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173 results for “vasculature”
Systemic Treatment with Cigarette Smoke Extract Affects Zebrafish Visual Behaviour, Intraocular Vasculature Morphology and Outer Segment Phagocytosis
<p>Underlying dataset and analysis tests of the results described in the article "Systemic Treatment with Cigarette Smoke Extract Affects Zebrafish Visual Behaviour, Intraocular Vasculature Morphology and Outer Segment Phagocytosis".</p>
EVO-NANO modified PhysiCell simulation run with 10K vasculature agents
<p>PhysiCell modifications:</p> <p>- added CSC</p> <p>-added vasculature</p> <p> </p> <p>this run:</p> <p>Cell cycle speed increased to 1/10 (default is 0.0432/60 = 0,00072)<br> snapshot is taken each 180 minutes (of simulation time)<br> There are 10000 vasculature agents with 100 branches<br> oxygen secretion rate for each fasculature agent is: phenotype.secretion.secretion_rates[0] = 10<br> </p>
EVO-NANO modified PhysiCell simulation run with 50K vasculature agents
<p>PhysiCell modifications:</p> <p>- added CSC</p> <p>-added vasculature</p> <p> </p> <p>this run:</p> <p>Cell cycle speed increased to 1/10 (default is 0.0432/60 = 0,00072)<br> snapshot is taken each 180 minutes (of simulation time)<br> There are 50000 vasculature agents with 500 branches<br> oxygen secretion rate for each fasculature agent is: phenotype.secretion.secretion_rates[0] = 10</p>
Data from: Identification of a minority population of LMO2+ breast cancer cells that integrate into the vasculature and initiate metastasis.
<p>Metastasis is responsible for the majority of breast cancer-related deaths, however, identifying the cellular determinants of metastasis has remained challenging. Here, we identified a minority population of immature THY1+/VEGFA+ tumor epithelial cells in human breast tumor biopsies that display angiogenic features and are marked by the expression of the oncogene, LMO2. Higher abundance of LMO2+ basal cells correlated with tumor endothelial content and predicted poor distant recurrence-free survival in patients. Using MMTV-PyMT/Lmo2CreERT2 mice, we demonstrated that Lmo2 lineage-traced cells integrate into the vasculature and have a higher propensity to metastasize. LMO2 knockdown in human breast tumors reduced lung metastasis by impairing intravasation, leading to a reduced frequency of circulating tumor cells. Mechanistically, we find that LMO2 binds to STAT3 and is required for STAT3 activation by TNFα and IL6. Collectively, our study identifies a population of metastasis-initiating cells with angiogenic features and establishes the LMO2-STAT3 signaling axis as a therapeutic target in breast cancer metastasis.</p>
Text-fig. 6. Cornaceae. Alangium (a–e), Mastixia (f–r). a–e: Alangium, DMNH EPI.47806. Scale bar = 1 cm. b, e: Reflected light, palladium coated. a, c, d: Micro-CT scan surface rendering. a: Locule cast, face view of slightly larger locule. b: Face view of slightly smaller locule. c: Lateral view of the endocarp, the slightly enlarged left carpel separated from the smaller carpel by a longitudinal septal groove; the faint pitting in the groove suggestive of the septal vasculature. d, e: Views of either end of the endocarp, illustrating the size difference between the two carpels and the pitting in the septal groove suggestive of the septal vasculature. f–k: Mastixia USNM PAL 772362. Scale bar = 1 cm. f, g, j, k: reflected light, palladium coated; h, i: micro-CT scan surface rendering. f: Lateral view of endocarp, inferred dorsal germination valve groove facing the viewer. Note irregular, rugose, longitudinal ridges. g: Lateral view of endocarp, inferred germination valve with median longitudinal groove to left. h: Lateral view of endocarp reoriented with the same longitudinal groove to the right. i: Lateral view, rotated to ventral surface. j: View of one end of the endocarp, germination valve groove up. k: Opposite end view, with prominent radial ridges and intervening grooves, germination valve groove up. l–r: Mastixia USNM PAL 772363. Scale bar = 1 cm. l: View of one face of endocarp, displaying a groove that may represent the surficial expression of the dorsal infold of a Mastixia-like germination valve. Surface badly eroded, reflected light, palladium coated. m: Opposite face of endocarp displaying extensive erosion and a central hole interpreted as feeding damage. n: Lateral view; m, n micro-CT scan surface renderings. o: A view of one end, displaying the prominent groove, reflected light, palladium coated. p: Opposite end to (o). q: View as in (o); p, q micro-CT scan surface renderings. r: Virtual transverse section showing curved locule (arrows). in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 6. Cornaceae. Alangium (a–e), Mastixia (f–r). a–e: Alangium, DMNH EPI.47806. Scale bar = 1 cm. b, e: Reflected light, palladium coated. a, c, d: Micro-CT scan surface rendering. a: Locule cast, face view of slightly larger locule. b: Face view of slightly smaller locule. c: Lateral view of the endocarp, the slightly enlarged left carpel separated from the smaller carpel by a longitudinal septal groove; the faint pitting in the groove suggestive of the septal vasculature. d, e: Views of either end of the endocarp, illustrating the size difference between the two carpels and the pitting in the septal groove suggestive of the septal vasculature. f–k: Mastixia USNM PAL 772362. Scale bar = 1 cm. f, g, j, k: reflected light, palladium coated; h, i: micro-CT scan surface rendering. f: Lateral view of endocarp, inferred dorsal germination valve groove facing the viewer. Note irregular, rugose, longitudinal ridges. g: Lateral view of endocarp, inferred germination valve with median longitudinal groove to left. h: Lateral view of endocarp reoriented with the same longitudinal groove to the right. i: Lateral view, rotated to ventral surface. j: View of one end of the endocarp, germination valve groove up. k: Opposite end view, with prominent radial ridges and intervening grooves, germination valve groove up. l–r: Mastixia USNM PAL 772363. Scale bar = 1 cm. l: View of one face of endocarp, displaying a groove that may represent the surficial expression of the dorsal infold of a Mastixia-like germination valve. Surface badly eroded, reflected light, palladium coated. m: Opposite face of endocarp displaying extensive erosion and a central hole interpreted as feeding damage. n: Lateral view; m, n micro-CT scan surface renderings. o: A view of one end, displaying the prominent groove, reflected light, palladium coated. p: Opposite end to (o). q: View as in (o); p, q micro-CT scan surface renderings. r: Virtual transverse section showing curved locule (arrows).
Data from: Identification of a minority population of LMO2+ breast cancer cells that integrate into the vasculature and initiate metastasis.
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I2K2020 Data for "Quantification of the 3D brain vasculature in zebrafish light sheet fluorescence microscopy data"
<p>Example data for the I2K2020 tutorial "Quantification of the 3D brain vasculature in zebrafish light sheet fluorescence microscopy data" (https://www.janelia.org/you-janelia/conferences/from-images-to-knowledge-with-imagej-friends/virtual-workshop-program)</p> <p>"Readme" file for data description included in folder.</p> <p><strong>Background:</strong> Zebrafish transgenic lines and light sheet fluorescence microscopy (LSFM) allow unrivalled insights into vascular development <em>in vivo</em> and 3D. The vascular architecture can be used to describe physiological status. However, assessment of the vasculature still relies on individual visual assessment rather than objective quantification. Thus, an image analysis pipeline is required to allow data assessment in 3D robustly and sensitively, while being able to handle LSFM data.</p> <p>Kugler et al have produced an image analysis workflow to quantify the zebrafish brain vasculature in 3D (https://www.biorxiv.org/content/10.1101/2020.08.06.239905v2).</p> <p><strong>Aim</strong>: In this tutorial we will use the analysis workflow produced by Kugler et al to examine and quantify the zebrafish brain vasculature in 3D with a hands-on practical (https://github.com/ElisabethKugler/ZFVascularQuantification).</p>
VessMAP - Feature-Mapped Cortex Vasculature Dataset
<h2>VessMAP Dataset</h2><p>The VessMAP Dataset comprises 100 manually labeled confocal microscopy images of mouse brain vasculature. Among these images, 20 randomly selected samples were labeled by two annotators. The dataset is organized using the following directory structure:</p><ul><li>images: 100 blood vessel images (.tiff). Each image has a size of 256x256 pixels with a single grayscale channel of 8-bit color depth.</li><li>annotator1:<ul><li>labels: 100 labels annotated by annotator 1. Each file is a 256x256 .png binary image, with a value of 255 assigned to pixels annotated as blood vessels and a value of 0 assigned to background pixels.</li><li>skeletons: skeletons of the labels annotated by annotator 1. Each file is a 256x256 .png binary image, with a value of 255 assigned to pixels belonging to the skeleton and a value of 0 assigned to background pixels. These skeletons were calculated using the Palàgyi-Kuba algorithm [1].</li><li>measures.json: a .json file with four metrics that can be used to map the dataset to a feature space. The four metrics included in the file are the contrast, blood vessel density, estimation of Gaussian noise level, and skeleton heterogeneity. A detailed explanation of the metrics can be found in [2]. These metrics are useful for testing possible biases when training supervised segmentation algorithms.</li></ul></li><li>annotator2:<ul><li>This directory contains annotations for 20 images that were randomly selected from the 100 images of the whole dataset. The organization is the same as for annotator 1.</li></ul></li></ul><p>1. K. Palágyi and A. Kuba, "A 3D 6-subiteration thinning algorithm for extracting medial lines," Pattern Recognit. Lett. 19, 613–627 (1998).</p><p>2. da Silva, MV., Santos, NdC., Lacoste, B., & Comin, CH. A new sampling methodology for creating rich, heterogeneous, subsets of samples for training image segmentation algorithms. arXiv preprint arXiv:2301.04517, (2023).</p>
Vasculature Segmentation Validation Dataset - Part IV - Biological Difference Dataset 2/2 (Development)
<p>Dataset to allow exploration of data enhancement, segmentation, and validation for: https://www.biorxiv.org/content/10.1101/2020.07.21.213843v1 and associated future publications<br> </p> <p><strong>Dataset description</strong>:</p> <p><strong>Development</strong>: Example data of the head vasculature of developing zebrafish.</p> <p><br> <strong>Data acquisition</strong>:<br> Experiments were performed according to the rules and guidelines of institutional and UK Home Office regulations under the Home Office Project Licence 70/8588 held by TC. Maintenance of adult zebrafish Tg(kdrl:HRAS-mCherry)s916 (Chi et al., 2008) was performed as described in standard husbandry protocols (Westerfield, 1993). Embryos, obtained from controlled mating, were kept in E3 medium buffer with methylene blue and staged as previously described (Kimmel et al., 1995).</p> <p>Embryos were embedded in 2% LMP-agarose with 0.01% Tricaine in E3 (MS-222, Sigma). Data were acquired using a Zeiss Z.1 light sheet microscope, Plan-Apochromat 20x/1.0 Corr nd=1.38 objective, dual-side illumination with online fusion, activated Pivot Scan, image acquisition chamber incubation at 28°C, with a scientific complementary metal-oxide semiconductor (sCMOS) detection unit. Data properties can be summarised as: 16bit image depth, voxel dimensions in x, y and z of 1920 x 1920 x 400-600, respectively, giving a voxel size of 0.33 x 0.33 x 0.5 µm). </p> <p><strong>Contact</strong>: kugler.elisabeth[at]gmail.com</p> <p><strong>Useful code links</strong>: </p> <ul> <li>https://github.com/ElisabethKugler/ZFVascularQuantification</li> <li>https://github.com/ElisabethKugler/Matlab3D-ImageAnalysis<br> </li> </ul>
Vasculature Segmentation Validation Dataset - Part III - Biological Difference Dataset 1/2 (Exsanguination)
<p>Dataset to allow exploration of data enhancement, segmentation, and validation for: https://www.biorxiv.org/content/10.1101/2020.07.21.213843v1 and associated future publications</p> <p><strong>Dataset description</strong></p> <p><strong>Exsanguination</strong>: Example data of the head vasculature of 4dpf of zebrafish. Data acquisition before and after exsanguination by opening of the heart cavity with forceps in Tg(kdrl:HRAS-mCherry)s916 (4dpf; n=16 embryos from 2 experimental repeats).</p> <p><br> <strong>Data acquisition</strong>:<br> Experiments were performed according to the rules and guidelines of institutional and UK Home Office regulations under the Home Office Project Licence 70/8588 held by TC. Maintenance of adult zebrafish Tg(kdrl:HRAS-mCherry)s916 (Chi et al., 2008) was performed as described in standard husbandry protocols (Westerfield, 1993). Embryos, obtained from controlled mating, were kept in E3 medium buffer with methylene blue and staged as previously described (Kimmel et al., 1995).</p> <p>Embryos were embedded in 2% LMP-agarose with 0.01% Tricaine in E3 (MS-222, Sigma). Data were acquired using a Zeiss Z.1 light sheet microscope, Plan-Apochromat 20x/1.0 Corr nd=1.38 objective, dual-side illumination with online fusion, activated Pivot Scan, image acquisition chamber incubation at 28°C, with a scientific complementary metal-oxide semiconductor (sCMOS) detection unit. Data properties can be summarised as: 16bit image depth, voxel dimensions in x, y and z of 1920 x 1920 x 400-600, respectively, giving a voxel size of 0.33 x 0.33 x 0.5 µm). </p> <p><strong>Contact</strong>: kugler.elisabeth[at]gmail.com</p> <p><strong>Useful code links</strong>: </p> <p>https://github.com/ElisabethKugler/ZFVascularQuantification<br> https://github.com/ElisabethKugler/Matlab3D-ImageAnalysis</p>
Vasculature Segmentation Validation Dataset - Part II - Decreasing Contrast-To-Noise Ratio
<p>Dataset to allow exploration of data enhancement, segmentation, and validation for: https://www.biorxiv.org/content/10.1101/2020.07.21.213843v1 and associated future publications</p> <p><strong>Dataset description:</strong></p> <ul> <li><em><strong>LaserForNoise</strong></em>: Example data of the head vasculature of 4dpf of zebrafish. The data were consecutively acquired with 1.2%, 0.8%, and 0.4% laser power (LP), respectively. This allowed us to produce data with decreasing contrast-to-noise ratio, and thus study robustness over a range of signal distributions.</li> </ul> <p><em><strong>Data acquisition</strong></em>:<br> Experiments were performed according to the rules and guidelines of institutional and UK Home Office regulations under the Home Office Project Licence 70/8588 held by TC. Maintenance of adult zebrafish Tg(kdrl: HRAS-mCherry)s916 (Chi et al., 2008) was performed as described in standard husbandry protocols (Westerfield, 1993). Embryos, obtained from controlled mating, were kept in E3 medium buffer with methylene blue and staged as previously described (Kimmel et al., 1995).</p> <p>Embryos were embedded in 2% LMP-agarose with 0.01% Tricaine in E3 (MS-222, Sigma). Data were acquired using a Zeiss Z.1 light sheet microscope, Plan-Apochromat 20x/1.0 Corr nd=1.38 objective, dual-side illumination with online fusion, activated Pivot Scan, image acquisition chamber incubation at 28°C, with a scientific complementary metal-oxide-semiconductor (sCMOS) detection unit. Data properties can be summarised as: 16bit image depth, voxel dimensions in x, y and z of 1920 x 1920 x 400-600, respectively, giving a voxel size of 0.33 x 0.33 x 0.5 µm). </p> <p><strong>Contact</strong>: kugler.elisabeth[at]gmail.com</p> <p><strong>Useful code links</strong>: </p> <ul> <li>https://github.com/ElisabethKugler/ZFVascularQuantification</li> <li>https://github.com/ElisabethKugler/Matlab3D-ImageAnalysis</li> </ul>
Vasculature Segmentation Validation Dataset - Part I - Simulated Tubes and Filter Responses
<p>Dataset to allow exploration of data enhancement, segmentation, and validation for: https://www.biorxiv.org/content/10.1101/2020.07.21.213843v1 and associated future publications</p> <p><strong>Dataset description:</strong></p> <ul> <li><em><strong>SimulatedTubes</strong></em>: Computationally produced tubes (3 different sizes) that are hollow, filled, or filled with Gaussian distribution. To study filter responses, artificial noise and blurring were added.</li> <li><em><strong>DataExFilterResponse</strong></em>: Example data of the head vasculature of 4dpf of zebrafish, where the response to different filters is tested (i.e. GF - general filtering and TF - tubular filtering / Sato enhancement; for TF also different scale sizes are examined; see also: https://www.mdpi.com/2313-433X/5/1/14).</li> </ul> <p><em><strong>Data acquisition:</strong></em><br> Experiments were performed according to the rules and guidelines of institutional and UK Home Office regulations under the Home Office Project Licence 70/8588 held by TC. Maintenance of adult zebrafish Tg(kdrl:HRAS-mCherry)s916 (Chi et al., 2008) was performed as described in standard husbandry protocols (Westerfield, 1993). Embryos, obtained from controlled mating, were kept in E3 medium buffer with methylene blue and staged as previously described (Kimmel et al., 1995).</p> <p>Embryos were embedded in 2% LMP-agarose with 0.01% Tricaine in E3 (MS-222, Sigma). Data were acquired using a Zeiss Z.1 light sheet microscope, Plan-Apochromat 20x/1.0 Corr nd=1.38 objective, dual-side illumination with online fusion, activated Pivot Scan, image acquisition chamber incubation at 28°C, with a scientific complementary metal-oxide semiconductor (sCMOS) detection unit. Data properties can be summarised as: 16bit image depth, voxel dimensions in x, y and z of 1920 x 1920 x 400-600, respectively, giving a voxel size of 0.33 x 0.33 x 0.5 µm). </p> <p><em><strong>Contact</strong></em>: kugler.elisabeth[at]gmail.com</p> <p><em><strong>Useful code links</strong></em>: </p> <ul> <li>https://github.com/ElisabethKugler/ZFVascularQuantification</li> <li>https://github.com/ElisabethKugler/Matlab3D-ImageAnalysis</li> </ul>
VesselExpress: Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs
<p>This dataset contains raw, segmented and skeletonized 3D light-sheet microscopic image volumes of blood vessels of different organs which were processed by VesselExpress. Please find the software here: https://github.com/RUB-Bioinf/VesselExpress. For details on how to run and setup the software please watch our tutorial (https://youtu.be/a8GWVKJNh68).</p>
Data for: On the identification of hypoxic regions in subject-specific cerebral vasculature by combined CFD/MRI
<p>A long-time exposure to lack of oxygen (hypoxia) in some regions of the cerebrovascular system is believed to be one of the causes of cerebral neurological disease. Performing {\em in vivo} studies on the human brain is complicated and can be highly risky for patients. In the present study, we show how a combination of Magnetic Resonance Imaging (MRI) and Computational Fluid Dynamics (CFD) can provide a non-invasive alternative for studying blood flow and transport of oxygen within the cerebral vasculature. We perform computer simulations of oxygen mass transfer in the subject-specific geometry of the Circle of Willis. The computational domain and boundary conditions are based on 4D flow MRI measurements. Two different oxygen mass transfer models are considered: passive (where oxygen is treated as a dilute chemical species in plasma) and active (where oxygen is bonded to hemoglobin) models. We show that neglecting hemoglobin transport results in a significant underestimation of the arterial wall-mass transfer of oxygen. We identified the hypoxic regions along the arterial walls by introducing the critical thresholds that are obtained by comparison of the estimated range of Damk\"{o}hler number ($Da\subset\langle9;57\rangle$) with the local Sherwood number. Finally, we recommend additional validations of the combined MRI/CFD approach proposed here for larger groups of subject- or patient-specific brain vasculature systems.</p>
Genetic and functional analysis of Raynaud’s syndrome implicates loci in vasculature and immunity
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Data for: On the identification of hypoxic regions in subject-specific cerebral vasculature by combined CFD/MRI
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Data from: D quantification of tumor vasculature in lymphoma xenografts in NOD/SCID mice allows to detect differences among vascular-targeted therapies
Quantitative characterization of the in vivo effects of vascular-targeted therapies on tumor vessels is hampered by the absence of useful 3D vascular network descriptors aside from microvessel density. In this study, we extended the quantification of planar vessel distribution to the analysis of vascular volumes by studying the effects of antiangiogenic (sorafenib and sunitinib) or antivascular (combretastatin A4 phosphate) treatments on the quantity and spatial distributions of thin microvessels. These observations were restricted to perinecrotic areas of treated human multiple myeloma tumors xenografted in immunodeficient mice and to microvessels with an approximate cross-sectional area lower than 75 µm2. Finally, vessel skeletonization minimized artifacts due to possible differential wall staining and allowed a comparison of the various treatment effects. Antiangiogenic drug treatment reduced the number of vessels of every caliber (at least 2-fold fewer vessels vs. controls; p<0.001, n = 8) and caused a heterogeneous distribution of the remaining vessels. In contrast, the effects of combretastatin A4 phosphate mainly appeared to be restricted to a homogeneous reduction in the number of thin microvessels (not more than 2-fold less vs. controls; p<0.001, n = 8) with marginal effects on spatial distribution. Unexpectedly, these results also highlighted a strict relationship between microvessel quantity, distribution and cross-sectional area. Treatment-specific changes in the curves describing this relationship were consistent with the effects ascribed to the different drugs. This finding suggests that our results can highlight differences among vascular-targeted therapies, providing hints on the processes underlying sample vascularization together with the detailed characterization of a pathological vascular tree.
Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression
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In Vivo Rodent Cervicothoracic Vasculature Imaging Using Photoacoustic Computed Tomography
<p>These video clips are the supplementary video for the manuscript "<em>In Vivo</em> Rodent Cervicothoracic Vasculature Imaging Using Photoacoustic Computed Tomography" submitted to <em>Photonics</em>.</p>
Effect of Chlorogenic Acids on the Human Vasculature
ClinicalTrials.gov study NCT03520452. IPD Sharing: NO. Countries: 1. Publications: 1.
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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.