Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
12,751
datasets available to search
ShareScore release 0.9.0
Dataset results
12,751 results for “blood”
Linked collectors and determiners for: NEON Biorepository Mammal Collection (Pathhogen Extracts [Blood]).
Natural history specimen data linked to collectors and determiners held within, "NEON Biorepository Mammal Collection (Pathhogen Extracts [Blood])". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1490fc23-bd7c-4898-87aa-1d4f1fa2d2c3">https://bionomia.net/dataset/1490fc23-bd7c-4898-87aa-1d4f1fa2d2c3</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1490fc23-bd7c-4898-87aa-1d4f1fa2d2c3">https://gbif.org/dataset/1490fc23-bd7c-4898-87aa-1d4f1fa2d2c3</a>. Formatted as a Frictionless Data package.
A Data-Driven Epigenetic Characterization of Morning Fatigue Severity in Oncology Patients Receiving Chemotherapy: Associations with Epigenetic Age Acceleration, Blood Cell Types, and Expression-Associated Methylation
<p>This dataset contains supplementary materials including the eCpG mapping analysis results and annotation. The manuscript has been accepted for publication at Cancer Medicine. Please cite both the paper as well as the DOI of this dataset if you make use of the data.</p>
Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.
<p><strong>DOCUMENTATION - Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.</strong></p> <p>Correspondence: fschmid@ethz.ch (Franca Schmid, ORCID: <a href="https://orcid.org/0000-0002-0689-9366">0000-0002-0689-9366</a>)</p> <p><strong>1. Related references:</strong><br> The data set is published in context with the manuscript: <br> [1]<em> The severity of microstrokes depends on local vascular topology and baseline perfusion</em>. <br> F Schmid, G Conti, P Jenny and B Weber. eLife. 2021. Doi: 10.7554/eLife.60208</p> <p>The bi-phasic blood flow simulations have been performed in realistic microvascular networks (MVNs) from the mouse somatosensory cortex first published in:<br> [2]<em> The cortical angiome: an interconnected vascular network with noncolumnar patterns of blood flow</em>. P Blinder, PS Tsai, JP Kaufhold, PM Knutsen, H Suhl and D Kleinfeld. Nature Neuroscience. 2013. Doi: 10.1038/nn.3426</p> <p>The bi-phasic blood flow model for realistic MVNs has first been published in:<br> [3]<em> Depth-dependent flow and pressure characteristics in cortical microvascular networks</em>. F Schmid, PS Tsai, D Kleinfeld, P Jenny and B Weber. PLoS Computational Biology. 2017. Doi: 10.1371/journal.pcbi.1005392</p> <p><em>For further information on how to perform bi-phasic blood flow simulation, please contact the corresponding authors of [1] or [3].</em></p> <p><strong>2. Requirements (software):</strong><br> <em>All simulations and analyses have been performed in Python 2.7. To execute the analysis script the following python libraries need to be installed: cPickle, python-igraph, pandas, seaborn, scipy. The individual analyses script can then be executed by in Python (e.g. “python plot_Figure3.py”).</em></p> <p><strong>3. Content:</strong><br> <em>All folders are stored as compressed archives (*.tar.bz2). On unix-based system the folders can be unpacked by: "</em>tar –jxf ARCHIVE_NAME"<br> <br> <strong>3a. Time-averaged simulation results (python dictionaries stored as python 2.7 pickle files):</strong><br> <strong>SimulationResults_Baseline.tar.bz2:</strong><br> Folders: MVN1, MVN2<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl (<em>generated from prepare_Figure4.py</em>), data_spatial_distribution_AVfactor.pkl (<em>generated from plot_Figure4.py</em>)</p> <p><strong>SimulationResults_SingleCapillaryOcclusions.tar.bz2:</strong><br> Folders: 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out, 2-in-2-out_high, 2-in-2-out_AL1, 2-in-2-out_AL2, 2-in-2-out_AL3, 2-in-2-out_AL4, 2-in-2-out_AL5, 2-in-2-out_closeToDA, 2-in-2-out_farFromDA<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl (<em>only folders:</em> 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out)</p> <p><strong>SimulationResults_MultiCapillaryOcclusions.tar.bz2:</strong><br> Folders: vesselsOccluded_1, vesselsOccluded_3, vesselsOccluded_5, vesselsOccluded_7, vesselsOccluded_9<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl</p> <p><strong>3b. Analysis scripts (python 2.7 scripts in folder Analyses_Scripts):</strong><br> <em>For details on the figure content see [1]. The verticesDict* and the edgesDict* are converted into graph structure (python-igraph) for all analyses. The functionality of python-igraph is used heavily throughout the various analyses.</em></p> <p><strong>helperFunctions.py</strong>: various functions used by the other analysis scripts</p> <p><strong>plot_Figure1_and_Figure1-supplement_1_a-d.py:</strong><br> <strong>Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl<br> <strong>Output:</strong> Figures/Figure_1/*, Supplementary_Figures/Figure_1-supplement_1_a-d/*</p> <p><strong>plot_Figure2_and_Figure2_supplement_1_a-d.py:<br> Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl<br> <strong>Output:</strong> Figures/Figure_2/*, Supplementary_Figures/Figure_2-supplement_1_a-d/*</p> <p><strong>plot_Figure3.py:<br> Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_MultiCapillaryOcclusion/*/edgesDict.pkl, SimulationResults_MultiCapillaryOcclusion/*/verticesDict.pkl <br> <strong>Output</strong>: Figures/Figure_3/*</p> <p><strong>prepare_Figure4.py </strong>(<em>long execution time!):</em><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN*/verticesDict_baseline.pkl,<br> <strong>Output:</strong> SimulationResults_Baseline/MVN*/pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl</p> <p><strong>plot_Figure4.py </strong>(<em>long execution time!):</em><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/*<br> <strong>Output:</strong> SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl (attribute Lfactor_median added), SimulationResults_Baseline/MVN*/data_spatial_distribution_AVfactor.pkl, Figures/Figure_4/*</p> <p><strong>plot_Figure5.py:</strong><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/* <br> <strong>Output:</strong> Figures/Figure_5/*</p> <p><strong>plot_Figure6.py:</strong><br> Input: SimulationResults_Baseline/MVN1/*, SimulationResults_SingleCapillaryOcclusion/2-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl Output: Figures/Figure_6/*</p> <p><strong>4. Attributes stored in python dictionaries:</strong><br> <br> <strong>4a. Baseline:</strong><br> <strong>verticesDict: </strong><em>contains all relevant information and data stored at vertices.</em></p> <ul> <li>index: index of vertex</li> <li>pressure: time averaged pressure at vertex [mmHg]</li> <li>inflowE: list of edges delivering blood to the vertex (inflows of the vertex)</li> <li>outflowE: list of edges removing blood from the vertex (outflows of the vertex)</li> <li>coords: coordinates of the vertex [µm]</li> <li>pBC: pressure boundary conditions [mmHg], None for internal vertices</li> <li>corticalDepth: depth from cortical surface [µm]</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> </ul> <p><strong>edgesDict:</strong> <em>contains all relevant information and data stored at edges.</em></p> <ul> <li>diameter: effective vessel diameter [µm]. See [3] for details.</li> <li>htd: time averaged discharge hematocrit [-]. </li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>mainAV: identifier for ascending venule (AV) main brain. 1: is AV main brain, 0: no AV main branch</li> <li>mainDA: identifier for descending arteriole (DA) main brain. 1: is DA main brain, 0: no DA main branch</li> <li>flow: time averaged flow rate [µm<sup>3</sup> ms<sup>-1</sup>]</li> <li>length: tortuous vessel length [µm] See [1] and [3] for details.</li> <li>tissueVolume: topological tissue volume supplied by vessel [µm<sup>3</sup>]. See [1] for details.</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> <li>edgesFulfillingSelection: identifier if vessels fulfils selection criteria to qualify for analysis. 1: vessel included for analysis, 0: vessel not included for analysis. Details on the selection criteria are provided in [1].</li> <li>htt: time averaged tube hematocrit [-]</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate.</li> <li>sign: sign describing the flow direction in the vessel. +: flow direction from source (vertex with lower index) to target (vertex with higher index), -: flow direction from target to source vertex. Based on time averaged pressure values.</li> <li>points: list of tortuous vessel coordinates of the edge [µm]. Starting at the source vertex. Ending at the target vertex.</li> <li>Lfactor_median: AV-factor of the vessel. None if no AV-factor can be assigned. See [1] for details. Attribute added by plot_Figure4.py</li> </ul> <p><strong>pathsDict_allPaths_from_DA_to_AV_mainBranch: </strong><em>contains all flow path from DA main brain to AV main branch. For details see [1]</em>.</p> <ul> <li>startPoint: list of vertex indices of the end point of the DA</li> <li>endPoint: list of vertex indices of the end point of the AV</li> <li>allPaths: list of lists of vertex indices describing all paths between a the associated startPoint and endPoint.</li> </ul> <p><strong>data_spatial_distribution_AVfactor:</strong> <em>contains information on the spatial distribution of venule-sided capillaries (AV-factor > 0.5). For details see [1].</em></p> <ul> <li>edges_L_mean_50um: list of all edges for which the average AV-factor in an analysis sphere of 50 µm has been computed.</li> <li>resulting_L_mean_50um: average AV-factor for an analysis sphere for 50 µm (see Figure4/AV_factor_delta_analysisSphere50_MVN*.pkl)</li> <li>shortest_distance_to_closest_vessel: list of shortest distances to any vessel for all discretization points along all venule sided capillaries.</li> <li>shortest_distance_to_Lfactor_lt_05: list of shortest distances to an arteriole-sided capillary (AV-factor < 0.5) for all discretization points along all venule sided capillaries.</li> </ul> <p><strong>4b. Occlusion scenarios (both single- and multi-capillary occlusions):</strong></p> <p><strong>verticesDict:</strong></p> <ul> <li>index: index of vertex</li> <li>coords: coordinates of the vertex [µm]</li> <li>pressure_strokeIndex_n: time averaged pressure at vertex [mmHg] for the simulation where edge n has been occluded. For details see [1].</li> </ul> <p><strong>edgesDict:</strong></p> <ul> <li>htd_strokeIndex_n: time averaged discharge hematocrit [-] for the simulation where edge n has been occluded. For details see [1]. </li> <li>flow: time averaged flow rate [µm<sup>3</sup> ms<sup>-1</sup>] for the simulation where edge n has been occluded. For details see [1].</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate for the simulation where edge n has been occluded. For details see [1].</li> <li>htt: time averaged tube hematocrit [-] for the simulation where edge n has been occluded. For details see [1].</li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>diameter_strokeIndex_n: effective vessel diameter [µm] for the simulation where edge n has been occluded (only given for multi-capillary occlusions).</li> </ul> <p><strong>pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased:</strong> <em>contains all flow path from DA main brain to AV main branch (unique vertex sequences). For details see helperFunctions.py --></em><em> function convert_pathsDict_to_unique_vertexSequence.</em></p> <ul> <li>startPoint_strokeIndex_n: list of vertex indices of the end point of the DA for the simulation where edge n has been occluded.</li> <li>endpoint_strokeIndex_n: list of vertex indices of the end point of the AV for the simulation where edge n has been occluded.</li> <li>allPaths_strokeIndex_n: list of lists of vertex indices describing all paths between a the associated startPoint and endpoint for the simulation where edge n has been occluded.</li> </ul>
Fig. 1 in Transport of jundiá Rhamdia quelen juveniles at different loading densities: water quality and blood parameters
Fig. 1. Plasma cortisol and blood glucose of jundiá juveniles transported at different loading densities. Different letters over the bars indicate significant differences (P <0.05) among treatments within each experimental time. Asterisks show significant differences (P <0.001) when compared to the initial value.
Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning
<p>This dataset is a subset of the MIMIC-III dataset used for non-invasive blood pressure prediction. PPG and ABP data were divided into windows of 7s length (875 data points). Systolic and diastolic blood pressure values were derived from the ABP windows. Each sample of the dataset consists of a PPG signal and blood pressure values as well as a unique subject identifier. The file consists of three datasets:</p> <ul> <li>PPG: PPG data of size 905,400 x 875</li> <li>label: BP data of size 905,400 x 2</li> <li>subject_idx: subject affiliation of each sample (size 905,400 x 1)</li> </ul> <p>Furthermore, this submission contains the following models:</p> <ul> <li>AlexNet</li> <li>ResNet50</li> <li>LSTM</li> <li>Architecture published by Slapnicar et al. 2019</li> </ul> <p>The architectures were trained using a non-mixed dataset derived from the MIMIC-III waveform database. Samples were divided between training, validation and test set based on their subject affiliation preventing contamination of validation and test sets with samples from subjects used for training.</p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/W6EH5l80NmU</p>
How to collect dried blood spot samples for hepatitis C testing (Spanish: Cómo obtener muestras de gota de sangre seca para el cribado de la hepatitis C)
<p>Short video in Spanish describing how to collect, store and ship to the laboratory, dried blood spot (DBS) samples for hepatitis C virus (HCV) testing.</p> <p>With proper training of the staff involved, DBS samples can be collected outside the healthcare setting, thus facilitating access to diagnosis of hepatitis C by the most vulnerable populations who attend different centers in the community.<br> With our experience in detecting HCV RNA from DBS samples since 2015, we have produced an explanatory video and a booklet with step-by-step instructions on how to obtain good quality DBS samples for laboratory HCV testing.<br> DBS samples not only allow us to improve the diagnosis rate of viremic HCV infection, but also to monitor the elimination of hepatitis C. Within the following website you can see the publications of different studies that we have carried out using DBS, which have also helped us to characterize the HCV epidemic at the local level.</p> <p>https://www.researchgate.net/project/Development-and-assessment-of-alternative-testing-strategies-for-the-detection-of-active-hepatitis-C-virus-infection-among-vulnerable-groups-at-risk-and-micro-elimination/update/61011c48647f3906fc8c31b8</p> <p> </p>
Data for: Effects of long-term ethanol storage of blood samples on the estimation of telomere length
<p>Telomeres, DNA structures located at the end of eukaryotic chromosomes, shorten with each cellular cycle. The shortening rate is affected by factors associated with stress, and, thus telomere length has been used as a biomarker of ageing, disease, and different life history trade-offs. Telomere research has received much attention in the last decades; however, there is still a wide variety of factors that may affect telomere measurements and to date no study has thoroughly evaluated the possible long-term effect of a storage medium on telomere measurements. In this study we evaluated the long-term effects of ethanol on relative telomere length (RTL) measured by qPCR, using blood samples of magpies collected over twelve years and stored in absolute ethanol at room temperature. We firstly tested whether storage time had an effect on RTL and secondly we modelled the effect of time of storage (from 1 to 12 years) in differences in RTL from DNA extracted twice in consecutive years from the same blood sample. We also tested whether individual amplification efficiencies were influenced by storage time, and whether this could affect our results. Our study provides evidence of an effect of storage time on telomere length measurements. Importantly, this effect shows a pattern of decreasing loss of telomere sequence with storage time that stops after approximate 4 years of storage, which suggests that telomeres may degrade in blood samples stored in ethanol. Our method to quantify the effect of storage time could be used to evaluate other storage buffers and methods. Our results highlight the need to evaluate the long-term effects of storage on telomere measurements, particularly in long-term studies.</p>
Future Blood Testing: Challenges & Opportunities
<p>This video shows the highlights of our Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 Music Licensing details: ASLC-1E1DFFF8-153AD9FEF0 The music is from Adobe Music, is called GUITAR-EPIC-TRAILER-VOCAL_AdobeStock_511629251</p> <p>YouTube Link: https://youtu.be/OfRa31Wvix8</p>
Malaria Parasite of Romanowsky-stained thick blood smears.
<p>We label 1000 Romanowsky-stained thick blood smear images previously used in actual diagnoses. This dataset has 1.000 Romanowsky-stained thick blood smear images from the INS (National Institute of Health in Colombia). The photographic records came from 100 thick blood smears (10 images of different fields per slide). There were 217 and 783 images of good quality and bad quality, respectively. Of these, 702 images were with the presence of parasites and 298 without parasites. A total of 6188 parasites (Trophozoites - 5927, Squizonts - 114, Gametocytes - 147) were labeled, whose height and width varied from 13 to 138 pixels. Each image with parasites included from one to ninety parasites, with an average of eight per image. In addition, there were 12.712 leukocytes in all the images, whose height and width varied from 15 to 222 pixels, and only in twelve of the images were no leukocytes found.</p>
Low-frequency oscillations of blood flow velocity
<p>figS4a-d.h5 - Intensity profiles obtained by scanning along a line (described by the below attribute) placed along a FITCdextran stained vessel. Record features traces of individual red blood cells from which the blood flow velocity in figure S4 was obtained.<br> File contains single dataset:<br> 1 - Imaging data_200807_111309 -> voltage from PMT collecting FITCdextran fluorescence signal<br> The dataset features two attributes:<br> 1 - Display Time -> hologram dwell time in microseconds<br> 2 - Image Dimentions -> dimensions of the full resolution image used prior the line-scans has been selected <br> 3 - ROI_XY -> coordinates of individual pixels of the scanning trajectory</p> <p> </p>
Heel and cord blood datasets for Bangladesh and Zambia cohorts
<div> <div> <div> <div> <p><strong>Background</strong>: Accurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low-income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical and metabolomic data.</p> <p><strong>Methods</strong>: We derived three GA estimation models using ELASTIC NET multivariable linear regression using metabolomic markers from heel-prick blood samples and clinical data from a retrospective cohort of newborns from Ontario, Canada. We conducted internal model validation in an independent cohort of Ontario newborns, and external validation in heel prick and cord blood sample data collected from newborns from prospective birth cohorts in Lusaka, Zambia, and Matlab, Bangladesh. Model performance was measured by comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound.</p> <p><strong>Results</strong>: Samples were collected from 311 newborns from Zambia and 1176 from Bangladesh. The best-performing model accurately estimated GA within about 6 days of ultrasound estimates in both cohorts when applied to heel prick data (MAE 0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for Bangladesh), and within about 7 days when applied to cord blood data (1.02 weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh).</p> <p><strong>Conclusions</strong>: Algorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data.</p> </div> </div> </div> </div>
Gene expression in monocytes, neutrophils and whole blood after stroke
<p>Dataset from:</p> <p>Carmona-Mora, P., Knepp, B., Jickling, G.C. <em>et al.</em> Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke. <em>BMC Med</em> <strong>21</strong>, 65 (2023). https://doi.org/10.1186/s12916-023-02766-1</p> <p>All methods available in the publication above.</p> <p>Abstract</p> <p>Background After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. Peripheral blood cells display distinctive gene expression signatures post IS and these transcriptional programs reflect changes in immune responses to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of immune and clotting responses at the molecular and cellular level that are involved in acute brain injury and may assist with time-targeted, cell-specific therapy.</p> <p>Methods The transcriptomic profiles from peripheral monocytes, neutrophils, and whole blood from 38 ischemic stroke patients and 18 controls were analyzed with RNAseq as a function of time and etiology after stroke. Differential expression analyses were performed at 0-24 h, 24-48 h, and >48 h following stroke.</p> <p>Results Unique patterns of temporal gene expression and pathways were distinguished for monocytes, neutrophils and whole blood with enrichment of interleukin signaling pathways for different timepoints and stroke etiologies. Compared to control subjects, gene expression was generally up-regulated in neutrophils and generally down- regulated in monocytes over all times for cardioembolic, large vessel and small vessel strokes. Self-Organizing Maps identified gene clusters with similar trajectories of gene expression over time for different stroke causes and sample types. Weighted Gene Co- expression Network Analyses identified modules of co-expressed genes that significantly varied with time after stroke and included hub genes of immunoglobulin genes in whole blood.</p> <p>Conclusions Altogether, the identified genes and pathways are critical for understanding how the immune and clotting systems change over time after stroke. This study identifies potential time- and cell-specific biomarkers and treatment targets.</p> <p>clinical_parameters_MON.txt: Clinical parameters from cohort used from monocyte samples.</p> <p>clinical_parameters_NEU.txt: Clinical parameters from cohort used from neutrophil samples.</p> <p>clinical_parameters_WB.txt: Clinical parameters from cohort used from whole blood samples.</p> <p>MON_gene_counts_filtered-WGCNA.txt: Filtered counts of each annotated gene from monocyte samples, cohort used for WGCNA analyses, (TPM normalized, non-log, filtered features where maximum <=40 reads were excluded).</p> <p>NEU_gene_counts_filtered-WGCNA.txt: Filtered counts of each annotated gene from neutrophil samples, cohort used for WGCNA analyses, (TPM normalized, non-log, filtered features where maximum <=40 reads were excluded).</p> <p>WB_gene_counts_filtered-WGCNA.txt: Filtered counts of each annotated gene from whole blood samples, cohort used for WGCNA analyses, (TPM normalized, non-log, filtered features where maximum <=80 reads were excluded).</p> <p>MON_gene_raw_counts.txt: raw counts for cohort used of monocyte samples.</p> <p>NEU_gene_raw_counts.txt: raw counts for cohort used of neutrophil samples.</p> <p>WB_gene_raw_counts.txt: raw counts for cohort used of whole blood samples.</p> <p>MON_Time_Course_filtered_normalized_counts_ready.txt: matrix counts of each annotated gene used for differential expression analyses of time points in monocyte samples. (TPM normalized, filtered features where maximum <=30 reads were excluded).</p> <p>NEU_Time_Course_filtered_normalized_counts_ready.txt: matrix counts of each annotated gene used for differential expression analyses of time points in neutrophil samples. (TPM normalized, filtered features where maximum <=30 reads were excluded).</p> <p>WB_Time_Course_filtered_normalized_counts_ready.txt: matrix counts of each annotated gene used for differential expression analyses of time points in whole blood samples. (TPM normalized, filtered features where maximum <=30 reads were excluded).</p> <p> </p> <p>All methods to generate the above files are available in the publication:</p> <p>Carmona-Mora, P., Knepp, B., Jickling, G.C. <em>et al.</em> Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke. <em>BMC Med</em> <strong>21</strong>, 65 (2023). https://doi.org/10.1186/s12916-023-02766-1</p> <p> </p>
The role of blood viscosity in hovering flight of hawkmoths
<p>Viscosity determines the resistance of hemolymph flow through vessels. For flying insects, viscosity is a major physiological parameter limiting flight performance by controlling the flow rate of fuel to the flight muscles, circulating nutrients, and rapidly removing metabolic waste products. The more viscous the hemolymph, the greater the metabolic energy needed to pump it through body cavities and hemolymph vessels. By employing Magnetic Rotational Spectroscopy with nickel nanorods, we showed that viscosity of hemolymph in resting hawkmoths (Sphingidae) depends on wing size non-monotonically. Viscosity increases for small hawkmoths with high wingbeat frequencies, reaches a maximum for middle-sized hawkmoths with moderate wingbeat frequencies, and decreases in large hawkmoths with slower wingbeat frequencies but greater lift. Accordingly, hawkmoths with small and large wings have viscosities approaching that of water, whereas hawkmoths with mid-sized wings have more than twofold greater viscosity. The metabolic demands of flight correlate with significant changes in circulatory strategies via modulation of hemolymph viscosity. Thus, the evolution of hovering flight would require fine-tuned viscosity adjustments to balance the need for the hemolymph to carry more fuel to the flight muscles while decreasing the viscous dissipation associated with its circulation.</p>
Red Blood Cell RedTell Dataset
<p>The dataset contains microscopic images of red blood cells (RBCs) and consists of two parts:</p> <p>1. dse_data.zip</p> <p>This dataset includes brightfield images of healthy control individuals obtained in the scope of the CoMMiTMenT study. The images are annotated on a single cell level for segmentation and classification (discocyte-stomatocyte-echinocyte sequence) purposes. </p> <p>2. anemia_data.zip</p> <p>This dataset includes brightfield and fluorescent images of RBCs of sickle cell disease (SCD) patients from the MemSID study as well as of thalassemia patients and healthy controls from the CoMMiTMenT study.</p> <p>The CoMMiTMenT study was funded by the European Seventh Framework Program under grant agreement number 602121 (CoMMiTMenT) and from the European Union’s FP7 Programme. The study protocols were approved by the Medical Ethical Research Board of the University Medical Center Utrecht, the Netherlands, under reference code 15/426M and by the Ethical Committee of Clinical Investigations of Hospital Clinic, Spain (IDIBAPS) under reference code 2013/8436. The MemSID study was performed at the University hospital Zurich (#NCT02615847 at https://clinicaltrials.gov/). The trial protocol was approved by the Ethics committee of Canton Zurich (KEK-ZH 2015-0297).</p>
PDMS-Polyimide transcutaneous blood gas collector with self-folding out-of-plane heater elements, dataset
<p>Recorded data and accompanying matlab code for the publication <em>PDMS-Polyimide transcutaneous blood gas collector with self-folding out-of-plane heater elements</em> accepted for publication in <em>Journal of Micromechanics and Microengineering</em>.</p>
Data for "Influences of Glyphosate Contaminations and Concentrate Feed on Performance, Blood Parameters, Blood Cell Functionality and DNA Damage Properties in Fattening Bulls"
<p>The deposited data consist of three data tables and three tables containing legends for the data:</p> <p><a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/oneTimepoint.txt">oneTimepoint.txt</a> contains data from statistical tests within one timepoint; <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/oneTimepoint_legend.txt">oneTimepoint_legend.txt</a> contains the corresponding legend.</p> <p><a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/threeTimepoints.txt">threeTimepoints.txt</a> contains data from statistical tests inclduing three distinct timepoints; <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/threeTimepoints_legend.txt">threeTimepoints_legend.txt</a> contains the corresponding legend.</p> <p><a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/Performance.txt">Performance.txt</a> contains data from statistical tests inclduing two time periods in fattening; <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/Performance_legend.txt">Performance_legend.txt</a> contains the corresponding legend.</p>
Whole blood RNA-seq demonstrates an increased host immune response in individuals with cystic fibrosis who develop nontuberculous mycobacterial pulmonary disease
<p><strong>Background </strong></p> <p>Individuals with cystic fibrosis have an elevated lifetime risk of colonization, infection, and disease caused by nontuberculous mycobacteria. A prior study involving non-cystic fibrosis individuals reported a gene expression signature associated with susceptibility to nontuberculous mycobacteria pulmonary disease (NTM-PD). In this study, we determined whether people living with cystic fibrosis who progress to NTM-PD have a gene expression pattern similar to the one seen in the non-cystic fibrosis population. <strong> </strong></p> <p><strong>Methods</strong></p> <p>We evaluated whole blood transcriptomics using bulk RNA-seq in a cohort of cystic fibrosis patients with samples collected closest in timing to the first isolation of nontuberculous mycobacteria. The study population included patients who did (n = 12) and did not (n = 30) develop NTM-PD following the first mycobacterial growth. Progression to NTM-PD was defined by a consensus of two expert clinicians based on reviewing clinical, microbiological, and radiological information. Differential gene expression was determined by DESeq2.</p> <p><strong>Results</strong></p> <p>No differences in demographics or composition of white blood cell populations between groups were identified at baseline. Out of 213 genes associated with NTM-PD in the non-CF population, only two were significantly different in our cystic fibrosis NTM-PD cohort. Gene set enrichment analysis of the differential expression results showed that CF individuals who developed NTM-PD had higher expression levels of genes involved in the interferon (α and γ), tumor necrosis factor, and IL6-STAT3-JAK pathways. <strong> </strong></p> <p><strong>Conclusion</strong></p> <p>In contrast to the non-cystic fibrosis population, the gene expression signature of patients with cystic fibrosis who develop NTM-PD is characterized by increased innate immune responses.</p>
Hummingbird blood traits track oxygen availability across space and time
<p>Predictable trait variation across environments suggests shared adaptive responses via repeated genetic evolution, phenotypic plasticity, or both. Matching of trait-environment associations at phylogenetic and individual scales implies consistency between these processes. Alternatively, mismatch implies that evolutionary divergence has changed the rules of trait-environment covariation. Here we tested whether species adaptation alters elevational variation in blood traits. We measured blood for 1,217 Andean hummingbirds of 77 species across a 4,600 m elevational gradient. Unexpectedly, elevational variation in hemoglobin concentration ([Hb]) was scale independent, suggesting that physics of gas exchange, rather than species differences, determine responses to changing oxygen pressure. However, mechanisms of [Hb] adjustment did show signals of species adaptation: Species at either low or high elevations adjusted cell size, whereas species at mid-elevations adjusted cell number. This elevational variation in red blood cell number-versus-size suggests that genetic adaptation to high altitude has changed how these traits respond to shifts in oxygen availability.</p>
Blood variation implicates respiratory limits on elevational ranges of Andean birds
<p><span>The extent to which species ranges reflect intrinsic physiological tolerances is a major, unsolved question in evolutionary ecology. To date, consensus has been hindered by the limited tractability of experimental approaches across most of the tree of life. Here, we apply a macrophysiological approach to understand how hematological traits related to oxygen transport shape elevational ranges in a tropical biodiversity hotspot. Along Andean elevational gradients, we measured traits that affect blood oxygen-carrying capacity—total and cellular hemoglobin concentration and hematocrit—for 2,355 individuals of 136 bird species. We used these data to evaluate the influence of hematological traits on elevational ranges. First, we asked whether the sensitivity of hematological traits to elevation is predictive of elevational range breadth. Second, we asked whether variance in hematological traits changed as a function of distance to the nearest elevational range limit. We found that the correlation between hematological sensitivity and elevational range breadth was slightly positive, consistent with a facilitative role for sensitivity in elevational range expansion. We further found reduced local variation in hematological traits near elevational range limits and at high elevations, patterns consistent with intensified natural selection, reduced effective population size, or compensatory changes in other cardiorespiratory traits. Our findings suggest that constraints on hematological sensitivity and local genetic adaptation to oxygen availability promote the evolution of the narrow elevational ranges that underpin tropical montane biodiversity.</span></p>
ScienceDex guides
Understand access before you commit
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.