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2,399 results for “fragmenter”
PUS10-induced tRNA fragmentation impacts retrotransposon-driven inflammation [CUT&RUN]
GEO Series GSE291044. Mus musculus. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Spatial co-fragmentation pattern of cell-free DNA recapitulates in vivo chromatin organization and identifies tissue-of-origin
GEO Series GSE124974. Homo sapiens. 3 samples. Type: Other.
tRNA-derived fragments regulate the functional maturation of neonatal β-cells [RNA-Seq]
GEO Series GSE163584. Rattus norvegicus. 8 samples. Type: Expression profiling by high throughput sequencing.
Role of tRNA-derived fragments in the cross-talk between immune cells and beta cells during type 1 diabetes pathogenesis (NOD EVs)
GEO Series GSE242565. Mus musculus. 3 samples. Type: Non-coding RNA profiling by high throughput sequencing.
C/D box snoRNA SNORD113-6 guides 2´-O-methylation and directs fragmentation of tRNALeu(TAA) in human arterial fibroblasts
GEO Series GSE190537. Mus musculus. 2 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Chemotherapy-induced CA-repeat DNA fragments of breast cancer triggers robust immune responses
GEO Series GSE302920. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
Sleep fragmentation aggravates myocardial infarction through promoting type I interferon-mediated SiglecFhi neutrophil expansion
GEO Series GSE276317. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
M6A IP from BrU-labeled and fragmented nascent RNA.
GEO Series GSE114543. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Fat cadherin cleavage releases a transcriptionally active nuclear fragment to regulate target gene expression [ChIP-Seq]
GEO Series GSE308417. Drosophila melanogaster. 34 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
RtcB RNAseq to map cyclic phosphate-bearing RNA fragments during poly I:C treatment
GEO Series GSE131130. Homo sapiens. 2 samples. Type: Other.
Serum tRNA-derived fragments (tRFs) as potential candidates for diagnosis of non-triple-negative breast cancer
GEO Series GSE134992. Homo sapiens. 3 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Identification of ribosome-protected mRNA fragments (RPFs) associated with NAT10 in mouse hearts
GEO Series GSE295333. Mus musculus. 4 samples. Type: Other.
Cellular active small molecule inhibitors of Mycobacterium tuberculosis by NMR fragment screen
GEO Series GSE17424. Mycobacterium tuberculosis H37Rv. 8 samples. Type: Expression profiling by array.
PUS10-induced tRNA fragmentation impacts retrotransposon-driven inflammation [iCLIP]
GEO Series GSE248956. Mus musculus. 3 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Role of tRNA-derived fragments in the cross-talk between immune cells and beta cells during type 1 diabetes pathogenesis
GEO Series GSE242568. Mus musculus. 52 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Soil rock fragment content (fragvol; % volume) soil maps of the Upper Colorado River Basin
<p>The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:</p> <p>Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release,<a href="http://https//doi.org/10.5066/P9SK0DO2"> https://doi.org/10.5066/P9SK0DO2</a>.</p> <p>Associated publication:</p> <p>Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. <a href="https://doi.org/10.1002/saj2.20080">https://doi.org/10.1002/saj2.20080</a>.</p> <p>Version 3: Updated cross validation graphs with correct units labeled as ln(%vol+1).</p> <p>Version 2: Unfortunately, errors were found in the original training data preparation in version 1. This version corrects those errors and has resulted in cross validation accuracy increases (R<sup>2</sup>) from ~0.4 to ~0.55-0.6 for the shallower depths.</p> <p>Repository includes maps of soil rock fragment (> 2mm) content (fragvol) as defined by United States soil survey program. </p> <p>These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates. </p> <p>The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.</p> <p>Nauman, T. W., Duniway, M. C., 2020. A hybrid approach for predictive soil property mapping using conventional soil survey data. Soil Science Society of America Journal.</p> <p>File Name Details:</p> <p>ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).</p> <p>Elements are separated by underscore (_) in the following sequence:</p> <p>property_r_depth_cm_geometry_model_additional_elements.extension</p> <p>Example: fragvol_r_0_cm_2D_QRF_bt.tif </p> <p>Indicates soil rock fragment content (fragvol) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. The _bt indicates that the map has been back transformed from ln or sqrt transformation used in modeling.</p> <p>**also for 15 and 30 cm, some files also had "_ART_SG100covs_bt.tif": these are the property prediction layers for those depths. These irregularities are due to file names not getting updated before upload, and issues creating a new version with corrected filenames (3/26/2019).</p> <p>The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).</p> <p>_95PI_h: Indicates the layer is the upper 95% prediction interval value.</p> <p>_95PI_l: Indicates the layer is the lower 95% prediction interval value.</p> <p>_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.</p> <p>References</p> <p> Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma</p>
CLOSED - Effect of sleep fragmentation on pain perception
<p>Moved primary listing to figshare.com.</p>
A Digital Fragmentation Method More Applicable to Artificial Intelligence Drug Design
Open the record for dataset details and reuse information.
cell-free fragmentation of urine and plasma samples of bladder cancder
<p>Cell-free DNA of urine and plasma samples (bladder cancer samples and control samples) was whole-genome sequenced. The reads were mapped to the human genome (GRCh37). Each zip file contains the locations of the mapped fragments of the corresponding sample. </p>
Bodhgayā, Bihār. Fragments of glass.
<p>Bodhgayā, Bihār. Fragments of glass. Identified as aquamarine in the nineteenth century. British Museum 1892,1103.62 presented by Alexander Cunningham.</p>
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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.