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30,948 results for “Profile”
Fig. 6 in Theodor Kotschy in Iran, 1841-1843. Botanical collections and an early printed vegetation profile
Fig. 6. – Lectotype of Aegilops kotschyi Boiss. (G-BOIS). Specimen collected in Iran, near Sabst-Buschom [Kuh-e Sabz Pushan] in May 1842 by Theodor Kotschy.
Fig. 4 in Theodor Kotschy in Iran, 1841-1843. Botanical collections and an early printed vegetation profile
Fig. 4. – Theodor Kotschy's passport, issued 29 December 1835. [recto; © Archiv, Naturhistorisches Museum, Vienna]
Fig. 7 in Theodor Kotschy in Iran, 1841-1843. Botanical collections and an early printed vegetation profile
Fig. 7. – Rudolf Friedrich Hohenacker, HÖhenprofil und Kärtchen des südwestlichen Theiles von Persien. Esslingen, 1846. (Annotation in unknown hand). Detail of the HÖhenprofil is shown below.
data sets of "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland"
<p>data sets from "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland"</p> <p>Monthly means ozone profiles data sets of MCH homogenized Dobson D051 and of Brewer B040 used in the article entitled: "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland" by Eliane Maillard Barras, Alexander Haefele, René Stübi, Achille Jouberton, Herbert Schill, Irina Petropavlovskikh, Koji Miyagawa, Martin Stanek, and Lucien Froidevaux.</p> <p><a href="https://doi.org/10.5194/acp-2022-344">https://doi.org/10.5194/acp-2022-344</a></p>
OMPS-LP ozone profiles retrieved at the University of Bremen - IUP
<p>This data set includes ozone profiles retrieved at the University of Bremen (IUP) from OMPS-LP observations. The retrieval range is 8.5 to 60.5 km. The format is harmonized according to ESA CCI guidelines. For detailed information about the retrieval and the data set, see "Retrieval of ozone profiles from OMPS limb scattering observations", Arosio, C. et al. (2018).</p>
Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies
<p>Supporting datasets for preprint version of "Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies".</p>
Research data supporting "Correlated heterospectral lipidomics for biomolecular profiling of remyelination in multiple sclerosis"
<p>Research data supporting the paper:</p> <p>Bergholt, M.S. et al., "Correlated heterospectral lipidomics for biomolecular profiling of remyelination in multiple sclerosis", ACS Central Science, 2017, DOI: 10.1021/acscentsci.7b00367.</p>
Selectivity profiling of multi-kinase inhibitors across the Human Kinome from ChEMBL
<p>Reported is the list of 596 protein kinase pairs, consisting of 141 kinases and selectivity profiles of 10,060 multi-kinase inhibitors found in ChEMBL23 high-confidence data. For each of the reported protein kinase pairs, UniProt IDs defining the kinase forming a pair is provided, as well as the shared inhibitors and their selectivity profiles. For each target within the pair, potency value for each compound is reported as pIC50 value, as well as the absolute potency difference used to assess the selectivity profiles.</p>
FIGURE 3. Picked bryozoan fragments from the disaggregated, sieved samples from the Finis Shale. A – profile B15 in Stenolaemate bryozoans from the Graham Formation, Pennsylvanian (Virgilian) at Lost Creek Lake, Texas, USA
FIGURE 3. Picked bryozoan fragments from the disaggregated, sieved samples from the Finis Shale. A – profile B15; B – profile B16; C – profile C5; D – profile C13.
Datasets to Transient Attracting Profiles in the Great Pacific Garbage Patch
<h1>Summary</h1> <p>This repository contains supplementary datasets to the publication Transient Attracting Profiles in the Great Pacific Garbage Patch (<a href="https://doi.org/10.5194/egusphere-2024-1215" target="_blank" rel="noopener">Kunz et al., 2024, preprint</a>). TRansient Attracting Profiles (TRAPs, <a href="http://doi.org/10.1038/s41467-020-16281-x" target="_blank" rel="noopener">Serra et al. (2020)</a>, <a href="https://doi.org/10.1063/1.4951720" target="_blank" rel="noopener">Serra and Haller (2016)</a>) have been computed using the TRAPs detection software by <a href="https://github.com/MattiaSerra/TRAPs" target="_blank" rel="noopener">Mattia Serra (2020)</a>. Raw TRAPs were further post-processed, tracked and analysed using software by <a href="https://github.com/kunzluca/trapsgpgp" target="_blank" rel="noopener">Luca Kunz (2024)</a>. Here we provide two comprehensive datasets:</p> <ol> <li> <p>the <strong>TRAPS GPGP</strong> dataset: This dataset contains 20 years of TRAP detections with various attributes like e.g. TRAP lifetimes, propagation speeds and pattern detections in the surrounding vorticity field. It comes in 20 yearly files named <code>MULTIOBS_24HI_e025_TRAPS_GPGP_20XX.pkl</code>.</p> </li> <li> <p>the <strong>TRAPS DRIFTERS HPS</strong> dataset: This dataset contains 20 years of drifter-TRAP pair detections with attributes like e.g. drifter-TRAP distances, drifter retention times and TRAP attraction strengths. It comes in one file named <code>MULTIOBS_24HI_e025_GDP_24HI_TRAPS_DRIFTERS_HPS_0019_r075.pkl</code>.</p> </li> </ol> <p> </p> <p>You can use these scripts to open the datasets and follow the analysis in Kunz et al. (2024):</p> <p><code>aa_define_classes.ipynb</code><br><code>rxa_read_TRAPS_GPGP.ipynb</code><br><code>rxb_read_TRAPS_DRIFTERS_HPS.ipynb</code><br><code>rxc_ANALYSIS_EXAMPLES.ipynb</code></p> <p>Make sure you adopt the directory structure that is provided here. You can use the .yml file to set up the working environment via <code>conda env create -f trapsgpgp_condaenvexport.yml</code> or <code>conda create -n trapsgpgp -f trapsgpgp_condaenvexport.yml</code>.</p> <p>For supplementary videos, see: <a href="https://doi.org/10.5281/zenodo.10943728">https://doi.org/10.5281/zenodo.10943728</a></p> <h1>Source data</h1> <p>Raw TRAPs and relative vorticity are derived from daily snapshots of near-surface geostrophic + Ekman currents from the product Global Total Surface and 15m Current (COPERNICUS-GLOBCURRENT) from Altimetric Geostrophic Current and Modeled Ekman Current Reprocessing that is provided by the E.U. Copernicus Marine Service (<a href="https://doi.org/10.48670/moi-00050" target="_blank" rel="noopener">CMEMS, 2022a</a>). Surface drifter positions have been consulted from the Global Drifter Program (<a href="https://doi.org/10.25921/7ntx-z961" target="_blank" rel="noopener">Lumpkin and Centurioni, 2019</a>). This repository does not include raw TRAP detections, raw drifter data or relative vorticity fields but they can be shared upon request.</p> <h1>Acknowledgements</h1> <p>This work is a contribution to the project <a href="https://www.trr-energytransfers.de/research/area-l/l3" target="_blank" rel="noopener">L3 Meso- to submesoscale turbulence in the ocean</a> of the Collaborative Research Centre TRR 181 Energy Transfer in Atmosphere and Ocean funded by the German Research Foundation (DFG) and has been conducted in collaboration with <a href="https://theoceancleanup.com/" target="_blank" rel="noopener">The Ocean Cleanup</a>.</p> <h1>Please cite</h1> <p>When using the data, please cite</p> <p>Kunz, L., Griesel, A., Eden, C., Duran, R., and Sainte-Rose, B.: Transient Attracting Profiles in the Great Pacific Garbage Patch, Ocean Sci., 20, 1611–1630, <a href="https://doi.org/10.5194/os-20-1611-2024">https://doi.org/10.5194/os-20-1611-2024</a>, 2024. </p> <p>and provide a link to the repository.</p>
Merged anvio profiles for Wolbachia MAGs from Culex pipiens midgut and ovary samples
<p>Anvi’o merged profile databases for <em>Wolbachia</em> MAGs from <em>Culex pipiens</em> midgut and ovary metagenomes. </p>
Fig. 8 Urine miRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 8 Urine miRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant miRNA between acutely infected rabbits and control rabbits. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of miRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant miRNA between chronically infected rabbits and control rabbits. c The volcano plot shows the individual statistically significant miRNA between acutely infected rabbits and chronically infected rabbits. d Venn diagram shows number of differentially expressed miRNA among different comparison pairs
Fig. 6 Serum miRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 6 Serum miRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant miRNA between acutely infected group and control group. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of miRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant miRNA between chronically infected rabbits and control rabbits. c The volcano plot shows the individual statistically significant miRNA between acutely infected rabbits and chronically infected rabbits. d Venn diagram shows number of differentially expressed miRNA among different comparison pairs. FDR represents false discovery rate
Fig. 7 Serum piRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 7 Serum piRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant piRNA between acutely infected rabbits and control rabbits. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of piRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant piRNA between chronically infected rabbits and control rabbits. c The volcano plot shows the individual statistically significant piRNA between acutely infected rabbits and chronically infected rabbits. d Venn diagram shows number of differentially expressed piRNA among different comparison pairs. FDR represents false discovery rate
Single-cell profiling reveals immune-based mechanisms underlying tumor radiosensitization by a novel Mn porphyrin clinical candidate, MnTnBuOE-2-PyP5+ (BMX-001)
<p>Manganese porphyrins reportedly exhibit synergic effects when combined with irradiation. However, an in-depth understanding of intratumoral heterogeneity and immune pathways, as affected by Mn porphyrins, remains limited. Here, we explored the mechanisms underlying immunomodulation of a clinical candidate, MnTnBuOE-2-PyP<sup>5+</sup> (BMX-001, MnBuOE), using single-cell analysis in murine carcinoma<em> </em>model. Mice bearing 4T1 tumors were divided into 4 groups: control, MnBuOE, radiotherapy (RT), combined MnBuOE, and radiotherapy (MnBuOE/RT). In epithelial cells, epithelial-mesenchymal transition, TNF-α signaling via NF-кB, angiogenesis, and hypoxia-related genes were significantly downregulated in the MnBuOE/RT compared to the RT. All subtypes of cancer-associated fibroblasts (CAFs) were reduced in MnBuOE and MnBuOE/RT. Inhibitory receptor-ligand interactions, in which epithelial cells and CAFs interacted with CD8+ T cells, were significantly lower in the MnBuOE/RT than in the RT. Trajectory analysis showed that DC maturation-associated markers were increased in MnBuOE/RT. M1 macrophages were significantly increased in the MnBuOE/RT compared to the RT, whereas myeloid-derived suppressor cells were decreased. CellChat analysis showed that the number of cell-cell communications was the lowest in the MnBuOE/RT. Our study is the first to provide evidence for the combined radiotherapy with a novel Mn porphyrin clinical candidate, BMX-001 from the perspective of each cell type within the tumor microenvironment.</p>
Fig. 9 Urine piRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 9 Urine piRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant piRNA between acutely infected group and control group. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of piRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant piRNA between chronically infected group and control group. c The volcano plot shows the individual statistically significant piRNA between acutely infected group and chronically infected group. d Venn diagram shows number of differentially expressed piRNA among different comparison pairs
Data for publication: A pipeline for in-depth analysis of DNA virus populations by profiling the low abundant virus variants and partial genomic components
<p>Raw and processed sequence data from Oxford Nanopore and BGI short read sequencing platforms used in the publication: "A pipeline for in-depth analysis of DNA virus populations by profiling the low abundant virus variants and partial genomic components".</p>
United States State Cancer Profiles data extract
<p>State Cancer Profiles (https://statecancerprofiles.cancer.gov) hosts an interactive website for tracking United States cancer incidence and prevalence. The dataset hosted here is an automated scraping from all locales for all demographic variables and diseases. </p> <p>Two tables are available:</p> <ol> <li>Cancer incidence</li> <li>Cancer mortality</li> </ol> <p>The dataset is meant for data mining or for building additional data products based on the state cancer profiles data, but without having to scrape the data yourself. </p>
Discrete Flow Cytometry of Depth Profile Samples from the Gradients 5 (2023) Cruise Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected during the Gradients 2023 (Gradients 5/TN412) oceanographic research cruise in the equatorial Pacific Ocean. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for heterotrophic bacteria, picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 μm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
Supplementary data for: Comparison of transcriptomic profiles between HFPO-DA and prototypical PPARa, PPARg, and cytotoxic agents in mouse, rat, and pooled human hepatocytes
<p>Like many per- or polyfluorinated alkyl substances (PFAS), toxicity studies with HFPO-DA (ammonium,2,3,3,3-tetrafluoro-2-(heptafluoropropoxy)-propanoate), a short-chain PFAS used in the manufacture of some types of fluorinated polymers, indicate that the liver is the primary target of toxicity in rodents following oral exposure. Although the current weight of evidence supports the PPARa mode of action (MOA) for liver effects in HFPO-DA-exposed mice, alternate MOAs have also been hypothesized including PPARg or cytotoxicity. To further evaluate the MOA for HFPO-DA in rodent liver, transcriptomic analyses were conducted on samples from primary mouse, rat and pooled human hepatocytes treated for 12, 24 or 72 hours with various concentrations of HFPO-DA, or agonists of PPARa (GW7647), PPARg (rosiglitazone), or cytotoxic agents (i.e., acetaminophen or d-galactosamine). Concordance analyses of enriched pathways across chemicals within each species demonstrated greatest concordance between HFPO-DA and PPARa agonist GW7647-treated hepatocytes compared to the other chemicals evaluated. These findings were supported by benchmark concentration modeling and predicted upstream regulator results. In addition, transcriptomic analyses across species demonstrated a greater transcriptomic response in rodent hepatocytes treated with HFPO-DA or agonists of PPARa or PPARg, indicating rodent hepatocytes are more sensitive to HFPO-DA or PPARa/g agonist treatment. These results are consistent with previously published transcriptomic analyses and further support that liver effects in HFPO-DA-exposed rodents are mediated through rodent-specific PPARa signaling mechanisms as part of the MOA for PPARa activator-induced rodent hepatocarcinogenesis. Thus, effects observed in mouse liver are not appropriate endpoints for toxicity value development for HFPO-DA in human health risk assessment.</p>
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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.