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The CAD WALK Healthy Controls Dataset
<p>This dataset contains the raw dynamic plantar pressure measurements of 55 healthy Dutch individuals collected at Sint Maartenskliniek, Nijmegen. For each individual, 24 dynamic plantar pressure measurements were collected from both feet. Also collected are walking speeds for each plantar pressure measurements, and demographic information of all individuals measured (age, height, weight, shoe size, sex, handedness, leg dominance).</p> <p>For more information, please see the Readme.pdf file accompanying this dataset.</p>
Blood Vessels Dataset obtained from Retina Images of Healthy and Diabetic Retinopathy Individual
<p>This dataset contains blood vessels image files extracted from publicly available fundus retina images</p>
Keystroke timing and pressure data captured during touchscreen typing by early Parkinson's disease patients and healthy controls
<p><strong>DATASET</strong></p> <p>The present dataset comprises keystroke timing and pressure data that correspond to short text excerpts typed by early Parkinson’s disease (PD) patients (n=18) and healthy controls (n=15) on a common touchscreen-equipped smartphone (LG Nexus 5X with a screen of 5.2 inches in diagonal and a resolution of 1080 × 1920 pixels, running native Android 7.0). Subjects were asked to transcribe up to 11 short text excerpts, with the initial one being 200 characters-long and common for all subjects, while the rest were 40-115 characters-long, pseudorandomly drawn from the fairy tale 'The Little Prince'. Data were recorded using a custom Android Operating System input method (keyboard), developed for the purposes of the study. Additional details on exepriment design, material and methods can be found in the related research article mentioned below. </p> <p>Data consist of sequences of raw press and release timestamps (in milliseconds), as well as of values of normalized pressure (0.000-1.000) applied to initiate keystrokes, corresponding to the consecutive keys tapped during the transcription of each text excerpt. Data included in the 'Data' folder are organised in sub-folders per subject. Each sub-folder contains a number of .txt files with each one corresponding to a text excerpt typed by the particular subject. Files are named using the format S##_TEX##.txt, with S## denoting the subject's coded ID and TEX## the serial number of the transcribed text excerpt. For all subjects, file S##_TEX01.txt corresponds to the initial and common 200 characters-long text excerpt. Each file contains the sequences of raw key press/release timestamps (Tp#, Tp#) and normalized pressure (NP#), applied to initiate each keystroke, in the following format:</p> <p>{<br> Press, Tp1, Release, Tr1, NP1<br> Press, Tp2, Release, Tr2, NP2<br> .<br> .<br> . <br> Press, Tpn, Release, Trn, NPn<br> }</p> <p>where 1,2,...,n denote the serial index of the key tapped during typing.</p> <p><em>Note:</em> Out of 33 subjects, 32 managed to transcribe 8 to 11 text excerpts, while the remaining one (Subject ID: 16) typed only 5. Ten subjects (Subject IDs: 6, 14, 16, 17, 25, 27, 29, 31, 32, 33) did not manage to type the initial 200 characters-long excerpt in its entirety.</p> <p>The dataset also includes a record, in Microsoft Excel format (Demographics_Clinical_Characteristics.xlsx), of the demographic and clinical characteristics (with respect to PD) of subjects. Entries of the Excel file are linked to subjects' sub-folders and individual keystroke data text files via the coded ID of the subject.</p> <p>Demographic characteristics included:</p> <p>Age; Gender; Education level; Years of smartphone usage; Dominant hand<sup>1</sup></p> <p>Clinical characteristics included:</p> <p>Group (PD, Control); Years from diagnosis; Hoehn-Yahr disease stage; Most affected side<sup>2</sup>; Levodopa Equivalent Daily Dose; UPDRS_III<sup>3</sup> total score; UPDRS_III Item 21 Tremor-Right hand; UPDRS_III Item 21 Tremor-Left hand; UPDRS_III Item 22 Rigidity-Right hand; UPDRS_III Item 22 Rigidity-Left hand; UPDRS_III Item 23 Finger taps-Right hand; UPDRS_III Item 23 Finger taps-Left hand; UPDRS_III Item 31 Body bradykinesia/ Hypokinesia</p> <p><sup>1</sup>Dominant hand: (Relating to handedness) the operant hand generally used for performing fine motor-skills tasks.<br> <sup>2</sup>Most affected body side by Parkinson's disease<br> <sup>3</sup>UPDRS_III: Unified Parkinson's Disease Rating Scale Part III (Motor section)</p> <p> </p> <p><strong>RELATED RESEARCH</strong></p> <p>This dataset was originally used and described in the OPEN ACCESS publication: </p> <p>[1] Iakovakis, D., Hadjidimitriou, S., Charisis, V., Bostantzopoulou, S., Katsarou, Z., & Hadjileontiadis, L. J. (2018). Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson’s disease. Scientific reports, 8(1), 7663. <a href="http://doi.org/10.1038/s41598-018-25999-0">https://doi.org/10.1038/s41598-018-25999-0</a> </p> <p>All documents and papers that report on research that uses this dataset will acknowledge this by citing the above publication.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected was approved by the Aristotle University of Thessaloniki Bioethics Committee of Medical School (approval no. 359/3.4.17), Thessaloniki, Greece. Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Dimitrios Iakovakis (Electrical & Computer Engineer, PhD candidate)</p> <p>Signal Processing & Biomedical Technology Unit<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building D, 6th floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996319<br> Fax: +30 2310 996312<br> E-mail: dimiiako12@gmail.com</p> <p> </p> <p><strong>LICENSE</strong></p> <p>This is an open access dataset, licensed under Creative Commons Attribution 4.0 International (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>).</p> <p> </p> <p><strong>WARRANTY</strong></p> <p>This dataset comes without any warranty. Administrators of this dataset can not be held accountable for any damage (physical, financial or otherwise) caused by the use of this dataset. </p>
Simulated tumor and healthy sample
<p>This dataset provides tumor and corresponding healthy sample from a simulation of somatic indels and SNVs using Venters genome. The reference genome (UCSC hg18) used for CRAM compression is delivered as well. Simulation was done as described here: <a href="https://doi.org/10.1101/741256">https://doi.org/10.1101/741256</a>.</p>
Resting State Perfusion in Healthy Aging
Open the record for dataset details and reuse information.
Bulk RNA-Seq PBMC data of SLE patients and healthy volunteers/ profiling of 29 individual immune cell types as well as PBMCs of healthy donors
<p>This Zenodo project contains processed gene expression data from two publicly available data sets. It includes the gene expression data of peripheral blood mononuclear cells (PBMCs) of systemic lupus erythematosus (SLE) patients as well as healthy volunteers (GSE122459). The project also comprises the bulk RNA-Seq profiling of 29 immune cell types as well as PBMCs of healthy individuals (GSE107011). In both cases, the raw RNA-Seq data was downloaded, aligned and processed. The gene expression data is available in form of a count matrix (GSE107011) or count matrix and transcript-per-million (TPM) values (GSE122459). For the latter, an annotation file is attached. Further details are provided in the information file. </p>
Metagenomic assembly and bin3C clustering result for a healthy human faecal microbiome transplant donor
<p>Metagenomic WGS assembly and Hi-C deconvolution of a healthy human faecal microbiome transplant donor.</p> <p>Metagenomic assembly was produced using Spades (v3.13.1).</p> <p>Extracted MAGs were produced using bin3C (v0.3.3) and QC'd using CheckM (v1.0.18).</p>
Structural and functional connectomes from 27 schizophrenic patients and 27 matched healthy adults
<p><strong><em>Data Acquisition</em></strong></p> <p>The cohort consists of a total of 27 healthy participants (age 35 ± 6.8 years) and 27 schizophrenic patients (age 41 ± 9.6), scanned in a 3-Tesla MRI scanner (Trio, Siemens Medical, Germany) using a 32-channel head-coil. The schizophrenic patients are from the Service of General Psychiatry at the Lausanne University Hospital (CHUV). All of them were diagnosed with schizophrenic and schizoaffective disorders after meeting the DSM-IV criteria (American Psychiatric Association (2000): Diagnostic and Statistical Manual of Mental Disorders, 4th ed. DSM-IV-TR. American Psychiatric Pub, Arlington, VA22209, USA). The Diagnostic Interview for Genetic Studies assessment was used to recruits the healthy controls (Preisig et al. 1999). 24 out of the 27 schizophrenics were under medication with mean chlorpromazine equivalent dose (CPZ) of 431 ± 288 mg. The written consent was obtained for all subjects - in accordance with institutional guidelines of the Ethics Committee of Clinical Research of the Faculty of Biology and Medicine, University of Lausanne, Switzerland, #82/14, #382/11, #26.4.2005). All subjects were fully anonymised.</p> <p>The session protocol consisted of (1) a magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence sensitive to white/gray matter contrast (1-mm in-plane resolution, 1.2-mm slice thickness), (2) a Diffusion Spectrum Imaging (DSI) sequence (128 diffusion-weighted volumes and a single b0 volume, maximum b-value 8,000 s/mm<sup>2</sup>, 2.2x2.2x3.0 mm voxel size), and (3) a gradient echo EPI sequence sensitive to BOLD contrast (3.3-mm in-plane resolution and slice thickness with a 0.3-mm gap, TE 30 ms, TR 1,920 ms, resulting in 280 images per participant). During the fMRI scan, participants were not engaged in any overt task, and the scan was treated as eyes-open resting-state fMRI (rs-fMRI).</p> <p><strong><em>Data Pre-processing </em></strong></p> <p>Initial signal processing of all MPRAGE, DSI, and rs-fMRI data was performed using the Connectome Mapper pipeline (Daducci et al. 2012). Grey and white matter were segmented from the MPRAGE volume using freesurfer (Desikan<em> </em>et al. 2006) and parcellated into 83 cortical and subcortical areas. The parcels were then further subdivided into 129, 234, 463 and 1015 approximately equally sized parcels according to the Lausanne anatomical atlas following the method proposed by (Cammoun et al. 2012). DSI data were reconstructed following the protocol described by (Wedeen et al. 2005), allowing us to estimate multiple diffusion directions per voxel. The diffusion probability density function was reconstructed as the discrete 3D Fourier transform of the signal modulus. The orientation distribution function (ODF) was calculated as the radial summation of the normalized 3D probability distribution function. Thus, the ODF is defined on a discrete sphere and captures the diffusion intensity in every direction.</p> <p><strong><em>Structural Connectivity</em></strong></p> <p>Structural connectivity matrices were estimated for individual participants using deterministic streamline tractography on reconstructed DSI data, initiating 32 streamline propagations per diffusion direction, per white matter voxel (Wedeen et al. 2008). Structural connectivity between pairs of regions was measured in terms of fiber density, defined as the number of streamlines between the two regions, normalized by the average length of the streamlines and average surface area of the two regions (Hagmann et al. 2008). The goal of this normalization was to compensate for the bias toward longer fibers inherent in the tractography procedure, as well as differences in region size. The number of fibers and fiber length were also included in the dataset. For the quantitative measure of structural connectivity, the generalised fractional anisotropy (gFA, Tuch et al. 2004) and average apparent diffusion coefficient (ADC, Sener et al. 2001) were also computed for each tract.</p> <p> </p> <p><strong><em>Functional Connectivity</em></strong></p> <p>Functional data were pre-processed using routines designed to facilitate subsequent network exploration (Murphy et al. 2009, Power et al. 2012). The first four time points were excluded from subsequent analysis to allow the time series to stabilize. The signal was linearly detrended and further physiological (white-matter and cerebrospinal fluid regressors) and motion artefacts (three translational and three rotational regressors) confounds were regressed. Then, the signal was spatially smoothed and bandpass-filtered between 0.01-0.1 Hz with Hamming windowed sinc FIR filter. To obtain the brain regions for different atlas scales the signal was linearly registered to the MPRAGE image and averaged within a given region (Jenkinson et al. 2012). Functional matrices were obtained by computing Pearson’s correlation between the individual pairs of regions. All of the above was carried out in subject’s native space (Daducci et al. 2012, Griffa et al. 2017).</p> <p>Brain cortical bert freesurfer rendering for the 5 scales of the Lausanne2008 atlas is available on <a href="https://github.com/jvohryzek/bert4lausanne2008">https://github.com/jvohryzek/bert4lausanne2008</a>.</p>
Functional Brain Networks of Picture Naming in Broca's Aphasia and Healthy Controls
<p>The data were from the picture-naming task with MEG scanning. </p> <p>Brain networks of ".net" format: Each network has 776 regions that were derived by subdividing USCBrain Atlas. Phase-locking values (PLV) were calculated between the 776 regions in a gamma-band of 30-45Hz. PLVs were normalized (z-PLVs) by using the mean and standard deviation of the 200-ms pre-stimulus baseline. The edges were weighted by z-PLVs. </p> <p>Vector files of ".vec" format: Each file contains activations, viz., amplitude, of regions. There are two types of amplitude. One is the estimated electric density in a physical unit of picoampere. Another is the z-score of amplitude calculated through comparison with a baseline of –200 ms.</p> <p>We provided both the group-averaged files (named as b999 for the Broca group and c999 for the control group) and the individuals' files (b1 to b5 for the Broca's aphasia and c1 to c5 for the control persons).</p> <p>We also provided two ".clu" files. One is the partition file of eight functional modules in two hemispheres. Another is the partition file of two hemispheres. </p> <p>The .net, .vec., and .clu files can be imported to Pajek for further interpretations and visualizations. </p> <p> </p>
Three dimensional MRF obtains highly repeatable and reproducible multi-parametric estimations in the healthy human brain at 1.5T and 3.0T
<p>3D MR Fingerprinting T1/T2/M0 maps of twelve healthy volunteers obtained in eight different sites (1.5T and 3.0T scanners, single vendor). Each subject/site dataset includes two acquisitions (test-retest) to assess repeatability of the measurement.</p>
Training dataset: Mass spectrometry based proteomics of healthy human serum samples
<p>The two raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>Serum of a healthy person was obtained by centrifugation of full blood in a serum-gelmonovette. One serum sample was depleted for high abundant proteins, the other not.<br> For the non-depleted sample: 5µl of serum was diluted with 0.1% Rapigest, resulting in a concentration of 1mg/ml.<br> Depletion was performed with the Seppro IgY14 Spin columns which are able to deplete 14 high abundent blood proteins by immunoaffinity. For the depleted sample 9µl of serum was diluted with TBS/HCl/NaCl buffer and added to the Seppro IgY14 spin column. After depletion the sample was buffered with Hepes pH 8.0 and Rapigest was added to a final 0.1% Rapigest concentration. From here on, both samples were reduced by adding TCEP, alkylated by IAA and quenched with DTT in solution. Digestion was performed by adding trypsin in a ratio of 1:50 to the samples. After incubation at 37°C, 600rpm, over night, the sample clean-up was performed with the PreOmics desalting columns. iRT peptides were added and the sample was measured with a Q-Exactive Plus mass spectrometer. Besides the two raw files, we uploaded a fasta file that serves as human protein sequence database and the Galaxy MaxQuant training result files: protein groups, peptides, mqpar and PTXQC.</p>
Resting-State High-Density EEG using EGI GES 300 with 256 Channels of Healthy Elders, People with Subjective and Mild Cognitive Impairment and Alzheimer's Disease
<p>This repository contains Matlab files including 4 samples of resting-state EEG recording for Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), Subjective Cognitive Decline (SCD), and Healthy Controls (HC) using the HD-EEG EGI GES 300.</p> <p><strong>[AD: i108, MCI: i100, SCD: i090, HC: s055]</strong></p> <p> </p> <p><strong>Participants & Settings</strong></p> <p>In total 230 participants have been recruited from the memory and dementia clinic of the Greek Association of Alzheimer’s Disease and Related Disorders (GAADRD) and the 1st Department of Neurology, U.H. AHEPA, Aristotle University of Thessaloniki, Greece.</p> <p>The full dataset includes:</p> <p><strong>Healthy Controls Elders (60+ years old)</strong>: 33 participants</p> <p><strong>Subjective Cognitive Decline:</strong> 34 participants</p> <p><strong>Mild Cognitive Impairment</strong>: 79 participants</p> <p><strong>Alzheimer's Disease</strong>: 48 participants</p> <p><strong>Healthy Young (25-40 years old):</strong> 36 participants</p> <p>The study was carried out in accordance with the Declaration of Helsinki and received approval by the Scientific and Ethics Committee of GAADRD (No56_27/11/2016), and written informed consent was obtained from all participants prior to their participation in the study. The diagnosis of AD was conducted by a neuropsychiatrist according to their medical history, neuropsychological performance, structural magnetic resonance imaging (MRI), and clinical and neurological examinations.</p> <p>Participants with AD fulfilled the National Institute of Neurological and Communication Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD, as well as the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criteria for dementia of Alzheimer’s type (American Psychological Association, 1994). On the other hand, the MCI participants fulfilled the Petersen criteria, while the SCD group met International Working Group-2 guidelines and the recent National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease (NI-AA), as well as the SCD-I Working Group instructions. </p> <p><strong>Resting-State EEG Recording</strong></p> <p>Fifteen-minute resting EEG activity was recorded for all the participants. For the whole duration of the resting state EEG recording, participants were advised to keep themselves relaxed as much as possible, close their eyes and open them after the researcher’s demand, sit still, minimize blinking or mouth movements and let their mind wander. The experimental procedure was monitored by a research assistant aiming to identify cases of horizontal eye movements, continued blinking, or excessive movement by visually inspecting the EEG traces during the experiment. More specifically, an EEG was registered for both resting conditions (eyes open, EO and eyes closed, EC) for at least 2–3 min for each period.</p> <p><strong>EEG Data Acquisition</strong></p> <p>The EEG data were collected by using the EGI 300 Geodesic EEG system (GES 300, CERTH-ITI, Thessaloniki, Greece) with a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz (EGI Eugene, OR). Moreover, the researcher placed the electrodes in accordance with the 256 HCGSN adult 1.0 montage system, while the signals were recorded relative to a vertex reference electrode (Cz), with AFz as the ground electrode with the electrodes’ impedance below 50 kΩ throughout the experimental procedure, as recommended for the high-input impedance amplifier. In detail, the HD-EEG data were analyzed offline in order to detect any artifact, as well as to conduct pre-processing (filtering, segmentation, bad channel replacement) using Net Station 4.3 software (EGI). HD-EEG data were initially filtered with a 5th-order bandpass Butterworth IIR filter of 0.3–30 Hz. Once the segmentation was completed, the detection of artifacts was performed by using the Net Station artifact detection tool for the automatic detection of excessive eye blinking and movement. Afterward, the signals were baseline corrected using 200 msec before the start of the experiment period and average re-referenced to transform them into reference-independent values.</p> <p> </p> <p><strong>Full Dataset Access</strong></p> <p>More information about the sample dataset and access to the full dataset can be available after request via e-mail:</p> <p><strong>Ioulietta Lazarou</strong> BSc, MSc, PhD candidate</p> <p>Neuropsychologist - Clinical Research Associate </p> <p>Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI)</p> <p>6th km Charilaou-Thermi Road, P.O. Box 60361, 57001 Thermi-Thessaloniki, Greece</p> <p>E-mail: <a href="mailto:iouliettalaz@iti.gr">iouliettalaz@iti.gr</a></p>
The Relationship between Vitamin D Status, Intake and Exercise Performance in UK University-level Athletes and Healthy Inactive Controls
<p>The potential ergogenic effects of vitamin D (vitD) in high performing athletes has received considerable attention in the literature and media. However, little is known about non-supplemented university athletes and students residing at a higher latitude. This study aimed to investigate the effects of vitD (biochemical status and dietary intake) on exercise performance in UK university athletes and sedentary students. Physically healthy male and female university students and athletes from the University of Surrey (51.2ºN) were recruited between January and March (2018) to take part in this study. A total of 50 participants (n= 24 males, n= 26 females) were included, 34 (n=18 male, n=16 female) were university athletes competing in a variety of sports. </p> <p>Fasted serum vitD status and sunlight exposure were assessed using LC-MS/MS and dosimetry, respectively. Body composition was measured through the use of a dual-energy x-ray absorptiometry (DEXA) whole body scan (Hologic QDR, Hologic inc. USA). Muscular strength of the upper and lower body was assessed using dominant arm handgrip and knee extensor dynamometry (KE) of the non-dominant leg. Countermovement jump (CMJ) and aerobic fitness were measured using an Optojump and VO<sub>2max</sub> test using a stationery cycle ergometer, respectively.</p>
Early postzygotic mutations contribute to de novo variation in a healthy monozygotic twin pair.
<p>Human de novo single-nucleotide variation (SNV) rate is estimated to range between 0.82-1.70×10(-8) mutations per base per generation. However, contribution of early postzygotic mutations to the overall human de novo SNV rate is unknown.</p> <p>METHODS:</p> <p>We performed deep whole-genome sequencing (more than 30-fold coverage per individual) of the whole-blood-derived DNA samples of a healthy monozygotic twin pair and their parents. We examined the genotypes of each individual simultaneously for each of the SNVs and discovered de novo SNVs regarding the timing of mutagenesis. Putative de novo SNVs were validated using Sanger-based capillary sequencing.</p> <p>RESULTS:</p> <p>We conservatively characterised 23 de novo SNVs shared by the twin pair, 8 de novo SNVs specific to twin I and 1 de novo SNV specific to twin II. Based on the number of de novo SNVs validated by Sanger sequencing and the number of callable bases of each twin, we calculated the overall de novo SNV rate of 1.31×10(-8) and 1.01×10(-8) for twin I and twin II, respectively. Of these, rates of the early postzygotic de novo SNVs were estimated to be 0.34×10(-8) for twin I and 0.04×10(-8) for twin II.</p> <p>CONCLUSIONS:</p> <p>Early postzygotic mutations constitute a substantial proportion of de novo mutations in humans. Therefore, genome mosaicism resulting from early mitotic events during embryogenesis is common and could substantially contribute to the development of diseases.</p>
Raw BRCA1/2 variants in breast cancer patients and healthy relatives produced with GATK.
<p>Aligned sequencing data is available in the NCBI Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra/) under accession SRP095082. Variants were called using GATK HaplotypeCaller (version 3.6). After joint performing joint genotyping multi-sample vcf file was generated. Next, SNPs and indels were extracted into two different vcf files and specific set of filters were applied for each case.</p> <p> </p> <p><strong>File descriptions</strong></p> <p><strong><em>Datasets</em></strong></p> <p><strong>BRCA_SNVs.vcf</strong> - this file contains SNPs called with GATK and hard filters applied. Following filtering options were applied: "QD < 2.0", "FS > 60.0", "MQ < 40.0", "MQRankSum < -12.5", "ReadPosRankSum < -8.0", "SB < -0.10" , "DP < 10" , "GQ < 30" , and "SOR > 3.0"</p> <p><strong>BRCA_indels.vcf</strong> - This file contains indels called with GATK and hard filters applied. Following filtering options were applied: "QD < 2.0", "FS > 200.0", "ReadPosRankSum < -20.0", "InbreedingCoeff < -0.8", "SOR > 10.0".</p> <p> </p> <p><strong><em>Scripts package (scritps.zip)</em></strong></p> <p>Scripts.zip file contains scripts and supporting files for genotype calling and filtering. </p> <p><strong>raw.variant.caling.sh </strong>– bam files preprocessing, alignment refining and raw genotype calling with HaplotypeCaller.</p> <p><strong>genotyping_and_filtering.sh </strong>– joint genotyping, variant hard filtering and callset refinement.</p> <p><strong>LIST.txt</strong> – supporting file that contains bam filenames containing aligned reads.</p> <p><strong>sample_order.txt</strong> – supporting file for sample renaming.</p> <p> </p> <p><strong><em>Reference files (hg19) used in variant calling scripts</em></strong></p> <p>Reference files can be downloaded from GATK bundle web-site at https://software.broadinstitute.org/gatk/download/bundle. </p> <p><strong>ucsc.hg19.fasta</strong> - human genome assembly;</p> <p><strong>Mills_and_1000G_gold_standard.indels.hg19.sites.vcf.gz</strong> – set of known indels to be used for local realignment;</p> <p><strong>1000G_phase1.indels.hg19.sites.vcf.gz</strong> – set of known indels to be used for local realignment;</p> <p><strong>dbsnp_138.hg19.vcf.gz</strong> – a recent dbSNP release (build 138); </p> <p><strong>1000G_phase3_v4_20130502.hg19.lifted.sites.vcf</strong> – the latest set from 1000G phase 3 (v4) for genotype refinement.</p> <p> </p>
Twenty four hour continuous tympanic temperature recordings in healthy volunteers and patients presented with Undifferentiated fever
<p>Twenty-four-hour continuous tympanic temperature recordings were obtained using high-accuracy tympanic probes placed at the auditory canal. Measurements were recorded at one-minute intervals over a 24-hour period, yielding 1,440 data points per subject and enabling high-resolution temporal profiling of body temperature.</p> <p>In Phase I, a total of 100 healthy adult volunteers were recruited to establish baseline circadian thermoregulatory patterns. These recordings were stratified by gender to examine physiological variability among individuals without fever.</p> <p>In Phase II, 184 adult patients presenting with undifferentiated fever of seven or more days’ duration were enrolled. Based on clinical examination and laboratory confirmation, the temperature profiles were analysed across a range of conditions, including tuberculosis, non-tubercular bacterial infections, dengue fever, malaria, leptospirosis, pyogenic sepsis, thyroiditis, malignancies, and non-infectious inflammatory diseases. This dual-phase dataset facilitated a comparative analysis between normative and pathological thermoregulatory patterns, supporting the development of classification models for diagnostic differentiation.</p>
Cine and real-time free-breathing CMR at rest and under exercise stress of healthy volunteers
<p>The dataset consists of cine and real-time images from 15 healthy volunteers (7 males; 8 females). All images were acquired in supine position using a 32-channel cardiac surface receiver coil at 3 T (Skyra, Siemens Healthineers, Germany).</p> <p>Conventional imaging at rest included a balanced steady-state free precession (bSSFP) ECG-gated cine sequence to create a short-axis stack covering the entire heart including both ventricles and atria. Real-time CMR data acquisition was performed during free-breathing and without ECG-synchronization at rest and under two different levels of exercise stress.</p> <p>The dataset includes automatically created contours (comDL) using Medis (version 4.0.56.4, QMass® 8.1, Medical Imaging Systems, Leiden, Netherlands) for all images, as well as manually corrected (mc) contours based on the comDL contours for all cine and real-time measurements at rest and under exercise stress for end-diastolic (ED) and end-systolic phases (ES).</p> <p>The dataset also includes segmentation masks in NIfTI format for cine and real-time CMR at rest and under exercise stress created with nnU-Net (DOI:10.1038/s41592-020-01008-z) with freely available weights based trained on the dataset of the cardiac segmentation challenge "Automated Cardiac Segmentation Challenge" (ACDC) (DOI:10.1109/TMI.2018.2837502).</p> <p>To minimize the influence of respiratory motion on clinical measures, images in the ED and ES phase of the cardiac cycle during end-expiration were manually selected for each slice. The dataset includes indices for these images for real-time CMR measurements at rest and under exercise stress. For intra-observer variability, manually corrected contours for the derivation of the clinical measures were created three to six months after the initial segmentation. For inter-observer variability, manually corrected contours for the derivation of the clinical parameters were created for the first five volunteers by a second reader with experience in cardiac segmentation. Single images in the ED and ES phase during end-expiration were once again chosen from each slice.</p> <p>Image data is provided in a file format used by the BART toolbox. <br>DOI:10.5281/zenodo.7110562</p>
Sex chromosomes and hormones independently influence healthy brain development but act similarly after cranial radiation
<h2><strong>Description</strong></h2> <p>Biological sex influences prevalence of developmental disorders through sex hormones and sex chromosomes. However, our understanding of their impacts in neurodevelopment and response to injury remains limited. In this project, we use high resolution magnetic resonance imaging (MRI) to investigate the four core genotype mouse model (FCG) that separates the influences of sex hormones and sex chromosomes during normal brain development and after cranial radiation therapy. </p> <p>Sex differences are attributed to either sex hormones or sex chromosomes. This can be distinguished by the FCG model which decouples the sex determining region (SRY) from the Y chromosome by moving SRY onto an autosome. This gives us four core sex genotypes: XX NULL, XY NULL, XX SRY, and XY SRY.</p> <p>This dataset represents the <em>most comprehensive mouse brain imaging study</em> employing the FCG model to date with 5 timepoints (P14, P23, P42, P63, P98), Ccl2 wildtype (+/+) and knockouts (-/-), irradiation (7Gy) and sham (0Gy) mice. All in all, a total of <strong>1071 images</strong>! The results presented here is published in PNAS.</p> <p>In vivo MRI scans were obtained using a 7-T MRI scanner (Bruker BioSpin, Ettlingen, Germany) equipped with four cryocoils for simultaneous imaging of four mice. The scans were performed with the following settings: T1-weighted, 3D-gradient echo sequence, 75μm isotropic resolution, TR=26ms, TE=8.25ms, flip angle=26°, field of view=25×22×22mm, and matrix size=334×294×294.</p> <p>All structural MR images are stored in <strong>images.tar.gz</strong>. Images were segmented and registered using an automated pipeline which are stored in <strong>labels.tar.gz</strong>. The consensus average and labels are <strong>final_average.mnc </strong>and <strong>final_labels.mnc</strong>, respectively. Extracted structure volumes alongside the metadata are included in <strong>df_micevolumes.csv</strong>. Structural MRIs are in MINC format and the <strong>readme.txt</strong> provides further information on this dataset. </p> <p>The authors express their sincere gratitude for the research funding recieved from the Canadian Institutes of Health Research (158622, 168037) and the Ontario Institute for Cancer Research (IA-024) with funding from the Government of Ontario and Restracomp from the SIckKids Research Training Centre.</p> <p><strong>Publication</strong>: https://www.pnas.org/doi/10.1073/pnas.2404042121</p> <h2><strong>Code/Software </strong></h2> <p><strong>MINC</strong><br>https://www.bic.mni.mcgill.ca/ServicesSoftware/MINC</p> <p><strong>RMINC</strong><br>https://github.com/Mouse-Imaging-Centre/RMINC</p> <p><strong>PydPiper</strong><br>https://github.com/Mouse-Imaging-Centre/pydpiper/tree/v2.0.19.1</p>
A personalized value-based justification in food swaps to stimulate healthy online food choices
<p>This study examined the effect of a personalized value-based justification in explaining the rationale behind healthy food swaps. Additionally, consumers' willingness to share their personal information with retailers to personalize swap recommendations is explored.</p>
Donor Age and Red Cell Age Contribute to the Variance in Lorrca Indices in Healthy Donors for Next Generation Ektacytometry: A Pilot Study
<p>This dataset refers to the article "Donor Age and Red Cell Age Contribute to the Variance in Lorrca Indices in Healthy Donors for Next Generation Ektacytometry: A Pilot Study" Front. Physiol. 12:639722 2021</p> <p>DOI: 10.3389/fphys.2021.639722</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.