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4,004 results for “In vivo”
In vivo rat brain for Ultrasound Localization Microscopy: raw and beamformed data.
<p><strong>Datasets provided for Open Platform for Ultrasound Localization Microscopy: Performance Assessment of Localization Algorithms.</strong></p> <p><strong>Abstract:</strong></p> <p>Ultrasound Localization Microscopy (<strong>ULM</strong>) is an ultrasound imaging technique that relies on the acoustic response of sub-wavelength ultrasound scatterers to map the microcirculation with an order of magnitude increase in resolution. Initially demonstrated <em>in vitro</em>, this technique has matured and sees implementation<em> in vivo</em> for vascular imaging of organs, and tumors in both animal models and humans. The performance of the localization algorithm greatly defines the quality of vascular mapping. We compiled and implemented a collection of ultrasound localization algorithms and devised three datasets<em> in silico</em> and<em> in vivo</em> to compare their performance through 18 metrics. We also present two novel algorithms designed to increase speed and performance. By openly providing a complete package to perform ULM with the algorithms, the datasets used, and the metrics, we aim to give researchers a tool to identify the optimal localization algorithm for their usage, benchmark their software and enhance the overall image quality in the field while uncovering its limits.</p> <p>This article provides all materials and post-processing scripts and functions.</p> <p><strong>Methods:</strong></p> <p>200.000 ultrasound images have been acquired <em>in vivo </em>on a rat brain with skull removal at 1000 Hz with a 15 MHz linear probe.</p> <p>This dataset contains raw radiofrequency data (<strong>RF</strong>) and beamformed images (<strong>IQ</strong>) of the brain vascularization with flowing microbubbles (ultrasound contrast agent).</p> <p><strong>Article to be cited:</strong> Heiles, Chavignon, Hingot, Lopez, Teston and Couture.<br> <a href="http://doi.org/10.1038/s41551-021-00824-8"><em>Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy</em>, Nature Biomedical Engineering, 2022, (doi.org/10.1038/s41551-021-00824-8)</a>.</p> <p><strong>Related processing scripts and codes:</strong> <a href="https://github.com/AChavignon/PALA">github.com/AChavignon/PALA</a></p> <p><strong>Related datasets:</strong> <a href="https://doi.org/10.5281/zenodo.4343435">doi.org/10.5281/zenodo.4343435</a></p> <p><strong>Acknowledgments:</strong></p> <p>We thank Cyrille Orset (INSERM UMR-S U1237, Physiopathology and Imaging of Neurological Disorders, GIP Cyceron, BB@C, Caen, France) for animals’ preparation and perfusion of contrast agent and the biomedical imaging platform CYCERON (UMS 3408 Unicaen/CNRS, Caen, France).</p>
In vivo T1w MRI of a TDP-43 knock-in mouse model of ALS-FTD
Open the record for dataset details and reuse information.
Mass spectrometry raw data for "Proteomics reveals substantial differences between in vitro matured abattoir-derived and in vivo matured oocytes in cattle"
<p><em><span>In vitro</span></em><span> production (IVP) of bovine embryos still has its limitations such as low blastocyst rate and lower embryo quality, resulting in lower pregnancy rates following the transfer of IVP embryos compared to <em>in vivo</em> produced embryos. </span><span>Given these differences in developmental competence, RNA sequencing and microarray technology have been applied to describe the differences in transcriptional activity between <em>in vitro</em> and <em>in vivo</em> produced embryos. All but one of these studies solely utilized oocytes obtained from slaughterhouse material for the <em>in vitro</em> production of embryos, thereby introducing the possibility, that differences between IVP and <em>in vivo</em> embryos are in part attributable to differing sources of oocytes. The aim of the present study was therefore to compare the proteome of oocytes retrieved from slaughterhouse material, with and without a period of <em>in vitro</em> maturation and <em>in vivo</em> matured oocytes obtained from donor cattle following superovulation. <span>For each group the protein pattern of four biological replicates containing ten oocytes each were analyzed via SWATH<sup>TM</sup>-MS.</span></span></p>
Dataset In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging
<p>This is the dataset related to the paper "In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging", E. Najdenovska*, Y. Aléman-Gómez*, G. Battistella, M. Descoteaux, P. Hagmann, S. Jacquemont, P. Maeder, J.-P. Thiran, E. Fornari and M. Bach Cuadra, Sci. Data. 5:180270 doi: 10.1038/sdata.2018.270 (2018). *Equally contributed authors.</p> <p>We provide NifTI-1 files representing a digital atlas of seven thalamic subparts per hemisphere. More precisely, the files include the spatial probabilistic atlas maps for each thalamic subpart (Thalamus_Nuclei-HCP-4DSPAMs.nii.gz) and the maximum likelihood atlas (Thalamus_Nuclei-HCP-MaxProb.nii.gz) in MNI space. The region corresponding to each labeled thalamic part respectively is given in the look-up table Thalamic_Nuclei-ColorLUT.txt. The NIFTI files can be visualised with the main available tools such as tkmedit, freeview or 3D-Slicer.</p> <p>We also provide a step by step pseudo code for creating the atlas.</p>
A database of FLASH Murine In-Vivo Studies
<p>The database contains peer-reviewed papers published until March 2024 on the FLASH in-vivo (murine) experiments. From each paper, previously defined parameters have been manually extracted and/or recalculated to ensure compatibility within the database entries. </p> <p>We provide two types of datasets: a user-friendly web-based Notion database and two spreadsheets. The database contains all the reviewed papers with extracted information in text or numeric form. Users can duplicate the database or view, search, filter, and reorganise online entries. The spreadsheets contain the data for the most analysed endpoints (skin toxicity, survival rate, and crypt cells), allowing a comparative analysis.</p> <p>Our goal with this database is to increase awareness of the results and their variability and provide the community with a useful research and analysis tool.</p>
7 Tesla MRI of the ex vivo human brain at 100 micron resolution
Open the record for dataset details and reuse information.
A2aR Oligomeric assemblies identified from MD simulations using in-vivo mimetic biomembranes
<p>GPCR oligomerisation is known to play an important role in the receptor signalling. However, due to the technical challenges, the structural information of GPCR oligomerisation is still very limited, which hinders our understanding of GPCR signalling in a fuller picture. In this deposit, we provide the structural coordinates of various oligomeric assemblies of Adenosine A2a receptor that were sampled from unbiased MD simulations.</p> <p>For more information regarding the MD simulation setup and definitions of the various calculated values, please check out our paper on <a href="https://www.biorxiv.org/content/10.1101/2020.06.24.168260v2">BioRxiv</a> (doi: https://doi.org/10.1101/2020.06.24.168260)</p> <p>** Simulation setup **<br> 9 copies of A2aR were randomly inserted into an <em>in-vivo </em>mimetic biomembrane (of size of 45nm x 45nm) to build the initial configuration of the simulations. 10 such systems were set up for A2aR in the inactive state, 10 for the active state and 10 for the active in complex with the mini Gs state. These systems were represented by MARTINI 2 coarse-grained models and were simulated for 50 micro-seconds. The use of MARTINI coarse-grained force field would freeze the receptor conformation in the initial configuration, thus decoupled the oligomerization from such process as ligand-induced conformational change. The more efficient sampling of coarse-grained force field therefore allowed us to explore fully the protein-protein associations in the oligomerisation process. Protein-protein associations were identified when any atoms from two protomers were getting closer than 0.75 nm. The oligomerisation process was monitored and the sampled various oligomeric assemblies were identified for calculation of oligomer residence time. </p> <p><br> ** Coordinate file explained **<br> These pdb files contain the A2aR oligomer structures in atomistic models. The coarse-grained oligomeric structures were converted back to atomistic models using CHARMM 36 force field. The identified oligomeric structures from each oligomeric order were clustered. 10 structures were randomly taken from each cluster and stored as individual models in the pdb files with a naming format <strong><em>{Conf. State}_OS{Oligomeric Order}_cl{Cluster id}.pdb</em></strong>. The pdb files can be viewed by such visualization tools as PyMol, Chimera or JMol etc.</p> <p><br> ** Spreadsheet file explained **<br> The calculated properties, including residence time and geometry, of each identified oligomer were stored in the Excel shreadsheet (Oligomeric_Assembly_Distribution.xlsx). Each oligomeric order, i.e. oligomer order = 2,3,4,5, opens an individual spreadsheet page where the calculated data were grouped by oligomers' conformational states (i.e. Inactive, Active and Act + mini Gs) and then ranked by oligomers' residence time. The measurements for describing the oligomer geometry were shown in columns after "Cluster ID" and before "Count". For definitions of these measurements, please refer to our paper. Pictures of the oligomers viewed from the extracellular side and intracellular side were also provided in the the spreadsheet to assist visualisation.</p>
Autofluorescence-Free In Vivo Imaging Using Polymer-Stabilized Nd3+-Doped YAG Nanocrystals
<p>Neodymium-doped yttrium aluminum garnet (YAG:Nd<sup>3+</sup>) has been widely developed during roughly the last sixty years and has been an outstanding fluorescent material. It has been considered as the gold standard among multipurpose solid-state lasers. Yet, the successful downsizing of this system into the nano regimen has been elusive, so far. Indeed, the synthesis of a garnet structure at the nanoscale, with enough crystalline quality for optical applications was found to be quite challenging. Here, we present an improved solvothermal synthesis method producing YAG:Nd<sup>3+</sup> nanocrystals of remarkably good structural quality. Adequate surface functionalization using asymmetric double-hydrophilic block copolymers, constituted of a metal-binding block and a neutral water soluble block, provides stabilized YAG:Nd<sup>3+</sup> nanocrystals with a long term colloidal stability in aqueous suspensions. These newly stabilized nanoprobes keep the spectroscopic quality (long lifetimes, narrow emission lines, and large Stokes shift) characteristic of bulk YAG:Nd<sup>3+</sup>. The narrow emission lines of YAG:Nd<sup>3+</sup> nanocrystals are exploited by differential infrared fluorescence imaging, thus achieving an autofluorescence-free <em>in vivo</em> readout. In addition, nanothermometry measurements, based on the ratiometric fluorescence of the stabilized YAG:Nd<sup>3+</sup> nanocrystals, are demonstrated. The progress here reported paves the way for the implementation of this new stabilized YAG:Nd<sup>3+</sup> system in the preclinical arena.</p>
geodata: Populated places for VIVO
<p>This dataset contains information about populated places and other geographical entities ready to use in current research information system VIVO. It was created at University of Applied Sciences and Arts Hannover, Germany. It is successfully tested with VIVO 1.6 and 1.7.</p>
In vivo treatment with insulin-like growth factor 1 reduces CCR5 expression on vaccine-induced activated CD4+ T-cells
<p>Dataset of the publication "In vivo treatment with insulin-like growth factor 1 reduces CCR5 expression on vaccine-induced activated CD4+ T-cells" by Bissa et al. on the journal Vaccines. </p><p>Each folder contains the original files reporting the data used to generate the manuscript.</p><p>For flowcytometry based assays the Flow panel is included in the folders. </p><p>For ELISA based assays the schemes of the plates are included in the folders. </p><p>The excel table "Bissa et al._Vaccines_2023_Animal IDs and viral acquisition" reports the IDs and grouping of the animals together with their viral acquisition</p><p>The excel table "Bissa et al._Vaccines_2023_Master table" reports each data used to generate the figures and supplemental materials included in the publication </p>
Amino Acids Modulate Liquid-Liquid Phase Separation in vitro and in vivo by Regulating Protein-Protein Interactions
<p>The metadata, plots and microscopy images for the manuscript "Amino Acids Modulate Liquid-Liquid Phase Separation in vitro and in vivo by Regulating Protein-Protein Interactions".</p>
Quantification of ROIs corresponding to MQs from fluorescence in vivo imaging experiments
<p>We injected DIr labelled macrophages into mice carring immunolgical hot and cold KPC pancreatic tumors and quantified the recruitment to the tumor sites and lungs of the injected cells at different days after injection using fluorescence imaging. We hypotesized that macrophages would be recruited into tumor tissue and in prevalence into cold tumors.</p>
Raw gel images accompanying the publication: Koralewska et al, NAR 2024, Short 2'-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production in vitro and in vivo, DOI 10.1093/nar/gkae284
<p>A set of raw gel images used in the article: Koralewska <em>et al</em>. Short 2’-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production <em>in vitro</em> and <em>in vivo, </em>NAR 2024, DOI 10.1093/nar/gkae284.</p>
Dataset In-vivo estimation of axonal morphology from MRI and EEG data
<p>This dataset includes the data underlying the conclusions made in the scientific article:<br> "In-vivo estimation of axonal morphology from MRI and EEG data"<br> Rita Oliveira, Andria Pelentritou, Giulia Di Domenicantonio, Marzia De Lucia, Antoine Lutti</p> <p><a href="https://www.frontiersin.org/articles/10.3389/fnins.2022.874023/full">https://www.frontiersin.org/articles/10.3389/fnins.2022.874023</a></p> <p>The main objective is to use data collected in-vivo in humans to estimate microscopic morphologic features of the white matter tracts.</p> <p>The in-vivo data estimated along a white matter tract of interest includes:<br> • the MRI g-ratio sampled along the visual transcallosal white matter tract<br> • a measure of conduction velocity estimated from an EEG measure of interhemispheric transfer time (IHTT)</p> <p>The microscopic morphologic features of white matter we estimate are:<br> • the axonal radius distribution, P(r)<br> • the g-ratio dependence on the radius, g(r)</p> <p>-------------------------------------------------------------------------<br> CONTENT:</p> <p>This package includes data for all the 14 subjects used in the corresponding scientific article:<br> • G-ratio values sampled along the transcallosal visual tract <br> double vector (# MRI_gratio samples x 1): G_ratio_samples.mat<br> • Length of the transcallosal visual tract <br> double: Tract_length.mat<br> • Current source densities (pA.m) of each trial, brain vertice and time <br> point for the left brain visual cortex <br> double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_left_brain_V1V2.mat <br> • Current source densities (pA.m) of each trial, brain vertice and time <br> point for the right brain visual cortex <br> double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_right_brain_V1V2.mat <br> • Vector of the time sample of the EEG epochs<br> double vector (1 x #time points): time_vec.mat</p> <p>The codes used in the analysis of this data are available on our online repository: <a href="https://github.com/LREN-physics/AxonalMorphology">https://github.com/LREN-physics/AxonalMorphology</a>.</p> <p>-------------------------------------------------------------------------<br> AUTHORS:</p> <p>Author: Rita Oliveira<br> PIs: Marzia De Lucia, Antoine Lutti</p> <p>Laboratory for Neuroimaging Research</p> <p>Lausanne University Hospital & University of Lausanne, Lausanne, Switzerland</p> <p>Copyright (C) 2022 Laboratory for Neuroimaging Research</p>
Toward a base-resolution panorama of the in vivo impact of cytosine methylation on transcription factor binding
<p>TF binding models built by JAMS (https://github.com/csglab/JAMS), ChIP-seq peak files (from ENCODE, Najafabadi et al. 2015, Schmitges et al. 2016, and Imbeault et al. 2017; called by MACS 1.4v), ChIP-seq pulldown and control tags from said peaks, input data for JAMS, and RCADE2 motifs for C2H2 zinc finger proteins. </p>
Supplementary material: Efficient in vivo screening method for the identification of C4 photosynthesis inhibitors based on cell suspensions of the single-cell C4 plant Bienertia sinuspersici
<p>Data described in Minges et al. (2019) Efficient <em>in vivo</em> screening method for the identification of C<sub>4</sub> photosynthesis inhibitors based on cell suspensions of the single-cell C<sub>4</sub> plant <em>Bienertia sinuspersici</em>. doi: <a href="https://doi.org/10.3389/fpls.2019.01350">10.3389/fpls.2019.01350</a></p> <p> </p>
in vivo electrophysiological data of DRN serotonin neurons
<p>This repository contains in vivo electrophysiological data of DRN serotonin neurons of freely behaving mice.</p> <p>The data is related to the following research article: Li, Y., Zhong, W., Wang, D. et al. Serotonin neurons in the dorsal raphe nucleus encode reward signals. Nat Commun 7, 10503 (2016). https://doi.org/10.1038/ncomms10503 Please refer to the original publication for details.</p> <p>Please refer to the Readme.txt in the files for details.</p> <p> </p>
Revealing real-time 3D in vivo pathogen dynamics in plants by label-free optical coherence tomography
<p>This repository contains all data and code underlying the publication: J. de Wit et al. "<em>Revealing real-time 3D in vivo pathogen dynamics in plants by label-free optical coherence tomography</em>" in Nature Communications (2024) (https://doi.org/10.1038/s41467-024-52594-x)</p> <p><strong>--------------Code description------------------</strong></p> <p>The set of scripts is largely organized around the figures. For each (sub)figure, also from supplementary materials, that involves data and plotting, there is a script that generates the plot from data that can be found in the different zip files that are present in the Zenodo repository under https://doi.org/10.5281/zenodo.11428245.</p> <p>The scripts use the data that is contained in the ZIP folders. The ZIP folders are organized by experiment (Experiment 1, including contrast optimization; Experiment 2), one for the other data (OtherData, the validation for with Trypan blue, and the Arabidopsis, Radish and nematode) and one as a smaller dataset to explain the method on a single B-scan (Example_Bscan_dynamicOCT).</p> <p>IMPORTANT: The folder where the ZIP files are unzipped should be put in the file '<em>basepath.txt</em>', such that the data can be automatically loaded.</p> <p>Besides the figures that mention 'MakeFig...' there are a few more scripts:</p> <ul> <li><em>pointcloud_generation_experiment1.py</em>: this file makes the point clouds from the dynamic OCT images as described in Fig 2b. The resulting data is saved as maximum intensity projections and axial sums(forming the basis for Fig S4, S6 and S7) and as voxel counts (forming the basis of Fig.2c and Fig S5)</li> <li><em>pointcloud_generation_timelapses.py</em>: this file does the segmentation for experiment 2 and saves the maximum intensity projections and axial sums of the different stages in the segmentation (forming the basis of Fig3a,d,e and FigS9a,b), and saves the point clouds of the data. These point clouds were refined manually in CloudCompare as described in methods. These segmented point clouds are contained in the data zip folder of experiment 2.</li> <li><em>StatisticalTests.R</em>: This R file calculates the statistical tests for Fig.2cd and Fig.S5d. Here the path is not automatically updated, and should be manually set. The input file is contained in "Experiment1/SegmentationData/segmentationdata_samples.csv" and the output of the file is "D:/DataZenodo/Experiment1/SegmentationData/data_combined_Rstats_output.csv"</li> <li><em>example_dynamic_Bscan.py</em>: This script gives an example of the dynamic OCT processing as proposed in this paper. First it shows the process from an OCT interference spectrum to a B-scan. Then it loads 100 B-scans and applies dynamic OCT, including normalization with histograms. Finally it gives a dynamic B-scan and plots this against the average normal OCT image. This script can be used with only the zip folder "Example_Bscan_dynamicOCT", which reduces the amount of data needed to download/unzip.</li> </ul> <p>The list of other script files to load the data and generate the figures (guiding to the path of uncropped figures) is:</p> <ul> <li><em>MakeFig1bce_Fig2e.py</em></li> <li><em>MakeFig1d.py</em></li> <li><em>MakeFig1agraphs_FigureS1.py</em></li> <li><em>MakeFig3acde_S9ab.py</em></li> <li><em>MakeFigS2_determine_dynamic_range_experiment1.py</em></li> <li><em>MakeFigS8.py</em></li> <li><em>MakeFigureS4-S6-S7.py</em></li> <li><em>MakeFigureS5.py</em></li> <li><em>MakeHistFig2b_makeFigS3b-e.py</em></li> <li><em>MakePlotsFig2ab.py</em></li> <li><em>MakePlotsFig2cd.py</em></li> </ul> <p>Code was all run in Python 3 using Anaconda Spyder.</p> <p>Moreover, the zip file with the code contains the folder '<em>figures</em>' with all subfigures. Some of them are automatically saved from the scripts, others (like photos, icons, but also the Trypan blue microscopy figure) are added in the respective folder. The are logically organized by figure number.</p> <p><strong>--------------Dataset Description-----------------</strong></p> <p>As mentioned above, the data is organized in four zip folders for both experiments, the other data (validation with Trypan blue, other plant-pathogens) and one for the dynamic OCT B-scan example. The data contain the following:</p> <p><strong>Experiment 1: </strong></p> <ul> <li>DynamicOCTimages whose subfolders (organized by date) contain a folder per volume dataset in experiment 1 with a z-stack of .tif files that form the imaged volume. The lateral sampling is 3 um and the axial sampling is 1.37 um. </li> <li>ContrastOptimization: This folder contains <ul> <li><em>Bscans_with_segmentation</em>: segmented B-scans for contrast optimization (Fig S3)</li> <li><em>Bscan_figS1_fig1</em>: The B-scans and segementation for Figure S1.</li> <li><em>histogramdata_dynamicrange</em>: The histograms, bins and deducted reference data for determining the dynamic range per color channel for experiment 1 (Fig S2)</li> <li><em>logcompressed_3value_dOCT_example</em>: An example data stack for obtaining histograms (see script MakeFigS2_determine_dynamic_range_experiment1.py)</li> <li><em>overlaps_threshold-100-98-95-92-90-85-80-75-70-65-60-55-50-45perc_red-1_2_blue_-3_0_green1_filt.npy</em>: A file with intermediate data for the contrast optimization, which can also be generated with the script "<em>MakeHistFig2b_makeFigS3b-e.py</em>"</li> </ul> </li> <li>SegmentationData: This folder contains <ul> <li><em>MIP_segmentation_stages</em>: maximum intensityp projections and axial sums for all images at different stages in the segmentation (basis for Fig S4,6,7)</li> <li><em>processed_masks and StackMasks</em>: the manually obtained masks (segmented in StackMasks, made into masks in the folder 'processed_masks') for filtering out stomata, veins and artefacts.</li> <li><em>Unmasked_axialsum_th34_formanualsegmentation</em>: This folder contains the images of Fig.S4 and were used for the segmentation (we addes a small offset, such that the in segmentation we could set it to 0 and have a unique mask). </li> <li><em>overview_samples_bremiayn.csv</em>: A dataframe with the data for all the samples in experiment 1 that is used as input for the segmentation. It also contains the result of the manual check whether it has infection (Fig2c, left).</li> <li><em>segmentationdata_samples.csv</em>: This supplements the file of overview_samples_bremiayn.csv with the results from the segmentation and is output to script "<em>pointcloud_generation_experiment1.py</em>". It forms the basis of Fig.2a-c, and FigS5, as well as for the R-script to do the statistical testing.</li> <li><em>qPCR_dOCT.csv</em>: This script contains the qPCR data and is input to Fig2d. </li> </ul> </li> </ul> <p><strong>Experiment 2:</strong></p> <ul> <li><em>DynamicOCTimages</em>: This contains the z-stacks of .tif files of the volumes for experiment 2 (and one extra, where a z-slice is used in Fig.1b, bottom). Sampling step size is here again 3 um in lateral direction and 1.37 um in axial direction.</li> <li><em>.npy files </em>with the histograms (with same bins as Experiment 1), maxvalues and reference values for the dynamic range calculation.</li> <li><em>segmentation_data</em>: this folder contains: <ul> <li><em>quantification_volume_disc160_33_10.csv</em> and <em>quantification_volume_disc160_33_10.xlsx</em>: data from the manually segmented point clouds that form the basis of Fig.3c.</li> <li><em>timelapse_sampleoverview.csv</em>: overview of the samples that is used as input in the file "<em>pointcloud_generation_timelapses.py</em>"</li> <li><em>pointclouds</em>: Folder with segmented point clouds for the three leaf discs. These files could be loaded in CloudCompare.</li> <li><em>overviewMIPs</em>: folder with overview maximum intensity projections for the different steps in segmentation, which also forms the input of Fig.3a, FigS9ab.</li> <li>rawpointclouds: folder with the automatically generated point clouds from file <em>pointcloud_generation_timelapses.py </em>which were imported into CloudCompare as the basis for the segmented point clouds.</li> </ul> </li> </ul> <p><strong>OtherData:</strong></p> <p>This folder contains the z-stacks of dynamic OCT tif images for Arabidopsis (here both a normal contrast and one that has been increased to only contain the original 0-180 range); nematodes, radish (called radijs_test_PP_py_0002), spores for Fig1c (SporesImaging) and the dynamic OCT image of Fig1d. </p> <p><strong>example_Bscan_dynamicOCT:</strong></p> <p>This folder contains data to run the script example_dynamic_Bscan.py to show the dynamic OCT imaging process from raw OCT spectra.</p> <ul> <li><em>raw_spectra_exampleframe:</em> contains interference spectra, a reference spectrum and interpolation grid to show how to get from a raw OCT spectrum to a normal single B-scan.</li> <li><em>abs_images:</em> contains 100 subsequent B-scans that can be used to generate a dynamic OCT image as done in example_dynamic_Bscan.py</li> </ul> <p> </p> <p> </p>
Data: An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction
<p><strong>Dataset supporting the manuscript "</strong>An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction<strong>" by the authors of this dataset.</strong></p> <p><strong>Where to start</strong></p> <p>This Zenodo repository contains both raw data and runnable code for the manuscript "An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction". The runnable code is best executed directly at CodeOcean (https://doi.org/10.24433/CO.6934377.v1). Alternatively, CodeOcean capsules are Docker images and can be run locally after download and unzipping. The full CodeOcean capsule is stored here as "CodeOceanCapsule_Injectable_meta_biomaterial.zip", it contains all the information and data to full reproduce the evaluation underpinning the manuscript " An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction".</p> <p>Quantitative raw data, in the form of text files, Excel files and R-data files useful for the data evaluation are included in "CodeOceanCapsule_Injectable_meta_biomaterial.zip". As especially the numerical simulation files are rather voluminous (100GB), we also provide a copy of the capsule without this large part, which however otherwise remains runnable for most evaluations ("CodeOceanCapsule_Injectable_meta_biomaterial_no_raw_simulation.zip"), and, for lightweight documentation of the code section only "CodeOceanCapsule_Injectable_meta_biomaterial_code_only.zip". The results of a capsule run are also provided, as "CodeOceanCapsule_Injectable_meta_biomaterial_results_run_4899036.zip".</p> <p>Besides archival of the CodeOcean evaluation capsule, this repository contains additional imaging data from which some of the quantitative data treated in the CodeOcean capsule was extracted, and additionally raw files for the illustrative figures in the manuscript. This data is contained in the files "Raw_images_For_Figure_1.zip", "Raw_images_For_Figure_3.zip", "Raw_images_For_Figure_4.zip"; "Raw_images_For_Figure_5.zip", "Raw_images_For_SFigure_S6.zip", "Raw_images_For_SFigure_S8.zip", "Raw_images_For_SFigure_S9.zip", "Raw_images_For_SFigure_S19.zip".</p> <p><strong>External dependencies</strong></p> <p>To facilitate centralized software development and installation, custom R and Python libraries used by the CodeOcean capsule "CodeOceanCapsule_Injectable_meta_biomaterial.zip" are hosted on Github, with releases archived in separate Zenodo repositories. These libraries are included automatically during the build phase of the CodeOcean capsule.</p> <p>This concerns the Python discrete particle simulation particleShear (DOI: <a href="https://doi.org/10.5281/zenodo.4589212">10.5281/zenodo.4589212</a>), and the R packages textureAnalyzerGels (for analysis of mechanical compression curves, DOI: <a href="https://doi.org/10.5281/zenodo.4589276">10.5281/zenodo.4589276</a>), rheologyEvaluation (for analysis of oscillatory sweep rheology, DOI: <a href="https://doi.org/10.5281/zenodo.4594353">10.5281/zenodo.4594353</a>), particleShearEvaluation (evaluation of the output of the Python simulations, DOI: <a href="https://doi.org/10.5281/zenodo.4594649">10.5281/zenodo.4594649</a>), plot.counts (convenience functions for scientific plotting, DOI: <a href="https://doi.org/10.5281/zenodo.4589498">10.5281/zenodo.4589498</a>) and reproducibleCalculationTools (numerical comparision of subsequent evaluations to validate reproducibility, DOI: <a href="https://doi.org/10.5281/zenodo.4594515">10.5281/zenodo.4594515</a>).</p> <p>For automated evaluation of ImageJ macros from Excel files, we also developed an Excel macro runner plugin in ImageJ, termed PoreSizeExcel (DOI: <a href="https://doi.org/10.5281/zenodo.4589546">10.5281/zenodo.4589546</a>). While the R and Python libraries listed above are actively loaded and used by the CodeOcean capsule, we used the PoreSizeExcel ImageJ plugin manually to streamline our quantitative image treatment, but not in a fully automated fashin.</p> <p>The Zenodo archives cited above reproducibly provide the state of the libraries as used for evaluation of this dataset, we continue to develop the libraries and continuously make them available at Github ( at <a href="https://github.com/tbgitoo">https://github.com/tbgitoo</a> ).</p> <p><strong>Version history</strong></p> <p>This is the third version of this Zenodo repository.</p> <p>We undertook major efforts from version v1.0 to the present version v2.0 to increase reprodubility of evaluation (via the use of the CodeOcean platform) and via separation of generic libraries (listed above, and installable on their own independently of this particular project) from specific project-associated data and evaluation (here). For this reason, while the data is maintained and in part completed due to new experiments having been carried out in the mean time, the structure of the repository has undergone major changes from v1.0 to the present version v2.0.</p> <p>With this version v3.0 we added raw data on cell transplantation, and completed the CodeOcean capsule, including adaptation to peer review changes to the manuscript.</p>
Dataset related to article "Molecular Studies and ex vivo Complement assay on Endothelium Highlight the Genetic Complexity of Atypical Hemolytic Uremic Syndrome: The Case of a Pedigree With a Null CD46 Variant".
<p><em>The files contain raw data related to the article "Molecular Studies and ex vivo Complement assay on Endothelium Highlight the Genetic Complexity of Atypical Hemolytic Uremic Syndrome: The Case of a Pedigree With a Null CD46 Variant", available from <a href="https://www.frontiersin.org/articles/10.3389/fmed.2020.579418/full">https://www.frontiersin.org/articles/10.3389/fmed.2020.579418/ful</a>l.</em></p> <p>File <strong>"Genetic and clinical data"</strong>:</p> <ul> <li>In the sheet "485 aHUS patients" are reported data obtained from the screening of 485 unrelated patients with aHUS including rare variants (RVs) in complement disease-associated genes (<em>CFH, CD46, CFI, C3, CFB </em>and <em>THBD</em>), the presence of <em>CFH-CFHR</em> genomic rearrangements and/or anti-FH antibodies.</li> <li>In the sheet "Pedigrees with c.286+2T>G" are listed all pedigrees carrying the c.286+2T>G variant, the diseases status of all subjects and the age of disease onset of patients. In bold are indicated pedigrees (n=7) used to study the penetrance of aHUS in c.286+2T>G carriers.</li> <li>In the sheet "Haplotypes" are reported genotypes used to evaluate the association between the presence of <em>CFH-H3</em> and <em>CD46<sub>GGAAC</sub></em> risk haplotypes and aHUS. Results of this analysis are reported in Table 3 of the published paper.</li> <li>In the sheet "Raw data Fig.2" are reported data of "platelet count" and "serum creatinine" of the proband used to elaborate Figure 2.</li> </ul> <p>In the file <strong>"C3 and C5b-9 deposition"</strong> is reported the quantification of serum-induced C3 and C5b-9 deposition on human microvascular endothelial cell line (HMEC-1). The fluorescent staining was evaluated with Image J and expressed as pixel<sup>2 </sup>per field analyzed. The fields with the lowest and highest values were excluded from calculation. These values were used to elaborate data included in Table 2 and in Figure 5.</p> <p>In the file <strong>"CD46 protein expression"</strong> are reported data of CD46 expression on peripheral blood mononuclear cells (PBMCs) isolated from the proband, his relatives and healthy volunteers. Data of specific expression of CD46 (evaluated for SCR1 or for SCR4 as reported in the materials and methods section) are indicated as median fluorescence intensity (MFI) percentage compared with the control.</p> <p>In the ppt file <strong>"cDNA amplification and sequencing results"</strong> is reported:</p> <ul> <li>the agarose gel image of the amplified cDNA from the control (ctr), the proband (IV-8) and his healthy father (III-7).</li> <li>Electropherograms obtained from the cDNA sequencing of the control (ctr), the proband (IV-8) and his healthy father (III-7).</li> </ul> <p>Additional data will be made available by the authors, without undue reservation, to any qualified researcher. </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.