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862 results for “Capillary”
Agrostis capillaris L. (BR0000021880896)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Agrostis capillaris L. (BR0000009842038)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Agrostis capillaris L. (BR0000011443889)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Agrostis capillaris L. (BR0000011444428)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Agrostis capillaris L. (BR0000012392056)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Agrostis capillaris L. (BR0000011444336)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Agrostis capillaris L. (BR0000011442615)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Optical Particle Tracking in the Pneumatic Conveying of Metal Powders through a Thin Capillary Pipe
<p>An experimental setup utilizing high-speed cameras and specialized optics was constructed to collect the conveying flow characteristics. The data here presented is pre-processed using ImageJ/Fiji, and uses the TrackMate package (see https://github.com/trackmate-sc/TrackMate/pull/296). The videos can be loaded to Fiji using the FFMPG package.</p>
Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.
<p><strong>DOCUMENTATION - Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.</strong></p> <p>Correspondence: fschmid@ethz.ch (Franca Schmid, ORCID: <a href="https://orcid.org/0000-0002-0689-9366">0000-0002-0689-9366</a>)</p> <p><strong>1. Related references:</strong><br> The data set is published in context with the manuscript: <br> [1]<em> The severity of microstrokes depends on local vascular topology and baseline perfusion</em>. <br> F Schmid, G Conti, P Jenny and B Weber. eLife. 2021. Doi: 10.7554/eLife.60208</p> <p>The bi-phasic blood flow simulations have been performed in realistic microvascular networks (MVNs) from the mouse somatosensory cortex first published in:<br> [2]<em> The cortical angiome: an interconnected vascular network with noncolumnar patterns of blood flow</em>. P Blinder, PS Tsai, JP Kaufhold, PM Knutsen, H Suhl and D Kleinfeld. Nature Neuroscience. 2013. Doi: 10.1038/nn.3426</p> <p>The bi-phasic blood flow model for realistic MVNs has first been published in:<br> [3]<em> Depth-dependent flow and pressure characteristics in cortical microvascular networks</em>. F Schmid, PS Tsai, D Kleinfeld, P Jenny and B Weber. PLoS Computational Biology. 2017. Doi: 10.1371/journal.pcbi.1005392</p> <p><em>For further information on how to perform bi-phasic blood flow simulation, please contact the corresponding authors of [1] or [3].</em></p> <p><strong>2. Requirements (software):</strong><br> <em>All simulations and analyses have been performed in Python 2.7. To execute the analysis script the following python libraries need to be installed: cPickle, python-igraph, pandas, seaborn, scipy. The individual analyses script can then be executed by in Python (e.g. “python plot_Figure3.py”).</em></p> <p><strong>3. Content:</strong><br> <em>All folders are stored as compressed archives (*.tar.bz2). On unix-based system the folders can be unpacked by: "</em>tar –jxf ARCHIVE_NAME"<br> <br> <strong>3a. Time-averaged simulation results (python dictionaries stored as python 2.7 pickle files):</strong><br> <strong>SimulationResults_Baseline.tar.bz2:</strong><br> Folders: MVN1, MVN2<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl (<em>generated from prepare_Figure4.py</em>), data_spatial_distribution_AVfactor.pkl (<em>generated from plot_Figure4.py</em>)</p> <p><strong>SimulationResults_SingleCapillaryOcclusions.tar.bz2:</strong><br> Folders: 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out, 2-in-2-out_high, 2-in-2-out_AL1, 2-in-2-out_AL2, 2-in-2-out_AL3, 2-in-2-out_AL4, 2-in-2-out_AL5, 2-in-2-out_closeToDA, 2-in-2-out_farFromDA<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl (<em>only folders:</em> 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out)</p> <p><strong>SimulationResults_MultiCapillaryOcclusions.tar.bz2:</strong><br> Folders: vesselsOccluded_1, vesselsOccluded_3, vesselsOccluded_5, vesselsOccluded_7, vesselsOccluded_9<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl</p> <p><strong>3b. Analysis scripts (python 2.7 scripts in folder Analyses_Scripts):</strong><br> <em>For details on the figure content see [1]. The verticesDict* and the edgesDict* are converted into graph structure (python-igraph) for all analyses. The functionality of python-igraph is used heavily throughout the various analyses.</em></p> <p><strong>helperFunctions.py</strong>: various functions used by the other analysis scripts</p> <p><strong>plot_Figure1_and_Figure1-supplement_1_a-d.py:</strong><br> <strong>Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl<br> <strong>Output:</strong> Figures/Figure_1/*, Supplementary_Figures/Figure_1-supplement_1_a-d/*</p> <p><strong>plot_Figure2_and_Figure2_supplement_1_a-d.py:<br> Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl<br> <strong>Output:</strong> Figures/Figure_2/*, Supplementary_Figures/Figure_2-supplement_1_a-d/*</p> <p><strong>plot_Figure3.py:<br> Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_MultiCapillaryOcclusion/*/edgesDict.pkl, SimulationResults_MultiCapillaryOcclusion/*/verticesDict.pkl <br> <strong>Output</strong>: Figures/Figure_3/*</p> <p><strong>prepare_Figure4.py </strong>(<em>long execution time!):</em><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN*/verticesDict_baseline.pkl,<br> <strong>Output:</strong> SimulationResults_Baseline/MVN*/pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl</p> <p><strong>plot_Figure4.py </strong>(<em>long execution time!):</em><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/*<br> <strong>Output:</strong> SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl (attribute Lfactor_median added), SimulationResults_Baseline/MVN*/data_spatial_distribution_AVfactor.pkl, Figures/Figure_4/*</p> <p><strong>plot_Figure5.py:</strong><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/* <br> <strong>Output:</strong> Figures/Figure_5/*</p> <p><strong>plot_Figure6.py:</strong><br> Input: SimulationResults_Baseline/MVN1/*, SimulationResults_SingleCapillaryOcclusion/2-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl Output: Figures/Figure_6/*</p> <p><strong>4. Attributes stored in python dictionaries:</strong><br> <br> <strong>4a. Baseline:</strong><br> <strong>verticesDict: </strong><em>contains all relevant information and data stored at vertices.</em></p> <ul> <li>index: index of vertex</li> <li>pressure: time averaged pressure at vertex [mmHg]</li> <li>inflowE: list of edges delivering blood to the vertex (inflows of the vertex)</li> <li>outflowE: list of edges removing blood from the vertex (outflows of the vertex)</li> <li>coords: coordinates of the vertex [µm]</li> <li>pBC: pressure boundary conditions [mmHg], None for internal vertices</li> <li>corticalDepth: depth from cortical surface [µm]</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> </ul> <p><strong>edgesDict:</strong> <em>contains all relevant information and data stored at edges.</em></p> <ul> <li>diameter: effective vessel diameter [µm]. See [3] for details.</li> <li>htd: time averaged discharge hematocrit [-]. </li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>mainAV: identifier for ascending venule (AV) main brain. 1: is AV main brain, 0: no AV main branch</li> <li>mainDA: identifier for descending arteriole (DA) main brain. 1: is DA main brain, 0: no DA main branch</li> <li>flow: time averaged flow rate [µm<sup>3</sup> ms<sup>-1</sup>]</li> <li>length: tortuous vessel length [µm] See [1] and [3] for details.</li> <li>tissueVolume: topological tissue volume supplied by vessel [µm<sup>3</sup>]. See [1] for details.</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> <li>edgesFulfillingSelection: identifier if vessels fulfils selection criteria to qualify for analysis. 1: vessel included for analysis, 0: vessel not included for analysis. Details on the selection criteria are provided in [1].</li> <li>htt: time averaged tube hematocrit [-]</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate.</li> <li>sign: sign describing the flow direction in the vessel. +: flow direction from source (vertex with lower index) to target (vertex with higher index), -: flow direction from target to source vertex. Based on time averaged pressure values.</li> <li>points: list of tortuous vessel coordinates of the edge [µm]. Starting at the source vertex. Ending at the target vertex.</li> <li>Lfactor_median: AV-factor of the vessel. None if no AV-factor can be assigned. See [1] for details. Attribute added by plot_Figure4.py</li> </ul> <p><strong>pathsDict_allPaths_from_DA_to_AV_mainBranch: </strong><em>contains all flow path from DA main brain to AV main branch. For details see [1]</em>.</p> <ul> <li>startPoint: list of vertex indices of the end point of the DA</li> <li>endPoint: list of vertex indices of the end point of the AV</li> <li>allPaths: list of lists of vertex indices describing all paths between a the associated startPoint and endPoint.</li> </ul> <p><strong>data_spatial_distribution_AVfactor:</strong> <em>contains information on the spatial distribution of venule-sided capillaries (AV-factor > 0.5). For details see [1].</em></p> <ul> <li>edges_L_mean_50um: list of all edges for which the average AV-factor in an analysis sphere of 50 µm has been computed.</li> <li>resulting_L_mean_50um: average AV-factor for an analysis sphere for 50 µm (see Figure4/AV_factor_delta_analysisSphere50_MVN*.pkl)</li> <li>shortest_distance_to_closest_vessel: list of shortest distances to any vessel for all discretization points along all venule sided capillaries.</li> <li>shortest_distance_to_Lfactor_lt_05: list of shortest distances to an arteriole-sided capillary (AV-factor < 0.5) for all discretization points along all venule sided capillaries.</li> </ul> <p><strong>4b. Occlusion scenarios (both single- and multi-capillary occlusions):</strong></p> <p><strong>verticesDict:</strong></p> <ul> <li>index: index of vertex</li> <li>coords: coordinates of the vertex [µm]</li> <li>pressure_strokeIndex_n: time averaged pressure at vertex [mmHg] for the simulation where edge n has been occluded. For details see [1].</li> </ul> <p><strong>edgesDict:</strong></p> <ul> <li>htd_strokeIndex_n: time averaged discharge hematocrit [-] for the simulation where edge n has been occluded. For details see [1]. </li> <li>flow: time averaged flow rate [µm<sup>3</sup> ms<sup>-1</sup>] for the simulation where edge n has been occluded. For details see [1].</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate for the simulation where edge n has been occluded. For details see [1].</li> <li>htt: time averaged tube hematocrit [-] for the simulation where edge n has been occluded. For details see [1].</li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>diameter_strokeIndex_n: effective vessel diameter [µm] for the simulation where edge n has been occluded (only given for multi-capillary occlusions).</li> </ul> <p><strong>pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased:</strong> <em>contains all flow path from DA main brain to AV main branch (unique vertex sequences). For details see helperFunctions.py --></em><em> function convert_pathsDict_to_unique_vertexSequence.</em></p> <ul> <li>startPoint_strokeIndex_n: list of vertex indices of the end point of the DA for the simulation where edge n has been occluded.</li> <li>endpoint_strokeIndex_n: list of vertex indices of the end point of the AV for the simulation where edge n has been occluded.</li> <li>allPaths_strokeIndex_n: list of lists of vertex indices describing all paths between a the associated startPoint and endpoint for the simulation where edge n has been occluded.</li> </ul>
Figure 3. Paraprionospio alata collected from Chesapeake Bay. A, neuropodial limbate capillary from setiger 8 in A revision of the genus Paraprionospio Caullery (Polychaeta: Spionidae)
Figure 3. Paraprionospio alata collected from Chesapeake Bay. A, neuropodial limbate capillary from setiger 8; B, neuropodial hooded hook from setiger 11; C, neuropodial non-limbate capillary from setiger 9; D, sabre seta from setiger 11; E, notopodial hooded hook from posterior fragment.
Expression Profile of CD157 Reveals Functional Heterogeneity of Capillaries in Human Dermal Skin
<p>CD157 acts as a receptor, regulating leukocyte trafficking and the binding of extracellular matrix components. However, the expression pattern and the role of CD157 in human blood (BEC) and the lymphatic endothelial cells (LEC) of human dermal microvascular cells (HDMEC), remain elusive. We demonstrated constitutive expression of CD157 on BEC and LEC, in fetal and juvenile/adult skin, in situ, as well as in isolated HDMEC. Interestingly, CD157 epitopes were mostly localized on BEC, co-expressing high levels of CD31 (CD31<sup>High</sup>), as compared to CD31<sup>Low</sup> BEC, whereas the podoplanin expression level on LEC did not affect CD157. Cultured HDMEC exhibited significantly higher numbers of CD157-positive LEC, as compared to BEC. Interestingly, separated CD157<sup>-</sup> and CD157<sup>+</sup> HDMEC demonstrated no significant differences in clonal expansion in vitro, but they showed distinct expression levels of cell adhesion molecules, before and after cytokine stimulation in vitro. In particular, we proved the enhanced and specific adherence of CD11b-expressing human blood myeloid cells to CD157<sup>+</sup> HDMEC fraction, using an in vitro immune-binding assay. Indeed, CD157 was also involved in chemotaxis and adhesion of CD11b/c monocytes/neutrophils in prevascularized dermo-epidermal skin substitutes (vascDESS) in vivo. Thus, our data attribute specific roles to endothelial CD157, in the regulation of innate immunity during inflammation.</p> <p> </p>
Fig. 3 in Vascular plants of Poaceae (Ⅰ) new to Korea: Vulpia bromoides (L.) Gray, Agrostis capillaris L. and Eragrostis pectinacea (Michx.) Nees
Fig. 3. Photograph of Eragrostis pectinacea (Michx.) Nees. A. Habits. B. Inflorescence. C. Ligule. D. Spikelet. E. Maturity spikelet. F. Caryopsis.
Fig. 1 in Vascular plants of Poaceae (Ⅰ) new to Korea: Vulpia bromoides (L.) Gray, Agrostis capillaris L. and Eragrostis pectinacea (Michx.) Nees
Fig. 1. Photograph of Vulpia bromoides (L.) Gray. A. Habit. B. Inflorescence. C. Ligule. D. Spikelet. E. Glumes. F. Lemma and Palea.
Fig. 2 in Vascular plants of Poaceae (Ⅰ) new to Korea: Vulpia bromoides (L.) Gray, Agrostis capillaris L. and Eragrostis pectinacea (Michx.) Nees
Fig. 2. Photograph of Agrostis capillaris L. A. Habits. B. Inflorescence. C. Ligule. D. Rhizome. E. Spikelet. F. Lemma and Palea.
An analytical study of capillary rise dynamics: Critical conditions and hidden oscillations - Research Data
<p>This dataset provides the research data and software for the publication:</p> <p>M. Fricke, E. Ouro-Koura, S. Raju, R. von Klitzing, J. De Coninck, D. Bothe: An analytical study of capillary rise dynamics:<br> Critical conditions and hidden oscillations</p> <p>which was published in the Journal Physica D: Nonlinear Phenomena. The experimental data for the capillary rise of silicon oil, ethanol and ether has been extracted from the publication</p> <p>D. Quéré: Inertial capillarity, EPL 39 533, DOI:10.1209/epl/i1997-00389-2 (1997)</p> <p>using image analysis methods. It is stored in the csv files Silicon_Oil_Quere1997, Ethanol_Quere1997.csv and Ether_Quere1997. Please use the Python scripts silicon-oil.py, ethanol.py and ether.py to solve the ordinary differential equation model and to generate the plots presented in our publication. The Python library matplotlib was used for plotting. A simple explicit Euler scheme proved sufficient to solve the ordinary differential equation in its dimensionless form. It is implemented in the file capRiseOdeSolver.py.</p>
Frequency dispersion of small-amplitude capillary waves in viscous fluids (Supporting Data)
<p>This data accompanies the paper "Frequency dispersion of small-amplitude capillary waves in viscous fluids", published in Physical Review E.</p>
Dispersion and viscous attenuation of capillary waves with finite amplitude (Supporting data)
<p>This data accompanies the paper "Dispersion and viscous attenuation of capillary waves with finite amplitude", published in European Physical Journal Special Topics. The spreadsheet (in OpenOffice format) contains the raw data pertaining to the studied capillary waves.</p>
Computational modeling of hemoglobin saturation heterogeneity in capillary networks
<p>This repository contains the C++ code based on OpenFOAM used for simulating oxygen transport with moving red blood cells. The OpenFOAM cases used to generate all results in the research article "The heterogeneity of hemoglobin saturation in capillaries and its relation to red blood cell transit time" are included.</p> <p>The archive 'code-axisymmetric.tgz' contains the code for the simulations in axisymmetric domains. This code works with OpenFOAM 2.1.1.</p> <p>The archive 'code-parallel_capillaries.tgz' contains the code for the simulations with parallel capillaries. This code is based on OpenFOAM 2.3.0.</p> <p>The archive 'code-graph.tgz' contains the simulation code for the simulations in reconstructed capillary networks. The postprocessing and plotting script are also in this archive. This code is based on OpenFOAM 2.3.0.</p> <p>The archive 'code-flow_reconstruction.tgz' contains the code for the flow reconstruction algorithm.</p> <p>The remaining archives contain the OpenFOAM cases that were used to run the oxygen transport simulations reported in the research article "The Heterogeneity of Hemoglobin Saturation in Capillary Networks and its Relation to Red Blood Cell Transit Time".</p>
Quantification of phosphorylated metabolites, organic acids, and intermediates of the TCA cycle using capillary ion chromatography tandem mass spectrometry (capIC-MS/MS) following treatment of Escherichia coli with ciprofloxacin
<p>Capillary ion chromatography tandem mass spectrometry (capIC-MS/MS) was used to quantify phosphorylated metabolites, organic acids, and intermediates of the TCA cycle of Escherichia coli treated with ciprofloxacin, BTP-001 (a novel antimicrobial peptide), and a combination of the two . Metabolite extracts were analyzed with a Xevo TQ-XS triple quadrupole mass spectrometer (Waters, USA).</p><p>Samples were gathered from E. coli cultures grown in batch cultivations using 1 liter bioreactors. Briefly, intracellular metabolites were extracted by cycling samples between −20 °C EtOH and N2 (<i>l</i>) in three consecutive freeze–thaw cycles, with vortexing every 10 min during the thawing phase. Filters were removed and the cell debris was pelleted (4500 rcf, 10 min, -9 °C). The supernatants were transferred to a new tube, snap frozen in N2 (<i>l</i>), and lyophilized. Lyophilized extracts were reconstituted in 500 µL cold Milli-Q H2O and cleared by spin-filtration with a 10 kDa molecular cutoff (20817 rcf, 10 min, 0 °C). A mix of 80 µL centrifuged sample and 20 µL 13C-labeled ISTD extract from yeast was sent to analysis. </p><p>Data processing and absolute quantification was performed as earlier described using the TargetLynx application manager of MassLynx v 4.1 (Waters) to interpolate calibration curves made with appropriate dilutions of analytical grade standards (Sigma-Aldrich). The response factor of the corresponding U13C-isotopologues were used to correct the standard and sample extract response factors. Extract concentrations were normalized to the CDW, which was calculated from interpolation of the OD600 vs. CDW (g/L) curve. </p><p>Further statistical analysis in MetaboAnalyst v 5.0 replaced missing values with 1/5 of the minimum value of the respective metabolite. An unpaired T-test with unequal variance determined differential enriched metabolites with a false discovery rate (FDR) < 0.05 which are presented as log2 fold-change compared to control. </p>
Nonlinear sound-sheet microscopy: imaging opaque organs at the capillary and cellular scale
<p>This dataset accompanies the article entitled: "Nonlinear sound-sheet microscopy: imaging opaque organs at the capillary and cellular scale".</p> <div> <h3><strong>Wells with E. Coli (Wells_EColi.zip)</strong></h3> <p><strong>Codes are hosted <a title="NSSM github repo" href="https://github.com/MarescaRenaudLabs/NSSM/releases/tag/v1.0.0" target="_blank" rel="noopener">here</a></strong></p> </div> <p>Representative dataset for paper figure 2D,E. The file <code>RFData_planes_figure_2E.mat</code> contains the RFData and the required parameters to reconstruct two orthogonal sound sheets, in SSM and NSSM mode. The file <code>demo_reconstruct_orthogonal_NSSM_images.m</code> shows how to reconstruct the sound sheet data.</p> <p>The file <code>beamformed_volume_figure_2E.mat</code> contains beamformed SSM and NSSM volumes as displayed in figure 2G. The file <code>demo_navigate_3D_NSSM_data.m</code> can be used to view the volumes.</p> <div> <h3><strong>mARG expression in orthopic tumors (mArg_tumors.zip)</strong></h3> <p><strong>Codes are hosted <a title="NSSM github repo" href="https://github.com/MarescaRenaudLabs/NSSM/releases/tag/v1.0.0" target="_blank" rel="noopener">here</a></strong></p> </div> <p>Representative dataset for paper figure 3B. The file <code>RFData_planes_figure_3B.mat</code> contains the RFData and the required parameters to reconstruct two orthogonal sound sheets, in SSM and NSSM mode. The file <code>demo_reconstruct_orthogonal_NSSM_images.m</code> shows how to reconstruct the sound sheet data.</p> <p>The file <code>beamformed_volume_figure_3B.mat</code> contains beamformed SSM and NSSM volumes as displayed in figure 3C. The file <code>demo_navigate_3D_NSSM_data.m</code> can be used to view the volumes.</p> <h3><strong>Nonlinear Soundsheet Localization Microscopy (NSSLM_zenodo_archive.zip)</strong></h3> <p><strong>Codes are hosted <a title="NSSLM github repo" href="https://github.com/MarescaRenaudLabs/NSSLM/releases/tag/v1.0.0" target="_blank" rel="noopener">here</a></strong></p> <p>A zip compressed dataset containing beamformed images and post-processed trajectories for Nonlinear Soundsheet Localization Microscopy.</p> <ul> <li>The files <code>ImgNSSM_00x.mat</code> are 4D data arrays of size (157x160x2x2100). They hold 2 soundsheets repeated for 2100 frames at 1000Hz (more details are provided in the Material and Methods section of accompanying papers). Use this dataset to run script <code>processNSSLM.m</code></li> <li>The file <code>Params.mat</code> is a file containing the <code>ULM</code> structure. This comprises the fields necessary to execute ULM codes from the <a href="https://github.com/AChavignon/PALA" target="_blank" rel="noopener">PALA toolbox</a></li> <li>A folder named Trajectories. This contains 50 files. Each contains the trajectories obtained from NSSLM processing of the entire sequence for soundsheet indexed 1 in the 4D matrix. Use this dataset to run script <code>renderingNSSLM.m</code></li> </ul> <p>Each of these datasets is to be used in conjunction with the different example scripts given on <strong><a title="NSSLM github repo" href="https://github.com/MarescaRenaudLabs/NSSLM/releases/tag/v1.0.0" target="_blank" rel="noopener">here</a></strong>.</p> <h3>Codes hosted on github</h3> <p>For accompanying codes, please refer to:</p> <ul> <li>https://github.com/MarescaRenaudLabs/NSSM/releases/tag/v1.0.0</li> <li>https://github.com/MarescaRenaudLabs/NSSLM/releases/tag/v1.0.0</li> </ul>
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.