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34 results for “vascular 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>
Parallel generation of extensive vascular networks with application to an archetypal human kidney model
<p>Given the relevance of the inextricable coupling between microcirculation and physiology, and the relation to organ function and disease progression, the construction of synthetic vascular networks for mathematical modelling and computer simulation is becoming an increasingly broad field of research. Building vascular networks that mimic in-vivo morphometry is feasible through algorithms such as constrained constructive optimisation (CCO) and variations. Nevertheless, these methods are limited by the maximum number of vessels to be generated due to the whole network update required at each vessel addition. In this work, we propose a CCO-based approach endowed with a domain decomposition strategy to concurrently create vascular networks. The performance of this approach is evaluated by analysing the agreement with the sequentially generated networks and studying the scalability when building vascular networks up to 200,000 vascular segments. Finally, we apply our method to vascularise a highly complex geometry corresponding to the cortex of a prototypical human kidney. The technique presented in this work enables the automatic generation of extensive vascular networks, removing the limitation from previous works. Thus, we can extent vascular networks (e.g., obtained from medical images) to pre-arteriolar level, yielding patient-specific whole-organ vascular models with an unprecedented level of detail.</p>
Datasets of Vascular Networks Extracted from Retinal Images of Hypertensive Retinopathy and Glaucoma Patients
<p>This dataset contains high resolution images of blood vessels extracted from Digital Retinal Images for Optic Nerve Segmentation Database (DRIONS-DB). It contains 110 blood vessels of individuals of which 23.1% are patients with chronic glaucoma while the remaining 76.9% are hypertensive retinopathy patients. </p>
Parallel generation of extensive vascular networks with application to an archetypal human kidney model
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Dataset for: Generation of model tissues with dendritic vascular networks via sacrificial laser-sintered carbohydrate templates
<p>Published in:<br> Nature Biomedical Engineering. doi: 10.1038/s41551-020-0566-1.</p> <p>Generation of model tissues with dendritic vascular networks via sacrificial laser-sintered carbohydrate templates</p> <p>Ian S. Kinstlinger (1), Sarah H. Saxton (2), Gisele A. Calderon (1), Karen Vasquez Ruiz (1), David R. Yalacki (1), Palvasha R. Deme (1), Jessica E. Rosenkrantz (3), Jesse D. Louis-Rosenberg (3), Fredrik Johansson (2), Kevin D. Janson (1), Daniel W. Sazer (1), Saarang S. Panchavati (1), Karl-Dimiter Bissig (4), Kelly R. Stevens (2,5), and Jordan S. Miller (1)</p> <p>1 Department of Bioengineering, Rice University, Houston, TX, USA.<br> 2 Department of Bioengineering, University of Washington, Seattle, WA, USA.<br> 3 Nervous System, Palenville, NY, USA.<br> 4 Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA.<br> 5 Department of Pathology, University of Washington, Seattle, WA, USA</p> <p>Sacrificial templates for patterning perfusable vascular networks in engineered tissues have been constrained in architectural complexity, owing to the limitations of extrusion-based 3D-printing techniques. Here we show that cell-laden hydrogels can be patterned with algorithmically generated dendritic vessel networks and other complex hierarchical networks by using sacrificial templates made from laser-sintered carbohydrate powders. We quantified and modulated gradients of cell proliferation and cell metabolism emerging as a result of fluid convection through these networks and of diffusion of oxygen and metabolites out of them. We also show scalable strategies for the fabrication, perfusion culture and volumetric analysis of large tissue-like constructs with complex and heterogeneous internal vascular architectures. Perfusable dendritic networks in cell-laden hydrogels may help sustain thick and densely cellularized engineered tissues, and assist interrogations of the interplay between mass transport and tissue function.</p>
REAVER Vascular Networks Fluorescent Image Dataset
<p><strong>Fluorescent Images of Vessel Networks from Various Murine Tissues</strong></p> <p> </p> <p><strong>Purpose</strong>: Image dataset of vascular networks with a diverse range of vessel architectures. Dataset is used to evaluate performance of several image processing programs (AngioQuant<sup>1</sup>, AngioTool<sup>2</sup>, RAVE<sup>3</sup>, REAVER). Manual analysis from ImageJ is used as ground truth to compare other programs against.</p> <ul> <li><strong>Labeling</strong>: IB4-Lectin with Alexa Flour 647</li> <li><strong>Modality</strong>: Confocal Microscope Nikon 80i CLSM</li> <li><strong>Objective</strong>: Mixture of 20x and 60x objective images</li> <li><strong>Image Format</strong>: Images originally acquired in Nikon IDS format, converted to 8-bit greyscale TIFs found in “_Original_Images” folder.</li> <li><strong>Questions</strong>: Email <a href="mailto:bac7wj@virginia.edu">bac7wj@virginia.edu</a> for inquiries.</li> </ul> <p> </p> <p><strong>External Links</strong></p> <ol> <li><strong>Manuscript</strong>:</li> <li><strong>Code repository: </strong><a href="https://github.com/bacorliss/REAVER_public">https://github.com/bacorliss/REAVER_public</a> for code to analyze this data (MATLAB 2019a).</li> </ol> <p> </p> <p><strong>Dataset Summary:</strong></p> <p>Each image folder contains 36 images. For each image:</p> <ol> <li>The first channel (red) is the segmented image with values of 0 or 255 (false or true).</li> <li>The second channel (green) is the skeleton image with values of 0 or 255 (false or true).</li> <li>The third channel (blue) is empty except for the Manual images where the third channel contains the original raw image.</li> </ol> <p> </p> <p><strong>Subfolders</strong></p> <ol> <li><strong>_Original_Images</strong>: contains raw input images.</li> <li><strong>AngioQuant_Auto</strong>: contains output images from automated analysis in AngioQuant.</li> <li><strong>AngioTool_Auto</strong>: contains output images from automated analysis in AngioTool.</li> <li><strong>ImageJ_Auto</strong>: contains output images from automated analysis in ImageJ.</li> <li><strong>ImageJ_Manual</strong>: contains output images from manual analysis in ImageJ.</li> <li><strong>RAVE_Auto</strong>: contains output images from automated analysis in RAVE.</li> <li><strong>REAVER_Auto</strong>: contains output images from automated analysis in REAVER.</li> </ol> <p> </p> <p><strong>Image Metadata and Output data</strong></p> <p>Each image folder has a .mat file called “Results.mat” containing the results of analysis in the form of the following variables all of which are 1x36 arrays (one entry for each image) unless specified otherwise:</p> <ol> <li><strong>branchpoint_RC</strong>: A 1x36 struct containing the row-column values for each branchpoint in the i<sup>th</sup> image (when organized in alphabetic order which is the order given everywhere else); Effectively the same as “BranchpointsByName.mat”</li> <li><strong>mean_diameter</strong>: The mean diameter of vessels in the image</li> <li><strong>num_branchpts</strong>: The number of branchpoints in the image</li> <li><strong>threshold_false_neg</strong>: The number of false negative pixels – a pixel is a false negative if the program has it as “false” and the manual image has the pixel as “true”</li> <li><strong>threshold_false_pos</strong>: The number of false positive pixels – a pixel is a false positive if the program has it as “true” and the manual image has the pixel as “false”</li> <li><strong>threshold_true_neg</strong>: The number of true negative pixels – a pixel is a true negative if the program has it as “false” and the manual image has the pixel as “false”</li> <li><strong>threshold_true_pos</strong>: The number of true positive pixels – a pixel is a false positive if the program has it as “true” and the manual image has the pixel as “true”</li> <li><strong>umppix</strong>: The length of the edge of one pixel in micrometers</li> <li><strong>vessel_area</strong>: The number of “true” pixels in the segmented image</li> <li><strong>vessel_length</strong>: The number of “true” pixels in the skeleton image</li> </ol> <p> </p> <p><strong>Dataset Output Data</strong></p> <p>The file “image_quantification.csv” in the base folder contains the aggregated results from each image folder. Each row contains the results for a given (Program, Image) pair. The columns are described below:</p> <ol> <li><strong>Program</strong>: Designates the program used to calculate the data for that row</li> <li><strong>Tissue_Type</strong>: Gives the tissue type for the image</li> <li><strong>Image_Name</strong>: Gives the specific name of the given image</li> <li><strong>Vessel_Length</strong>: The number of “true” pixels in the skeleton image</li> <li><strong>Vessel_Area</strong>: The number of “true” pixels in the segmented image</li> <li><strong>Mean_Diameter</strong>: The mean diameter of vessels in the image</li> <li><strong>Num_Branchpoints</strong>: The number of branchpoints in the image</li> <li><strong>Sensitivity</strong>: (Number of True Positive pixels) / (Number of True Positive pixels + Number of False Negative pixels)</li> <li><strong>Specificity</strong>: (Number of True Negative pixels) / (Number of True Negative pixels + Number of False Positive pixels)</li> <li><strong>Accuracy</strong>: (Number of True Positive pixels + Number of True Negative pixels) / (Total number of pixels)</li> <li><strong>umppix</strong>: The length of the edge of one pixel in micrometers</li> <li><strong>pix_dim</strong>: The edge length in pixels of the square image</li> </ol> <p> </p> <p><strong>References</strong></p> <p>1. Niemisto, A., Dunmire, V., Yli-Harja, O., Wei Zhang & Shmulevich, I. Robust quantification of in vitro angiogenesis through image analysis. <em>IEEE Trans. Med. Imaging</em> <strong>24</strong>, 549–553 (2005).</p> <p>2. Zudaire, E., Gambardella, L., Kurcz, C. & Vermeren, S. A Computational Tool for Quantitative Analysis of Vascular Networks. <em>PLOS ONE</em> <strong>6</strong>, e27385 (2011).</p> <p>3. Seaman, M. E., Peirce, S. M. & Kelly, K. Rapid Analysis of Vessel Elements (RAVE): A Tool for Studying Physiologic, Pathologic and Tumor Angiogenesis. <em>PLoS ONE</em> <strong>6</strong>, e20807 (2011).</p>
Vascular network in the pericarp of tomato fruit and implications for fruit size, quality, and drought response
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Printability, durability, contractility and vascular network formation in 3D bioprinted cardiac endothelial cells using alginate-gelatin hydrogels
<p>TBC</p>
Perifoveal Vascular Network Assessed by OCT-Angiography in Type I Diabetes Mellitus
ClinicalTrials.gov study NCT03422965. IPD Sharing: UNDECIDED. Countries: 1. Publications: 23.
Retinal Vascular Network and Obstructive Sleep Apnea
ClinicalTrials.gov study NCT03979001. IPD Sharing: NO. Countries: 1. Publications: 0.
Supplementary material for "A large-scale demonstration and sustainability evaluation of ductile-porous vascular networks for self-healing concrete"
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Data from: Graph analysis of cell clusters forming vascular networks
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The lipodystrophic hotspot lamin A p.R482W mutation deregulates the mesodermal inducer T/Brachyury and early vascular differentiation gene networks
GEO Series GSE98675. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
SLIT3-ROBO4 Activation Promotes Vascular Network Formation in Human Engineered Tissue and Angiogenesis in Vivo
GEO Series GSE48302. Homo sapiens. 8 samples. Type: Expression profiling by array.
Identification of circRNA and mRNA expression profiles and functional networks of vascular tissue in lipopolysaccharide-induced rat septic shock model
GEO Series GSE147775. Rattus norvegicus. 20 samples. Type: Non-coding RNA profiling by array; Expression profiling by array.
Splicing regulatory network of master regulator RNA Binding Protein Multiple Splicing (RBPMS) in human Embryonic stem cell derived Vascular Smooth Muscle Cells.
GEO Series GSE153614. Homo sapiens. 21 samples. Type: Expression profiling by high throughput sequencing.
LncRNA-mRNA expression profile and functional network of vascular dysfunction in endotoxemic rats
GEO Series GSE144439. Rattus norvegicus. 10 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
Vascular network-inspired diffusible scaffolds for engineering functional neural organoids
GEO Series GSE276401. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Identification of circRNA and mRNA expression profiles and functional networks of vascular tissue in lipopolysaccharide-induced rat septic shock model [circRNA]
GEO Series GSE147772. Rattus norvegicus. 10 samples. Type: Non-coding RNA profiling by array.
Gene Regulatory Network Analysis and Engineering Directs Development and Vascularization of Multilineage Human Liver Organoids
GEO Series GSE159491. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
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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.