Atrial fibrillation (AF) is common and increases stroke risk and mortality. Many knowledge gaps remain with respect to practice patterns and outcomes. Electronic medical records (EMRs) may serve as powerful research t...
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Atrial fibrillation (AF) is common and increases stroke risk and mortality. Many knowledge gaps remain with respect to practice patterns and outcomes. Electronic medical records (EMRs) may serve as powerful research tools if AF status can be properly ascertained. We sought to develop an algorithm for identifying subjects with and without AF in the EMR and compare it to previous methods. Using a hospital network EMR (n = 5,737,846), we randomly selected 8,200 subjects seen at a large academic medical center in January 2014 to derive and validate 7 AF classification schemas (4 cases and 3 controls) to construct a composite AF algorithm. In an independent sample of 172,138 subjects, we compared this algorithm against published AF classification methods. In total, we performed manual adjudication of AF in 700 subjects. Three AF schemas (AF1, AF2, and AF4) achieved positive predictive value (PPV) >0.9. Two control schemas achieved PPV>0.9 (control 1 and control 3). A combination algorithm AF1, AF2, and AF4 (PPV 88%;8.2% classified) outperformed published classification methods including >1 outpatient International Statistical Classification of Diseases, Ninth Revision code or 1 outpatient code with an electrocardiogram demonstrating AF (PPV 82%;5.9% classified), =1 inpatient International Statistical Classification of Diseases, Ninth Revision code or electrocardiogram demonstrating AF (PPV 88%;6.1% classified), or the intersection of these (PPV 84%;7.4% classified). When applied simultaneously, the case and control algorithms classified 98.4% of the cohort with zero disagreement. In conclusion, we derived a parsimonious and portable algorithm to identify subjects with and without AF with high sensitivity. If broadly applied, this algorithm can provide optimal power for EMR-based AF research. Copyright (C) 2016 Elsevier Inc. All rights reserved.
Compact separators are increasingly used for subsea separation of hydrocarbons, because of their low weight and low cost. A problem is their small volume, which makes them very sensitive to flow variations. This can d...
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ISBN:
(纸本)9781509045839
Compact separators are increasingly used for subsea separation of hydrocarbons, because of their low weight and low cost. A problem is their small volume, which makes them very sensitive to flow variations. This can degrade separation performance, which in turn can cause operational problems and economic loss. Improved control can increase robustness and therefore, the focus of this paper is to derive a control-oriented model based on first principles to enable the development of robust control algorithms. The derived model is controlled by a PI feedback control algorithm and tuned using the SIMC tuning rules. The model is qualitatively verified in simulations, and the behaviour confirmed with reported observations from experimental work and field applications from literature.
We present a new R package for training gapped-kmer SVM classifiers for DNA and protein sequences. We describe an improved algorithm for kernel matrix calculation that speeds run time by about 2 to 5-fold over our ori...
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We present a new R package for training gapped-kmer SVM classifiers for DNA and protein sequences. We describe an improved algorithm for kernel matrix calculation that speeds run time by about 2 to 5-fold over our original gkmSVM algorithm. This package supports several sequence kernels, including: gkmSVM, kmer-SVM, mismatch kernel and wildcard kernel. Availability and Implementation: gkmSVM package is freely available through the Comprehensive R Archive Network (CRAN), for Linux, Mac OS and Windows platforms. The C++ implementation is available at ***/gkmsvm
Within electrified vehicle powertrains, lithium-ion battery performance degrades with aging and usage, resulting in a loss in both energy and power capacity. As a result, models used for system design and control algo...
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ISBN:
(纸本)9781509045839
Within electrified vehicle powertrains, lithium-ion battery performance degrades with aging and usage, resulting in a loss in both energy and power capacity. As a result, models used for system design and control algorithm development would ideally capture the impact of those efforts on battery capacity degradation, be computationally efficient, and simple enough to be used for algorithm development. This paper provides an assessment of the state-of-the-art in lithium-ion battery degradation models, including accuracy, computational complexity, and amenability to control algorithm development. Various aging and degradation models have been studied in the literature, including physically-based electrochemical models, semi-empirical models, and empirical models. Some of these models have been validated with experimental data;however, comparisons of pre-existing degradation models across multiple experimental data sets have not been previously published. Three degradation models, a 1-d electrochemical model (AutoLion ST, or ALST), a semi-empirical model (from the National Renewable Energy Laboratory) and an empirical model (published in the literature), are compared against three published experimental data sets for a 2.3-Ah commercial graphite/LiFePO_4 cell. The results show that the physically-based model is best able to capture results across all three representative data sets with an error less than 10 %, but is 24× slower than the empirical model, and 4000× slower than the semi-empirical model, making it unsuitable for powertrain system design and model-based algorithm development. Despite being computationally efficient, the semi-empirical and empirical models, when used under conditions that lie outside the calibration data set, exhibit up to 60% error in capacity loss prediction. Such models require expensive experimental data collection to recalibrate for every new application. Thus, in the author's opinion, there exists a need for a physically-based model tha
In a genome-wide association study (GWAS) of an admixed population, such as Hispanic Americans, ancestry-specific allele frequencies can inform the design of a replication GWAS. We derive an EM algorithm to estimate a...
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In a genome-wide association study (GWAS) of an admixed population, such as Hispanic Americans, ancestry-specific allele frequencies can inform the design of a replication GWAS. We derive an EM algorithm to estimate ancestry-specific allele frequencies for a bi-allelic marker given genotypes and local ancestries on a 3-way admixed population, when the phase of each admixed individual's genotype relative to the pair of local ancestries is unknown. We call our algorithm Ancestry Specific Allele Frequency Estimation (ASAFE). We demonstrate that ASAFE has low error on simulated data.
Biological sequence databases are integral to efforts to characterize and understand biological molecules and share biological data. However, when analyzing these data, scientists are often left holding disparate biol...
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Biological sequence databases are integral to efforts to characterize and understand biological molecules and share biological data. However, when analyzing these data, scientists are often left holding disparate biological currency-molecular identifiers from different databases. For downstream applications that require converting the identifiers themselves, there are many resources available, but analyzing associated loci and variants can be cumbersome if data is not given in a form amenable to particular analyses. Here we present BISQUE, a web server and customizable command-line tool for converting molecular identifiers and their contained loci and variants between different database conventions. BISQUE uses a graph traversal algorithm to generalize the conversion process for residues in the human genome, genes, transcripts and proteins, allowing for conversion across classes of molecules and in all directions through an intuitive web interface and a URL-based web service.
An increasingly common method for studying evolution is the collection of time-resolved short-read sequence data. Such datasets allow for the direct observation of rapid evolutionary processes, as might occur in natur...
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An increasingly common method for studying evolution is the collection of time-resolved short-read sequence data. Such datasets allow for the direct observation of rapid evolutionary processes, as might occur in natural microbial populations and in evolutionary experiments. In many circumstances, evolutionary pressure acting upon single variants can cause genomic changes at multiple nearby loci. SAMFIRE is an open-access software package for processing and analyzing sequence reads from time-resolved data, calling important single-and multi-locus variants over time, identifying alleles potentially affected by selection, calculating linkage disequilibrium statistics, performing haplotype reconstruction and exploiting time-resolved information to estimate the extent of uncertainty in reported genomic data. Availability and Implementation: C++ code may be found at https://***/cjri/samfire/.
Motivation: Drop-seq has recently emerged as a powerful technology to analyze gene expression from thousands of individual cells simultaneously. Currently, Drop-seq technology requires refinement and quality control (...
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Motivation: Drop-seq has recently emerged as a powerful technology to analyze gene expression from thousands of individual cells simultaneously. Currently, Drop-seq technology requires refinement and quality control (QC) steps are critical for such data analysis. There is a strong need for a convenient and comprehensive approach to obtain dedicated QC and to determine the relationships between cells for ultra-high-dimensional datasets. Results: We developed Dr. seq, a QC and analysis pipeline for Drop-seq data. By applying this pipeline, Dr. seq provides four groups of QC measurements for given Drop-seq data, including reads level, bulk-cell level, individual-cell level and cell-clustering level QC. We assessed Dr. seq on simulated and published Drop-seq data. Both assessments exhibit reliable results. Overall, Dr. seq is a comprehensive QC and analysis pipeline designed for Drop-seq data that is easily extended to other droplet-based data types. Availability and Implementation: Dr. seq is freely available at: http://***/similar to zhanglab/drseq and https://***/tarela/drseq
The wound healing assay (or scratch assay) is a technique frequently used to quantify the dependence of cell motility-a central process in tissue repair and evolution of disease-subject to various treatments condition...
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The wound healing assay (or scratch assay) is a technique frequently used to quantify the dependence of cell motility-a central process in tissue repair and evolution of disease-subject to various treatments conditions. However processing the resulting data is a laborious task due its high throughput and variability across images. This Robust Quantitative Scratch Assay algorithm introduced statistical outputs where migration rates are estimated, cellular behaviour is distinguished and outliers are identified among groups of unique experimental conditions. Furthermore, the RQSA decreased measurement errors and increased accuracy in the wound boundary at comparable processing times compared to previously developedmethod (TScratch).
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