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162 results for “msi”
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of tobacco seedlings
<p>Mass spectrometry imaging (MSI) data set in imzML format, of complete tobacco (<em>Nicotiana tabacum</em>) seedling using Laser Desorption Low-Temperature Plasma ionization. Mapping the ion that corresponds to nicotine shows accumulation in the roots and at the borders of leaves.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry <strong>2019</strong> <em>91</em> (4), 2734-2743</p>
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of San Pedro cactus
<p>Mass spectrometry imaging (MSI) data set in imzML format, obtained from San Pedro cactus (<em>Echinopsis pachanoi</em>) cross-section using Laser Desorption Low-Temperature Plasma ionization. Mapping the ion that corresponds to mescaline shows a star-like distribution of this interesting alkaloid.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry <strong>2019</strong> <em>91</em> (4), 2734-2743</p> <p>DOI: 10.1021/acs.analchem.8b04406</p> <p> </p>
Annual land cover maps of Germany based on Sentinel-2 MSI Level 3A (WASP) data
<p>Overview:<br> This annual land cover product is available for the years 2016, 2019, 2020, 2021 for the whole of Germany. It was generated based on Sentinel-2 MSI L3A WASP Data provided by DLR (https://geoservice.dlr.de/data-assets/4hcq6dgkj648.html). For a complete description of the classification procedure please refer to<br> Riembauer, G.; Weinmann, A.; Xu, S.; Eichfuss, S.; Eberz, C.; Neteler, M.: Germany-wide Sentinel-2 based land cover classification and change detection for settlement and infrastructure monitoring. In: Proceedings of the 2021 conference on Big Data from Space (doi:10.2760/125905), 2021.</p> <p>Source data:</p> <ul> <li>Satellite data <ul> <li>German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648</li> </ul> </li> <li>Auxiliary data <ul> <li>European Union, Copernicus Land Monitoring Service, European Environment Agency (EEA), <strong>Copernicus High Resolution Layer: Imperviousness Status Map, 2018 </strong>(https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/imperviousness-density-2018)</li> <li><strong>OpenStreetMap</strong> Planet dump retrieved from https://planet.osm.org, https://www.openstreetmap.org</li> <li><strong>S2GLC Map of Europe</strong> (R. Malinowski, S. Lewiński, M. Rybicki, E. Gromny, M. Jenerowicz, M. Krupiński, A. Nowakowski, C. Wojtkowski, M. Krupiński, E. Krätzschmar, and P. Schauer, "Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery," Remote Sensing, vol. 12, no. 21, p. 3523, 2020.)</li> </ul> </li> </ul> <p>File naming:<br> classification_map_germany_[year].tif example: classification_map_germany_2020.tif</p> <p>Projection + EPSG code:<br> WGS 84 / UTM zone 32N (EPSG: 32632)</p> <p>Spatial extent:<br> north: 55:03:38.646483N<br> south: 47:08:24.738401N<br> west: 5:33:47.816647E<br> east: 15:34:24.108516E</p> <p>Spatial resolution:<br> 10 m</p> <p>Format: COG (Cloud-Optimized GeoTIFF)</p> <p>Pixel values:<br> 10: forest<br> 20: low vegetation<br> 30: water<br> 40: built-up<br> 50: bare soil<br> 60: agriculture</p> <p>Temporal coverage:<br> Years 2016, 2019, 2020, 2021</p> <p>Software used:<br> GRASS 7.8, actinia</p> <p>Original dataset license:<br> The Sentinel-2 level 3A data produced and distributed by DLR are based on Copernicus Sentinel-2 level 1C data, which are subject to the following license: https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf One of the following citations is mandatory for using the provided MAJA/WASP L3A product: German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648 or Contains modified Copernicus Sentinel data, processed by DLR, licensed under CC-BY 4.0</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
Laser Ablation Electrospray Ionization Mass Spectrometry Imaging (LAESI MSI) of Arabidopsis thaliana leaf
<p>Mass spectrometry imaging (MSI) data set in imzML format, obtained from the 5th leaf of an Arabidopsis thaliana wildtype plant using Laser Ablation Electrospray Ionization. Laser ablation took place with 20 pulses per pixel at an energy of 58.4 µJ/pulse. The ROI measures 9 mm by 5 mm and was sampled with a step size of 200 µm.</p>
Low-temperature plasma mass spectrometry imaging (LTP-MSI) of Chili pepper
<p>Low-temperature plasma mass spectrometry imaging of a chili pepper. The data set contains raw data and the scripts to prepare the imzML file. The final imzML/ ibd files are also included.</p> <p>The creation of the data is described in: Maldonado-Torres, Mauricio, José Fabricio López-Hernández, Pedro Jiménez-Sandoval, and Robert Winkler. 2014. “‘Plug and Play’ Assembly of a Low-Temperature Plasma Ionization Mass Spectrometry Imaging (LTP-MSI) System.” <em>Journal of Proteomics</em> 102C (March): 60–65. doi:10.1016/j.jprot.2014.03.003.</p> <p>The data set was re-analyzed using R scripts, as reported in: Gamboa-Becerra, Roberto, Enrique Ramírez-Chávez, Jorge Molina-Torres, Robert Winkler, Enrique Ramírez-Chávez, Jorge Molina-Torres, and Robert Winkler. 2015. “MSI.R Scripts Reveal Volatile and Semi-Volatile Features in Low-Temperature Plasma Mass Spectrometry Imaging (LTP-MSI) of Chilli (Capsicum Annuum).” <em>Analytical and Bioanalytical Chemistry</em> 407 (19): 5673–84. doi:10.1007/s00216-015-8744-9.</p>
Harmonized Chlorophyll-a dataset from Landsat-8/9 OLI and Sentinel 2 MSI in lakes of the Yunnan-guizhou Plateau, China
<p>We generated a harmonized Chl-a dataset for the lakes in the Yunnan–Guizhou Plateau in China from 2013 to 2022 by the Landsat 8/9 and Sentinel-2A/B virtual constellation. Here, we shared the mean chlorophyll-a in nine major lakes in studied area. </p><p>These dataset were aggregated from MSI- and OLI-derived Chl-a images and were subseted using the boundary of each lake. Data format is Geo-Tiff (*.tif) and zero values are INVALID values. More details on Chl-a retrievals can be found in: </p><p>Z. Cao et al., "Harmonized Chlorophyll-a Retrievals in Inland Lakes From Landsat-8/9 and Sentinel 2A/B Virtual Constellation Through Machine Learning," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022, Art no. 4209916, doi: 10.1109/TGRS.2022.3207345.</p>
Sentinel-2 derived Sphagnum and herbaceous CI, GCC, NDVI, MSI, SL2P10 LAI, and hourly temperature, water table depth, PAR on the Bernadouze peatland from 2017 to 2021 and 2D scans LAI over 2021.
<p>This release contains data from field campaign over the Bernadouze Peatland and satellite sentinel-2 derived vegetation indices from 2017-01-01 to 2021-12-31.</p> <p>Sentinel-2 derived Sphagnum and herbaceous chlorophyll index, green chromatic coordinate, normalised difference index, moisture soil index retrived on google earth engine from 2017-01-01 to 2021-12-31 on the Bernadouze peatland.</p> <p>Sentinel-2 sphagnum and herbaceous leaf area index (m².m-²) computed with the SL2P10 algorithm from 2017-01-01 to 2021-12-31 thanks to google earth engine.</p> <p>Sphagnum leaf area index (m².m-²), measured with a 2D-scan (LI3100 Area Meter) over the 2021 season on the Bernadouze peatland.</p> <p>Reflectance over the 12 bands of Sentinel-2 on two areas of the Bernadouze peatland : one dominated by Sphagnum mosses and the other by herbaceous vegetation. Data related to an image acquired the 2021-07-21.</p> <p>Hourly air temperature (°C) and photosynthetically active radiations (umol.m-².s-1) derived from the S2M (SAFRAN–SURFEX, ISBA–Crocus–MEPRA) reanalysis chain on the Bernadouze peatland. Vertical resolution of 300m on the 'Couseran' massif.</p> <p>Hourly water table depth (m) from 10 piezometers (PZ1, ..., PZ10) over the Bernadouze peatland. Measured with 10 Orpheus Mini Water Level Logger, OTT HydroMet, Germany.</p> <p>Growth primary productivity of dominant peatland vegetation (umol.m-².s-1) calculated by the difference of measured net primary productivity and of measured ecosystem respiration flux under dark conditions. Measurements of GPP and ER are made with a soil chamber connected to a LI-COR LI-7810 analyser from 2017-01-01 to 2021-12-31 on the Bernadouze peatland.</p>
Data from: SLICE-MSI: A machine learning interface for system suitability testing of mass spectrometry imaging platforms
Open the record for dataset details and reuse information.
Strong histopathological relevance patches for MSI vs. MSS classification
<p>This is a subset of dataset "Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samples" CRC_DX[1]. For more details, please visit [1]. After a fused distillation, a neural network based framework will vote for the candidates for the histopathological strong relevance patches. We incorporate the morphology knowledge from pathologists to sort out the most representative samples and tag them to this subdata.</p> <p>The dataset has two labels i.e., MSIMUT_strong and MSS_strong.</p> <p>MSIMUT_strong : 28 307 patches.</p> <p>MSS_strong : 27 146 patches.</p> <p>[1] Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samples. https://doi.org/10.5281/zenodo.2530835</p>
MSI-MLR_Detail on surface topography and varnish on coffin of Butehamun, RMAH-Brussels-Inv. E.5288
<p>Detail on the coffin of Butehamun (panel of the legs of the left wall), Royal Museums of Art and History, Brussels, Inv. E.5288 (© RMAH). Compilation of MS PLD screenshots (PLD viewer 7.01.04).<br> <strong>a</strong>. General colour view of the investigated area surface with indication of the location of zoom 1; <strong>b</strong>. Zoom 1, demonstrating the ability to measure the surface topography in three dimensions; <strong>c</strong>. Five visualisations of the same area (zoom 2, indicated in Figure 7b) with different underlying normal maps, based on the image datasets made with the five spectral bands in the MS PLD system.</p>
MSI-MLR_Treated area coffin Butehamun_RMAH-Brussels-E.5288
<p>Treated area on the coffin of Butehamun (top frieze and panel of the trunk of the left wall), Royal Museums of Art and History, Brussels, Inv. E.5288 (© RMAH). Compilation of MS PLD screenshots (PLD viewer 7.01.04).</p> <p><strong>a</strong>. General view of the investigated area surface;<strong> b</strong>. Result with the UV reflections; <strong>c</strong>. Result with the IR reflections; <strong>d</strong>. False colour Green-Blue-UV; Zoom 1 and Zoom 2. Visualisation of surface relief with normal map based on reflections by the IR dataset.</p>
Identificação de nuvens em imagens do Sentinel-2 MSI
<p>Dataset usado para desenvolver projeto na disciplina PTC3567 com rede neural destinada a estimar a probabilidade de presença de nuvem em cada pixel da imagem.</p>
Cell Graph data for predicting MSI vs. MSS from histological images
<p><strong>Abstract: </strong>The cell graph data extracted from histological images for predicting microstatellite status. This is a patch-level (patch refers to the sub-image tessellated from the whole-slide image) binary classification task: the two datasets classify images patches to either MSS (microsatellite stable) or MSIMUT (microsatellite instable or highly mutated). The original histology patches are available from [1]. </p> <p><strong>Cell Graph Contraction:</strong> In this dataset, we establish a cell graph for each patch. The cell graph can represent the cell-cell interaction and the collection of cell graphs for all patches provide a precise characterization of tumor microenvironment. With only the availability of raw image patches, we leverage the nuclei regions segmented by a well-tuned CA<sup>2.5</sup>-Net [2] to extract the node features of each single nuclei node. And then we extract a total number of 94 pre-defined pathomics features for each nuclei region as the corresponding graph node feature. As the morphological signals are believed relative to cell-cell interplay, the cell-specific features which include the nuclei coordination, optical, and representations, then characterize the cell-level morphological behavior. We then calculate the pair-wise Euclidean distance between nuclei centroids to establish edges of a cell graph</p> <p><strong>Reference</strong></p> <p>For more details about the construction of cell-graph, please visit our paper: https://arxiv.org/abs/2206.07599.</p> <p>The associated codes and usage for the datasets are available at: https://github.com/yiqings/HEGnnEnhanceCnn.</p> <p>[1] Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samples. https://doi.org/10.5281/zenodo.2530835</p> <p>[2] Huang, J., Shen, Y., Shen, D., & Ke, J. (2021, September). CA 2.5-Net Nuclei Segmentation Framework with a Microscopy Cell Benchmark Collection. In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em> (pp. 445-454). Springer, Cham.</p>
Study of MK-3475 in Patients With Microsatellite Unstable (MSI) Tumors (Cohorts A, B and C)
ClinicalTrials.gov study NCT01876511. IPD Sharing: NO. Countries: 1. Publications: 2.
Study of Pembrolizumab Following Surgery in Patients With Microsatellite Instability High (MSI-H) Solid Tumors
ClinicalTrials.gov study NCT03832569. IPD Sharing: YES. Countries: 1. Publications: 2.
Avelumab in Patients With MSS, MSI-H and POLE-mutated Recurrent or Persistent Endometrial Cancer and of Avelumab/Talazoparib and Avelumab/Axitinib in Patients With MSS Recurrent or Persistent Endometr
ClinicalTrials.gov study NCT02912572. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluation of Co-formulated Pembrolizumab/Quavonlimab (MK-1308A) Versus Other Treatments in Participants With Microsatellite Instability-High (MSI-H) or Mismatch Repair Deficient (dMMR) Stage IV Color
ClinicalTrials.gov study NCT04895722. IPD Sharing: YES. Countries: 22. Publications: 1.
A Study of Nivolumab, Nivolumab Plus Ipilimumab, or Investigator's Choice Chemotherapy for the Treatment of Participants With Deficient Mismatch Repair (dMMR)/Microsatellite Instability High (MSI-H) M
ClinicalTrials.gov study NCT04008030. IPD Sharing: Not stated. Countries: 24. Publications: 2.
Demonstrating voxel-by-voxel (v×v) single-point calibration in liver tissue by IR-MALDESI quantitative MSI
Open the record for dataset details and reuse information.
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of jimsonweed
<p>Mass spectrometry imaging (MSI) data set in imzML format, obtained from jimsonweed (Datura stramonium) fruits and seeds using Laser Desorption Low-Temperature Plasma ionization. The data indicate the non-uniform distribution of tropane alkaloids.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry 2019 91 (4), 2734-2743</p> <p>DOI: 10.1021/acs.analchem.8b04406</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.