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162 results for “Quantitative Imaging”
Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI) [raw datasets]
<p>Raw datasets accompanying the analysis in "Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI)"</p> <p>The datasets contain raw fluorescence microscopy images aimed to be processed in a SOFI analysis. They are acquired with different camera technologies, allowing for direct comparison of an industry-grade CMOS detector with both a scientific-grade sCMOS and emCCD detector.</p>
Quantitative Content Analysis Data for Hand Labeling Road Surface Conditions in New York State Department of Transportation Camera Images
<p><strong>Foundational Codebook and Data: </strong></p> <p>Traffic camera images from the New York State Department of Transportation (511ny.org) are used to create a hand-labeled dataset of images classified into to one of six road surface conditions: 1) severe snow, 2) snow, 3) wet, 4) dry, 5) poor visibility, or 6) obstructed. Six labelers (authors Sutter, Wirz, Przybylo, Cains, Radford, and Evans) went through a series of four labeling trials where reliability across all six labelers were assessed using the Krippendorff’s alpha (KA) metric (Krippendorff, 2007). The online tool by Dr. Freelon (Freelon, 2013; Freelon, 2010) was used to calculate reliability metrics after each trial, and the group achieved inter-coder reliability with KA of 0.888 on the 4th trial. This process is known as quantitative content analysis, and three pieces of data used in this process are shared, including: 1) a PDF of the codebook which serves as a set of rules for labeling images, 2) images from each of the four labeling trials, including the use of New York State Mesonet weather observation data (Brotzge et al., 2020), and 3) an Excel spreadsheet including the calculated inter-coder reliability (ICR) metrics and other summaries used to asses reliability after each trial. The data are included in NYSDOT_quantitative_content_analysis.zip.</p> <p>The broader purpose of this work is that the six human labelers, after achieving inter-coder reliability, can then label large sets of images independently, each contributing to the creation of larger labeled dataset used for training supervised machine learning models to predict road surface conditions from camera images. The xCITE lab (xCITE, 2023) is used to store camera images from 511ny.org, and the lab provides computing resources for training machine learning models.</p> <p><strong>Obstructed Class Variation: </strong></p> <p>There are many applications for labeling roadside camera images, and as a variation of the foundational codebook, an addendum codebook provides another version of labeling the obstructed class. Specifically, this variation prioritizes labeling an image as “obstructed” only in extreme circumstances where there is a camera- or image- specific problem that prevents the assessment of any road surfaces. For labelers who want to use this version of the obstructed class (in this document) and also the other five weather-related classes (in the foundational codebook), the guidance is to use both documents in tandem, making sure to use the obstructed rules/definitions in this document while disregarding the obstructed rules/definitions in the foundational codebook. Alternatively, this codebook may be used alone in applications where the goal is to solely classify obstructed vs not obstructed. To ensure reliability and quality of this variation, quantitative content analysis was conducted on this addendum codebook, just as it was for the foundational codebook. Two labelers were tested with a sample of 30 images and achieved inter-coder reliability with Krippendorff's Alpha of 0.934 after one trial. The data, including the addendum codebook and labeling trial data (images and results) are included in ObstructedVariation_quantitative_content_analysis.zip.</p> <p>This material is based upon work supported by the U.S. National Science Foundation under Grant No. RISE-2019758.</p>
Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging – dataset
<p>Raw volumetric data used in the work "Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging" (<a href="http://doi.org/10.1364/BOE.466403">doi.org/10.1364/BOE.466403</a>). The data is packaged using the FIJI BigStitcher into HDF5 file. The file is split into 89 parts in ZIP format. Additionally we provide XML file needed for opening the data with BigStitcher and the TXT file with the nominal locations of the volumes based on the readings from the X-Y translation stage. The volumes inside the HDF5 file are already registered for stitching using the BigStitcher pairwise registration and global optimization procedure. Using the BigStitcher option "Resave to TIFF" one can access the raw data that we processed in the work. The processing code which operates on TIFF files is available here: <a href="https://github.com/biopto/QPI-stitching-2D-3D">https://github.com/biopto/QPI-stitching-2D-3D</a>.</p>
3D IQ Test Task (3D-IQTT) - A Dataset for Quantitative Evaluation of 3D Reconstruction from 2D Images
<p>3D reconstruction is mostly evaluated qualitatively. With this dataset, we are introducing a new difficult quantitative task, the 3D IQ test task (3D-IQTT).</p> <p>It is designed to be similar to mental rotation questions found in some IQ tests. Each element in the dataset consists of 4 images: reference object and answers 1-3. One of the answers is the reference object but randomly rotated. For every question, dataset users have to use their model to pick the rotated model out of the 3 possible answers.</p> <p>The dataset encourages semi-supervised or unsupervised 3D reconstruction because it contains a large corpus of unlabeled data and only a small set of labeled data where the correct answer is known.</p> <p>All the images are of blocky 3D shapes floating in space in front of a black background.</p> <p>Demo scripts for loading/processing the dataset can be found at <a href="https://github.com/fgolemo/3D-IQTT">https://github.com/fgolemo/3D-IQTT</a></p> <p>The dataset consists of:</p> <ul> <li> <pre>3diqtt-v2-train.h5 (XZ-compressed)</pre> <strong>(Training Dataset)</strong> <ul> <li> <pre>/labeled</pre> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format: [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> <li> <pre>/unlabeled</pre> <ul> <li> <pre>/questions</pre> format: [100,000 x 4 x 128 x 128 x 3], corresponding to (100k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> </ul> </li> </ul> </li> <li> <pre>3diqtt-v2-test.h5</pre> <strong>(Test Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1].<br> <strong>Important! This is what you have to evaluate yourself on. We have the correct answers but they are not public.</strong></li> </ul> </li> <li> <pre>3diqtt-v2-val.h5</pre> <strong>(Validation Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> </ul> <p> </p> <p><strong>Important:</strong> Before use, the main training dataset (3diqtt-v2-train.h5.xz) needs to be decompressed. This can take up to 24h depending on your hardware. We apologize for any inconvenience caused by this. The uncompressed file has a size of ~74GB. The reason for this compression was a restriction on the size of individual files. The command for decompression is "<strong>unxz</strong><strong> 3diqtt-v2-train.h5.xz</strong>" on Unix machines.</p> <p><strong>If you use this dataset, please cite it.</strong></p>
High-Resolution Quantitative Phase Imaging of Plasmonic Metasurfaces with Sensitivity down to a Single Nanoantenna_experimental dataset
<p>This dataset shares the data presented in the paper "Geometric-phase microscopy for high-resolution quantitative phase imaging of plasmonic metasurfaces with sensitivity down to a single nanoantenna" available in open access under <a href="https://doi.org/10.5281/zenodo.3355170">10.5281/zenodo.3355170</a>. The archive contains experimental files titled with references to the figures as they appear in the paper. </p>
Supplementary Movies and Source Data for: Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation
<p>Supplementary Movies and raw data for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation":</p> <p>Source_Data.zip: Supplementary Code, Supplementary Data and Weka Analysis</p> <p>Lan_supplementary_movies_AVI.zip: Supplementary movies as AVI</p> <p>Lan_supplementary_movies_MP4.zip: Supplementary movies as MP4</p> <p>Lan_raw_movies.zip: Raw TIFF stacks of the movies.</p> <p>Lan_supplementary_movies.zip: Old version of the movies.</p>
Dataset for demonstration of quantitative label-free imaging with phase and polarization
<p>The QLIPP_Reconstruction_Resources_20x.zip file contains raw images of mouse brain slice and anisotropic glass target acquired with QLIPP. The file also contains the configuration files to reconstruct the phase, retardance, and orientation from this data with the recOrder pipeline. The tutorial slides for using this dataset for reconstruction can be found here (10.5281/zenodo.5135889).</p> <p> </p> <p>v1.1.0: Upload two zip files for automated testing of recOrder and waveOrder repositories.</p> <p>v1.2.0: add pycromanager dataset for testing the reader and converter in waveOrder.</p> <p>v1.3.0: reduce the size of recOrder test dataset</p> <p>v 1.4.0: add datasets for new data schema defined for recOrder 0.4.0 </p> <p>v1.5.0: add a dataset that shows images of an embryo </p>
Single cell analysis by Quantitative image-based cytometry (QIBC)
<p>Quantitative image-based cytometry (QIBC): Employing automated multichannel wild-field microscopy using the Olympus ScanR screening system. This system includes an inverted motorized Olympus IX83 microscope, a motorized stage, IR-laser hardware autofocus, a fast emission filter wheel with single band emission filters. </p> <p>Images were analyzed and processed using ScanR analysis software and TIBCOSpotfire software was used to plot total nuclear pixel intensities and mean (total pixel intensities divided by nuclear area) nuclear intensities.</p>
Images supporting: Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation
<p>Two image datasets (as zip files) including all images analyzed in the manuscript Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation. Images are of pancreatic adenocarcinoma (PDAC) cystic spheroid samples grown in either BME or Matrigel. Some images have background noise in the form of iron oxide nanoparticles introduced to them.</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>
Quantitative magnetic resonance imaging of Scots pine seeds and the assessment of germination potential
<p>This dataset contains all the raw source data and MATLAB analysis functions that comprise the study:</p> <p><strong>Quantitative magnetic resonance imaging of Scots pine seeds and the assessment of germination potential</strong></p> <p>Canadian Journal of Forest Research | DOI: 10.1139/cjfr-2021-0273.</p> <p>Tuomainen, TV (1), Himanen, K (2), Helenius, P (2), Kettunen, MI (3), Nissi, MJ (1,4)*<br> 1. University of Eastern Finland, Department of Applied Physics, Kuopio, Finland<br> 2. Natural Resources Institute Finland, Suonenjoki Unit, Suonenjoki, Finland.<br> 3. University of Eastern Finland, Kuopio Biomedical Imaging Unit, A.I. Virtanen Institute for Molecular Sciences, Kuopio, Finland <br> 4. University of Oulu, Research Unit of Medical Imaging, Physics and Technology, Oulu, Finland</p> <p>*Corresponding author:<br> Mikko J. Nissi<br> Department of Applied Physics,<br> University of Eastern Finland<br> POB 1627<br> FI-70211, Kuopio, Finland<br> mikko.nissi@uef.fi<br> +358-50-5955517</p> <p>Keywords: Pinus sylvestris, seed germination, MRI, radiography, relaxation time mapping</p> <p> </p> <p><strong>Study and data description</strong></p> <p>Altogether 90 Scots pine (Pinus sylvestris L.) seeds were MR imaged using RAREVTR, MSME, MGE and ZTE pulse sequences with reference radiograph from each seed.</p> <p>The data includes MR images and relaxation time data as well as individual X ray radiographs of Scots pine seeds. </p> <p>The data includes all data ('fid' and '2dseq' for MRI, and .jpeg/.png for radiographs), metadata (acquisition and reconstruction MRI parameters), figures of manuscript, and calculated relaxation time maps (in MATLAB MAT-file format).</p> <p> </p> <p>Included folders and files in the zenodo_repo_scotspine_MRI_zip_20012022 are:</p> <ul> <li><strong>additional_info_scotspine</strong>: Information on the seed batches, their germination and structure in .xlsx file format. Translated into English from Finnish on 06.10.2021.</li> <li><strong>manuscript_figures:</strong> Figures in .eps vector file format (fig1.eps-fig7.eps)</li> <li><strong>matlab_scripts:</strong> Contains MATLAB functions and scripts for data analysis of the MRI data, processed together with 'aedes' GUI (aedes.uef.fi/, redirects to github.com).</li> <li><strong>mri_scotspine</strong>: Contains the MRI data using 5 mm and 10 mm RF coils at 11.7 T (Bruker). The folders 'discard_folder/' contain ZTE data that are not processed with carbon_collector.m MATLAB script (i.e. processed separately).</li> <li><strong>radiography_scotspine: </strong>Contains radiographs of invidual seeds in two folders: old (lower resolution, Faxitron MX-20, Faxitron Bioptics LLC, <em>Tucson, Az, USA</em>) and new (higher resolution, Faxitron MultiFocus, Faxitron Bioptics LLC, <em>Tucson, Az, USA</em>).</li> <li><strong>readme.txt: </strong>More information on the file and folder structure and datatypes.</li> </ul> <p> </p> <p>Please see the included readme.txt for further details.</p> <p> </p> <p>(Teemu Tuomainen, Jan 25, 2022)</p>
Image data for bioRxiv article named: mtFociCounter - Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci
<p>Raw imaging data to reproduce and test the findings of the bioRxiv article: <strong>mtFociCounter </strong>- Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci. It contains data from three imaging days and 2 or three technical replicates on each day.</p> <p> </p>
Mp4-Version of the supplementary Movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"
<p>Videos in 'mp4'-format of the 8 supplementary movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"</p>
Trapalyzer: A computer program for quantitative analyses in fluorescent live-imaging studies of Neutrophil Extracellular Trap formation.
<p>This data set contains a set of fluorescent microscopy images of a co-culture of neutrophil cells and E. coli bacteria used to study the Neutrophil Extracellular Trap (NET) formation stimulated by bacteria. </p> <p>NETs and live cells were visualized with a double fluorescent staining of DNA using Hoechst 33342 and SYTOX Green. </p> <p><strong>Reagents.</strong></p> <p>Roswell Park Memorial Institute (RPMI) 1640 medium, HEPES, SYTOX<sup>TM</sup> Green, and Hoechst 33342 were purchased from Thermo Fisher Scientific (Waltham, USA). LB broth was purchased from Sigma Aldrich (St Louis, MO, USA).</p> <p><strong>Preparation of blood neutrophils.</strong></p> <p>Neutrophils were obtained from peripheral blood of one healthy blood donor. Blood sample was purchased at Local Blood Donation Centre and according to local regulations, the blood donor enabled blood donation center to sell their blood samples for scientific purposes and the consent of bioethical committee was not required. Blood was collected into a citrate tube and processed within 2 hours from collection. Neutrophils were isolated using density gradient centrifugation followed by polyvinyl alcohol sedimentation, exactly as described in [1]. Isolated neutrophils were suspended in RPMI 1640 medium with 10 mM HEPES (RH). </p> <p><strong>Preparation of bacteria.</strong></p> <p><em>Escherichia coli</em> (American Type Culture Collection(ATCC) 25922 strain) were grown overnight in LB broth with shaking. In the morning, an aliquot of bacterial culture was taken, diluted 100 x in a fresh LB medium and grown for subsequent 2-3 hours. Subsequently, bacterial cultures were washed and resuspended in RH medium.</p> <p><strong>Co-culture of neutrophils with bacteria</strong><br> Neutrophils were seeded into the wells of 48-well plates at the density of 2 ⨉ 10<sup>4</sup> cells/well and allowed to settle for 30 minutes at 37°C, 5% CO2. Subsequently, <em>E. coli</em> was added into the appropriate wells at the multiplicity of infection of 4 or 1 (<em>E.coli</em>: neutrophil). Neutrophils incubated without bacteria were used as a control group. A technical duplicate for each condition was prepared. <br> For each intended timepoint (t=0, 60, 90, 120, 180 minutes), a separate 48 well plate was prepared. The plates were centrifuged for 5 minutes at 250 g to allow the contact of bacteria with neutrophils. The plates were incubated at 37°C, 5\% CO2 for a specified time and then the samples were stained with SYTOX<sup>TM</sup> Green (100 nM) and Hoechst 33342 (1.25 μM) for 10 minutes. Four images of each well were taken with Leica DMi8 fluorescent microscope equipped with a 10× magnification objective (Leica, Wetzlar, Germany). Overall, 120 images have been obtained.</p> <p> </p> <p><strong>2019_04_24--ecoli_neu_tiff_channel_merged.zip:</strong> Images in .tif format, each containing 5 channels: channel 1 for SYTOX Green fluorescent stain (green fluorescence), channel 2 for Hoechst 33342 fluorescent stain (blue fluorescence), and three channels for transmission light encoded in RGB values. </p> <p> </p> <p><strong>2019_04_24--ecoli_neu_tiff_raw_exported.zip:</strong> Images split by different light sources: transmission light (_ch00.tif), SYTOX Green fluorescence (_ch01.tif), Hoechst 33342 fluorescence (_ch02.tif).</p> <p> </p> <p>[1] Bystrzycka W, Moskalik A, Sieczkowska S, Manda-Handzlik A, Demkow U, Ciepiela O. The effect of clindamycin and amoxicillin on neutrophil extracellular trap (NET) release. <em>Cent Eur J Immunol</em>. 2016;41(1):1-5. doi:10.5114/ceji.2016.58811</p>
Data for: Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations
<p>Magnetic Resonance Imaging measurement data used in our work about "Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p> <p><br> Further information about the individual datasets:</p> <p>data_GSM_t1<br> Type: Gold-Standard T1 measurement<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: IR Single-Echo Spin-Echo<br> TR|TE [ms]: 8000|15<br> FOV [mm]: 200<br> T_INV [ms]: 30:250:2530</p> <p>data_GSM_t2<br> Type: Gold-Standard T2 measurement<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: Single-Echo Spin-Echo<br> TR|TE [ms]: 8000|(15:40:455)<br> FOV [mm]: 200</p> <p>data_05b_b1map<br> Type: B1 Map<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_05b_kspace<br> Type: Radial Single-Shot Dataset<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.88|2.44<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_06_irbssfp_long<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 10.8|5.4<br> FA [deg]: 45<br> T_RF [ms]: 2.5<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_irbssfp_short<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.88|2.44<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 4.1|2.58<br> FA [deg]: 6<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_s03_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_s03_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.75|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_2_5ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 6.14|3.07<br> FA [deg]: 35<br> T_RF [ms]: 2.5<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_2_1ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 5.5|2.75<br> FA [deg]: 35<br> T_RF [ms]: 2.1<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_1_6ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 5.0|2.5<br> FA [deg]: 35<br> T_RF [ms]: 1.6<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_1_2ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.6|2.3<br> FA [deg]: 35<br> T_RF [ms]: 1.2<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_0_6ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4|2<br> FA [deg]: 35<br> T_RF [ms]: 0.6<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_0_4ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 3.8|1.9<br> FA [deg]: 35<br> T_RF [ms]: 0.4<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p> </p>
Data for: Quantitative Multi-Parameter Mapping in Magnetic Resonance Imaging
<p>Magnetic Resonance Imaging measurement data used in the PhD thesis "Quantitative Multi-Parameter Mapping in Magnetic Resonance Imaging". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).<br> </p> <p>Further information about the individual datasets:</p> <p>data_invivo_b0map<br> Type: B0 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence<br> TR|TE1|TE2 [ms]: 400|4.92|7.38<br> FA [deg]: 60<br> FOV [mm]: 240</p> <p>data_invivo_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 6830|2.19<br> FA [deg]: 8<br> FOV [mm]: 240</p> <p>data_invivo_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.8|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7</p> <p>data_invivo_irbssfp<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7</p> <p>data_invivo_irbssfp_shim<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7<br> missing shim break</p> <p>data_vfa_b0map<br> Type: B0 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence<br> TR|TE1|TE2 [ms]: 400|4.92|7.38<br> FA [deg]: 60<br> FOV [mm]: 240</p> <p>data_vfa_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 6830|2.19<br> FA [deg]: 8<br> FOV [mm]: 240</p> <p>data_vfa_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.8|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_20<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 20<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_40<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 40<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_45<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_50<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 50<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_60<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 60<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_70<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 70<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_77<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 77<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p>
Dataset of PNT1A and PC-3 cells - efect of FITC phototoxicity, quantitative phase imaging (1/2)
<p>Part of article Feith, M,Vičar, T., Gumulec, J., Raudenská, M. Wingren, AG, Masařík, M., Balvan, J. Quantitative Phase Dynamics of Cancer Cell Populations Affected by Blue Light, <em>Appl. Sci.</em> <strong>2020</strong>, <em>10</em></p> <p>Increased exposition to blue light may induce many changes in cell behavior and significantly affect the critical characteristics of cells. Here we show that multimodal holographic microscopy (MHM) within advanced image analysis is capable of correctly distinguishing between changes in cell motility, cell dry mass, cell density, and cell death induced by blue light. We focused on the effect of blue light with a wavelength of 485 nm on morphological and dynamical parameters of four cell lines, malignant PC-3, A2780, G361 cell lines, and the benign PNT1A cell line. We used MHM with blue light doses 24 mJ/cm<sup>2</sup>, 208 mJ/cm<sup>2 </sup>and two kinds of expositions (500 and 1000 ms) to acquire real-time quantitative phase information about cellular parameters. It has been shown that specific doses of the blue light significantly influence cell motility, cell dry mass and cell density. These changes were often specific for the malignant status of tested cells. Blue light dose 208 mJ/cm<sup>2 </sup>× 1000 ms affected malignant cell motility but did not change the motility of benign cell line PNT1A. This light dose also significantly decreased proliferation activity in all tested cell lines but was not so deleterious for benign cell line PNT1A as for malignant cells. Light dose 208 mJ/cm<sup>2 </sup>× 1000 ms oppositely affected cell mass in A2780 and PC-3 cells and induced different types of cell death in A2780 and G361 cell lines. Cells obtained the least damage on lower doses of light with shorter time of exposition.</p> <p><strong>Materials and Methods </strong></p> <p><em>Cell Lines</em></p> <p>The PC-3, A2780, PNT1A, and G361 cell lines were purchased from HPA Culture Collections (Salisbury, UK). PC-3 prostate cancer cell line was derived from bone metastasis of a 4-grade prostatic adenocarcinoma of a 62-year-old Caucasian male The A2780 cell line was derived from the ovarian carcinoma of a nontreated patient according to ECACC. PNT1A cell line was established from prostatic epithelial tissue of healthy 35-years old male and immortalized by plasmid transfection containing the SV40 genome with defective replication origin. The G361 cell line was established from a malignant melanoma of a 31-year-old male Caucasian. The G361 cells produce melanin for up to 50 population doublings. As the aim of this study is to compare the effect of blue light on the cell lines differing by morphology, transformation state, sensitivity to cell death, and origin, we decided to use the cell lines listed above. PC-3 cells are larger in comparison with small A2780 cells. Benign PNT1A cell line differs from malignant PC-3, and all four cell lines are derived from diverse tissues of origin. Furthermore, melanoma G361 cells expressing melanin may differ in the reaction of cells to blue light exposure.</p> <p></p> <p><em>Cell Cultivation</em></p> <p>All four cell lines were cultivated in 25 cm<sup>2</sup> flasks with 5 ml of media at 37 °C in a humidified incubator (60%) with 5% CO<sub>2 </sub>(Sanyo, Osaka City, Japan). Cell lines A2780, PNT1A and G361 were cultured in RPMI-1640 medium with phenol red indicator, L–glutamine, FBS and antibiotics penicillin/streptomycin (Sigma Aldrich Co., St. Louise, MO, USA). For the PC-3 cell line cultivation, Ham´s F-12 medium with FBS and antibiotics (Sigma Aldrich Co., St. Louis, MO, USA) was used. The same supplementation with antibiotics (penicillin 100 U/mL and streptomycin 0.1 mg/mL) and 10% FBS was used in both media. The cell medium was changed two times per week. Cell subculturing was done with 10% of trypsin solution (PAA, Pasching, Austria) with previous washing with EDTA (0.02% in PBS buffer).</p> <p><em>QPI and Holographic Microscopy and Fluorescence Setting</em></p> <p>QPI was performed by using a Q-PHASE multimodal holographic microscope (Telight, Brno, CZ). The Q-PHASE is equipped with fluorescence module using a halogen lamp as a non-coherent source of blue light. In this work, the module was used as a source of blue light for treatment of observed cell lines. The 485 nm light waves are emitted by the fluorescence light source of the attached module. Before the imaging experiment, cells were cultivated overnight in a concentration of 7000 cells/mL in flow chamber µ-Slide I Lauer Family (Ibidi, Martinsried, Germany). During the measurements, the chamber with cells was incubated in 37 °C humidified, 5% CO<sub>2</sub> atmosphere in H201–for Mad City Labs Z100/Z500 piezo Z-stages (Okolab, Ottaviano NA, Italy). Images and holograms were captured with lens Nikon Plan 10/0.3 and CCD camera (XIMEA MR4021 MC-VELETA, Münster, Germany) respectively. The fluorescence mode used was a plasma light source (Sutter Instrument Lambda XL Novato, CA, USA). Cells were irradiated with a 485 nm light with a 25 nm bandwidth. Light doses 0 mJ/cm<sup>2</sup>, 24 mJ/cm<sup>2</sup> and 208 mJ/cm<sup>2 </sup>were achieved by the combination of time exposition and light intensity.</p> <p>The images were acquired automatically from seven positions every 3 min for 24 h. Holographic images were collected by custom software and raw data were numerically reconstructed. The numerical reconstruction was performed by custom software where the established methods of the fast Fourier-transform and phase unwrapping are implemented. The output from the software is an unwrapped phase image. This image has high intrinsic contrast and can be processed by an available image processing software. The unwrapped phase image is integrated phase shift through the cell and it is proportional to integrated cell dry mass density.</p> <p></p>
Retinal status analysis method based on feature extraction and quantitative grading in OCT images
<p>The raw database includes 200 retinal OCT images judged as normal by ophthalmologists and 100 images with various abnormalities. The software includes the main steps for retinal status analysis. The software was carried out in Matlab.</p>
Dataset for: AI-enabled Lorentz microscopy for quantitative imaging of nanoscale magnetic spin textures
<p>Data used in the publication: AI-enabled Lorentz microscopy for quantitative imaging of nanoscale magnetic spin textures</p>
Source data for: Electrochemically controlled blinking of fluorophores for quantitative STORM imaging
<p>Stochastic optical reconstruction microscopy (STORM) allows widefield imaging with single-molecule resolution by calculating the coordinates of individual fluorophores from the separation of the fluorophore emission in both time and space. Such separation is achieved by photoswitching the fluorophores between a long-lived OFF state and an emissive ON state. While STORM can image single molecules, molecular counting remains challenging due to undercounting errors from photobleached or overlapping dyes and overcounting artifacts from the repetitive random blinking of the dyes. Here, we show that fluorophores can be switched electrochemically for STORM imaging (EC-STORM), with excellent control over the switching kinetics, duty cycle, and recovery yield. Using EC-STORM, we demonstrate molecular counting by using electrochemical potential to control the photophysics of dyes. The random blinking of dyes is suppressed by a negative potential but the switching ON event can be activated by a short pulsed positive potential, such that the frequency of ON events scales linearly with the number of underlying dyes. We also demonstrate the EC-STORM of tubulins in fixed cells with a spatial resolution as low as ~28 nm and counting of single Alexa 647 fluorophores on various DNA nanoruler structures. This control over fluorophore switching will enable EC-STORM to be broadly applicable in super-resolution imaging and molecular counting.</p>
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Allen Brain Atlas
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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
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OpenNeuro
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