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4,004 results for “In vivo”
Dataset Single-subject EEG measurement of interhemispheric transfer-time for the in-vivo estimation of axonal morphology
<p>This dataset is a subset of the data presented in the article Single‐subject electroencephalography measurement of interhemispheric transfer time for the in‐vivo estimation of axonal morphology Rita Oliveira, Marzia De Lucia, Antoine Lutti</p> <p><a href="https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26420">https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26420</a></p> <p><br> For a complete description of our approach for axonal morphology estimation in-vivo, see: <em>Oliveira, R., Pelentritou, A., Di Domenicantonio, G., De Lucia, M., and Lutti, A. (2022). In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data. Front. Neurosci. 16, 1–18. doi: 10.3389/fnins.2022.874023.</em></p> <p>This dataset contains the following Matlab files:</p> <ul> <li>CD_CondNameVisualField_LeftBrainOccipital.mat - Current source densities (pA.m) of each brain vertice, EEG trial, and time point for the left brain occipital cortex [#trials x #vertices x #timepoints]</li> <li>CD_CondNameVisualField_RightBrainOccipital.mat - Current source densities (pA.m) of each brain vertice, EEG trial, and time point for the right brain occipital cortex [#trials x #vertices x #timepoints]</li> <li>Stats_Source_CondNameVF_Occipital_LeftBrainOccipital.mat - Result of the cluster permutation for the left brain cortex for the CondNameVF, CondName being Left or Right visual stimulation (Fieldtrip stat structure)</li> <li>Stats_Source_CondNameVF_Occipital_RightBrainOccipital.mat - Result of the cluster permutation for the right brain cortex for the CondNameVF, CondName being Left or - Right visual stimulation (Fieldtrip stat structure)</li> <li>time_vec.mat - Time vector associated with the timecourses [1 x #timepoints]</li> <li>Occipital_vertices.mat - Structure containing the vertices of the brain mesh of the region of interest. Occipital_vertices.Vertices [1 x #vertices]</li> <li>G_ratio_samples.mat - MRI g-ratio sampled along the occipital transcallosal tract [#samples x 1]</li> <li>Tract_length.mat - Length of the occipital transcallosal tract (double)</li> </ul> <p>The analysis scripts that allow the users to replicate the results of the original publication can be found here: <a href="https://github.com/DNC-EEG-platform/SingleSubjectIHTTEstimation">https://github.com/DNC-EEG-platform/SingleSubjectIHTTEstimation</a><br> <br> Funding: Swiss National Science Foundation (grant no 320030 184784 and 32003B 212981), ROGER DE SPOELBERCH Foundation and Bertarelli Catalyst Foundation.</p> <p> </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> <p> </p>
Sensor data associated with Lucius et al. 2020 – Using machine learning to correct for nonphotochemical quenching in high-frequency in vivo fluorometer data.
This document describes a dataset used to produce Using machine learning to correct for nonphotochemical quenching in high-frequency, in vivo fluorometer data, as reported in: Lucius, M.A., Johnston, K.E., Eichler, L.W., Farrell, J.L., Moriarty, V.W. and Relyea, R.A. (2020), Using machine learning to correct for nonphotochemical quenching in high‐frequency, in vivo fluorometer data. Limnol Oceanogr Methods, 18: 477-494. https://doi.org/10.1002/lom3.10378 The dataset consists of high-frequency water quality and meterological sensor data collected from two autonomous vertical profiling platforms deployed on Lake George, NY during the ice-free months of 2017-2019. Water quality data include depth-referenced measurements of chlorophyll fluorescence, water temperature and dissolved oxygen. Meteorological data include surface-incident total radiation as well as two derived values: solar azimuth and 1-hr rolling average of total radiation. Finally, using interpolated data from regularly collected subsurface profiles of photosynthetically active radiation, estimates of subsurface total radiation were estimated and included in this dataset. This dataset does not include raw data. The data used were subjected to quality control procedures of the Jefferson Project, as well as additional outlier removal measures and the creation of derived data (as previously described and described in detail in Lucius et al. 2020).
Instantaneous In Vivo Imaging of Acute Myocardial Infarct by NIR‐II Luminescent Nanodots
<p>Fast and precise localization of ischemic tissues in the myocardium after an acute infarct is required by clinicians as the first step toward accurate and efficient treatment. Nowadays, diagnosis of a heart attack at early times is based on biochemical blood analysis (detection of cardiac enzymes) or by ultrasound‐assisted imaging. Alternative approaches are investigated to overcome the limitations of these classical techniques (time‐consuming procedures or low spatial resolution). As occurs in many other fields of biomedicine, cardiological preclinical imaging can also benefit from the fast development of nanotechnology. Indeed, bio‐functionalized near‐infrared‐emitting nanoparticles are herein used for in vivo imaging of the heart after an acute myocardial infarct. Taking advantage of the superior acquisition speed of near‐infrared fluorescence imaging, and of the efficient selective targeting of the near‐infrared‐emitting nanoparticles, in vivo images of the infarcted heart are obtained only a few minutes after the acute infarction event. This work opens an avenue toward cost‐effective, fast, and accurate in vivo imaging of the ischemic myocardium after an acute infarct.</p>
Silver Nanoparticles Alter Cell Viability Ex Vivo and in Vitro and Induce Proinflammatory Effects in Human Lung Fibroblasts
<p>Dataset for data generated and presented in following article by Löfdahl et al in Nanomaterials 2020, 10, 1868. doi:10.3390/nano10091868</p>
Sodium [18F]Fluoride PET Can Efficiently Monitor In Vivo Atherosclerotic Plaque Calcification Progression and Treatment
<p>Dataset for the research article entitled "Sodium [<sup>18</sup>F]Fluoride PET Can Efficiently Monitor <em>In Vivo </em>Atherosclerotic Plaque Calcification Progression and Treatment" published in the MDPI journal Cells.</p>
Supplementary Material: Evaluation of Cyanea capillata Sting Management Protocols Using Ex Vivo and In Vitro Envenomation Models
<p>Supplementary files for Doyle, T.K.; Headlam, J.L.; Wilcox, C.L.; MacLoughlin, E.; Yanagihara, A.A. Evaluation of <em>Cyanea capillata</em>Sting Management Protocols Using Ex Vivo and In Vitro Envenomation Models. <em>Toxins</em> <strong>2017</strong>, <em>9</em>, 215. Video S1: Vinegar Application to Gelatin-Adherent Cnidae</p>
Understanding in vivo Models of Depression: A Systematic Review - Records of Full Search
<p>This Zenodo record outlines the full list of journal articles retrieved from the search string as well as a a subset of articles that have been screened by two independent human reviews and reconciled by a third independent screener. </p> <p> </p> <p>We carried out a search of 2 online databases for studies reporting animal models of depression. This search, carried out in May 2016, identified 70,365 unique publications (File: Depression-Dataset-SLIM-AllRecords.txt )</p> <p> </p> <p>Two independent investigators have, to date, screened 5749 of these publications for inclusion or exclusion and these publications form the dataset for this study (File: Updated-training-data.txt ). </p> <p>Several text-mining approaches will be developed for this depression literature search using the results from manual screening to train the machine, where machine-learning software set rules to automatically define each publication as included or excluded without the need for human screening. In this project, we seek to identify the best performing machine learning algorithm for this depression literature search. Performance is measured on sensitivity, specificity, and precision. </p> <p> </p> <p>Column Names in Datasets: </p> <p>Depression-Dataset-SLIM-AllRecords.txt - ID, Author, Year, Title, Journal, Volume, Issue, Pages, Abstract, URL, SetNumber</p> <p>Depression-Dataset-SLIM-DevelopmentTrainingSet.txt - ID, Author, Year, Title, Journal, Volume, Issue, Pages, Abstract, URL, Incl(1)/Excl(0)</p> <p>Updated-training-data.txt - ID, Author, Year, Title, Journal, Volume, Issue, Pages, Abstract, URL, Incl(1)/Excl(0)</p> <p> </p>
Raw diffraction images of polyhedra in-vivo crystals
<p>Diffraction images of wild type cypovirus polyhedra in-vivo crystals (WTPhC) and the mutant (Δ3-PhC) related to PDB codes 5GQM and 5GQN, respectively.</p> <p>Small-wedge (5°/crystal) datasets were collected from loop-harvested microcrystals using EIGER X 9M detector at a wavelength of 1 Å on BL32XU, SPring-8. The datasets for 5GQN were collected automatically using ZOO system.</p> <p>The crystals belonged to space group <em>I</em>23 with unit cell parameter a~103 Å. 14 and 41 datasets were merged at 1.68 and 1.55 Å resolution in the published result (Abe <em>et al</em>. <em>ACS Nano</em> 2017; PDB codes: 5GQM & 5GQN, respectively) using KAMO; see https://github.com/keitaroyam/yamtbx/wiki/Processing-Polyhedra-data-(5GQM-&-5GQN)</p> <p>NOTE</p> <ul> <li> <p>flatfield correction was not applied to the images and you need to apply it using the correction table saved in master.h5 files.</p> </li> <li> <p>master.h5 files were modified; see https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md</p> </li> <li> <p>Most frames have ice (rings).</p> </li> </ul>
FIGURE 4 in Ex vivo three-dimensional reconstruction of Acutiramus: a giant pterygotid sea scorpion
FIGURE 4. Acutiramus macrophthalmus showing arrangement of ventral structures. AMNH-FI 2253; specimen from the Silurian (Pridoli), Bertie Group, Waterville, Oneida County, NY (Clarke and Ruedemann, 1912: pl. 71, fig. 6): A. complete specimen; B. close-up of ventral structures; C. interpretive drawing of B. Abbreviation: Ap.II.co, appendage II coxae. Specimens photographed under ethanol.
FIGURE 9. 3D in Ex vivo three-dimensional reconstruction of Acutiramus: a giant pterygotid sea scorpion
FIGURE 9. 3D reconstruction of Acutiramus based on examined specimens in lateral view. A. Reconstruction with chelicerae outstretched. B. Reconstruction with chelicerae rotated. The 3D pdf associated with this reconstruction, figure S1, is available in the online supplement (https://doi.org/10.5531/sd.sp.61).
FIGURE 8. 3D in Ex vivo three-dimensional reconstruction of Acutiramus: a giant pterygotid sea scorpion
FIGURE 8. 3D reconstruction of Acutiramus based on examined specimens in dorsal and ventral view. A, D. Reconstruction with chelicerae outstretched: A. dorsal view; D. ventral view. B, E. Reconstruction with chelicerae rotated: B. dorsal view; E. ventral view. C, F. Close-up of prosomal region in ventral view: C. all prosomal appendages; F. close-up of appendages II–V with appendages VI and metastoma removed. The 3D pdf associated with this reconstruction, figure S1, is available in the online supplement (https:// doi.org/10.5531/sd.sp.61).
FIGURE 7 in Ex vivo three-dimensional reconstruction of Acutiramus: a giant pterygotid sea scorpion
FIGURE 7. Acutiramus macrophthalmus showing ventral morphology. YPM IP 208195 from the Late Silurian (Pridoli), Bertie Group, Fiddlers Green dolomite, Phelps Waterline Member, SW of Spinnersville, Millers Mills Quadrangle, Herkimer County, NY (Briggs and Roach, 2020: figs. 10, 11): A. complete specimen. B. close-up of rectangle in A showing reduced appendage II (white arrow); C. interpretive drawing of close-up in B showing key morphologies and the possible articulation locality for the chelicerae (black arrows). B converted to grayscale. Abbreviation: ELS, epistomal lateral sutures. A, B. Photographs by Jessica Utrup.
FIGURE 5 in Ex vivo three-dimensional reconstruction of Acutiramus: a giant pterygotid sea scorpion
FIGURE 5. Erettopterus bilobus from the Silurian (latest Llandovery and Wenlock) Patrick or Kip Burn formation, Scotland. NHMUK PI In 59343 (Kjellesvig-Waering, 1964: pl. 53, fig. 1): A. complete specimen; B. closeup of rectangle in A showing the purported cheliceral articulation with the epistoma; C. interpretive drawing of B showing the reduction in cheliceral width proximal to the epistoma. Abbreviation: ELS, epistomal lateral sutures. Specimen photographed under ethanol.
Data from: In vivo functional phenotypes from a computational epistatic model of evolution
<p><span>Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways, and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with <em>in vivo </em>functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for <em>in vivo</em> beta-lactamase activity in E. coli TEM-1 variants. These variants retain family-like functionality while being more active than their WT predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. In this dataset, we include input datasets, simulation trajectories as well as experimental data to support the publication: "In vivo functional phenotypes from a computationa epistatic model of evolution".</span></p>
In vivo parameter maps for: Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools
<p>Quantitative magnetization transfer and relaxometry maps as described in the Paper <em>Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools</em>.</p> <p>.</p>
Volumetric and morphological analysis of the clades based on the in vivo confetti imaging
<p>This dataset contains a script in programming language that describes the analytical pipeline for image processing of the in vivo data from the Confetti mice skin. The algorithm describes volumetric analysis, 3D reconstruction, density analysis, as well multiple other morphological parameters. </p>
Polyadenylation landscape of in vivo long-term potentiation in the rat brain
<h3><strong>This repository contains data published along our corresponding manuscript and additional data resources generated/used throughout this work. <br>______________________________________________________________________________</strong></h3> <h2><strong>Source data accompanying the manuscript:<br></strong></h2> <p><strong>Supplementary Table 1. Key resource table. (A) </strong>Characteristics of analyzed material (e.g. sample identifiers, number of animals used, reads produced, accession numbers). <strong>(B)</strong> Key resources (antibodies, reagents, software).<strong><br><br>Supplementary Table 2. Summary of dentate gyri DRS data per gene. (A)</strong> Differential expression and differential adenylation data for 10 min timepoint. <strong>(B)</strong> Differential expression and differential adenylation data for 60 min timepoint. <strong>(C)</strong> GO-terms for genes with significantly elongated poly(A) tails in 10 min timepoint. <strong>(D)</strong> GO-terms for genes with significantly elongated poly(A) tails in 60 min timepoint. <strong>(E)</strong> GO-terms for upregulated genes in 10 min timepoint. <strong>(F)</strong> GO-terms for upregulated genes in 60 min timepoint. <strong>(G)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 10 min timepoint. <strong>(H)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 60 min timepoint.</p> <p><strong>Supplementary Table 3. Summary of dentate gyri cDNA data per gene. (A)</strong> Differential expression and differential adenylation data for 10 min timepoint. <strong>(B)</strong> Differential expression and differential adenylation data for 60 min timepoint. <strong>(C)</strong> GO-terms for genes with significantly elongated poly(A) tails in 10 min timepoint. <strong>(D)</strong> GO-terms for genes with significantly elongated poly(A) tails in 60 min timepoint. <strong>(E)</strong> GO-terms for upregulated genes in 10 min timepoint. <strong>(F)</strong> GO-terms for upregulated genes in 60 min timepoint. <strong>(G)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 10 min timepoint. <strong>(H)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 60 min timepoint.</p> <p><strong>Supplementary Table 4.</strong> <strong>High-confidence PASs.</strong> <strong>(A)</strong> PASs predicted for datasets obtained 10 min after LTP induction by TAPAS. <strong>(B)</strong> High confidence poly(A) clusters predicted by LAPA for datasets obtained 10 min after LTP induction. <strong>(C)</strong> PASs predicted for datasets obtained 60 min after LTP induction. <strong>(D)</strong> High confidence poly(A) clusters predicted by LAPA for datasets obtained 60 min after LTP induction.</p> <p><strong>Supplementary Table 5.</strong> <strong>Nonadenosine profiling upon LTP induction. (A)</strong> Summary of Ninetails pipeline for dentate gyrus. <strong>(B)</strong> List of genes containing semi-templated poly(A) tails with their adjacent nucleotide contexts.</p> <p><strong>Supplementary Table 6. Summary of synaptoneurosomal DRS/cDNA data per gene.</strong> <strong>(A)</strong> Differential expression and differential adenylation data for unfractionated synaptoneurosomes DRS sequencing. (B) <strong> </strong>Summary of Ninetails pipeline for unfractionated synaptoneurosomes DRS sequencing. (C) Differential expression and differential adenylation data for monoribosome-bound mRNA synaptoneurosomes cDNA sequencing. (D) Differential expression and differential adenylation data for polyribosome-bound mRNA synaptoneurosomes cDNA sequencing. (E) Differential expression and differential adenylation data for unfractionated synaptoneurosomes cDNA sequencing.<br><br><strong>Supplementary Information</strong> - supplementary figures and captions.<br><br><strong>______________________________________________________________________________</strong></p> <h2><strong>Additional data resources:</strong></h2> <p><strong>CPEB1_motif.meme </strong>- CPE1 motif sequence represented as position-dependent letter-probability matrice required by FIMO to make predictions.<br><strong><br>CPEB2_4_motif.meme</strong> - CPE2,4 motif sequence represented as position-dependent letter-probability matrice required by FIMO to make predictions.<strong><br></strong></p> <p><strong>mRatBN7.2_TAPAS_ref_flat.txt</strong> - mRatBN7.2 reference annotation in format required by TAPAS<br><br><strong>mRatBN7.2_LAPA.gtf </strong>- mRatBN7.2 reference annotation in format required by LAPA</p> <p><strong>FIMO_output.zip</strong> - compressed folder with motif predictions provided by FIMO software.<strong><br><br>LAPA_output_dentate_gyrus.zip </strong>- compressed folder with poly(A) clusters predicted by LAPA software. Each timepoint is represented by separate output. <br><strong><br>TAPAS_output_dentate_gyrus.zip </strong>- compressed folder with raw outputs produced by TAPAS software. Each timepoint is represented by separate output. <strong><br><br>Ninetails_output_dentate_gyrus.zip </strong>- compressed folder with raw outputs produced by Ninetails software for samples from dentate gyrus. Subfolders are named according to the sample identifiers provided in Supplementary Table 1. For each sequencing run, 2 tsv files are produced: read classification and nonadenosine residue classification.<br><strong><br>Ninetails_output_synaptoneurosomes.zip </strong>- compressed folder with raw outputs produced by Ninetails software for samples from synaptoneurosomes. Subfolders are named according to the sample identifiers provided in Supplementary Table 1. For each sequencing run, 2 tsv files are produced: read classification and nonadenosine residue classification.<strong><br><br>PolyA_clusters_high_confident.bed </strong>- High-confidence poly(A) clusters annotation in bed format.<strong><br></strong></p>
Data Set: Raman Investigation of In Vivo Radiation Exposure on Melanin in Murine Hair
<p>Updated version contains additional data added during peer review. Files contains Raw Raman spectra collected from the hair of mice irradiataed with gamma rays of specified dose. The time following exposure (in days) that the hair was sampled, the sex of the mouse, and the total dose (Gy) is given for each spectrum. The Raman spectra were collected with excitation wavelengths of 532 nm and 785 nm. The Raman shift labels for each excitation wavelength is given the first row of the data table prior to the raw spectra.</p>
Data and analysis codes for "In vivo visualization of butterfly scale cell morphogenesis in Vanessa cardui"
<p>Butterfly scale data and data analysis codes for "In vivo visualization of butterfly scale cell morphogenesis in <em>Vanessa cardui</em>."</p> <p> </p> <p>It is recommended to download all files and folders into a single root folder for use in MATLAB.<br> This code was prepared for use in MATLAB R2019b, and some scripts or functions require the Image Processing Toolbox.</p>
Metadata of "Light-Emitting Biosilica by In Vivo Functionalization of Phaeodactylum tricornutum Diatom Microalgae with Organometallic Complexes"
<p>Metadata of "Light-Emitting Biosilica by In Vivo Functionalization of Phaeodactylum tricornutum Diatom Microalgae with Organometallic Complexes"</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.