second, Cell Hashing yields cell identities corresponding to 8 donors which are independently analyzed by the jackstraw. Third, peripheral blood mononuclear cells (PBMCs) is used to explore heterogeneous immune populations. The proposed p-values and PIPs lead to probabilistic feature selection of single cells, that can be visualized using PCA, t-SNE, and others. By learning uncertainty in clustering high-dimensional data, the proposed methods enable unsupervised evaluation of cluster membership. AVAILABILITY AND IMPLEMENTATION https//cran.r-project.org/package=jackstraw. SUPPLEMENTARY INFORMATION Supplementary figures are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press.Radiomics is a novel image analysis technique, whereby voxel-level information is extracted from digital images and used to derive multiple numerical quantifiers of shape and tissue character. Cardiac magnetic resonance (CMR) is the reference imaging modality for assessment of cardiac structure and function. Conventional analysis of CMR scans is mostly reliant on qualitative image analysis and basic geometric quantifiers. Small proof-of-concept studies have demonstrated the feasibility and superior diagnostic accuracy of CMR radiomics analysis over conventional reporting. CMR radiomics has the potential to transform our approach to defining image phenotypes and, through this, improve diagnostic accuracy, treatment selection, and prognostication. The purpose of this article is to provide an overview of radiomics concepts for clinicians, with particular consideration of application to CMR. We will also review existing literature on CMR radiomics, discuss challenges, and consider directions for future work. © The Author(s) 2020. Published by Oxford University Press on behalf of the European Society of Cardiology.AIMS Frailty is characterized by reduced biological reserves and weakened resistance to stressors, and is common in older adults. This study evaluated the prognostic implications of frailty at hospitalization in elderly patients with acute myocardial infarction (AMI) who undergo percutaneous coronary intervention (PCI). METHODS AND RESULTS We prospectively analyzed 546 AMI patients aged ≥80 years undergoing PCI from 2009 to 2017. Frailty was classified based on impairment in walking (unassisted, assisted, wheelchair/non-ambulatory), cognition (normal, mildly impaired, moderately to severely impaired), and basic activities of daily living. Impairment in each domain was scored as 0, 1, or 2, and patients were categorized into the following 3 groups based on total score no frailty (0), mild frailty (1 to 2), moderate-to-severe frailty (≥3). The median follow-up period was 589 days. Of the 546 patients, 27.8% were frail (mild or moderate-to-severe), and this proportion significantly increased to 35.5% at discharge (P less then 0.001). read more Compared to non-frail patients, frail patients were older, less likely to be male, and had a higher rate of advanced Killip class. Major bleeding (no frailty, 9.6%; mild frailty, 16.9%; moderate-to-severe frailty, 31.8%; P less then 0.001) and in-hospital mortality (no frailty, 8.4%; mild frailty, 15.4%; moderate-to-severe frailty, 27.3%; P less then 0.001) increased as frailty worsened. After adjusting for confounders, frailty was independently associated with higher mid-term all-cause mortality (hazard ratio, 1.81; 95% confidence interval, 1.23-2.65; P=0.002). CONCLUSIONS Frailty in AMI patients aged ≥80 years undergoing PCI was associated with major bleeding, in-hospital death, and mid-term mortality. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2020. For permissions please email [email protected] The subcellular location of a protein can provide useful information for protein function prediction and drug design. Experimentally determining the subcellular location of a protein is an expensive and time-consuming task. Therefore, various computer-based tools have been developed, mostly using machine learning algorithms, to predict the subcellular location of proteins. RESULTS Here, we present a neural network based algorithm for protein subcellular location prediction. We introduce SCLpred-EMS a subcellular localization predictor powered by an ensemble of Deep N-to-1 Convolutional Neural Networks. SCLpred-EMS predicts the subcellular location of a protein into two classes, the endomembrane system and secretory pathway versus all others, with an MCC of 0.75-0.86 outperforming the other state-of-the-art web servers we tested. AVAILABILITY SCLpred-EMS is freely available for academic users at http//distilldeep.ucd.ie/SCLpred2/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email [email protected].
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