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220 results for “In-Vitro”
Instantaneous, three-dimensional velocity fields past a bio-prosthetic aortic valve measured in-vitro with tomographic particle image velocimetry.
<p>Each folder contains instantaneous, three-dimensional velocity vector data obtained in a simplified model of the aortic root with a distinct size and geometry (small, medium, large, and sinus-less). The specific geometry of each aortic root model is contained in the corresponding folder.</p> <p>The velocity data is structured in the following way: Two separate folders for velocity data in the "ascending aorta" domain (AAo) and in the "sinus of Valsalva" domain (SOV). Each domain contains velocity datasets for instances t=0.00, 0.03, 0.06, ..., 0.39 s (t000, t003, t006, ..., t039). Each velocity dataset contains N=16 phase-locked instantaneous 3D velocity fields.</p> <p>The data was acquired using tomographic particle image velocimetry and a custom built hydraulic setup capable of replicating normal physiological flow conditions in the human aorta (heart rate = 72 bpm, cardiac output = 4.8 l)</p> <p>Data format:</p> <p>- aortic root geometry: STL (the geometry is provided with respect to the reference frame of the velocity data)</p> <p>- velocity data: NPY (NumPy), shape= (N_nodes, 6), columns contain X, Y, Z, U, V, W data, where U, V, W are the X, Y, Z components of the instantaneous vector field</p>
Modified poly(L-lysine)-based structures as novel antimicrobials for diabetic foot infections, an in-vitro study.
<p><strong>Data used to generate Figures 2 to 7 </strong></p> <p>Figure 2. Bactericidal activity of poly-L-lysine polymers against <em>S. aureus</em> and <em>P. aeruginosa</em> laboratory strains compared to antibiotics.</p> <p>Figure 3. Bactericidal activity of poly-L-lysine polymers against <em>S. aureus</em> and <em>P. aeruginosa</em> laboratory strains.</p> <p>Figure 4. Comparative bactericidal activity of poly-L-lysine G3(16) copolymers series with hydrophobic amino acid isoleucine, tyrosine and phenylalanine.</p> <p>Figure 5. Comparison of Bactericidal activity of poly-L-lysine polymers, PLL<sub>160 </sub>and G2(8)PLL<sub>20</sub> against <em>S. aureus</em> clinical isolates from wound infections. </p> <p>Figure 6. Bactericidal activity of linear PLL<sub>160 </sub>against <em>S. aureus</em> and <em>P. aeruginosa </em>isolates from suspected diabetic foot infections.</p> <p>Figure 7. Investigation of PLL polymer-induced loss of biofilm viability by resazurin staining of 24 h biofilms.</p>
Raman spectra from "Discrimination of immune cell activation using Raman micro-spectroscopy in an in-vitro & ex-vivo model"
<p>The uploaded files are data from Chaudhary et al, 2021 (Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Discrimination of immune cell activation using Raman micro-spectroscopy in an in-vitro & ex-vivo model, https://doi.org/10.1016/j.saa.2020.119118).</p> <p>There are two files in .mat format. In one (Preprocessed.mat) the data has been completely pre-processed according to the methods described in the paper.</p> <p>In the second (Unpreprocessed.mat) the data has been calibrated using the methods described in the paper, but has not received further pre-processing.</p> <p>Within both files there are datasets for the spectral measurement from each cell (‘spectra’), together with the treatment which was applied to each sample (‘treatment’) and the wavenumber at which the spectral measurements were made (‘wavenumber’).</p>
In-vitro Major Arterial Cardiovascular Simulator: Benchmark Data Set for in-silico Model Validation
<p><strong>Background</strong><br> <br> The data described here supplements the paper "In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark Data Sets for in-silico Model Validation" (to be submitted). It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper M. Wisotzki, A. Mair, P. Schlett, B. Lindner, M. Oberhardt, S. Bernhard, In Vitro Major Arterial Cardiovascular Simulator to Generate Benchmark Data Sets for In Silico Model Validation (2022), Data 7(11), DOI: 10.3390/data7110145 and the Zenodo doi when using this dataset.</p> <p><strong>General description / Dataset Structure</strong></p> <p>Each mat-File describes a different stenosis degree at the popliteal artery of the in-vitro simulator MACSim (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function.</p> <p>The different stenosis degrees for each degree are:<br> ScenarioI: 100 % Area fraction (no stenosis)<br> ScenarioII: 37,5 % Area fraction<br> ScenarioIII: 23,4 % Area fraction<br> ScenarioIV: 6,56 % Area fraction</p> <p><strong>Data fields for each file</strong></p> <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>name of the scenario according to the paper, corresponds to filename</td> </tr> <tr> <td>configuration</td> <td>parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table> <p> </p> <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the paper (node numbering, not sensor numbers) or in the software SISCA (https://gitlab.com/agbernhard.lse.thm/sisca) in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor, unit mmHg for type 'p' and ml/s for type 'q' </td> </tr> <tr> <td>anatomicalPosition</td> <td> <p>name of the corresponding anatomical position</p> </td> </tr> </tbody> </table>
In-vitro dataset for classification and regression of stenosis: dependence on heart rate, waveform and location
<p><strong>Background</strong></p> <p>This data supplements the paper "Classification and regression of stenosis using an in-vitro pulse wave dataset:<br> dependence on heart rate, waveform and location". It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper (<a href="https://doi.org/10.1016/j.compbiomed.2022.106224">https://doi.org/10.1016/j.compbiomed.2022.106224</a>) and the Zenodo doi when using this dataset.</p> <p><strong>General description / Dataset structure</strong></p> <p>Each mat-File describes a different measurement (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. We did our best to make the beginnings end endings align as close as possible (by removing buffer artefacts and aligning the input signal of the monitor), however algorithms should not rely on a global time axis. This similar to patient measurements without an ekg, this does also not share a global time axis comparable among patients.</p> <p>The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function.</p> <p>The data is structure first by stenosis "state" (or location) then by heart rate and then by heart waveform. The stenosis "states" can devided in 1 subset of 10 folders created for regression and 6 created for classification. Excerpt of the folder structure:</p> <ul> <li>No Stenosis <ul> <li>HR 50 <ul> <li>WaveForm1.mat</li> <li>WaveForm2.mat</li> <li>...</li> </ul> </li> <li>HR 55 <ul> <li>...</li> </ul> </li> <li>...</li> </ul> </li> <li>Regression - Stenosis at Pos01 <ul> <li>HR 50 <ul> <li>...</li> </ul> </li> <li>...</li> </ul> </li> <li>...</li> </ul> <p>The tools also available at this page help with traversing this folder structure and are available for Python and Matlab.</p> <p><strong>Data Fields of each file</strong></p> <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>internal database id</td> </tr> <tr> <td>name</td> <td>stenosis location</td> </tr> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>definition of automatic parameter sweep range</td> </tr> <tr> <td>configuration</td> <td>concrete parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table> <p> </p> <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the technical paper describing the MACSim simulator (node numbering, not sensor numbers) or in the software SISCA in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor, unit mmHg for type 'p' and ml/s for type 'q'</td> </tr> <tr> <td>anatomicalPosition</td> <td>name of the corresponding anatomical position</td> </tr> </tbody> </table> <p><strong>Tools:</strong></p> <p>This Tools should make it easier to load the dataset. The usage is documented in the respective code files.</p> <p>Code for the publication is available here:<br> https://gitlab.com/agbernhard.lse.thm/publication_macsim_machinelearning<br> </p> <p> </p> <p> </p>
In-vitro gas production kinetics and methane emission potential of selected indigenous legume fodder tree and shrubs in the semi-humid condition of the southern Ethiopia
<p>The data is about the gas production production characteristics and methane emission potential of the selected eleven species of indigenous legume fodder tree and shrubs in the semi-humid condition of the southern Ethiopia. </p>
A systematic assessment of deep learning methods for drug response prediction: from in-vitro to clinical application
<p>https://github.com/LihongLab/Suppl-data-Benchmark</p> <p>## GDSC dataset</p> <p>**Table S3.** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**Table S4.** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**Table S5.** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**Table S6.** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout.</p> <p> </p> <p>**Table S7.** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage.</p> <p>## TCGA dataset</p> <p>**Table S8.** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**Table S9.** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**Table S10.** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**Table S11.** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder.</p>
Impact of background input on memory consolidation in In-Vitro neural networks
<p>Memory consolidation is a complex process, that can be divided into two stages: first, memories are temporary stored in hippocampus and in the second stage, repeated replay slowly transfers memories to the neo-cortex for long-term consolidation. This 2<sup>nd</sup> stage occurs during slow wave sleep, a phase characterized in the cortex by low cholinergic tone and low afferent input. A recent in-vitro study showed that high cholinergic tone hampers memory consolidation, probably due to lowered network excitability (defined as the mean network response to one neuron spiking). Here we investigate whether low background input contributes to memory consolidation.</p> <p>We used cortical neuronal networks on multi electrode arrays to study memory. When input deprived, these networks develop an activity-connectivity balance. Focal stimuli initially disrupt the existing balance, inducing connectivity changes. When repeated, this effect fades and the response becomes part of spontaneous patterns (memory formation). Application of the same stimulus hours later does not affect connectivity indicating that memory was consolidated.</p> <p>We applied five periods (10 min each) of focal electrical stimulation at different electrodes (A B A), separated by 1 hour of spontaneous activity . Some cultures were transfected to express channelrhopsins (ChR2) enabling global optogenetic background stimulation. We used 12 control, 15 ChR2 cultures with no background input and 8 ChR2 cultures with superimposed random optogenetic stimulation during electrical stimulation periods (f<sub>mean</sub>=5 Hz) to mimic afferent input.</p> <p>Background stimulation acutely reduced network excitability during stimulation without persisting effects after cessation. ChR2 cultures showed significantly more dispersed spiking outside network bursts, and network excitability tended to be lower than in control cultures. Stimulation at electrodes A and B induced memory traces in control cultures. Return to electrode A did not further affect connectivity, showing that memory trace A had been consolidated. Background stimulation impeded the formation of memory traces following electrical stimulation at either electrode. ChR2 expression alone also obstructed memorization.</p> <p>These findings confirm the importance of low background afferent input for memory consolidation. The presence of background afferent inputs reduced network excitability, similar to high cholinergic tone. This leads to the conclusion that sufficient network excitability is crucial for memory consolidation, and high network excitability may be a critical feature of slow wave sleep that makes it more suitable for memory consolidation than the awake state.</p>
Following the mixtures of organic micropollutants with in-vitro bioassays in a large lowland river from source to sea - bioassays CRC
<p>Supportive material for the submitted paper: </p> <p><strong><em><span>Following the mixtures of organic micropollutants with in-vitro bioassays in a large lowland river from source to sea </span></em></strong></p> <p><span>from Hommel et al.</span></p> <p><span>The data includes the with R automated evaluation of the AhR-CALUX, AREc32, ERa-GeneBLAzer and SH-SY5Y assay with respective plots and excel files of the concentrations response curves.</span></p>
A hermetically closed sample chamber enables time-lapse nano-characterization of pathogenic microorganisms in-vitro
<p>Videos showing <span>biosafety compliance of sample chamber (airtightness and liquid leakproof tests) </span></p>
In-vitro selection of lactic acid bacteria to combat Salmonella enterica and Campylobacter jejuni in broiler chickens
<p><em><span>In-vitro</span></em><span> selection of LAB strains for antimicrobial applications in livestock production required specific focus on certain LAB strains. Accordingly, six commercial LAB strains (homofermentative, obligatory heterofermentative and facultative heterofermentative) belonging to different genera, were chosen for screening against strains of <em>Salmonella </em>and <em>Campylobacter jejuni </em>under <em>in-vitro </em>conditions.</span></p> <p> </p>
RNA-seq data and results from in-vitro experiments for "Genetic control of fetal placental genomics contributes to development of health and disease" (Bhattacharya et al 2021)
<p>This dataset includes data and results from RNA-seq data collected from in-vitro experiments presented in "Genetic control of fetal placental genomics contributes to development of health and disease" (Bhattacharya et al 2021). Please check the README.txt file for more details.</p>
A Study Comparing the Efficacy, Safety and Tolerability of Oral Dydrogesterone 30 mg Daily Versus Crinone 8% Intravaginal Progesterone Gel 90 mg Daily for Luteal Support in In-Vitro Fertilization (LOT
ClinicalTrials.gov study NCT02491437. IPD Sharing: Not stated. Countries: 10. Publications: 1.
Enhancement of in-Vitro GC Function in Patients With COPD
ClinicalTrials.gov study NCT00241631. IPD Sharing: NO. Countries: 1. Publications: 3.
A Multicenter Study Comparing the Efficacy, Safety and Tolerability of Oral Dydrogesterone 30 mg Daily Versus Intravaginal Micronized Progesterone Capsules 600 mg Daily for Luteal Support in In-Vitro
ClinicalTrials.gov study NCT01850030. IPD Sharing: Not stated. Countries: 7. Publications: 1.
Subcutaneous Progesterone Versus Vaginal Progesterone Gel for Luteal Phase Support in Patients Undergoing In-Vitro Fertilization (IVF)
ClinicalTrials.gov study NCT00827983. IPD Sharing: Not stated. Countries: 5. Publications: 2.
Impact of background input on memory consolidation in In-Vitro neural networks
Open the record for dataset details and reuse information.
Dataset for "Spatial distribution and physicochemical properties of respirable volcanic ash from the 16-17 August 2006 Tungurahua eruption (Ecuador), and alveolar epithelium response in-vitro" published in GeoHealth
<p>Data Repository for:</p> <p><strong>"Spatial distribution and physicochemical properties of respirable volcanic ash from the 16-17 August 2006 Tungurahua eruption (Ecuador), and alveolar epithelium response <em>in-vitro" </em></strong>published in GeoHealth.<br> </p> <p>Julia Eychenne<sup>1,2*</sup>, Lucia Gurioli<sup>1</sup>, David Damby<sup>3</sup>, Corinne Belville², Federica Schiavi<sup>1</sup>, Geoffroy Marceau<sup>2,4</sup>, Claire Szczepaniak<sup>5</sup>, Christelle Blavignac<sup>5</sup>, Mickael Laumonier<sup>1</sup>, Emmanuel Gardés<sup>1</sup>, Jean-Luc Le Pennec<sup>6,7</sup>, Jean-Marie Nedelec<sup>8</sup>, Loïc Blanchon², Vincent Sapin<sup>2,4 </sup></p> <p><sup>1</sup> Université Clermont Auvergne, CNRS, IRD, OPGC, Laboratoire Magmas et Volcans, F-63000 Clermont-Ferrand, France</p> <p><sup>2</sup> Université Clermont Auvergne, CNRS, INSERM, Institut de Génétique Reproduction et Développement, F-63000 Clermont-Ferrand, France</p> <p><sup>3</sup> U.S. Geological Survey, California Volcano Observatory, Moffett Field, CA, USA</p> <p><sup>4</sup> Biochemistry and Molecular Genetic Department, University Hospital, F-63000 Clermont-Ferrand, France</p> <p><sup>5</sup> Université Clermont Auvergne, UCA PARTNER, Centre Imagerie Cellulaire Santé, F-63000 Clermont-Ferrand, France</p> <p><sup>6</sup> Geo-Ocean, CNRS, Ifremer, UMR6538, F-29280 Plouzané, France</p> <p><sup>7</sup> IRD Office for Indonesia & Timor Leste, Jalan Kemang Raya n°4, Jakarta 12730, Indonesia</p> <p><sup>8</sup> Université Clermont Auvergne, Clermont Auvergne INP, CNRS, ICCFn, F-63000 Clermont-Ferrand, France</p> <p><strong>This repository includes the grainsize distributions of the individual tephra fall samples, the grainsize distribution of the respirable ash sample isolated from F2, the Raman point counting data and individual spectra, the SEM images and EDX maps of the respirable ash sample, the SEM and TEM images of the <em>in-vitro</em> experiments, and the data from the LDH assays, multiplex immunoassays and RT-qPCR.</strong></p>
Datsets for Publication "In-Vitro MPI-Guided IVOCT Catheter Tracking in Real Time for Motion Artifact Compensation"
<p>This dataset contains Magnetic Particle Imaging and Intravascular optical coherence tomography data for the profiles</p> <ul> <li>Standard Profile (3x)</li> <li>Bending Profile (3x)</li> <li>Heart Beat Profile (3x)</li> </ul> <p>used in the publication "In-Vitro MPI-Guided IVOCT Catheter Tracking in Real Time for Motion Artifact Compensation".</p>
Fig. 5 in In-vitro antioxidative potential of different fractions from Prunus dulcis seeds: Vis a vis antiproliferative and antibacterial activities of active compounds
Fig. 5. Anti-proliferative effect of isolated compounds and standard adriamycin at different concentrations. Growth between 0% and 50% indicated a cytostatic effect, while growth <0% indicated a cytocidal effect. (a) Percent control growth for MCF-7 cell line and (b) percent control growth for MDA-MB-468.
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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.