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1,552 results for “patch”
total RNA-seq from Peyer's patch (ENCSR544SAU)
GEO Series GSE88350. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Histone ChIP-seq from Peyer's patch (ENCSR927PLE)
GEO Series GSE143111. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Histone ChIP-seq from Peyer's patch (ENCSR349GPJ)
GEO Series GSE139746. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Transcriptomic profiles of jejunum, liver, spleen and jejunal Peyer's patches in piglets exposed to Fumonisin B1
GEO Series GSE189198. Sus scrofa. 47 samples. Type: Expression profiling by array; Third-party reanalysis.
Shared and unique features distinguishing follicular T helper (TFH) and regulatory (TFR) cells from peripheral lypmh node and Peyer's Patches [Agilent-048306]
GEO Series GSE106490. Mus musculus. 20 samples. Type: Expression profiling by array.
NeuroD1-driven differentiation of medulloblastoma cells from Patched 1 deficient mice
GEO Series GSE150752. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
ChIP-seq from Peyer's patch (ENCSR124GAS)
GEO Series GSE95934. Homo sapiens. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Histone ChIP-seq from Peyer's patch (ENCSR440PMP)
GEO Series GSE139774. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
The G-patch protein NF-κB-repressing factor mediates the recruitment of the exonuclease XRN2 and activation of the RNA helicase DHX15 in human ribosome biogenesis
GEO Series GSE92733. Homo sapiens. 4 samples. Type: Other.
Mononuclear phagocytes in neonatal Peyer's patches
GEO Series GSE188714. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Histone ChIP-seq from Peyer's patch (ENCSR718LSM)
GEO Series GSE143067. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Uniprot datasets with variable patch sizes for testing taxonomic classification
<p>These datasets can be used to test the performance of the taxonomic classification model deposited at <a href="https://zenodo.org/record/4306499">https://zenodo.org/record/4306499</a> and trained using the data deposited at <a href="https://zenodo.org/record/4306240">https://zenodo.org/record/4306240</a> with different patch sizes:</p> <ul> <li>300 +- 10 bases</li> <li>300 +- 25 bases</li> <li>300 +- 50 bases</li> </ul> <p>Those are the same sequences as used in the test dataset mentioned above, but sampled at different lengths.</p>
Quadrophonic Recordings of free improvisation by the MS&I workshop using alps-autopan Max patch
<p>XXXXXXXXXXXXX<br> XX READ ME: X<br> XXXXXXXXXXXXX</p> <p>The audio-files in these subfolders are MP3 - stems for a 4 channel surround sound system.</p> <p>These recordings have been left unedited due to their academic nature, demonstrating the Autopan Max Patch <a href="https://doi.org/10.5281/zenodo.5607120">(https://doi.org/10.5281/zenodo.5607120)</a> in conjunction with the Acoustic Localisation Positioning System (ALPS) algorithm <a href="https://doi.org/10.5281/zenodo.5602869">(https://doi.org/10.5281/zenodo.5602869</a>). They are not intended to be played as musical works in their own rights. For the recording, the loudspeakers were at equal distance within a 3 m radius. The files sound best if played back on loudspeakers in a similar arrangement.Due to their raw nature, THERE MAY BE CLICKS AND LOUD PEAKS TO BE AWARE OF DURING PLAYBACK!! Also, Although acoustic feedback on these recordings are intentional, they are not level-limited, please be aware.</p> <p>We also would like to underline that these are recordings of a free interdisciplinary improvisation, so only form one aspect of a participatory practice.</p> <p>On behalf of the MS&I Collective, Dominik Schlienger 2021</p>
Improving Histopathological Image Classification with Patch-based Embeddings and Ensemble Learning: A Study on Enteroscope Biopsy Images
<p>This dataset contains the application codes developed for Enteroscope Biopsy Histopathological images and the images divided into patches in the dataset.</p>
Figure 2 from: Al-Razi H, Maria M, Bin Muzaffar S (2019) Mortality of primates due to roads and power lines in two forest patches in Bangladesh. Zoologia 36: 1-6. https://doi.org/10.3897/zoologia.36.e33540
Figure 2 Type of mortality of different primate species in both study sites.
Figure 1 in Food patch particularity and forging strategy of reintroduced Przewalski's horse in North Xinjiang, China
Figure 1. Study area.
For wireless applications, design and analysis of patch antenna at 2.45 GHz
Open the record for dataset details and reuse information.
SD4EO: Sentinel-2 Northern France Patch Dataset (RGB+NIR)
<p>This dataset has been created as part of the deliverables for ESA’s <a href="https://eo4society.esa.int/projects/sd4eo/">SD4EO project</a>.</p> <p>It consists of orthogonal patches from real satellite images of Sentinel-2, covering the visible and near-infrared bands, taken over large areas in northern France. The following tiles are included:</p> <div> <table> <tbody> <tr> <td> <p><strong>Tile ID</strong></p> </td> <td> <p><strong>Sampling Date</strong></p> </td> <td> <p><strong>Number of patches</strong></p> </td> </tr> </tbody> <tbody> <tr> <td> <p>T31TCJ</p> </td> <td> <p>2021/04/15</p> </td> <td> <p>3306</p> </td> </tr> <tr> <td> <p>T31TCN</p> </td> <td> <p>2021/04/25</p> </td> <td> <p>3120</p> </td> </tr> <tr> <td> <p>T31TDN</p> </td> <td> <p>2021/04/25</p> </td> <td> <p>3481</p> </td> </tr> <tr> <td> <p>T31UDP</p> </td> <td> <p>2021/04/25</p> </td> <td> <p>3540</p> </td> </tr> <tr> <td> <p>T31UEP</p> </td> <td> <p>2021/04/25</p> </td> <td> <p>3599</p> </td> </tr> <tr> <td> <p>T30UWU</p> </td> <td> <p>2023/11/07</p> </td> <td> <p>2928</p> </td> </tr> <tr> <td> <p>T31TCL</p> </td> <td> <p>2024/05/09</p> </td> <td> <p>3422</p> </td> </tr> <tr> <td> <p>T31TDL</p> </td> <td> <p>2024/05/09</p> </td> <td> <p>3306</p> </td> </tr> <tr> <td> <p>T31TDM</p> </td> <td> <p>2021/03/31</p> </td> <td> <p>2891</p> </td> </tr> <tr> <td> <p>T31TEM</p> </td> <td> <p>2021/04/27</p> </td> <td> <p>3480</p> </td> </tr> <tr> <td> <p>T31TEN</p> </td> <td> <p>2021/04/27</p> </td> <td> <p>3038</p> </td> </tr> <tr> <td> <p>T31TFM</p> </td> <td> <p>2021/04/27</p> </td> <td> <p>2576</p> </td> </tr> <tr> <td> <p>T31UDQ</p> </td> <td> <p>2023/10/07</p> </td> <td> <p>3599</p> </td> </tr> <tr> <td> <p>T31UEQ</p> </td> <td> <p>2023/10/07</p> </td> <td> <p>3540</p> </td> </tr> <tr> <td> <p>T30TXT</p> </td> <td> <p>2023/10/10</p> </td> <td> <p>3420</p> </td> </tr> <tr> <td> <p>T30TYS</p> </td> <td> <p>2023/10/10</p> </td> <td> <p>3534</p> </td> </tr> <tr> <td> <p>T30UXU</p> </td> <td> <p>2023/10/10</p> </td> <td> <p>3599</p> </td> </tr> <tr> <td> <p>T30UYU</p> </td> <td> <p>2023/10/10</p> </td> <td> <p>3654</p> </td> </tr> <tr> <td> <p>T31TFN</p> </td> <td> <p>2024/05/11</p> </td> <td> <p>3363</p> </td> </tr> <tr> <td> <p>T31UFP</p> </td> <td> <p>2024/05/11</p> </td> <td> <p>3658</p> </td> </tr> <tr> <td> <p>T31UFQ</p> </td> <td> <p>2024/05/11</p> </td> <td> <p>3660</p> </td> </tr> <tr> <td> <p>T30TVK</p> </td> <td> <p>2024/05/11</p> </td> <td> <p>2970</p> </td> </tr> <tr> <td> <p><strong>TOTAL</strong></p> </td> <td> <p><strong>73,684</strong></p> </td> </tr> </tbody> </table> </div> <p> </p> <p>The tiles were selected to minimize cloud cover, which is why the sampling dates are spread across a wide range. Additionally, care was taken to capture images close to winter and late spring, ensuring that lighting conditions were either at dawn or dusk, which helps reduce saturation effects on highly reflective surfaces such as flat industrial rooftops.</p> <p>Each patch covers an area of approximately 1700x1700 meters (though the exact size may vary slightly with latitude). Patches located at the edges of tiles were excluded to avoid potential cutting or distortion issues. Likewise, patches near large bodies of water, including coastlines, were also discarded.</p> <p>It's important to note that, unlike the synthetic image dataset [<a href="https://zenodo.org/records/13208361">link</a>], in this case, the patches are always disjoint.</p> <p>To facilitate the use of these images for training generative AI models (such as GANs and Diffusion models), the patch size has been standardized to 512x512 pixels, providing an effective resolution of 3.3 meters per pixel. The color channels were scaled according to the accumulated histograms in order to embrace most of the energy (>90%): Thus, the values from 0 to 256 in the red channel correspond to the range of 460.0 to 6700.0 in the irradiance captured by Sentinel-2 for the patches in this dataset. The values in the green channel correspond to a range of 740.0 to 5800.0, and the blue channel values span from 400.0 to 4840.0 as recorded by Sentinel-2 sensors.</p> <p>To simplify their usage, the images are stored in lossless PNG format.</p> <p>Each patch is also accompanied by pixel-level labels based on the color-coded annotations from OpenStreetMap data in the dataset <a href="https://zenodo.org/records/13958096">doi:10.5281/zenodo.13958096</a> Both this dataset and the OpenStreetMap-labeled dataset have been used to train a conditional diffusion model, which generated a synthetic dataset of 46.5 GB that can be accessed at this [<a href="https://zenodo.org/records/13208361">link</a>].</p> <p>The file naming convention is straightforward:</p> <p> Patch_<Tile Name>_<Column Number>_<Row Number>_S2.png</p> <p>Columns and rows are always referenced within the same tile.</p> <p> </p> <p>The images from Sentinel satellites, part of the European Union's Copernicus program, are available under an open access policy. Specifically, they are distributed under the <strong>Creative Commons Attribution-ShareAlike 3.0 IGO (CC BY-SA 3.0 IGO)</strong> license. This means that you are free to share, use, and adapt the images, even for commercial purposes, as long as appropriate credit is given, and any derivative work is shared under the same license: <a href="https://open.esa.int/copernicus-sentinel-satellite-imagery-under-open-licence/">Open Access at ESA</a>. This open access policy allows wide usage for research, education, and even commercial applications, with the goal of supporting environmental monitoring and other societal needs. So, <strong>we have selected the closest avaliable licence in Zenodo, as this dataset is a derivative work.</strong></p> <p>The SD4EO Project is funded by ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and is supervised by ESA Φ-lab.</p>
Single-arm, open-label, multicenter first in human study to evaluate the safety and performance of Dura Sealant Patch in reducing CSF leakage following elective cranial surgery: The ENCASE trial
<p><span><span><span><span><span><span><span><span><span><span><span><b>Objective:</b> The Dural Sealant Patch (DSP) is designed for watertight dural closure after cranial surgery. The goal of this study is to assess, for the first time, safety and performance of the DSP as a means of reducing CSF leakage in patients undergoing elective cranial intradural surgery with a dural closure procedure.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Design:</b> First in human, open-label, single-arm, multicenter study with 360 days (12 months) follow up. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Setting: </b>Three large tertiary reference neurosurgical centers, 2 in the Netherlands and 1 in Switzerland. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Participants</b>: Forty patients undergoing elective cranial neurosurgical procedures, stratified into 34 supra- and 6 infratentorial trepanations.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Intervention:</b> Each patient received one DSP after cranial surgery and closure of the dura mater with sutures.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Outcome measures:</b> Primary composite endpoint was occurrence of one of the following events: postoperative percutaneous CSF leakage, intraoperative leakage at 20 cm H2O positive end-expiratory pressure or postoperative wound infection. Overall success was defined as achieving the primary endpoint in no more than 2 patients. Secondary endpoints were device related serious adverse events (SAEs) or adverse events (AEs), pseudomeningocele and thickness of dura + DSP. Additional endpoints were reoperation in 30 days and user satisfaction. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results:</b> No patients met the primary endpoint. No device related (S)AEs were observed. There were two incidences of self-limiting pseudomeningocele as confirmed on MRI. Thickness of dura and DSP were (mean +/- SD) 3.5 mm +/- 2.0 at day 7 and 2.1 mm +/- 1.2 at day 90. No patients were re-operated within 30 days. Users reported a satisfactory design and intuitive application.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><strong>Conclusions: </strong>DSP, later officially named Liqoseal, is a safe and potentially efficacious device for reducing CSF leakage after intracranial surgery, with favorable clinical handling characteristics. A randomized controlled trial is needed to assess Liqoseal efficacy against the best current practice for reducing postoperative CSF leakage.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Strengths and limitations</b></span></span></span></span></span></span></span></span></span></span></span></p> <ul> <li>The trial studies a device to prevent postoperative cerebrospinal fluid (CSF) leakage, which is one of the most common neurosurgical complications.</li> <li>The study protocol was performed in multiple centers, registered, pre-published and strictly followed.</li> <li>The composite endpoint of the trial reduced the number of inclusions needed.</li> <li>The study did not involve a comparison to current clinical standard and has a potential selection bias, so generalization of results with regard to DSP efficacy needs to be cautiously undertaken.</li> </ul>
Figure 2 from: Hałaj R, Osiadacz B (2015) On foreign land: the conquest of Europe by Cinara curvipes (Patch, 1912). Deutsche Entomologische Zeitschrift 62(2): 261-265. https://doi.org/10.3897/dez.62.6457
Figure 2 - Cinara curvipes on the trunk of Abies concolor (29 May 2015) [photo by M. Kręciała].
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.