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Fig. 2 in The Flora Of Vascular Plants In Nature Reserve "Eglone"
Fig. 2. Distribution of forest stands in nature reserve "Eglone" by forest types.
Figura 2 in Lista atualizada e aspectos sobre a conservação DA floRA VAScuLAR do Parque Estadual do Turvo, Rio Grande do Sul, Brasil
Figura 2. Mapa de localização do Parque Estadual do Turvo, RS, Brasil (Google Earth).
Data from: Aged-vascular niche hinders osteogenesis of mesenchymal stem cells through paracrine repression of Wnt-axis
<p>Raw data set for: Fleischhacker V, Milosic F, Bricelj M, Kührer K, Wahl-Figlash K, Heimel P, Diendorfer A, Nardini E, Fischer I, Stangl H, Pietschmann P, Hackl M, Foisner R, Grillari J, Hengstschläger M, and Osmanagic-Myers S. Aged-vascular niche hinders osteogenesis of mesenchymal stem cells through paracrine repression of Wnt-axis. Aging Cell. 2024 Jun;23(6):e14139. doi: 10.1111/acel.14139. Epub 2024 Apr 5.</p> <p><span>Age-induced decline in osteogenic potential of bone marrow mesenchymal stem cells (BMSCs) potentiates osteoporosis and increases risk for bone fractures. Despite e</span><span>pidemiology studies reporting concurrent development of vascular- and bone diseases in the elderly, the underlying mechanisms for the vascular-bone cross-talk in aging are largely unknown. </span><span>In this study, we show that accelerated endothelial aging deteriorates bone tissue through paracrine repression of Wnt-driven-axis in BMSCs. <span>Here, we utilize </span>physiologically aged mice<span> in conjunction with our transgenic endothelial progeria mouse model </span>(Hutchinson-Gilford progeria syndrome; HGPS) that<span> displays hallmarks of an aged bone marrow vascular niche</span>. We find bone defects associated with diminished BMSC osteogenic differentiation that implicate the existence of angiocrine factors with long-term inhibitory effects. microRNA-transcriptomics of HGPS-patient plasma combined with aged-vascular niche analyses in progeria mice reveal abundant secretion of Wnt-repressive microRNA-31-5p. Moreover, we show that inhibition of microRNA-31-5p as well as selective Wnt-activator CHIR99021 boost the osteogenic potential of BMSCs through de-repression and activation of the Wnt-signalling, respectively. Our results demonstrate that the vascular niche significantly contributes to osteogenesis defects in aging and pave ground for microRNA-based therapies of bone loss in elderly.</span></p> <h1> </h1>
Fig. 11 - Trachelium caeruleum L in New floristic data of vascular plants from central Italy
Fig. 11 - Trachelium caeruleum L. subsp. caeruleum. (Photo / Foto F. Bartolucci).
Fig. 9 in New floristic data of vascular plants from central Italy
Fig. 9 - Iris pallida Lam. (Photo / Foto F. Falcinelli).
Fig. 10 - Opuntia scheerii F.A.C in New floristic data of vascular plants from central Italy
Fig. 10 - Opuntia scheerii F.A.C.Weber. (Photo / Foto F. Conti).
Fig. 8 - Cotoneaster lacteus W.W in New floristic data of vascular plants from central Italy
Fig. 8 - Cotoneaster lacteus W.W.Sm. (Photo / Foto F. Falcinelli).
Fig. 1 - Astragalus exscapus L in New floristic data of vascular plants from central Italy
Fig. 1 - Astragalus exscapus L. subsp. exscapus. (Photo / Foto F. Falcinelli).
Fig. 6 - Oxytropis ocrensis F in New floristic data of vascular plants from central Italy
Fig. 6 - Oxytropis ocrensis F.Conti & Bartolucci. (Photo / Foto F. Conti).
Fig. 2 in New floristic data of vascular plants from central Italy
Fig. 2 - Calendula tripterocarpa Rupr. (Photo / Foto F. Conti).
Fig. 1 in Short communication Contribution to the vascular flora of Ventotene and Santo Stefano islands (Pontine Islands, Lazio, Italy) with two taxa new to Lazio
Fig. 1 - Oenothera speciosa Nutt. (Foto F. Conti).
Fig. 7 - Salix pentandra L in New floristic data of vascular plants from central Italy
Fig. 7 - Salix pentandra L. (Photo / Foto F. Conti).
Fig. 3 in New floristic data of vascular plants from central Italy
Fig. 3 - Cytisus villosus Pourr. (Photo / Foto F. Falcinelli).
Fig. 3 in Vascular plant diversity of the Gogunsan Archipelago in the Korean Peninsula
Fig. 3. Variations of percentage of habitat affinity types in the Go- gunsanArchipelago.
Fig. 1. A in Vascular plant diversity of the Gogunsan Archipelago in the Korean Peninsula
Fig. 1. A map of investigated area in the Gogunsan Archipelago.
Fig. 2 in Correction of the holotype citations of three vascular plants at the herbarium of the National Institute of Biological Resources, Korea
Fig. 2. Holotype of Isoetes coreana Y.H. Chung & H.K. Choi.
Fig. 3 in Correction of the holotype citations of three vascular plants at the herbarium of the National Institute of Biological Resources, Korea
Fig. 3. Holotype of Huperzia jejuensis B.Y. Sun & J. Lim.
Stylized urban landscapes optimized for compactness, climate regulation and vascular plant species richness
<p>The data set provides the output of a genetic algorithm optimizing a stylized urban region with respect to three target functions: urban compactness, climate regulation as an exemplary ecosystem service and vascular plant species richness as a measure of biodiversity.</p> <p>The optimisation varies the spatial allocation of three types of land cover blocks in a stylized urban region: high- and low-density and park blocks which consist of green and/or built-up cells. We systematically vary landscape composition at the block level, but keep city size constant.</p> <p>The data set is related to a publication submitted to Frontiers in Environmental Science.</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>
Effects of flavoring compounds used in electronic cigarette refill liquids on endothelial and vascular function
<p>raw data of all figures and tables</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.