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720 results for “Identification data”
Data and scripts for Journal of Geophysical Research – Earth Surface publication: Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina
<p>This data source contains scripts and data associated with the JGR Earth Surface publication <strong>“Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina”</strong> by A. Mueting, B. Bookhagen, and M. R. Strecker. The Digital Elevation Model (DEM) of the lower part of the Quebrada del Toro and Río Capilla catchment in the NW Argentinian Andes was generated from SPOT-7 tri-stereo images using Ames Stereo Pipeline. The final dataset has a spatial resolution of 3 m. A full description of the DEM generation process and accuracy assessment can be found in the associated paper. The scripts are also available at https://github.com/UP-RS-ESP/DEM_ConnectedComponents.</p>
Data from: Analysis of the PEBP gene family and identification of a novel FLOWERING LOCUS T orthologue in sugarcane
<p>Sugarcane (<i>Saccharum</i> spp.) is an important economic crop for both sugar and biomass, the yields of which are negatively affected by flowering. The molecular mechanisms controlling flowering in sugarcane are nevertheless poorly understood. RNA-seq data analysis and database searches have enabled a comprehensive description of the PEBP gene family in sugarcane. It is shown to consist of at least 13 <i>FLOWERING LOCUS T </i>(<i>FT</i>)-like genes, two <i>MOTHER OF FT AND TFL </i>(<i>MFT</i>)<i>-</i>like genes, and four <i>TERMINAL FLOWER </i>(<i>TFL</i>)-like genes. As expected, these genes all show very high homology to their corresponding genes in <i>Sorghum</i>, and also to <i>FT</i>-like, <i>MFT-</i>like, and <i>TFL</i>-like genes in maize, rice, and Arabidopsis. Functional analysis in Arabidopsis showed that the sugarcane <i>ScFT3</i> gene can rescue the late flowering phenotype of the Arabidopsis <i>ft-10</i> mutant, whereas <i>ScFT5</i> cannot. High expression levels of <i>ScFT3</i> in leaves of short day-induced sugarcane plants coincided with initial stages of floral induction in the shoot apical meristem as shown by histological analysis of meristem dissections. This suggests that <i>ScFT3</i> is likely to play a role in floral induction in sugarcane; however, other sugarcane <i>FT</i>-like genes may also be involved in the flowering process.</p>
Data from: Identification of selection signals on the X-chromosome in East Adriatic sheep: a new complementary approach
<p>Sheep are one of the most important livestock species in Croatia, found mainly in the Mediterranean coastal and mountainous regions along the East Adriatic coast, well adapted to the environment and mostly kept extensively. Our main objective was therefore to map the positive selection of the X-chromosome (18,983 SNPs that passed quality control), since nothing is known about the adaptation genes on this chromosome for any of the breeds from the Balkan cluster. Analyses were performed on a sample of eight native Croatian breeds (101 females and 100 males) representing the East Adriatic metapopulation and on 10 mouflons (five females and males), all sampled in Croatia. Three classical within-population approaches (extreme Runs of Homozygosity islands, integration Haplotype Scores, and number of Segregating Sites by Length) were applied along with our new approach called Haplotype Richness Drop (HRiD), which uses only the information contained in male haplotypes. We have also shown that phylogenetic analyses, such as the Median-joining network, can provide additional information when performed with the selection signals identified by HRiD. Our new approach identifies positive selection signals by searching for genomic regions that exhibit a sudden decline in haplotype richness. In total, we identified 14 positive selection signals, 11 using the classical approach and three using the HRiD approach, all together containing 34 annotated genes. High repeatability (86%) of results was observed, as 12 identified selection signals were also confirmed in other studies with sheep. HRiD offers an interesting possibility to be used complementary to other approaches or when only males are genotyped, which is often the case in genomic breeding value estimations. These results highlight the importance of the X-chromosome in the adaptive architecture of domestic ruminants, while our novel HRiD approach opens new possibilities for research.</p>
Data from: Molecular identification of an avian predator of mimetic salamanders
<p>Natural field observations of salamander predation are uncommon, in some cases limiting the ability of scientists to study the evolution of toxicity, aposematism, and mimicry in these amphibians. Here, we document field observations of a recently depredated <em>Pseudotriton ruber </em>(Red Salamander) and two <em>Gyrinophilus porphyriticus </em>(Spring Salamanders), members of a putative Müllerian mimetic complex involving <em>Notophthalmus viridescens</em> (Eastern Newts). We use molecular methods to identify the likely avian predator of the Spring Salamanders and underscore the opportunities for future research enabled by the pairing of traditional natural history with modern molecular techniques.</p>
Supplementary data for 'scROSHI - robust supervised hierarchical identification of single cells'
<p>This record contains some of the supplementary files for the manuscript "scROSHI - robust supervised hierarchical identification of single cells" by Prummer et al.</p> <p>The file "scROSHI_Fig03_counts.zip" contains SingleCellExperiment R objects as RDS files, "sce_A.RDS", "sce_B.RDS", "sce_C.RDS", corresponding to the three samples shown in Figure 3. The 'assay' slot of the SingleCellExperiment objects contains the gene x cell raw count matrix, the 'colData' slot contains the description for each cell: barcode, celltype_major, celltype_final, cnv_status.</p> <p>Please see https://github.com/ETH-NEXUS/scROSHI for code generating the cell type labels.</p>
Data from: Integrative identification of incipient lineages in Heuchera longiflora (Saxifragaceae)
Following on emerging understanding of the diversification process, many recent workers have considered infraspecific taxa as valuable for formally recognizing incompletely speciated entities. The distinction between a species and an infraspecific taxon represents a fundamentally subjective weighting of evidence, yet this points further to the need for an evidential basis for these decisions. We explore these concepts in Heuchera longiflora (Saxifragaceae), which is morphologically variable and has a disjunct range across several physiographic provinces in the eastern U.S. using a tiered sampling approach and a combination of Sanger and next generation sequencing (NGS) techniques. We investigated 56 populations with seven markers to investigate population structure, and for a subset of 12 representative populations we sequenced 277 nuclear markers to characterize gene tree discord. Using a variety of methods to overcome high levels of gene conflict, we find evidence for the traditionally recognized taxa H. longiflora var. longiflora and H. longiflora var. aceroides in molecular and morphological data partitions. Our results comprise a case study on the use of multiple sources of data and analytical methods to delimit infraspecific taxa.
Microscopy data from: Identification of genetic interactions with priB links the PriA/PriB DNA replication restart pathway to double-strand DNA break repair in Escherichia coli
<p>Collisions between DNA replication complexes (replisomes) and impediments such as damaged DNA or proteins tightly bound to the chromosome lead to premature dissociation of replisomes at least once per cell cycle in <em>Escherichia coli</em>. Left unrepaired, these events produce incompletely replicated chromosomes that cannot be properly partitioned into daughter cells. DNA replication restart, the process that reloads replisomes at prematurely terminated sites, is therefore essential in <em>E. coli</em> and other bacteria. Three replication restart pathways have been identified in <em>E. coli</em>: PriA/PriB, PriA/PriC, and PriC/Rep. A limited number of genetic interactions between replication restart and other genome maintenance pathways have been defined, but a systematic study placing replication restart reactions in a broader cellular context has not been performed. We have utilized transposon insertion sequencing to identify new genetic interactions between DNA replication restart pathways and other cellular systems. Known genetic interactors with the <em>priB</em> replication restart gene (uniquely involved in the PriA/PriB pathway) were confirmed and several novel <em>priB </em>interactions were discovered. Far fewer connections were found with the PriA/PriC or PriC/Rep pathways, suggesting a primacy role for the PriA/PriB pathway in <em>E. coli</em>. Targeted genetic and imaging-based experiments with <em>priB</em> and its genetic partners revealed significant double-strand DNA break (DSB) accumulation in strains with mutations in <em>dam</em>, <em>rep</em>, <em>rdgC</em>, <em>lexA</em>, or <em>polA</em>. Modulating the activity of the RecA recombinase partially suppressed the detrimental effects of <em>rdgC</em> or <em>lexA</em> mutations in Δ<em>priB</em> cells. Taken together, our results highlight roles for several genes in DSB homeostasis and define a genetic network that facilitates DNA repair/processing upstream of PriA/PriB-mediated DNA replication restart in <em>E. coli</em>.</p>
Data for COLLAPSE: A representation learning framework for identification and characterization of protein structural sites
<p>Data for methods described in the paper "COLLAPSE: A representation learning framework for identification and characterization of protein structural sites" by Alexander Derry and Russ B. Altman (BioRxiv, 2022). https://www.biorxiv.org/content/10.1101/2022.07.20.500713v1</p>
Data & model products from "Identification of carbon dioxide in an exoplanet atmosphere"
<p>Associated Publication: <a href="https://www.nature.com/articles/s41586-022-05269-w">https://www.nature.com/articles/s41586-022-05269-w</a><br> <br> OVERVIEW: Carbon dioxide (CO2) is a key chemical species that is found in a wide range of planetary atmospheres. In the context of exoplanets, CO2 is an indicator of the metal enrichment (i.e., elements heavier than helium, also called “metallicity”), and thus formation processes of the primary atmospheres of hot gas giants. It is also one of the most promising species to detect in the secondary atmospheres of terrestrial exoplanets. Previous photometric measurements of transiting planets with the Spitzer Space Telescope have given hints of the presence of CO2, but have not yielded definitive detections due to the lack of unambiguous spectroscopic identification. Here we present the detection of CO2 in the atmosphere of the gas giant exoplanet WASP-39b from transmission spectroscopy observations obtained with JWST as part of the Early Release Science Program (ERS). The data used in this study span 3.0 - 5.5 µm in wavelength and show a prominent CO2 absorption feature at 4.3 µm (26σ significance). The overall spectrum is well matched by one-dimensional, 10x solar metallicity models that assume radiative-convective-thermochemical equilibrium and have moderate cloud opacity. These models predict that the atmosphere should have water, carbon monoxide, and hydrogen sulfide in addition to CO2, but little methane. Furthermore, we also tentatively detect a small absorption feature near 4.0 µm that is not reproduced by these models.</p>
Data set for the figures in the manuscript "Real-Time Identification of Aerosol-Phase Carboxylic Acid Production Using Extractive Electrospray Ionization Mass Spectrometry"
Open the record for dataset details and reuse information.
Code and Data for "Probabilistic Eddy Identification with Uncertainty Quantification"
<p>The code and data used in the paper "Probabilistic Eddy Identification with Uncertainty Quantification".</p>
"Systematic Identification of Post-Transcriptional Regulatory Modules": preprocessed data
<p>Processed functional genomic data supplementing the paper "<strong>Systematic Identification of Post-Transcriptional Regulatory Modules"</strong></p>
Data from: Identification of reindeer fine-scale foraging behaviour using tri-axial accelerometer data
<p>Animal behavioural responses to the environment ultimately affect their survival. Monitoring animal fine-scale behaviour may improve understanding of animal functional response to the environment and provide an important indicator of the welfare of both wild and domesticated species. In this study, we illustrate the application of collar-attached acceleration sensors for investigating reindeer fine-scale behaviour. Using data from 19 reindeer, we tested the supervised machine learning algorithms random forests, support vector machines, and hidden Markov models to classify reindeer behaviour into seven classes: grazing, browsing low from shrubs or browsing high from trees, inactivity, walking, trotting, and other behaviours. We implemented leave-one-subject-out cross-validation to assess generalizable results on new individuals. Our main results illustrated that hidden Markov models were able to classify collar-attached accelerometer data into all our pre-defined behaviours of reindeer with reasonable accuracy while random forests and support vector machines were biased towards dominant classes. Random forests using 5-second windows had the highest overall accuracy (85%), while hidden Markov models were able to best predict individual behaviours and handle rare behaviours such as trotting and browsing high. We conclude that hidden Markov models provide a useful tool to remotely monitor reindeer and potentially other large herbivore species behaviour. These methods will allow us to quantify fine-scale behavioural processes in relation to environmental events.</p>
Cataloging Distant Galactic Open Clusters: Identification of 739 New Star Clusters Beyond 5 kpc Utilizing GAIA DR3 Data
<p><span>The figures of the 739 open clusters that report in our paper (Cataloging Distant Galactic Open Clusters: Identification of 739 New Star Clusters Beyond 5 kpc Utilizing GAIA DR3 Data)</span></p> <p><span> complete King's model<span> </span>profile fitting, sky charts and 5-panels ( spatial distribution, proper-motion distribution,</span></p> <p><span><span>parallax statistics, parallax distribution, and CMD)</span> .</span></p>
FIGURE 10 in An analysis of fossil identification guides to improve data reporting in citizen science programs
FIGURE 10. An example of a †Cosmopolitodus hastalis photo enhanced by illustration.
Data accompanying "Identification of capsid-like proteins in venomous and parasitic animals"
<p>This data accompanies the pub "Identification of capsid-like proteins in venomous and parasitic animals". The DOI for this pub is <a href="https://doi.org/10.57844/arcadia-14b2-6f27" target="_blank" rel="noopener noreferrer">https://doi.org/10.57844/arcadia-14b2-6f27</a></p> <p> </p> <p><strong>Files and folders included: </strong></p> <p> </p> <p><strong>1.</strong> Manually annotated results of searching VOG HMMs against venom transcriptomes: Annotated_capsid_no_cutoffs_summary_with_seqs_05092024.xlsx</p> <p><strong>2.</strong> Manually annotated results of running preHGT on the genomes of venomous species: Annotated_hgt_results_venom_050924.xlsx</p> <p><strong>3. </strong>The genomes we input into preHGT: preHGT_input_genomes.csv</p> <p><strong>4.</strong> Genbank files of the genomic neighborhood of putatively horizontally transferred viral genes in the tick genome. There are seven GenBank files, one for each contig. Named with protein name.</p> <p><strong>5.</strong> BLAST results from searching putatively endogenized viral proteins against tick salivary transcriptomes: TSA_blast_results.csv</p> <p><strong>6.</strong> Results from searching the putatively endogenized viral capsid proteins against AlphaFold/UniProt50 version 4, AlphaFold/Swiss-Prot version 4, and AlphaFold/Proteome version 4 using the Foldseek webserver: Foldseek_capsid_followup_05092024.xlsx</p> <p><strong>7.</strong> Newick files of the three trees displayed in Figure 3 and Figure 5 . </p>
ProteinCartograhpy data accompanying "Identification of capsid-like proteins in venomous and parasitic animals"
<p>This is the data for the <a href="https://github.com/Arcadia-Science/ProteinCartography/releases/tag/v0.4.2">ProteinCartography</a> analysis in the pub "<a href="https://doi.org/10.57844/arcadia-14b2-6f27">Identification of capsid-like proteins in venomous and parastic animals.</a>" Note that ProteinCartography (v0.4.2) was run in "Cluster" mode or "From-folder" mode using the parameters set in the <code>config_ff.yml</code> and the <em>Ornithodoros turicata </em>proteins. Notebooks used to fetch data and prepare custom plots can be found in the capsids GitHub <a href="https://github.com/Arcadia-Science/capsids">repository</a>.</p> <p> </p> <p><strong>Files in this data repository include: </strong></p> <p><code>output.zip</code> is a folder containing all of the output files for the <em>Ornithodoros</em> ProteinCartography run, including the maps, aggregated features files, and the all-v-all similarity matrix.</p> <p><code>structures.zip</code> is a folder containing all the structures used in the ProteinCartography analysis. Structures beginning with "VOG" are viral capsid proteins folded using <a href="https://doi.org/10.1126/science.ade2574">ESMFold</a>.</p> <p><code>ornithodoros_aggregated_features.tsv</code> is a file containing all the metadata gathered for each protein in the analysis from either <a href="https://www.uniprot.org">UniProt</a> or the <a href="https://vogdb.org">VOG database</a>.</p> <p><code>ornithodoros_aggregated_features_pca_umap.html</code> is the final map of the capsid proteins with the <em>Ornithodoros</em> proteins with metadata overlays.</p> <p><code>ornithodoros_aggregated_features_pca_umap.tsv</code> is a file containing all of the metadata gathered for each protein in the analysis, as well as the coordinates for the map.</p> <p><code>ornithodoros_leiden_similarity.html</code> is a heatmap showing the average between-cluster and within-cluster similarity between every cluster in the analysis.</p> <p><code>tick_or_virus_umap.html</code> is a version of the final map that specifically highlights which proteins are from capsids and which proteins are from <em>Ornithodoros</em>. </p> <p><code>uniprot_features1.tsv</code> is a file containing all of the UniProt metadata fetched for the <em>Ornithodoros</em> proteins, as well as the viral capsid VOG identifiers. This file is used as an input for the ProteinCartography analysis.</p> <p><code>ornithodoros.txt</code> us a file containing all of the <em>Ornithodoros</em> proteins fetched from the <a href="https://doi.org/10.1038/s41586-021-03819-2">AlphaFold</a> <a href="https://doi.org/10.1093/nar/gkab1061">database.</a></p> <p><code>config_ff.yml</code> is the configuration file for the ProteinCartography run. </p>
Raw data for figures used in manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures
<p>Raw Image files used for generating the figures (Fig 2, Fig3, Fig4, Fig5 and Fig6, Supplementary figures, files needed for background subtraction and image processing tutorial (docker image)) in the manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures</p> <p>Use command tar xfz[v] *.tar.gz to retain the file structure. </p> <p>Docker image in image processing tutorial works on Linux platforms only.</p> <p>File structure after un-compressing each *.tar.gz is as follows - </p> <p>1. acetylated_background_signalsFiles.tar.gz</p> <p> - Folders for the different experiments with the name expt[1..30]</p> <p> - *SIGNALS.pkl - Pickle file (python encoded) containing the information of the histogram of the peptide step-drops</p> <p> - acetylated_backgroundFiles_list.csv (file formatted for performing iterative_background.py)</p> <p> - README.txt (information on the contents and the use of the files in the directory)</p> <p>2. fig2.tar.gz</p> <p> - fig2A/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig2B/ (contains raw image folders, processed_results and README.txt)</p> <p>2. fig3and4.tar.gz</p> <p> - acPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - bocPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - README.txt</p> <p>3. fig5.tar.gz</p> <p> - fig5A_panel1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5A_panel2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A3/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5C/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5D/ (contains raw image folders, processed_results and README.txt)</p> <p>5. fig6.tar.gz</p> <p> - fig6B_top/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig6B_bottom/ (contains raw image folders, processed_results and README.txt)</p> <p>6. fig_supplementary08.tar.gz</p> <p> - (contains raw image folders, processed_results and README.txt)</p> <p>7. fig_supplementart12.tar.gz</p> <p> - supplementary_fig14A/ (contains raw image folders, processed_results and README.txt)</p> <p> - supplementary_fig14B/ (contains raw image folders, processed_results and README.txt)</p> <p>8. imageProcessingTutorial.tar.gz</p> <p> - walkthrough_docker_image.tar.xz (contains the docker image with necessary code pre-installed. Includes small example dataset; Works only in linux docker and not macOS)</p> <p> - README.txt (information on the image processing tutorial). </p>
Data for "Identification of Killer Defects in Kesterite Thin-Film Solar Cells"
<p>**README**</p> <p>Data for "Identification of Killer Defects in Kesterite Thin-Film Solar Cells"</p> <p>DOI: 10.1021/acsenergylett.7b01313</p> <p><br> * File Tree <br> ---<br> * DFT_CALC // Row input file for DFT calculation (VASP)<br> * XX_DEFECT_CZTS(e) // Data for CZTS (or CZTSe)<br> * XX_PRIM(ORTHO/221) // Data for bulk (primitive, orthogonal or 2X2X1 supercell)<br> * XX_Defect // Data for defect (V_S, Sn_Cu, Sn_Zn, Cu_Sn)<br> * XX_q // SCF calculation with charge state q <br> * fig // data used to draw figures<br> * 00_atomic_structure <br> * 01_charge_transition_level<br> * 02_charge_density<br> * 03_configuration_coordinate<br> * 20_SI_PHASE_DIAGRAM</p>
NGS data produced in 'Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries'; Nature Communications (2019)
<p>Sharma, G et al. Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries. <em>Nature Communications</em>. Accepted (August 2019)</p> <p><strong>Abstract:</strong></p> <p>Cytotoxic CD8+ T-cells recognize and eliminate infected or malignant cells that present, at their cell surfaces, short peptide epitopes derived from intracellularly processed antigens. However, broadly searching for specific major histocompatibility complex (MHC)-bound peptide epitopes that are naturally processed and capable of eliciting a functional T-cell response has been challenging. Here, we report a method for deep and unbiased T-cell epitope profiling, which is done by using <em>in vitro</em> co-culture of CD8+ T-cells and target cells transduced with high-complexity epitope-encoding minigene libraries. Target cells that are subject to cytotoxic attack from T-cells in co-culture are isolated, before they are lost to apoptosis, by fluorescence-activated cell sorting and characterized by sequencing the minigenes encoded within. In the present study, we validate this highly parallelized method using known murine T-cell receptor/peptide-MHC pairs and diverse minigene-encoded epitope libraries to identify naturally processed and MHC-presented peptide epitopes unambiguously and with high sensitivity.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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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.