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10,068 results for “heart”
Four-Chamber Human Heart Model for the Simulation of Cardiac Electrophysiology and Cardiac Mechanics
<p><strong>Changes in version 1.1 compared to version 1.0:</strong></p> <ul> <li>Ventricular fiber orientation changed to 66° on the endocardial and −41° on the epicardial surface</li> <li>Electrophysiology mesh was resampled</li> <li>Updated material tags in EP mesh</li> <li>Further details can be found in the <a href="https://github.com/KIT-IBT/CardioMechanics/tree/main">CardioMechanics GitHub repository</a> new reference paper:</li> </ul> <blockquote> <p>Gerach, T.; Loewe, A. Differential effects of mechano-electric feedback mechanisms on whole-heart activation, repolarization, and tension. <em>The Journal of Physiology</em> <strong>2024. </strong>https://doi.org/10.1113/JP285022 </p> </blockquote> <p>This repository contains a four-chamber model of the human heart which is ready to use for simulations of cardiac electrophysiology and cardiac mechanics problems. When using this dataset, please also cite the accompanying paper</p> <blockquote> <p>Gerach, T.; Schuler, S.; Fröhlich, J.; Lindner, L.; Kovacheva, E.; Moss, R.; Wülfers, E.M.; Seemann, G.; Wieners, C.; Loewe, A. Electro-Mechanical Whole-Heart Digital Twins: A Fully Coupled Multi-Physics Approach. <em>Mathematics</em> <strong>2021</strong>, <em>9</em>, 1247. https://doi.org/10.3390/math9111247</p> </blockquote> <p>The cardiac anatomy was manually segmented from magnetic resonance imaging (MRI) data of a 33 year old male volunteer. The volunteer provided informed consent and the study was approved by the IRB of Heidelberg University Hospital (Fritz et al., 2014).<br>The MRI data were acquired using a 1.5 T MR tomography system and consist of a static whole heart image stack taken during diastasis as well as time-resolved images in several long and short axis slices. Based on the segmentation, we first labeled the atria and the ventricles. The geometry was extended by a representation of the mitral valve, the tricuspid valve, the aortic valve and the pulmonary valve. Additionally, we closed the endo- and epicardial surfaces of the atria and added truncated pulmonary veins, vena cavae as well as the ascending aorta and pulmonary artery. Furthermore, we added a concentric layer of tissue around the entire heart which phenomenologically represents the influence of the pericardium and the surrounding tissue.</p> <p>Two tetrahedral meshes were created using Gmsh (Geuzaine et al., 2009): (1) the mechanical reference domain (<strong>M.vtu</strong>) with 128,976 elements (on average 3.17 mm edge length) and (2) the electrophysiological reference domain (<strong>EP.vtu</strong>) as a subset of M with 50,058,295 elements (on average 0.4 mm edge length).<br>We used rule-based methods to assign the myofiber orientation on EP: Wachter et al. (2015) was used for the atria and Bayer et al. (2012) for the ventricles. The fiber angle in the ventricles was chosen as +60° and -60° on the endocardial and epicardial surface, respectively. The sheet angle was set to -65° on the endocardium and 25° on the epicardium. Github repositories to these fiber generation tools are given in the sidebar. All geometry files are given in millimeter (mm).</p> <ul> <li><strong>Data:</strong><br>We provide time resolved data evaluated from cine MRI data, which can be used for model calibration. <ul> <li>Wall thickening (<strong>17AHA_WT.txt</strong>) / fractional wall thickening (<strong>17AHA_fractionalWT.txt</strong>) in the 17 AHA segments of the left ventricle</li> <li>Atrioventricular plane displacement (<strong>AVPD.txt</strong>) and velocity (<strong>AVPV.txt</strong>) as well as the displacement of all tracked points used for AVPD calculation (<strong>AVPD_trackedPoints.txt</strong>)</li> <li>Left and right ventricular volume (<strong>Volume_LV_RV.txt</strong>). RV volume is only available for end-diastole and end systole.</li> </ul> </li> <li><strong>Surfaces:</strong><br>This directory contains *.stl files with triangulated surfaces on which boundary conditions can be applied. <ul> <li><strong>cavityXX.stl</strong>: blood volume of the LV, RV, LA, RA</li> <li><strong>epicard.stl</strong>: the whole epicardium</li> <li><strong>outerPeri.stl</strong> and <strong>outerTrunks.stl</strong>: surfaces for Dirichlet boundary conditions</li> <li><strong>master.stl </strong>and <strong>slave.stl</strong>: surfaces used for the frictionless contact problem described in Fritz et al. (2014)</li> </ul> </li> <li><strong>TetGen:</strong><br>Contains the geometry <strong>M.vtu</strong> in the TetGen file format. T4 mesh with 4 node tetrahedrons and 3 node triangles. <ul> <li>.bases: fiber, sheet, and normal orientation at quadrature points of all elements</li> <li>.node: vertex coordinates</li> <li>.ele: list of tetrahedra</li> <li>.sur: list of triangles</li> </ul> </li> <li><strong>EP.vtu:</strong><br>Contains the cell arrays Fiber and Material.</li> <li><strong>M.vtu:</strong><br>Contains the cell arrays Fiber, Sheet, Sheetnormal, Material, and Label.</li> <li><strong>LabelIDs.txt:</strong><br>List of Label and Material identification numbers and corresponding anatomical structures.</li> </ul> <p> </p>
The influence of heart rate variability biofeedback on cardiac regulation and functional brain connectivity
Open the record for dataset details and reuse information.
PsPM-trSP1: SCR, and heart beat measurement in response to aversive/neutral IAPS pictures while subjected to auditory distractors
<p>This dataset includes skin conductance response (SCR) and pulse time stamp (HB) measurements for each of 60 healthy unmedicated participants (30 males and 30 females aged 23.7 +/- 4.8 years) in response to the 45 most arousing negative, and 45 least arousing neutral IAPS pictures, each presented for 1 s each, while listening to regular or random distractor sounds, as described in Bach et al. (2015). ITI was selected randomly on each trial from 7.65 s, 9 s, or 10.35 s. The experiment was preceded by a 2-minute resting period and divided into 3 blocks, separated by resting periods. Each resting period begins and ends with an event marker in the psychophysiological recordings.</p>
DATASET: De novo assembly and functional annotation of the heart + hemolymph transcriptome in the Caribbean spiny lobster Panulirus argus
<p>The spiny lobster <em>Panulirus argus</em> is an ecologically relevant species in shallow water coral reefs and target of the most lucrative fishery in the greater Caribbean region. This study reports, for the first time, the heart + hemolymph transcriptome of the Caribbean spiny lobster<em> Panulirus argus</em> assembled from short Illumina 150 bp PE raw reads. A total 80,152,094 raw reads were assembled using the Oyster River Protocol pipeline that aspires to become the standard protocol for <em>de novo</em> transcriptome assembly. The assembly resulted in a total of 254,773 transcripts. Functional gene annotation was conducted using the software package 'dammit' that also aspires to become the standard protocol for <em>de novo</em> transcriptome annotation. Lastly, gene enrichment analyses were conducted using the Gene Ontology (GO), KEGG pathway analyses (Kaas), and KOG (WebMGA) databases. This resource will be of utmost importance in future research aiming at exploring the effect of local and regional anthropogenic disturbances as well as global climate change on the molecular physiology of this overexploited species.</p>
IPI values of heart beats
<p>InterPuls Interval (IPI) is the time difference between two R-R peak of the heartbeat based on mili-second. Current dataset belongs to 4223 subjects and the IPI values are extracted from ECG datasets provided by PhysioNet. The main file IPI-All.txt contains the value of IPIs and each line represents a subject. In the rest of the files <em>n</em>bit.txt.7z, <em>n </em>is the number of bits in converting the IPI values to binary. For instance, if n=2, then after converting the IPI value to binary, only the least two significant bits were inserted in the 2bit.txt.7z. </p>
TomoBreast randomized clinical trial's lung-heart outcomes and mortality through the 2020 COVID-19 pandemic: data and software
<p>Dataset and R script to reproduce the analyses of the manuscript:</p> <p>Vinh-Hung V, Gorobets O, Adriaenssens N, Van Parijs H, Storme G, Verellen D, Nguyen NP, Magne N, De Ridder M.</p> <p><strong>Lung-heart outcomes and mortality through the 2020 COVID-19 pandemic in a prospective cohort of breast cancer radiotherapy patients.</strong></p> <p>Cancers 2022; 14(24):6241. https:// doi.org/10.3390/cancers14246241</p> <p>https://www.mdpi.com/2072-6694/14/24/6241</p> <p>PubMed: PMID: 36551726</p> <p>PMCID: PMC9777311</p> <p>Info on the variables in file "aelq6_public.R"</p> <p>reproduced in "aelq_2_3_readme.txt":</p> <p>"aelq2_base2.txt" = baseline characteristics.</p> <p>"aelq3.txt" = longitudinal maesurements.</p> <p>Variables in "aelq2_base2.txt":</p> <p>"<strong>aelq2_base2.txt</strong>" = baseline characteristics. <br># Age at randomization, years. <br># RTdose: cf TomoBreast papers. <br># 51 Gy = hypofractionated, simultaneous integrated boost<br># 42 Gy = hypofractionated, no boost, mastectomy cases only<br># 50 Gy = conventional, no boost, mastectomy cases only<br># 66 Gy = conventional, sequential boost<br># Weight kg, Height cm, <br># Detection 1=found by screening (senology follow-up/controle)<br># 2=found by symptoms (pain, palpable)<br># 9=unknown<br># Smoker 0= Not smoker<br># 1= Smoker<br># 2=ex-smoker<br># Mastectomy (and other binary coded) 1= yes<br># chemosched 0=none<br># 1= planned after RT (sequential)<br># 2= prior to RT and is finished (sequential)<br># 3= chemo is on-going or is planned to start with RT (concomitant)<br># hormonetherapy 0=no<br># 1=tamoxifen (nolvadex)<br># 2=Femara (Letrozole)<br># 3=zoladex<br># 4=tamoxifen + zoladex<br># Laterality 1,=Right, 2=Left, 3=Bilateral<br># LengthFU: length of follow-up, days from randomization</p> <p>"<strong>aelq3.txt</strong>" = longitudinal maesurements.<br># "Nr" = Case ID<br># "Time" in days from origin (origin =date of randomization), <br># if negative =before randomization<br># "KPS" "Weight" <br># "Died" "LocalRec" "Metast" "NewPrim" = binary code, 0=no, 1=yes<br># "fAEBreast" "fAEHeart" "fAELung" "fAEOther" <br># fAE = freedom from breast, heart, lung, other adverse event score<br># "LVEF2" = ejection fraction, %<br># "MacIver" = estimated cardiac strain</p> <p># the following are pulmonary function tests, untransformed units<br># "FVC", "FEV1", "PEF", "VC", "TLC", "RV", "FRC", "Raw", "sRaw", "DLCO",<br># "VA", "PF"</p> <p># "fDY", "fFA", "fPA" = freedom from dyspnea, from fatigue, from pain<br># range 0 to 100 (best)<br># see papers:</p> <p># Van Parijs, H.; Vinh-Hung, V.; Fontaine, C.; Storme, G.; Verschraegen, C.;<br># Nguyen, D.M.; Adriaenssens, N.; Nguyen, N.P.; Gorobets, O.; De Ridder, M.<br># Cardiopulmonary-related patient-reported outcomes in a randomized clinical<br># trial of radiation therapy for breast cancer. BMC Cancer 2021, 21, 1177,<br># doi:10.1186/s12885-021-08916-z.</p> <p># preprint:<br># Van Parijs, H.; Cecilia-Joseph, E.; Gorobets, O.; Storme, G.; <br># Adriaenssens, N.; Heyndrickx, B.; Verschraegen, C.; Nguyen, N.P.;<br># De Ridder, M.; Vinh-Hung, V. Lung-heart toxicity in a randomized <br># clinical trial of hypofractionated image guided radiation therapy for<br># breast cancer. Preprints 2022, 202212, 0214.<br># https://doi.org/10.20944/preprints202212.0214.v1</p> <p># <br># "Year" = year of the observation<br># example: randomized 1/1/2011, measurement done 1/31/2011, time = 30 days,<br># Year =2011<br>#<br> </p>
What the heart wants: adaptive significance of cordate leaf morphology in Arnica (Asteraceae)
We studied how the leaf inclination of basal leaves of two species, heartleaf arnica (Arnica cordifolia Hook.) and broadleaf arnica (Arnica latifolia Bong.) varied with canopy cover in the Greater Yellowstone Ecosystem, Wyoming, USA in July and August, 2022. Basal leaves of heartleaf arnica possess cordate leaf bases while those of broadleaf arnica do not, leading to potential biomechanical limitations of the latter to persist in shaded forest understories. Leaf inclination was measured as the angle (degrees) between the petiole and leaf planes of basal leaves for each species; cordateness was measured as the ratio of leaf length on either side of the petiole insertion point in basal leaves of heartleaf arnica. Data collection are complete.
Data inputs and results from AI-supported title and abstract screening "Lack of evidence regarding markers identifying acute heart failure in patients with COPD: an AI-supported systematic review"
<p>These comma-separated data files were used to conduct the AI supported screening of [Lack of Evidence Regarding Markers Identifying Acute Heart Failure in Patients with COPD: An AI-supported Systematic Review (working title)], following the methodology described in the publication (URL/doi to be uploaded).</p> <p>These files provide insight into the AI-supported screening process and the choices made by the human reviewer.</p>
Age-related proteostatic imbalance exacerbates heart failure with preserved ejection fraction pathogenesis in old mice
<p>Heart failure with preserved ejection fraction (HFpEF) is a leading cause of hospitalization and death in the elderly. While aging strongly increases the incidence of HFpEF, the specific influences of aging on HFpEF at molecular and pathophysiological levels remain unclear. Here, we show that aged mice, when subjected to chronic metabolic and hypertensive stress (2-hit stress), develop an aggravated cardiometabolic HFpEF phenotype compared to younger counterparts. Aged HFpEF mice also display unique pathological characteristics reminiscent of those found in HFpEF patients. We demonstrate that age-related dysfunction in protein quality control (PQC) exacerbates proteostatic stress in HFpEF. Specifically, we demonstrate that increased protein synthesis induced by 2-hit stress combines with age-related impairment in protein degradation in aged HFpEF hearts, culminating in the accumulation of protein aggregates. These findings underscore the importance of incorporating aging into preclinical HFpEF models and support the therapeutic potentials of targeting PQC mechanisms to ameliorate disease outcomes.</p> <p>The deposited data are lc-ms data acquired on the Thermo QEx-Plus system. For any questions, please contact mike kinter mike-kinter at omrf.org</p> <p>This upload contains the bulk of the LC-MS data. But, due to file sizes, and addition group of files can be found at doi 10.5281/zenodo.11094720</p>
Interstage single ventricle heart disease infants show dysregulation in multiple metabolic pathways: targeted metabolomics analysis - Data
<p>The data in this Zenodo entry corresponds to the data used to produce the results in <a href="https://www.jacc.org/doi/full/10.1016/j.jacadv.2022.100169">https://www.jacc.org/doi/full/10.1016/j.jacadv.2022.100169</a>. The zipped folder contains three files</p> <ul> <li>Metabolite Data.csv - The meatobilte measurements for all the samples</li> <li>Clinical Data.csv - Values for the clinical variables</li> <li>Clinical Data Descriptions.csv - More in depth explanation of clinical variables as well as possible values of the variables</li> </ul> <p><span>This study was supported by the American Heart Association (AHA</span><span>20CDA35310498 and AHA18IPA34170070) and the National Institutes </span><span>of Health (NIH/NCATS Colorado CTSA, No. UL1 TR001082 and NIH/</span><span>NHLBI K23HL12363</span></p>
Myocardial ultrastructure of human heart failure with preserved ejection fraction
<p>These transmission electron micrographs were obtained from endocardial biopsies of patients with heart failure and preserved ejection fraction, or from non-failling control myocardium. The myocardium is from the right side of the ventricular septum. Images are shown at various magnification levels indicated in the title of the image. Images with titles: HH_DM+ or HH_DM-; Mixed_DM+ or Mixed_DM-; OB_DM+ or OB_DM-; or NF_DM+ or NF_DM- show examples from the primary groups, HH represents HFpEF patients with primarily hypertensive hypertrophic heart disease and the least obesity; OB represents HFpEF patients with primarily severe obesity and the least hypertensive hypertrophic disease; Mixed matches obesity and hypertensive hypertrophic heart disease in HFpEF patients to levels obsserved in the HH and OB groups, and NF is non-failing controls. </p> <p>Additional images are shown for NF, HH, OB, and Mixed from the remaining patients in this study are provided at two magnification levels. These are provided as individual pictures as well. </p> <p> </p>
Heart Failure eQTLs companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure"
<p>These are the results of a QTL analysis companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure". We performed RNA expression measurements and obtained genotype information in genome-wide markers for 313 patients (177 failing hearts , 136 donor, non-failing [control] hearts) using Affymetrix expression and Affymetrix Human 6.0 respectively.<strong> </strong>Prior to eQTL discovery, we used PEER to find hidden covariates that could confound signals in our data as well as filtering any genotypes with major allele frequencies less than 5%. To test associations between gene expression in each cohort separately, we used QTLTools with an additive model accounting for gender, age, sample site, and the PEER factors as covariates. We corrected for eQTL multiple association testing using a 10000 permutations per locus in a 2 megabase window and a false discovery rate cutoff of 5%. To select the number of PEER factors, we performed the full analysis multiple times from 1 to 15 PEER factors and observed a saturation of new QTLs being discovered when using 10 factors.</p> <p>Four files are provided, two for each cohort (cases and controls):</p> <p>- peer_[cases|controls]_nominal.txt: Nominal associations with a p-value threshold of 0.001</p> <p>- peer_[cases|controls]_permutations_all.significant.txt: All significant associations detected after the QTLtools permutation test.</p> <p>The column names are those from QTLtools, in order:</p> <p><br> 1. The phenotype ID<br> 2. The chromosome ID of the phenotype<br> 3. The start position of the phenotype<br> 4. The end position of the phenotype<br> 5. The strand orientation of the phenotype<br> 6. The total number of variants tested in cis<br> 7. The distance between the phenotype and the tested variant (accounting for strand orientation)<br> 8. The ID of the tested variant ( in Affy 6.0 SNP ids)<br> 9. The chromosome ID of the variant<br> 10. The start position of the variant<br> 11. The end position of the variant<br> 12. The nominal P-value of association between the variant and the phenotype<br> 13. The corresponding regression slope<br> 14. A binary flag equal to 1 is the variant is the top variant in cis</p>
Hatchling Medaka Heart (HyLFM)
<p>dataset RDF to display https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BSST604 on bioimage.io</p>
KG for heart failure gene expression data
<p>Pre processed gene expression data for different heart failure. Includes count table, gene patiens metadata, gene lenght</p>
A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model
<p>A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model</p>
PineTime heart rate dataset
<p>Dataset of heart rate measurements collected from the PineTime wristband, with a gold standard reference.</p> <p><strong>Contents</strong></p> <p>The repository contains both the raw and the "merged", clean data. The merged data is much easier to work with and should be used when building machine learning models. The raw data is provided for transparency, reproducibility, and to allow for studies that could use the other data collected from the Equivital device.</p> <ul> <li><code>schedule.md</code> – schedule of the study, indicating the start and end times of each exercise and break.</li> <li><code>data_raw/</code> – raw data collected from the PineTime wristband and the Equivital device. Each subdirectory corresponds to one participant. The files are in the <a href="https://arrow.apache.org/docs/python/feather.html">Feather format</a>.</li> <li><code>data_merged/</code> – merged data series that can be used for building ML models. The files are in JSON format and follow a nested structure, where each heart rate measurement is associated with a series of acceleration measurements that preceded it. Each file corresponds to one continuous measurement session – there are sometimes multiple sessions per participant due to intermittent hardware failures.</li> </ul> <p><strong>Citation</strong></p> <p>If you use this data in research works, please cite the following paper:</p> <p>Sowiński, P., Rachwał, K., Danilenka, A., Bogacka, K., Kobus, M., Dąbrowska, A., Paszkiewicz, A., et al. (2023). Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites. <em>Sensors</em>, <em>23</em>(14), 6464. MDPI AG. Retrieved from http://dx.doi.org/10.3390/s23146464</p> <p>BibTeX:</p> <pre><code>@article{sowinski2023frugal, title={Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites}, author={Sowi{\'n}ski, Piotr and Rachwa{\l}, Kajetan and Danilenka, Anastasiya and Bogacka, Karolina and Kobus, Monika and D{\k{a}}browska, Anna and Paszkiewicz, Andrzej and Bolanowski, Marek and Ganzha, Maria and Paprzycki, Marcin}, journal={Sensors}, volume={23}, number={14}, pages={6464}, year={2023}, publisher={MDPI}, url = {https://www.mdpi.com/1424-8220/23/14/6464}, doi = {10.3390/s23146464} }</code></pre> <p><strong>Authors</strong></p> <ul> <li><a href="https://orcid.org/0000-0003-3217-1050">Monika Kobus</a> – data collection</li> <li><a href="https://orcid.org/0000-0003-4295-3005">Anna Dąbrowska</a> – data collection, methodological supervision</li> <li><a href="https://orcid.org/0000-0002-2543-9461">Piotr Sowiński</a> – data collection and processing</li> </ul> <p><strong>Acknowledgements</strong></p> <p>This work is part of the <a href="https://assist-iot.eu/">ASSIST-IoT project</a> that has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement No 957258.</p> <p>The <a href="https://www.ciop.pl/en">Central Institute for Labour Protection – National Research Institute</a> provided facilities and equipment for data collection.</p> <p><strong>License</strong></p> <p>The dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
Dielectric Measurements of Ovine Heart
<p>This file includes datasets from ex vivo dielectric properties measurements performed on four ovine hearts at National University of Ireland Galway in July 2019. </p> <p>The preliminary results of this experiment were to be presented at EuCAP 2020 under the title "Detailed Dielectric Characterisation of the Heart and Great Vessels."</p>
Full summary statistics of mixQTL for GTEx v8 Heart_Left_Ventricle
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Effects of COVID-19 lockdown on heart rate variability
<p><strong>Introduction: </strong>Strict lockdown rules were imposed to the French population from 17 March to 11 May 2020, which may result in limited possibilities of physical activity, modified psychological and health states. This report is focused on HRV parameters kinetics before, during and after this lockdown period.</p> <p><strong>Methods:</strong> 95 participants were included in this study (27 women, 68 men, 37 ± 11 years, 176 ± 8 cm, 71 ± 12 kg), who underwent regular orthostatic tests (a 5-minute supine followed by a 5-minute standing recording of heart rate (HR)) on a regular basis before (BSL), during (CFN) and after (RCV) the lockdown. HR, power in low- and high-frequency bands (LF, HF, respectively) and root mean square of the successive differences (RMSSD) were computed for each orthostatic test, and for each position. Subjective well-being was assessed on a 0-10 visual analogic scale (VAS). The participants were split in two groups, those who reported an improved well-being (WB+, increase >2 in VAS score) and those who did not (WB-) during CFN.</p> <p><strong>Results:</strong> Out of the 95 participants, 19 were classified WB+ and 76 WB-. There was an increase in HR and a decrease in RMSSD when measured supine in CFN and RCV, compared to BSL in WB-, whilst opposite results were found in WB+ (i.e. decrease in HR and increase in RMSSD in CFN and RCV; increase in LF and HF in RCV). When pooling data of the three phases, there was a moderate significant correlation between VAS and HR, RMSSD, HF, respectively, in the supine position; the higher the VAS score (i.e., subjective well-being), the higher the RMSSD and HF and the lower the HR. In standing position, HRV parameters were not modified during CFN.</p> <p><strong>Conclusion:</strong> Our results suggest that the strict COVID-19 lockdown likely had opposite effects on French population as 20% of participants improved parasympathetic activation (RMSSD, HF) and rated positively this period, whilst 80% showed altered responses and deteriorated well-being. The changes in HRV parameters during and after the lockdown period were in line with subjective well-being responses. The observed recordings may reflect a large variety of responses (anxiety, anticipatory stress, change on physical activity…) beyond the scope of the present study. However, these results confirmed the usefulness of HRV as a non-invasive means for monitoring well-being and health in the general population.</p>
dataset for paper Vanhaebost J, Faouzi M, Mangin P, Michaud K: New reference tables and user-friendly Internet application for predicted heart weights. Int J Legal Med 2014, 128(4):615-620.
<p>The heart weight is the most important parameter in the determination of cardiac hypertrophy. The obtained heart weight value should be compared against tables of normal weights by age, gender and body weight and height</p> <p>In the study by Vanhaebost<em> et al</em>. has been shown in the Swiss population that the heart weight increases along with the increase of the body weight, body height, BMI and body surface area (BSA). The mean heart weight is greater in men than in women at a similar body weight. The reference tables for predicted heart weights obtained from this study are presented as an user-friendly internet application (<a href="http://calc.chuv.ch/Heartweight">http://calc.chuv.ch/Heartweight</a>) enabling the comparison of heart weights observed at autopsy with the reference values.</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.