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2,100 results for “nodes”
Data from: Microvascular rarefaction in the sinoatrial node
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Cortical VIP neurons as a critical node for dopamine actions
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Two notorious nodes: A critical examination of relaxed molecular clock age estimates of the bilaterian animals and placental mammals
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The TCR assigns naive T cells to a preferred lymph node
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Data from: Influence of periaqueductal gray on other salience network nodes predicts social sensitivity
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Phylogenetic comparative methods are problematic when applied to gene trees with speciation and duplication nodes: correcting for biases in testing the ortholog conjecture
<p>This repository contains “manuscript_dunn.RData” file, which is reproduced by using the files and scripts of Dunn et al. (Dunn CW, Zapata F, Munro C, Siebert S, Hejnol A (2018) Pairwise comparisons across species are problematic when analyzing functional genomic data. Proc Natl Acad Sci U S A 115: E409–E417. <a href="http://dx.doi.org/10.1073/pnas.1707515115">doi:10.1073/pnas.1707515115</a>).</p> <p>In this repository, we also supplied “Data_TMRR_latest.rda” file, containing the results generated by using our own scripts. Our scripts are available on GitHub: <a href="https://github.com/tbegum/Testing_the_ortholog_conjecture">https://github.com/tbegum/Testing_the_ortholog_conjecture</a>.</p> <p> </p>
Node connectivity measurements for Hetionet v1.0 metapaths
<p><a href="https://github.com/hetio/hetionet">Hetionet</a> v1.0 is a hetnet (heterogeneous network) with 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. This record contains computed connectivity measurements for Hetionet v1.0 for all metapaths (types of paths) up to length 3. These measurements are designed to assess the extent of connectivity between two nodes along a given metapath. Several types of data are included:</p> <ol> <li><strong>Path counts</strong>: Path counts measure the number of paths from a source node to a target node along a specified metapath. The path count is a special case of the degree-weighted path count (DWPC) metric where the damping exponent parameter is set to 0.0. Path counts for all source–target node combinations of a given metapath are stored in a matrix with source nodes as rows and target nodes as columns.</li> <li><strong>Degree-weighted path counts</strong>: DWPCs measure the abundance of paths from a source to target node along a given metapath (like path counts), but are adjusted for the degrees along the path such that paths through higher degree nodes are downweighted according to a damping parameter. The DWPCs here use a damping exponent of 0.5 and the same matrix serialization as the path count datasets. The values are not scaled/transformed. To compare to the null DWPCs discussed below, divide each value by the mean DWPC for the entire matrix and apply an inverse hyperbolic sine transformation.</li> <li><strong>Degree-grouped permutation summaries</strong>: Degree-grouped permutations (DGP) are used to compute the significance of DWPC values. Specifically, they are used to estimate null distribution for DWPCs from the unpermuted hetnet. DGP summaries provide summary statistics of DWPCs computed on permuted hetnets. The permuted hetnets are derived from Hetionet v1.0 using the XSwap algorithm. This approach preserves node degree but randomizes edges to muddle their meaning. DWPCs were computed for <a href="https://github.com/hetio/hetionet/tree/a95ae76581af604e91d744680aee3f888fa18887/hetnet/permuted/matrix">200 permuted networks</a> and grouped by source–target node degree within each metapath. Permuted DWPCs were scaled by dividing by the unpermuted DWPC mean and then inverse hyperbolic sine transformed. Every degree pair for a given metapath has corresponding statistics that summarize its values across permuted hetnets. These statistics include the number of observed DWPCs, the number of nonzero DWPCs, the sum of the DWPCs, and the sum of squared DWPCs. These values are sufficient to calculate the parameters of a gamma-hurdle null DWPC distribution.</li> </ol> <p><strong>Data Format</strong>: the .zip files are HetMat archive files. This simply means that the directory structure and file formats of the archived files conform to the HetMat data structure for storing hetnets on disk. Matrices are stored as scipy.sparse .npz files. .npz is a numpy array serialization format that scipy uses to write sparse matrices to disk.</p> <p>TSV files in this upload report information on the contents of the archives. The .zip-info.tsv files contain a list of all files included in the zip archives. metapath-dwpc-stats.tsv contains summary information on the unpermuted path counts and DWPCs. Note that results are archived by path length, such that all metapaths of length 1 are in a different archive than metapaths of length 2. Therefore, users who only need results for shorter metapaths, do not need to download the large archives for longer metapaths. There are 24 metapaths of length 1, 242 metapaths of length 2, and 1939 metapaths of length 3.</p> <p><strong>Connectivity Search Database</strong>: connectivity-search-pg_dump.sql.gz is a PostgreSQL database dump for use with the <a href="https://github.com/greenelab/connectivity-search-backend">connectivity-search-backend</a> repository.</p> <p><strong>Source code</strong>: These datasets were computed by the <a href="https://github.com/greenelab/hetmech/blob/34e95b9f72f47cdeba3d51622bee31f79e9a4cb8/explore/bulk-pipeline/bulk.ipynb">bulk.ipynb notebook</a> from <a href="https://github.com/greenelab/hetmech/commit/34e95b9f72f47cdeba3d51622bee31f79e9a4cb8">greenelab/hetmech@34e95b9</a>.</p> <p><strong>Funding</strong>: This work was supported through a research collaboration with <a href="https://www.pfizer.com/partners/research-and-development">Pfizer Worldwide Research and Development</a>. This work is funded in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through Grants <a href="https://www.moore.org/grant-detail?grantId=GBMF4552">GBMF4552</a> and <a href="https://www.moore.org/grant-detail?grantId=GBMF4560">GBMF4560</a>.</p> <p><strong>More information</strong>: See the manuscript titled <a href="https://greenelab.github.io/connectivity-search-manuscript/">Hetnet connectivity search provides rapid insights into how two biomedical entities are related</a>.</p> <ul> </ul>
GeoVectors-Germany-Nodes-Relations-tags (v1.0)
<p><strong>Description</strong></p> <p>The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities.</p> <p><strong>Contents</strong></p> <p>This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors).</p> <p>We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the nodes and relations of the region "Germany" including the following countries:</p> <ul> <li>Germany</li> </ul> <p><strong>File format</strong></p> <p>The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv)</p> <p><strong>Further information:</strong></p> <p>For further information, please visit <a href="http://geovectors.l3s.uni-hannover.de">http://geovectors.l3s.uni-hannover.de</a></p> <p><strong>Funding</strong>:</p> <p>This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).</p>
Data from: Node-based measures of connectivity in genetic networks
At-site environmental conditions can have strong influences on genetic connectivity, and in particular on the immigration and settlement phases of dispersal. However, at-site processes are rarely explored in landscape genetic analyses. Networks can facilitate the study of at-site processes, where network nodes are used to model site-level effects. We used simulated genetic networks to compare and contrast the performance of 7 node-based (as opposed to edge-based) genetic connectivity metrics. We simulated increasing node connectivity by varying migration in two ways: we increased the number of migrants moving between a focal node and a set number of recipient nodes, and we increased the number of recipient nodes receiving a set number of migrants. We found that two metrics in particular, the average edge weight and the average inverse edge weight, varied linearly with simulated connectivity. Conversely, node degree was not a good measure of connectivity. We demonstrated the use of average inverse edge weight to describe the influence of at-site habitat characteristics on genetic connectivity of 653 American martens (Martes americana) in Ontario, Canada. We found that highly connected nodes had high habitat quality for marten (deep snow and high proportions of coniferous and mature forest) and were farther from the range edge. We recommend the use of node-based genetic connectivity metrics, in particular, average edge weight or average inverse edge weight, to model the influences of at-site habitat conditions on the immigration and settlement phases of dispersal.
Data from: Integrated network analysis identifies fight-club nodes as a class of hubs encompassing key putative switch genes that induce major transcriptome reprogramming during grapevine development
We developed an approach that integrates different network-based methods to analyze the correlation network arising from large-scale gene expression data. By studying grapevine (Vitis vinifera) and tomato (Solanum lycopersicum) gene expression atlases and a grapevine berry transcriptomic data set during the transition from immature to mature growth, we identified a category named "fight-club hubs" characterized by a marked negative correlation with the expression profiles of neighboring genes in the network. A special subset named "switch genes" was identified, with the additional property of many significant negative correlations outside their own group in the network. Switch genes are involved in multiple processes and include transcription factors that may be considered master regulators of the previously reported transcriptome remodeling that marks the developmental shift from immature to mature growth. All switch genes, expressed at low levels in vegetative/green tissues, showed a significant increase in mature/woody organs, suggesting a potential regulatory role during the developmental transition. Finally, our analysis of tomato gene expression data sets showed that wild-type switch genes are downregulated in ripening-deficient mutants. The identification of known master regulators of tomato fruit maturation suggests our method is suitable for the detection of key regulators of organ development in different fleshy fruit crops.
Data from: Inferring node dates from tip dates in fossil Canidae: the importance of tree priors
Tip-dating methods are becoming popular alternatives to traditional node calibration approaches for building time-scaled phylogenetic trees, but questions remain about their application to empirical datasets. We compared the performance of the most popular methods against a dated tree of fossil Canidae derived from previously published monographs. Using a canid morphology dataset, we performed tip-dating using BEAST v. 2.1.3 and MrBayes v. 3.2.5. We find that for key nodes (Canis, approx. 3.2 Ma, Caninae approx. 11.7 Ma) a non-mechanistic model using a uniform tree prior produces estimates that are unrealistically old (27.5, 38.9 Ma). Mechanistic models (incorporating lineage birth, death and sampling rates) estimate ages that are closely in line with prior research. We provide a discussion of these two families of models (mechanistic versus non-mechanistic) and their applicability to fossil datasets.
FIGURE 9. Phylogram obtained with RAxML and the combined mitochondrial sequence data. Bootstrap values above nodes. See Table 1 in Stictonectes rebeccae sp. n. from the Iberian Peninsula, with notes on its phylogenetic position (Coleoptera, Dytiscidae)
FIGURE 9. Phylogram obtained with RAxML and the combined mitochondrial sequence data. Bootstrap values above nodes. See Table 1 for localities.
GBIF data summaries: GBIF national node type records: Sweden
GBIF—the Global Biodiversity Information Facility—is an international network and research infrastructure funded by the world__s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. <p></p>https://www.gbif.org
GBIF data summaries: GBIF national node type records: Brazil
GBIF—the Global Biodiversity Information Facility—is an international network and research infrastructure funded by the world__s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. <p></p>https://www.gbif.org
GBIF data summaries: GBIF national node type records: UK
GBIF—the Global Biodiversity Information Facility—is an international network and research infrastructure funded by the world__s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. <p></p>https://www.gbif.org
GBIF data summaries: GBIF national node type records: Netherlands
GBIF—the Global Biodiversity Information Facility—is an international network and research infrastructure funded by the world__s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. <p></p>https://www.gbif.org
GBIF data summaries: GBIF national node type records: Germany
GBIF—the Global Biodiversity Information Facility—is an international network and research infrastructure funded by the world__s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. <p></p>https://www.gbif.org
Random Woody Crown Network (WCN) from 1 to 248 nodes
<p>Those woody crown networks are part of data of manuscript to consider publication </p>
Data of decomposition, topology, and the corresponding graphs of Random Woody Crown Networks with size from 10 to 248 nodes
<p>Data of decomposition, topology and the corresponding graphs of Random Woody Crown Networks with size from 10 to 248 nodes</p>
Single-cell RNA sequencing of Lymph node-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.
<p><strong>Single-cell RNA sequencing of Lymph node-infiltrating HSC-derived phagocytes of <em>Ms4a3</em><sup>Ai14</sup> at onset and peak EAE using 10X Genomics platform.</strong></p> <p>The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell.</p> <p>Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from <em>Ms4a3</em><sup>Ai14</sup> mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.</p>
ScienceDex guides
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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.