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333 results for “functional network”
Summary statistics accompanying the article "Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency" in Scientific Reports (2022)
<p>Summary statistics for genome-wide association studies reported in:</p> <p>Bell, S., Tozer, D.J., & Markus H.S. (2022). Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency. <em>Scientific Reports</em>, DOI: <a href="https://dx.doi.org/10.1038/s41598-022-19106-7">10.1038/s41598-022-19106-7</a>. </p> <p><strong>Abstract</strong></p> <p>Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component.</p> <p> </p> <p>Further information on the files uploaded here can be found in the README. Users interested in bulk downloading these summary statistics may find <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> helpful.</p>
NUT01 Nutrient Network: Investigating the roles of nutrient availability and vertebrate herbivory on grassland structure and function at Konza Prairie
The goals and focal research questions are copied below from the Nutrient Network website. More information can be found at nutnet.org. NutNet focal research questions: (1) How general is our current understanding of productivity-diversity relationships? (2) To what extent are plant production and diversity co-limited by multiple nutrients in herbacoues-dominated communities? (3) Under what conditions do grazers or fertilization control plant biomass, diversity, and composition? NutNet goals: (1) To collect data from a broad range of sites in a consistent manner to allow direct comparisons of environment-productivity-diversity relationships among systems around the world. This is currently occurring at each site in the network and, when these data are compiled, will allow us to provide new insights into several important, unanswered questions in ecology. (2) To implement a cross-site experiment requiring only nominal investment of time and resources by each investigator, but quantifying community and ecosystem responses in a wide range of herbaceous-dominated ecosystems (i.e., desert grasslands to arctic tundra).
Deep learning models predicting gene functions and pathways using public DRKG knowledge graph and graph neural network
<p>The attached dataset contains pretrained link prediction models, as described in our paper 'Morphological Map of Under- and Over-Expression of Genes in Human Cells'.</p>
Functional networks of inhibitory neurons orchestrate synchrony in the hippocampus: optogenetical stimulation data
<p>This dataset contains 2-photon calcium imaging data from the paper 'Functional networks of inhibitory neurons orchestrate synchrony in the hippocampus'. This is the calcium imaging data from CA1 pyramidal cells and interneurons, including both spontaneous activity and activity in response to optogenetic stimulation.</p> <p><strong>Data organization</strong></p> <p>This dataset contains all the data related to the all-optical part of the paper and was analyzed using the code from the <a href="https://gitlab.com/cossartlab/bocchio-vorobyev-et-al-2023/-/tree/main/Optogenetical%20stimulation?ref_type=heads">lab repository</a>. The original calcium imaging movies are excluded due to size limitations.</p> <p><strong>Further information</strong></p> <p>Please email vorobev[a t]phystech.edu if you need further information on the data or if you wish to access the raw calcium imaging movies (not uploaded here due to storage limitations).</p>
Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
<p>Dataset linked to the article with the same title</p> <p>The model itself is very similar to its non-plastic counterpart under the following DOI: <a href="../record/7930275">10.5281/zenodo.7930275</a>, i.e. a 1.5 mm diameter cortical tissue comprising 211,712 neurons and their connectivity in the front limb and jaw subregions and the dysgranular zone of the Paxinos & Watson rat brain atlas. It's formatted in the open <a href="https://github.com/AllenInstitute/sonata">SONATA</a> standard and contains neuron locations and their properties (such as morphological types, cortical layer, etc.), their detailed morphologies, and synaptic connectivity (with all their anatomical and physiological parameters). The main difference from the non-plastic version is the addition of plasticity related parameters to <em>O1/S1nonbarrel_neurons__S1nonbarrel_neurons__chemical/edges.h5. </em>Extrinsic synaptic connections from the thalamus are included in this release, but for inputs from neurons in the remainder of non-barrel somatosensory cortex please see the non-plastic version of the circuit.</p> <p><strong>Analyzing the model</strong></p> <p>The model can be analyzed in terms of its anatomy, physiology and connectivity using the packages <a href="https://neurom.readthedocs.io/en/stable/">NeuroM</a>, <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a> and <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>. (see first Jupyter notebook)</p> <p><strong>Simulating the model</strong></p> <p>To simulate the model we'd recommend using out using our open-source simulator <a href="https://github.com/BlueBrain/neurodamus">Neurodamus</a>. The reference version is the branch <em>nbS1-2023</em>, which is archived under the following DOI: <a href="http://doi.org/10.5281/zenodo.8075202">10.5281/zenodo.8075202</a>. Instructions on how to use the simulator are provided on the GitHub page linked above. Briefly, you'll first have to <a href="https://github.com/BlueBrain/neurodamus#install-neurodamus">install Neurodamus</a>. Next, build a <em>"special"</em> executable that include compiled versions of ion channel and synapse models. To do that, follow <a href="https://github.com/BlueBrain/neurodamus#build-special-with-mod-files">these instructions</a>, where <em>mod-files-from-released-circuit </em>is replaced by the location of <em>O1/mods</em> on your system. Finally, <a href="https://github.com/BlueBrain/neurodamus#examples">run a simulation</a>. The specific simulation conditions and stimuli are specified in simulation configuration files. An exemplary simulation configuration is included in this release (<em>simulation_config.zip</em>).</p> <p><strong>Analyzing simulation results</strong></p> <p>Simulation results can be analyzed with <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a>, <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>, and <a href="https://github.com/BlueBrain/assemblyfire">assemblyfire</a>. Notebooks 2-5 go though these analysis and recreate some of the panels from our article. In most cases the notebooks can be run with the shared HDF5 files and don't require running any simulations.</p> <p><strong>Version 2</strong></p> <p>Bug fix in simulation_config.json and therefore new version of results (and corresponding notebooks). The underlying circuit model (O1.xz) did not change from v1.</p> <p>--</p> <p><em>The development of this dataset was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.</em></p>
Sensitivity Datasets - Leveraging Implicit Knowledge in Neural Networks for Functional Dissection and Engineering of Proteins
<p><strong>Leveraging Implicit Knowledge in Neural Networks for Functional Dissection and Engineering of Proteins</strong></p> <p>The Sensitivity datasets cover more than 800 proteins and are structured as follows. The sensitivity values are the mean of four DeeProtein replicates.</p> <p>It is uploaded as tar.gz. and contains one directory.</p> <p>File names contain the PDB<sup>1</sup> identifier and the respective chain identifier. </p> <p>The sequences and secondary structure information were downloaded from the RCSB Protein Databank and are available here: <a href="https://cdn.rcsb.org/etl/kabschSander/ss_dis.txt.gz">https://cdn.rcsb.org/etl/kabschSander/ss_dis.txt.gz</a> This URL can be found with some explanation at <a href="http://www.rcsb.org/pdb/static.do?p=download/http/index.html">http://www.rcsb.org/pdb/static.do?p=download/http/index.html</a></p> <p>The secondary structure annotation relies on the DSSP Algorithm by Kabsch and Sander<sup>2</sup>.</p> <p> </p> <p><strong>The files are tab-separated and contain the following columns:</strong></p> <ul> <li><strong>Pos</strong> Position in the sequence, starting from zero</li> <li><strong>AA</strong> Amino acid in that position</li> <li><strong>sec</strong> Secondary structure as annotated in the RCSB Protein Databank</li> <li><strong>dis</strong> if a region has not been experimentally observed (sometimes explains mismatches with crystal structures)</li> <li><strong>GO:_______</strong> Sensitivity for the GO term</li> </ul> <p><strong>References</strong></p> <ol> <li>The Protein Data Bank H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne (2000) Nucleic Acids Research, 28: 235-242. doi:10.1093/nar/28.1.235</li> <li>Kabsch, W. & Sander, C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577-2637, doi:10.1002/bip.360221211 (1983).</li> </ol>
Coping with Collapse: Functional Robustness of Coral-Reef Fish Network to Simulated Cascade Extinction
<p>Data set, codes and results related to the article "Coping with Collapse: Functional Robustness of Coral-Reef Fish Network to Simulated Cascade Extinction", accepted in the periodic Global Change Biology. Stored are the full results of site occupancy models fitted to fish data, with coral and turf algae cover as predictor variables (results published in Luza et al. 2022, Scientific Reports), and the results of the present article. The RData also contains site coordinates, and the fish traits used in trait-based analyzes.</p>
Differentiation of functional networks during long-term memory retrieval in children and adolescents
Open the record for dataset details and reuse information.
Data from "Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques"
<p>DATA FILES from the study below:</p> <p><strong><a href="https://www.biorxiv.org/content/10.1101/603530v1">Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques</a></strong></p> <p>Jérôme Sallet, MaryAnn P Noonan, Adam Thomas, Jill X O’Reilly, Jesper Anderson, Georgios KPapageorgiou, Franz X Neubert, Bashir Ahmed, Jackson Smith, Andrew H Bell, Mark J Buckley, LéaRoumazeilles, Steven Cuell, Mark E Walton, Kristine Krug, Rogier B Mars, Matthew FS Rushworth</p> <p>bioRxiv 603530; doi: <a href="https://doi.org/10.1101/603530">https://doi.org/10.1101/603530</a></p> <p>*.nii.gz files could be opened with FSLeyes -<a href="https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)">https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)</a></p> <p>Dara are also available from : https://www.jeromesallet.org/data-ofc-reversal-learning</p>
Functional Brain Networks of Picture Naming in Broca's Aphasia and Healthy Controls
<p>The data were from the picture-naming task with MEG scanning. </p> <p>Brain networks of ".net" format: Each network has 776 regions that were derived by subdividing USCBrain Atlas. Phase-locking values (PLV) were calculated between the 776 regions in a gamma-band of 30-45Hz. PLVs were normalized (z-PLVs) by using the mean and standard deviation of the 200-ms pre-stimulus baseline. The edges were weighted by z-PLVs. </p> <p>Vector files of ".vec" format: Each file contains activations, viz., amplitude, of regions. There are two types of amplitude. One is the estimated electric density in a physical unit of picoampere. Another is the z-score of amplitude calculated through comparison with a baseline of –200 ms.</p> <p>We provided both the group-averaged files (named as b999 for the Broca group and c999 for the control group) and the individuals' files (b1 to b5 for the Broca's aphasia and c1 to c5 for the control persons).</p> <p>We also provided two ".clu" files. One is the partition file of eight functional modules in two hemispheres. Another is the partition file of two hemispheres. </p> <p>The .net, .vec., and .clu files can be imported to Pajek for further interpretations and visualizations. </p> <p> </p>
Divergent molecular networks program functionally distinct CD8+ skin-resident memory T cells
<p>Skin-resident CD8+ T cells comprise distinct IFN-γ- (TRM1) and IL-17-producing (TRM17) subsets that differentially contribute to immune responses. However, whether these populations employ common mechanisms to establish tissue residence is unknown. Here, we show that TRM1 and TRM17 cells navigate divergent trajectories to acquire tissue residency in skin. While TRM1 cells depend on a T-bet-Hobit-IL-15 axis, TRM17 cells develop independently of these factors. Instead, c-Maf commands a tissue-resident program in TRM17 cells parallel to that induced by Hobit in TRM1 cells, with an ICOS-c-Maf-IL-7 axis pivotal to TRM17 cell commitment. Accordingly, targeting this pathway enables ablation of skin TRM17 cells without compromising their TRM1 counterparts. Thus, skin-resident T cells rely on distinct molecular circuitries, which can be exploited to strategically modulate local immunity.</p>
Dataset for "Thresholds in road network functioning on US Atlantic and Gulf barrier islands"
<p>This dataset accompanies the paper "Thresholds in road network functioning on US Atlantic and Gulf barrier islands" (<a href="https://doi.org/10.31223/X55D1G">https://doi.org/10.31223/X55D1G</a>) and is intended to be used to reproduce the analysis. In this dataset you can find the graphml files generated for each island (103 islands with drivable roads). Each intersection of the island road network is a node, and has an associated elevation and extreme water level value. Also included are the statistics for each network, and a single table with the analysis results for each of the networks with >100 nodes.</p>
Combined network file for "FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data"
<p><strong>Combined network from scRNA-seq and proteomics data</strong></p> <p>Given the complementary nature of the networks based on scRNA-seq and proteomics data individually, we decided to combine them into a single network. As the Pearson Correlation Coefficient scores from FAVA cannot be assumed to be directly comparable across the two networks, we converted them to probabilistic scores based on the KEGG benchmarks. These calibrated scores were then combined to produce a single network based on scRNA-seq as well as proteomics data. As should be expected, this network outperforms the individual networks, combining the best aspects of both.</p>
Functional precision profiling reveals non-mutational rewiring of kinase signaling networks in colorectal cancer
<p>Multi-omics profiling of colorectal cancer (CRC) patients and associated patient-derived organoids. Tumor organoids were characterized in steady-state and perturbed using kinase inhibitors.</p>
Dataset of pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use
<p>This is the dataset of the manuscript entitled "Pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use", which was submitted for publication. The dataset include the functional traits of 162 insect pollinator species and 315 interactions with the mangrove species <em>Avicennia germinans, Conocarpus erectus, Laguncularia racemosa,</em> and <em>Rhizophora</em> <em>mangle</em>. The manuscript evaluates the effects of mangrove patch size and surrounding land use on pollinator functional diversity and plant-pollinator interactions in seven mangrove patches from the Colombian Caribbean region. Data variables are pollinator order, family, species, functional traits (pollinator guilds, body size, feeding preference, sociality, and nesting site) and frequency, interacting mangrove species, mangrove patch name, coordinates and size (ha), surrounding land use areas (urban areas, croplands, conserved dry forest, degraded vegetation areas, beach and water) and landscape diversity (Shannon H').</p>
Subgraphs of functional brain networks identify dynamical constraints of cognitive control
<p>Post-processed BOLD fMRI functional connectivity data from human subjects performing two distinct cognitive control tasks.</p> <p>See enclosed README file for information regarding data organization and handling.</p>
main source codes and files of "Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks"
<p>These source codes and their related files are associated the study. "Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks"</p> <p>This study is in process of publication.</p>
Adjacency matrices for dApps contracts and functions network
<p>This dataset encompasses the network structure of decentralized applications (dApps) mainly deployed in the Ethereum blockchain and other platforms such as Binance, Optimism, Polygon, Astar, Shiden, and Thundercore.</p>
Using neural networks to model Main Belt Asteroid albedos as a function of their proper orbital elements
<p>This repository contains a copy of the following repository https://github.com/r-zachary-murray/Asteroid-Albedos. It contains weights for an ensemble of neural nets trained on the Asteroid Family Portal proper elements and NEOWISE albedos. These weights can be used to predict albedos of asteroids based of their proper elements. Example.ipynb contains an ipython notebook that shows how these predictions can be made.</p>
Quality-aware Analysis and Optimisation of Virtual Network Function
<p># SPLC'22 Quality-aware Analysis and Optimisation of Virtual Network Functions</p><p> </p><p>DATA: Quality-aware Analysis and Optimisation of Virtual Network Functions</p><p> </p><p>This repository contains the models, operations and results empirically used in [Quality-aware Analysis and Optimisation of Virtual Network Functions](https://doi.org/10.1145/3546932.3547007) at SPLC 2022.</p><p>Due to copyright issues, it does not contain the tools (i.e., automated reasoners), although their official sites are provided.</p><p> </p><p>It is licensed under the [MIT license](https://github.com/danieljmg/SPLC22/blob/main/LICENSE).</p><p> </p><p> </p><p>## SPLC'22 Models, Categorical Operations and Datasets</p><p> </p><p>This data-set contains:</p><p> </p><p>1. The 5 SPL categories in CQL alongside the 11 tested operations.</p><p>2. The 5 SPL Clafer models.</p><p>3. The 5 SPL XMLs (for the AAFM Python Framework).</p><p>4. The 5 SPL XMLs (for SATIBEA).</p><p>5. The previous models are enriched with quality attributes measurements at feature and configuration levels.</p><p>6. A Microsoft Excel file with the scalability results obtained.</p><p> </p><p> </p><p>## Automated Reasoners</p><p> </p><p>- CQL IDE: https://github.com/CategoricalData/CQL</p><p>- Clafermoo: http://t3-necsis.cs.uwaterloo.ca:8092/</p><p>- AAFM Python Framework: https://pypi.org/project/famapy/</p><p>- SATIBEA: https://github.com/jmguo/SMTIBEA</p><p> </p><p> </p><p>## Requirements</p><p> </p><p>The data-set has been generated using Java JDK 18.0.2 for CQL IDE, Clafermoo, and SATIBEA, and Python 3.9.13 x86_64 for AAFM Python Framework.</p><p> </p><p>## Authors</p><p> </p><p>1. **[Daniel-Jesus Munoz](https://github.com/danieljmg)**: [ITIS Software](https://www.uma.es/institutos-uma/info/118460/instituto-de-tecnologias-e-ingenieria-del-software/), [CAOSD](http://caosd.lcc.uma.es/), Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain</p><p>2. **Mónica Pinto**: [ITIS Software](https://www.uma.es/institutos-uma/info/118460/instituto-de-tecnologias-e-ingenieria-del-software/), [CAOSD](http://caosd.lcc.uma.es/), Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain</p><p>3. **Lidia Fuentes**: [ITIS Software](https://www.uma.es/institutos-uma/info/118460/instituto-de-tecnologias-e-ingenieria-del-software/), [CAOSD](http://caosd.lcc.uma.es/), Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain</p>
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.