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141 results for “Cell tracking”
Cell Tracking Dataset (E. coli growing in the Mother Machine)
<pre>Dataset collected by the Dunlop lab (Boston University) E. coli growing in the mother machine Images taken every 5 minutes Each video consists of a single chamber within the mother machine 58 videos in the train dataset 15 videos in the val dataset 29 videos in the test dataset Data is formatted in the Cell Tracking Challenge Format (https://celltrackingchallenge.net/datasets/)</pre>
PDAC cells CD44 siRNA perfusion tracking dataset
<p>This dataset contains tracking results of CD44 siRNA-silenced AsPC1, and MiaPaca cells perfused on endothelial monolayers under physiological flow speeds.</p> <p>Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.</p> <p>Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate. Tracking results were analyzed using a custom CellTracksColab notebook. </p> <p>The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). <strong> </strong></p> <h3>Specifications</h3> <ul> <li> <p>Sample information</p> </li> <ul> <li> <p>AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). </p> </li> <li> <p>CD44 siRNA silencing of PDACs prior to perfusion</p> </li> </ul> <li> <p>Imaging specs</p> </li> <ul> <li> <p>Microscope: Nikon Eclipse Ti2-E, 20x objective</p> </li> <li> <p>Data Type: Brightfield microscopy images (16-bit)</p> </li> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> <li> <p>Recording speed 25 frames/s</p> </li> </ul> <li> <p>DL models:</p> </li> <ul> <li> <p>Cancer cells: <a href="https://doi.org/10.5281/zenodo.10572122">https://doi.org/10.5281/zenodo.10572122</a> </p> </li> <li> <p>Neutrophils: <a href="https://doi.org/10.5281/zenodo.10572231">https://doi.org/10.5281/zenodo.10572231</a></p> </li> <li> <p>Mononucleated cells: <a href="https://doi.org/10.5281/zenodo.10572200">https://doi.org/10.5281/zenodo.10572200</a></p> </li> <li> <p>Model Training and predictions: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <li> <p>Tracking parameters (TrackMate):</p> </li> <ul> <li> <p>Detection: label detector</p> </li> <li> <p>Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. </p> </li> <li> <p>Track filtering: min number of spots in the tracks 11.79 </p> </li> </ul> <li> <p>Tracking analysis</p> </li> <ul> <li> <p>Tracks were analyzed using a customized CellTracksColab notebook (<a href="https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab">https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab</a>)</p> </li> </ul> </ul> <h3><strong> </strong>Contents of the repository</h3> <ul> <li> <p>Analysis.zip</p> </li> <li> <p>As_TCsi1.zip dataset</p> </li> <li> <p>As_TCsi2.zip dataset</p> </li> <li> <p>As_TCsi3.zip dataset</p> </li> <li> <p>As_TCsiCtrl.zip dataset</p> </li> <li> <p>Mia_TCsi1.zip dataset</p> </li> <li> <p>Mia_TCsi2.zip dataset</p> </li> <li> <p>Mia_TCsi3.zip dataset</p> </li> <li> <p>Mia_TCsiCtrl.zip dataset</p> </li> </ul> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
Fast-track Blood Test for Suspected Fever by Deficiency of a Kind of White Blood Cells As Main Defense Against Infection
ClinicalTrials.gov study NCT05393505. IPD Sharing: NO. Countries: 1. Publications: 22.
Tracking Inflammatory Cells Using Superparamagnetic Particles of Iron Oxide (SPIO) and Magnetic Resonance Imaging (MRI)
ClinicalTrials.gov study NCT01169935. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Added Value of Speckle Tracking in the Evaluation of Patients With Sickle Cell Disease
ClinicalTrials.gov study NCT02394431. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Handheld MPI Imaging to Track Stem Cells in Osteoarthritis
ClinicalTrials.gov study NCT07313020. IPD Sharing: YES. Countries: 1. Publications: 10.
Cell Tracking Using Superparamagnetic Particles of Iron Oxide (SPIO) and Magnetic Resonance Imaging (MRI) - A Pilot Study
ClinicalTrials.gov study NCT00972946. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Performance of a Fast-track Pathway for Giant Cell Arteritis Diagnosis
ClinicalTrials.gov study NCT06742671. IPD Sharing: YES. Countries: 1. Publications: 2.
Tracking Mutations in Cell Free Tumour DNA to Predict Relapse in Early Colorectal Cancer
ClinicalTrials.gov study NCT04050345. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Tracking T-Cell Responses to Evaluate Pembrolizumab Effectiveness in Advanced Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT06951399. IPD Sharing: NO. Countries: 1. Publications: 1.
TRAcking Non-small Cell Lung Cancer Evolution Through Therapy (Rx)
ClinicalTrials.gov study NCT01888601. IPD Sharing: Not stated. Countries: 1. Publications: 10.
Data from: In vivo tracking of dendritic cell using MRI reporter gene, ferritin
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Videos for tracking the movement of KT2440 and UWC1 cells
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THP-1 Monocyte invasion cell tracks through guest-host MAP over 18 hours.
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Empirical single-cell tracking and cell-fate simulation reveal dual roles of p53 in tumor suppression
<p>The tumor suppressor p53 regulates various stress responses via increasing its cellular levels. The lowest p53 levels occur in unstressed cells; however, the impact of these low levels on cell integrity remains unclear. To address the impact, we used empirical single-cell tracking of p53-expressing and silenced cells, and developed a fate-simulation algorithm. Here we show that p53-silenced cells underwent more frequent cell death and cell fusion, which further induced multipolar cell division to generate aneuploid progeny. Those results suggest that the low levels of p53 in unstressed cells indeed have a role to maintain the integrity of a cell population. Results of a cell-fate simulation that provides a flexible and dynamic virtual culture space confirmed that these aneuploid progeny could propagate. However, p53-silenced cells were unable to propagate in a virtual cell population that was mainly comprised of p53-expressing cells, supporting the notion that p53 acts as a tumor suppressor. In contrast, when DNA damage responses were repeatedly induced in 53-expressed cells, p53-silenced cells became the major cell population, as the growth of p53-expressed cells was more tightly suppressed by the response than that of p53-silenced cells. In this context, the p53-mediated damage response that is supposed to suppress tumor formation has a pro-malignant function. The cellular microenvironment could thus be a major factor to determine the fate of cancer cells and the fate-simulation algorithm can be used to reveal the fate. </p>
Data from: Trajectory energy minimisation for cell growth tracking and genealogy analysis
Cell growth experiments with a microfluidic device produce large-scale time-lapse image data, which contain important information on cell growth and patterns in their genealogy. To extract such information, we propose a scheme to segment and track bacterial cells automatically. In contrast with most published approaches, which often split segmentation and tracking into two independent procedures, we focus on designing an algorithm that describes cell properties evolving between consecutive frames by feeding segmentation and tracking results from one frame to the next one. The cell boundaries are extracted by minimizing the distance regularized level set evolution (DRLSE) model. Each individual cell was identified and tracked by identifying cell septum and membrane as well as developing a trajectory energy minimization function along time-lapse series. Experiments show that by applying this scheme, cell growth and division can be measured automatically. The results show the efficiency of the approach when testing on different datasets while comparing with other existing algorithms. The proposed approach demonstrates great potential for large-scale bacterial cell growth analysis.
Using E-Nose Technology to Track Treatment Response in People With Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT07218601. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Trajectory energy minimisation for cell growth tracking and genealogy analysis
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Empirical single-cell tracking and cell-fate simulation reveal dual roles of p53 in tumor suppression
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Live cell interferometry cell division tracking data files
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Allen Brain Atlas
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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.