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11,177 results for “identify”
Supplemental Datasets for: WhatsGNU: A Tool For Identifying Proteomic Novelty.
<p>Five precompressed databases are available for WhatsGNU:</p> <p>Ortholog Mode:</p> <ol> <li>WhatsGNU_TB_Ortholog.zip: <em>Mycobacterium tuberculosis</em> Version-07/09/2019 (compressed 26,794,006 proteins in 6563 genomes to 434,725 protein variants).</li> <li>WhatsGNU_Pa_Ortholog.zip: <em>Pseudomonas aeruginosa</em> Version-07/06/2019 (compressed 14,475,742 proteins in 4712 genomes to 1,288,892 protein variants).</li> <li>WhatsGNU_Sau_Ortholog.zip: <em>Staphylococcus aureus</em> Version-06/14/2019 (compressed 27,213,667 proteins in 10350 genomes to 571,848 protein variants).</li> </ol> <p>Big Data basic Mode:</p> <ol> <li><em>Senterica_Enterobase_basic_216642.pickle.gz: Salmonella enterica</em> Enterobase Version-08/29/2019 (compressed 975,262,506 proteins in 216,642 genomes to 5,056,335 protein variants).</li> <li><em>Sau_Staphopia_basic_43914.pickle: Staphylococcus aureus</em> Staphopia Version-06/27/2019 (compressed 115,178,200 proteins in 43,914 genomes to 2,228,761 protein variants).</li> </ol> <p>Figures_datasets.zip: The FASTA files and the WhatsGNU reports as input and output, respectively, which were used to produce panels in Figures 1 and 2 and supplementary figures 1, 2 and 3.</p>
Spatial genetic structure to identify populations at risk
<p>Microsatellite genotypes, locality information, and raster data for Miller et al</p>
Co-expression clustering across flower development identifies modules for diverse floral forms in Achimenes (Gesneriaceae)
<p>Data and code for the analyses published in:</p> <p>Roberts WR, Roalson EH. 2020. Co-expression clustering across flower development identifies modules for diverse floral forms in <em>Achimenes</em> (Gesneriaceae). <em>PeerJ</em>. In Press.</p>
Functional genomic study to identify key genes involved in terpenoid and rosmarinic acid biosynthesis in lemon balm (Melissa officinalis)
<p><em>Melissa officinalis</em> (lemon balm) is a medicinal plant producing high<em>-</em>value secondary metabolites that are used in traditional medicine all over the world since the earliest times. Despite pharmacological importance, there is a lack of knowledge about the genes and enzymes involved in secondary metabolite biosynthetic pathways in lemon balm. We identified the key genes and pathways associated with biosynthesis of terpenoid and rosmarinic acid through functional analysis of transcriptomic data. In this study, a comprehensive transcriptome assembly containing 37,055 unigenes was generated by analyzing 42,837,601 Illumina paired-end reads by employing an efficient pipeline. Functional annotation of the unigenes showed that 35,822 (96.67%) and 27,363 (73.84%) had BLAST hits to known proteins in the NR and SwissProt databases, respectively. The KEGG pathway analysis revealed that 10,062 (36.83%) unigenes were assigned to 399 KEGG pathways. The focus of this study was on pathways associated with the production of important metabolites such as terpenes and rosmarinic acid. A total of 149 unigenes were identified that are associated with biosynthesis of terpenoids, including 75 mevalonate and methyl-erythritol phosphate (MEP) pathway genes, terpenoid backbone<strong> </strong>biosynthesis genes, and 74 terpene synthase genes. Furthermore, 144 and 30 unigenes were detected that are related to the phenylpropanoid biosynthesis and the rosmarinic acid pathway. Therefore, this study lays an accurate and comprehensive transcriptome foundation for future research in the metabolic engineering and identification of novel genes and pathways in lemon balm.</p>
Machine readable code lists for an algorithm to identify incident lung cancer in United States healthcare claims data
<p>Machine readable code lists for an algorithm to identify incident lung cancer in United States healthcare claims data</p>
2000 AU-scale cores identified by dendrogram in three massive clouds in the CMZ
<p>The full catalog of the 2000 AU-scale cores identified by dendrogram in three massive clouds in the CMZ. Paper will be submitted to AAS journals.</p>
Mobile EEG identifies the re-allocation of attention during real-world activity
<p>All the datasets from the three studies reported in the scientific article "Mobile EEG identifies the re-allocation of attention during real-world activity" <a href="https://www.nature.com/articles/s41598-019-51996-y">10.1038/s41598-019-51996-y </a></p>
A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-derived Natural Compounds
<p>The latent knowledge, molecular interaction, and chemical property features of the drugs and natural compounds</p>
Data from: Identifying the effects of an upgraded 'fever clinic' on COVID-19 control and the workload of emergency department: a retrospective study in a tertiary hospital in China
<p>Objectives: COVID-19 started spreading widely in China. Outpatient fever clinics (FC),were upgraded to serve for COVID-19 screening and prevention of disease transmission in large tertiary hospitals in China. We aimed to evaluate the effect of upgrading the FC system on rates of nosocomial COVID-19 infection and ED patient attendance at PUMCH Methods: The FC of PUMCH was upgraded on January 20, 2020. We performed a retrospective study of patients presenting to the FC between December 12, 2019 and February 29, 2020. January 20, 2020 was the date when COVID-19 was declared an outbreak in Beijing. Two groups of data were collected and subsequently compared with each other: The first group of data was collected within 40 days before January 20, 2020; The second group of data was collected within 40 days after January 20, 2020. All necessary data, including patient baseline information, diagnosis, follow-up conditions, and the transfer records between the FC and ED were collected and analyzed. Results: 6,365 patients were screened in the FC, among whom 2,912 patients were screened before January 21, 2020, while 3,453 were screened afterward. Screening results showed that upper respiratory infection was the major disease associated with fever. After the outbreak of COVID-19, the number of patients who were transferred from the FC to ED decreased significantly [39.21% vs 15.75%, p<0.001], and patients generally spent more time in the FC [55 vs 203minutes, p, compared with before the outbreak. For critically ill patients waiting for their screening results, the total length of stay in the FC was 22minutes before the outbreak, compared with 442minutes after the outbreak Conclusion: The workload of the FC increased significantly after the COVID-19 outbreak. New protocols regarding the use of FC likely helped prevent the spread of COVID-19 l. The upgraded FC reduce the burden on the ED</p>
Identifying galaxies, quasars and stars with machine learning: a new catalogue of classifications for 111 million SDSS sources without spectra - parquet format
<p>This is the same as the published data available under 10.5281/zenodo.3768398, but in the format of parquet files. This means you can access it using Dask for convenience when using cloud compute facilities. </p> <p>Abstract: We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this machine learning model to 111 million previously unlabelled sources from the SDSS photometric catalogue which did not have existing spectroscopic observations. Our new catalogue contains 50.4 million galaxies, 2.1 million quasars, and 58.8 million stars. We provide individual classification probabilities for each source, with 6.7 million galaxies (13%), 0.33 million quasars (15%), and 41.3 million stars (70%) having classification probabilities greater than 0.99; and 35.1 million galaxies (70%), 0.72 million quasars (34%), and 54.7 million stars (93%) having classification probabilities greater than 0.9. Precision, Recall, and F1 score were determined as a function of selected features and magnitude error. We investigate the effect of class imbalance on our machine learning model and discuss the implications of transfer learning for populations of sources at fainter magnitudes than the training set. We used a non-linear dimension reduction technique (Uniform Manifold Approximation and Projection: UMAP) in unsupervised, semi-supervised, and fully-supervised schemes to visualise the separation of galaxies, quasars, and stars in a two-dimensional space. When applying this algorithm to the 111 million sources without spectra, it is in strong agreement with the class labels applied by our random forest model.</p> <p>When using this dataset, please reference our paper via the journal (<a href="https://arxiv.org/abs/1909.10963">https://arxiv.org/abs/1909.10963</a>) and this DOI (10.5281/zenodo.4060257). If you make use of our scripts please reference our Github repository DOI (10.5281/zenodo.3855160).</p> <p>File descriptions:</p> <p>All of these files are Pandas Dataframes, saved as uncompressed parquet files for ease of access when using cloud compute such as Dask. df_spec_classprobs.parquet contains the spectroscopically observed sources used for training and testing. This has been cleaned, and has the results of the random forest classifier added as additional columns (sources used for training have NaNs in the class_pred column). SDSS-ML-all.parquet contains the 111 million photometrically observed sources, with our class labels and probabilities added.</p>
Supporting Information: Potential of Hydraulic Tomography in Identifying Boundary Conditions of Groundwater Basins
<p>This file contains the supplementary data for this manuscript, including the basic model input files (such as grid discretization, true boundary conditions, wells, true fields, etc.) and the generated animations of transient groundwater flow fields for Scenarios C and D.</p>
InSet: A Tool to Identify Architecture Smells Using Machine Learning
<p>Architectural smells (ASs) are architectural decisions that negatively affect the maintenance and evolution of software. Most of the existing tools able to identify AS rely on few metrics with fixed thresholds. However, it is not possible to define specific metrics and thresholds that meet all the cases, i.e., the classification of a piece of code in smell or not can depend on the domain, the experience of developers, organization patterns or even from a vast set of features - so there is a subjective ingredient in this decision.<br> Machine Learning (ML) can help to make these decisions/classifications more precise by taking into consideration a vast set of features and also feedback from experts.<br> This paper presents a machine learning-based tool to detect the architectural smells Unstable Dependency(UD) and God Component(GC). Our tool is able to take into consideration users' feedback to retrain the algorithms and constantly improve their performance. Our tool got good result in terms of accuracy, precision, recall, F-measure and Kappa's coefficient.</p>
Identifying factors determining the distribution of the invasive pest fall armyworm, Spodoptera frugiperda
<p>The raw data of Identifying factors determining the distribution of the invasive pest fall armyworm, Spodoptera frugiperda</p>
Identifying kaige and (proto-)Lucianic readings in 2 Kings with the help of Old Latin manuscript La115 (Collected cases)
<p>Cases for the paper Identifying kaige and (proto-)Lucianic readings in 2 Kings with the help of Old Latin manuscript La115.<br> A paper given at Boston SBL Annual 2017.</p>
Data from: Genome-wide association studies in dogs and humans identify ADAMTS20 as a risk variant for cleft lip and palate
Cleft lip with or without cleft palate (CL/P) is the most commonly occurring craniofacial birth defect. We provide insight into the genetic etiology of this birth defect by performing genome-wide association studies in two species: dogs and humans. In the dog, a genome-wide association study of 7 CL/P cases and 112 controls from the Nova Scotia Duck Tolling Retriever (NSDTR) breed identified a significantly associated region on canine chromosome 27 (unadjusted p=1.1 x 10-13; adjusted p= 2.2 x 10-3). Further analysis in NSDTR families and additional full sibling cases identified a 1.44 Mb homozygous haplotype (chromosome 27: 9.29 – 10.73 Mb) segregating with a more complex phenotype of cleft lip, cleft palate, and syndactyly (CLPS) in 13 cases. Whole-genome sequencing of 3 CLPS cases and 4 controls at 15X coverage led to the discovery of a frameshift mutation within ADAMTS20 (c.1360_1361delAA (p.Lys453Ilefs*3)), which segregated concordant with the phenotype. In a parallel study in humans, a family-based association analysis (DFAM) of 125 CL/P cases, 420 unaffected relatives, and 392 controls from a Guatemalan cohort, identified a suggestive association (rs10785430; p =2.67 x 10-6) with the same gene, ADAMTS20. Sequencing of cases from the Guatemalan cohort was unable to identify a causative mutation within the coding region of ADAMTS20, but four coding variants were found in additional cases of CL/P. In summary, this study provides genetic evidence for a role of ADAMTS20 in CL/P development in dogs and as a candidate gene for CL/P development in humans.
Data from: Single‐cell profiling screen identifies microtubule‐dependent reduction of variability in signaling
Populations of isogenic cells often respond coherently to signals, despite differences in protein abundance and cell state. Previously, we uncovered processes in the Saccharomyces cerevisiae pheromone response system (PRS) that reduced cell‐to‐cell variability in signal strength and cellular response. Here, we screened 1,141 non‐essential genes to identify 50 "variability genes". Most had distinct, separable effects on strength and variability of the PRS, defining these quantities as genetically distinct "axes" of system behavior. Three genes affected cytoplasmic microtubule function: BIM1, GIM2, and GIM4. We used genetic and chemical perturbations to show that, without microtubules, PRS output is reduced but variability is unaffected, while, when microtubules are present but their function is perturbed, output is sometimes lowered, but its variability is always high. The increased variability caused by microtubule perturbations required the PRS MAP kinase Fus3 and a process at or upstream of Ste5, the membrane‐localized scaffold to which Fus3 must bind to be activated. Visualization of Ste5 localization dynamics demonstrated that perturbing microtubules destabilized Ste5 at the membrane signaling site. The fact that such microtubule perturbations cause aberrant fate and polarity decisions in mammals suggests that microtubule‐dependent signal stabilization might also operate throughout metazoans.
Data from: Chloroplast phylogenomic analysis of chlorophyte green algae identifies a novel lineage sister to the Sphaeropleales (Chlorophyceae)
Background: The class Chlorophyceae (Chlorophyta) includes morphologically and ecologically diverse green algae. Most of the documented species belong to the clade formed by the Chlamydomonadales (also called Volvocales) and Sphaeropleales. Although studies based on the nuclear 18S rRNA gene or a few combined genes have shed light on the diversity and phylogenetic structure of the Chlamydomonadales, the positions of many of the monophyletic groups identified remain uncertain. Here, we used a chloroplast phylogenomic approach to delineate the relationships among these lineages. Results: To generate the analyzed amino acid and nucleotide data sets, we sequenced the chloroplast DNAs (cpDNAs) of 24 chlorophycean taxa; these included representatives from 16 of the 21 primary clades previously recognized in the Chlamydomonadales, two taxa from a coccoid lineage (Jenufa) that was suspected to be sister to the Golenkiniaceae, and two sphaeroplealeans. Using Bayesian and/or maximum likelihood inference methods, we analyzed an amino acid data set that was assembled from 69 cpDNA-encoded proteins of 73 core chlorophyte (including 33 chlorophyceans), as well as two nucleotide data sets that were generated from the 69 genes coding for these proteins and 29 RNA-coding genes. The protein and gene phylogenies were congruent and robustly resolved the branching order of most of the investigated lineages. Within the Chlamydomonadales, 22 taxa formed an assemblage of five major clades/lineages. The earliest-diverging clade displayed Hafniomonas laevis and the Crucicarteria, and was followed by the Radicarteria and then by the Chloromonadinia. The latter lineage was sister to two superclades, one consisting of the Oogamochlamydinia and Reinhardtinia and the other of the Caudivolvoxa and Xenovolvoxa. To our surprise, the Jenufa species and the two spine-bearing green algae belonging to the Golenkinia and Treubaria genera were recovered in a highly supported monophyletic group that also included three taxa representing distinct families of the Sphaeropleales (Bracteacoccaceae, Mychonastaceae, and Scenedesmaceae). Conclusions: Our phylogenomic study advances our knowledge regarding the circumscription and internal structure of the Chlamydomonadales, suggesting that a previously unrecognized lineage is sister to the Sphaeropleales. In addition, it offers new insights into the flagellar structures of the founding members of both the Chlamydomonadales and Sphaeropleales.
Data from: Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease
Coronary heart disease (CHD) is top risk factor for health in modern society, causing high mortality rate each year. However, there is no reliable way for early diagnosis and prevention of CHD so far. So study the mechanism of CHD and development of novel biomarkers is urgently needed. In this study, metabolomics and metagenomics technology are applied to discover new biomarkers from plasma and urine of 59 CHD patients and 43 healthy controls and trace their origin. We identify GlcNAc-6-P which has good diagnostic capability and can be used as potential biomarkers for CHD, together with mannitol and 15 plasma cholines. These identified metabolites show significant correlations with clinical biochemical indexes. Meanwhile, GlcNAc-6-P and mannitol are potential metabolites originated from intestinal microbiota. Association analysis on species and function levels between intestinal microbes and metabolites suggest a close correlation between Clostridium sp. HGF2 and GlcNAc-6-P, Clostridium sp. HGF2, Streptococcus sp. M143, Streptococcus sp. M334 and mannitol. These suggest the metabolic abnormality is significant and gut microbiota dysbiosis happens in CHD patients.
Data from: A methylation-to-expression feature model for generating accurate prognostic risk scores and identifying disease targets in clear cell kidney cancer
Many researchers now have available multiple high-dimensional molecular and clinical datasets when studying a disease. As we enter this multi-omic era of data analysis, new approaches that combine different levels of data (e.g. at the genomic and epigenomic levels) are required to fully capitalize on this opportunity. In this work, we outline a new approach to multi-omic data integration, which combines molecular and clinical predictors as part of a single analysis to create a prognostic risk score for clear cell renal cell carcinoma. The approach integrates data in multiple ways and yet creates models that are relatively straightforward to interpret and with a high level of performance. Furthermore, the proposed process of data integration itself captures relationships in the data that represent highly disease-relevant functions.
Data from: Identifying metabolic subpopulations from population level mass spectrometry
Metabolism underlies many important cellular decisions, such as the decisions to proliferate and differentiate, and defects in metabolic signaling can lead to disease and aging. In addition, metabolic heterogeneity can have biological consequences, such as differences in outcomes and drug susceptibilities in cancer and antibiotic treatments. Many approaches exist for characterizing the metabolic state of a population of cells, but technologies for measuring metabolism at the single cell level are in the preliminary stages and are limited. Here, we describe novel analysis methodologies that can be applied to established experimental methods to measure metabolic variability within a population. We use mass spectrometry to analyze amino acid composition in cells grown in a mixture of 12C- and 13C-labeled sugars; these measurements allow us to quantify the variability in sugar usage and thereby infer information about the behavior of cells within the population. The methodologies described here can be applied to a large range of metabolites and macromolecules and therefore have the potential for broad applications.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.