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Lab disease outcomes data evaluating how antibiotic tolerant vs. non-tolerant cell-free supernatant from Pseudomonas aeruginosa affects the interaction between a fungal pathogen (Batrachochytrium dendrobatidis) and amphibian (Rana sylvaticus), 2022.
Microbes living on hosts and in the environment can play a key role in helping hosts to combat pathogens. However, antibiotic-induced alterations to microbial metabolite production could disrupt this dynamic. Here, we investigated whether antibiotic tolerance influences the anti-pathogenic properties of host-associated (living on the host; biofilms) and environmental (living in the soil of water column; planktonic) microbes in vitro and in vivo. For our model host and pathogen, we used the amphibian (Rana sylvatica)-Batrachochytrium dendrobatidis (Bd) system. For our model host-associated (biofilm) and environmental (planktonic) microbes, we used four strains of Pseudomonas aeruginosa that vary in their tolerance to antibiotics and their biofilm-forming capabilities: Planktonic, non-antibiotic tolerant (ΔsagS/VC); Planktonic, antibiotic tolerant (ΔsagS::sagS_L154A); Biofilm, non-antibiotic tolerant (ΔsagS::sagS_D105A); Biofilm, antibiotic tolerant (ΔsagS::sagS). We collected cell-free supernatants (CFS) from each strain to examine the effects of metabolites. We conducted four experiments. In our pathogen-only exposures to test direct effects of metabolites on Bd, we exposed Bd zoospores to each P. aeruginosa CFS at six concentrations. After 11 days of growth, we measured relative abundance of Bd across each treatment. In our host-only exposures to test effects of metabolites on host disease outcomes, we placed R. sylvatica tadpoles in individual units containing each P. aeruginosa CFS. After 48 hours, water was changed into clean well water (no CFS). Bd zoospores were immediately added to each experimental unit following the water change. After 5 days of Bd exposure, we measured snout-vent length (SVL), mass, developmental stage, and Bd quantification in the mouthparts using qPCR for each tadpole. In our host-pathogen exposures to test interactive effects of metabolites on hosts in the presence of the pathogen, we conducted the same experiment as above. However, ins
Silica Nanoparticles Enhance Disease Resistance in Arabidopsis Plants - RAW DATA
<p>These datasets are used to produce the figures/graphs published in our article</p> <p><strong>Silica Nanoparticles Enhance Disease Resistance in <em>Arabidopsis</em> Plants</strong></p> <p>in <em>Nat. Nanotechnol.</em> (2020). <a href="https://doi.org/10.1038/s41565-020-00812-0">https://doi.org/10.1038/s41565-020-00812-0</a></p> <p> </p><p><strong>Correspondence: </strong></p> <p></p> <p>fabienne.schwab@alumni.ethz.ch, Tel: +41 78 736 00 19;</p> <p>m.shetehy@uky.edu, Tel. +41 76 455 56 02</p> <p>Further raw data related to qPCR and microbiology are available upon reasonable request from M.H. El‑Shetehy.</p> <p>Further raw data related to the nanoparticles and plant microscopy are available upon reasonable request by F. Schwab.</p> <p> </p> <p><strong>Abstract</strong></p> <p>In plants, pathogen attack can induce an immune response known as systemic acquired resistance (SAR) that protects against a broad spectrum of pathogens. In the search for safer agrochemicals, silica nanoparticles (SiO<sub>2</sub>‑NPs, food additive E551) have recently been proposed as a new tool. However, initial results are controversial, and the molecular mechanisms of SiO<sub>2</sub>‑NP-induced disease resistance are unknown. Here, we show that SiO<sub>2</sub>‑NPs, as well as soluble orthosilicic acid (Si(OH)<sub>4</sub>), can induce SAR in a dose-dependent manner, that involves the defence hormone salicylic acid. Nanoparticle uptake and action occurred exclusively through stomata (leaf pores facilitating gas exchange) and involved extracellular adsorption in leaf air spaces of the spongy mesophyll. In contrast to treatment with SiO<sub>2</sub>‑NPs, induction of SAR by Si(OH)<sub>4 </sub>was problematic, since high concentrations caused stress. We conclude that SiO<sub>2</sub>‑NPs have the potential to serve as an inexpensive, highly efficient, safe, and sustainable alternative for plant disease protection.</p>
Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls
<p>For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>.</p> <p>Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.</p> <p> </p> <p><strong>DESCRIPTION</strong></p> <p>The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels.</p> <p>The dataset is organized into 5 pickle files:</p> <p>1. <strong>imu_sdata.pickle</strong>: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples.</p> <p>Sample Python code for accessing the acceleration data of a subject</p> <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> <p>2. <strong>imu_gdata.pickle</strong>: Same layout as imu_sdata.pickle but with data ffrom GData subjects.</p> <p>3. <strong>typing_sdata.pickle</strong>: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502.</p> <p>Sample Python code for accessing the typing data of a subject:</p> <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> <p>4. <strong>typing_gdata.pickle</strong>: Same layout as typing_sdata.pickle but with data from GData subjects.</p> <p>5. <strong>subject_metadata.pickle</strong>: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:<br> 'age': Year of birth,<br> 'gender_id': 0 indicates male, 1 indicates female<br> 'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history</p> <p>In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). 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. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><br> </p> <p> </p>
Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping
<h3>TCGA pan-cancer mRNA and DNA data augmented with artificial confounders utilised in "Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping" by Zuqi Li and Sonja Katz (manuscript in preparation).</h3> <p>The following data curation steps were carried out: </p> <ul> <li><strong>Step 1. Download data from TCGA</strong> <ul> <li>R package `TCGAbiolinks`</li> <li>2547 patients (after step 2) with 6 cancer types: <ul> <li>BRCA (731)</li> <li>THCA (408)</li> <li>BLCA (387)</li> <li>LUSC (297)</li> <li>HNSC (412)</li> <li>KIRC (312)</li> </ul> </li> <li>mRNA expression profiles</li> <li>DNAm expression profiles</li> <li>Clinical data: <ul> <li>tumor stage: i, ia, ib, ii, iia, iib, iii, iiia, iiib, iiic, iv, iva, ivb, ivc, x</li> <li>age at diagnosis</li> <li>race: 'white', 'black or african amarican', 'asian', 'american indian or alaska native'</li> <li>gender<br><br></li> </ul> </li> </ul> </li> <li><strong>Step 2. Removal criteria</strong> <ul> <li>Patients with <ul> <li>NA or 'not reported' clinical data</li> <li>race 'american indian or alaska native'</li> <li>tumor stage x</li> </ul> </li> <li>mRNA and DNAm probes with <ul> <li>0 variance across all included patients</li> <li>not shared across all cancer types</li> <li>with missing values<br><br></li> </ul> </li> </ul> </li> <li> <strong>Step 3. Encode clinical vairables and save datasets</strong> <ul> <li>mRNA dataset: 2547 patients x 58,456 mRNAs</li> <li>DNAm dataset: 2547 patients x 232,088 DNAm</li> <li>clinic dataset: 2547 patients x 6 variables<br> 1. patient ID<br> 2. tumor stage: 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4<br> 3. age at diagnosis<br> 4. race: asian(1), black or african amarican(2), white(3)<br> 5. gender: female(0), male(1)<br> 6. cancer type: BRCA(1), THCA(2), BLCA(3), LUSC(4), HNSC(5), KIRC(6)<br> </li> </ul> </li> <li><strong> Step 4. Pre-process the datasets</strong> <ul> <li>mRNA dataset: '<em>TCGA_mRNAs_processed.csv'</em><br> <ul> <li>Take the 2000 mRNAs with highest variance</li> <li>Rescale every feature to [0,1]</li> <li>--> 2547 patients x 2000 mRNAs</li> </ul> </li> <li>DNAm dataset: <em>'TCGA_DNAm_processed.csv'</em><br> <ul> <li>Take the 2000 DNAm with highest variance</li> <li>Rescale every feature to [0,1]</li> <li>--> 2547 patients x 2000 DNAm</li> </ul> </li> <li>clinic dataset:<em> 'TCGA_clinic.csv'<br><br></em></li> </ul> </li> <li><strong>Step 5. Simulate confounders (instructions can be found in Methods section of manuscript)</strong> <ul> <li>Linear confounder: <ul> <li><em>'TCGA_confounder_linear.csv' -</em> linear confounding classes<em><br></em></li> <li><em>'TCGA_DNAm_confounded_linear.csv' </em>- linearly confounded DNAm data<em><br></em></li> <li><em>'TCGA_mRNA2_confounded_linear.csv' </em> - linearly confounded mRNA data<em><br></em></li> </ul> </li> <li>Squared confounder <ul> <li><em>'TCGA_confounder.csv' -</em> squared confounding classes<em><br></em></li> <li><em>'TCGA_DNAm_confounded.csv' </em>- squared confounded DNAm data<em><br></em></li> <li><em>'TCGA_mRNA2_confounded.csv' </em> - squared confounded mRNA data</li> </ul> </li> <li>Categorical confounder <ul> <li><em>'TCGA_confounder_categ2.csv' -</em> categorical confounding classes<em><br></em></li> <li><em>'TCGA_DNAm_confounded_categ2.csv' </em>- categorically confounded DNAm data<em><br></em></li> <li><em>'TCGA_mRNA2_confounded_categ2.csv' </em> - categorically confounded mRNA data</li> </ul> </li> <li>Multiple confounders - combined effect (linear + squared + categorical)<br> <ul> <li><em>'TCGA_confounder_multi.csv' -</em> confounding classes for combined effect<em><br></em></li> <li><em>'TCGA_DNAm_confounded_multi.csv' </em>- DNAm data with combined effect<em><br></em></li> <li><em>'TCGA_mRNA2_confounded_multi.csv' </em> - mRNA data with combined effect</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
Image data of co-localization of IgG and HEV ORF2 protein in a case of hepatitis E-associated kidney disease
<p><span>Image data for a co-localization study of IgG with HEV ORF2 protein in a </span><span>de novo immune complex-mediated glomerulonephritis (GN) case in</span><span> a kidney transplant recipient </span><span>with chronic hepatitis E (Leblond and Helmchen, et al. 2024).<span> </span>Immunofluorescence images are provided for 25 glomeruli at low magnification (20x, 0.227 micron/pixel) and for 16 glomeruli at high magnification (100x, 0.0454 micron/pixel). For each example glomeruli the green channel represents IgG antibody staining with FITC, and the magenta channel represent anti-HEV ORF2 staining using Alexa Fluor 546.</span></p> <p><span>Methods: </span></p> <p><span>Mouse monoclonal antibody clone 1E6 against the HEV ORF2 protein was incubated for 1h at a dilution of 1:125 followed by a mix of Alexa Fluor 546-conjugated goat anti-mouse antibody (Invitrogen BV, A11018) and FITC-conjugated Rabbit anti-Human IgG (Gamma chain, Diagnostic Biosystem, F008) for 1hat a dilution of 1:50. Following automated staining, the slides were hand -washed in distilled H<sub>2</sub>O. Tissue was covered with Vectashield® Antifade Mounting Medium with DAPI (VectorLaboratories, H-1200), covered with a coverslip and stored at 4°C until evaluation.</span></p> <p><span>Immunofluorescence images were acquired with an upright fluorescence microscope (AxioImager.Z2 controlled by ZEN Blue software; 89 North Photofluor LM-75 light source, and Axiocam 503 mono camera; Zeiss, Jena, Germany), equipped with the following objectives: 20x (NA 0.5, Plan-NEOFLUAR), 40x (NA 1.4 oil, Plan-APOCHROMAT), and 100x (NA 1.45 oil, Plan-APOCHROMAT) objectives. This setup provides an excellent spatial resolution (nominally about 200 nm lateral resolution in our study; pixel size was 45.4 nm for 100x objective). High resolution images were taken with the 100x objective using the ApoTome.2 module with deconvolution (grid 5 lp/mm; section thickness 0.7 µm). We used Vysis Abbott Chroma filter sets (Blue: excitation (ex) 335-383 nm, emission (em) 420-470; green: ex 481-507 nm; em 521-551 nm; red: ex 534-556 nm, em 574- 606 nm). Co-localization of IgG and HEV ORF2 staining was quantified using Fiji software (Schindelin et al., 2012) and the JACoP ImageJ plug-in. </span></p>
Wearable data and self reported fatigue scores from a remote observational study in Sjogren's disease, SLE and healthy participants
<p>Fatigue is a subjective, complex, and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. To date, there is no consensus about reliable quantitative assessments of fatigue.</p> <p>We collected observational data for a period of one month from 296 participants (healthy volunteers, Sjogren’s Syndrome, and Systemic Lupus Erythematosus patients) in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity, and sleep daily features, and app-based daily and weekly questions (e.g., pain, mood, general physical activity, and fatigue). When matching both sensor data and PROs, and excluding missing data, the dataset contains data from 183 subjects and 3950 recording days.</p> <p>The analysis of the association of digital data to self-reported fatigue was published at <em><strong>Rao C., et. al. (2023), Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease, Frontiers in Digital Health</strong></em>.</p> <p>Demographics, digital parameters, and other information on this dataset can be found in the aforementioned manuscript and related supplementary material. Details on the data files can be found under README.txt.</p>
Monipar Database: smartwatch movement data to monitor motor competency in subjects with Parkinson's disease
<p>Movement data was collected through smartwatches to monitor motor competence in subjects with Parkinson's Disease (PD). The data set collected for the Monipar study consists of triaxial acceleration data from 21 subjects with PD and 7 healthy control subjects when performing a set of physical exercises while wearing an off-the-shelf smartwatch. Each participant performed the complete set of eight exercises once a week, commonly on the same day and at a similar time. Three Matlab files are provided that contain the raw data of the experimental subgroups: (1) Supervised, (2) Remote, and (3) Healthy control. Additionally, two Matlab files are provided containing the Tremor Labels for selected subjects in the experimental subgroups: (1) Supervised and (2) Remote.</p><p>While the implementation of the experimental protocol for collecting movement data followed a consistent approach for all participants, three distinct experimental subgroups were established:</p><p>Remote group: This subgroup consisted of individuals diagnosed with Parkinson's disease (PD) who completed the experimental protocol at their regular PD association.</p><p>Supervised group: This subgroup comprised PD patients who underwent the experimental protocol under circumstances similar to the remote group. Additionally, clinical scoring (MDS-UPDRS) is reported for this group in the file "MONIPAR SUBJECTS DATA.xlsx"</p><p>Healthy control group: This subgroup consisted of healthy participants who performed exercises under the supervision of research project team members.</p><p>Data was collected using a sample rate of 50Hz and expressed in m/s^2.</p><p>Check the "Monipar_README.txt" file for details about this dataset. Further details are contained in the following reference -- if you use this dataset, please cite:</p><p>Sigcha, L., Polvorinos-Fernández, C., Costa, N., Costa, S., Arezes, P., Gago, M., ... & Pavón, I. "<strong>Monipar: Movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones</strong>". <i>Frontiers in Neurology</i>, <i>14</i>, 1326640. <a href="https://doi.org/10.3389/fneur.2023.1326640">https://doi.org/10.3389/fneur.2023.1326640</a></p><p>References:</p><p>Sigcha, L. et al. (2022). Bradykinesia Detection in Parkinson's Disease Using Smartwatches' Inertial Sensors and Deep Learning Methods. Sensors 11, 3879</p><p>Sigcha, L. et al. (2021). Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks. Sensors 21, 291.</p><p><strong>Funding:</strong></p><p>This research was funded by the following projects:</p><p>(1) "Tecnologías Capacitadoras para la Asistencia, Seguimiento y Rehabilitación de Pacientes con Enfermedad de Parkinson". Centro Internacional sobre el envejecimiento, CENIE (código 0348_CIE_6_E) Interreg V-A España-Portugal (POCTEP).</p><p>(2) FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.</p>
Tree Health Conditions (mortality, damage, disease, bark beetles) in Fuel Reduction Treatments Located Near Communities in Interior Alaska and the Cook Inlet Region of Alaska - Observations from July-August 2023
This dataset contains tree-, transect-, and site-level observations of forest stands at sites that received a fuel reduction treatment. Tree-level observations include species, diameter, living status, damage, disease, and bark beetle presence. Transect-level observations include level of coarse woody debris and bark beetle presence. Sites are categorized by region (recent/ongoing spruce beetle oubreak or endemic spruce beetle population levels) and treatment type (hand-thinned or mechanincally felled and masticated). These observations are from July-August 2023. Sites are located near communities in Interior Alaska and the Cook Inlet Region.
Effects of crown gall disease on natural microbiota of Vitis vinifera - genome annotations
<p>Young grapevines (Vitis vinifera) frequently die due to the crown gall (CG) disease induced by the plant pathogen Allorhizobium vitis (Rhizobiaceae). Virulent members of A. vitis harbour a tumor-inducing (Ti) plasmid and cause formation of CGs due to genes encoded on the T-DNA. Expression of the oncogenes by transformed host cells induce cell proliferation, metabolic and physiological changes. The CG produces opines uncommon to plants, which provide an important nutrient source for A. vitis harbouring opine catabolism enzymes. CGs host a defined bacterial community and the mechanisms establishing a CG-specific bacterial community are currently unknown. Thus, we were interested in whether genes homologous to those of the Ti-plasmid coexist in the genomes of the microbial species coexisting in CGs. We isolated eight bacterial strains from grapevine CGs, sequenced their genomes and tested their virulence and opine utilization ability in bioassays. In addition, the eight genome sequences were aligned to the sequences of a Ti-plasmid and seven published bacterial genomes, including closely related plant associated bacteria but not from CGs. Homologous genes for virulence and opine anabolism were only present in the virulent Rhizobiaceae. By contrast, homologs of the opine catabolism genes were present in all strains including the non-virulent members of the Rhizobiaceae and non-Rhizobiaceae, indicating horizontal gene transfer of the opine degradation cluster from virulent to non-virulent strains. These results along with those of the opine utilization assay support the important role of opine utilization for co-colonization of virulent and non-virulent bacteria in CGs, thereby shaping the CG community.</p> <p>This dataset contains the prokka annotations of the genomes as used in "Opportunistic bacteria of grapevine crown galls are equipped with the genomic repertoire for opine utilization"</p>
An integrated polygenic tool substantially enhances coronary artery disease prediction
<p>Summary-level CAD GWAS data generated by Genomics plc as presented in:</p> <p>Riveros-Mckay F. et al. An integrated polygenic tool substantially enhances coronary artery disease prediction. Circulation: Genomics and Precision Medicine (in press). </p> <p>If you have any questions or comments regarding these files, please contact Genomics plc at research@genomicsplc.com</p> <p> </p> <p>NOTES<br> -----------------------------<br> These analyses were carried out using the full UK Biobank imputation data release (v3b). Analyses were restricted to a subset of UK Biobank, described as “Group I” in the published paper. Group I, “no PCE/QRISK3 available”, included 114,196 European-ancestry individuals with missing data that prevented PCE or QRISK3 calculation.</p> <p>CAD case phenotypes were defined as described in the “Phenotype definitions” section of the paper’s Supplementary Materials, using both prevalent (pre-baseline) and incident (post-baseline) events.</p> <p>All analyses included Age at assessment, sex, genotyping chip, and 10 principal components as covariates. </p> <p>We used plink2.0 logistic regression. For chromosome X variants males were treated as having 0 or 2 alternative alleles. </p> <p>The results are not adjusted for genomic control.</p> <p> </p> <p>DATA FILE CONTENT DESCRIPTION<br> -----------------------------<br> cpra Variant ID in ‘CPRA’ format. Position reflects position in b37. <br> chrom Chromosome<br> pos Position in base pairs (b37, 1-based)<br> alt Alternative allele (effect allele)<br> beta Effect size (log odds ratio)<br> standard_error Standard error of beta <br> minus_log10_p Minus log(base 10) of P-value<br> ref Reference allele (non-effect allele)<br> ncase Number of cases<br> ncontrol Number of controls</p>
Transcriptomic atlas reveals organ-specific disease tolerance in sickle cell mice: dataset bone marrow HbAA mice injected or not with heme
<p>The objective of this experiment was to explore the transcriptome of the HbSS Townes mouse model of sickle cell disease. Townes model mice carry several human hemoglobin knock-in genes replacing the endogenous mouse genes and may be useful in studying sickle cell disease. All mice were genotyped, age- and sex-matched littermates. All HbAA (control, normal human hemoglobin) vs HbSS (sickle cell disease, mutated human hemoglobin) mice were used for experimentations at 6-8 weeks of age, to limit intra-group heterogeneity. Hemin (Ferriprotoporphyrin IX) was purchased from Frontiers Scientific and injected intravenously (iv.) in a retroorbital sinus at a concentration of 24 µmol/kg. Control mice received PBS instead. Mice were anesthetized with isoflurane 2-3% for injections, blood collection and sacrifice. All mice were sacrificed by cervical dislocation, 4 hours after injection.</p> <p>This dataset contains the results of the HbAA mice with and without heme.</p> <p>The corresponding HbSS mice with and without heme are deposited under number 10.5281/zenodo.10962782</p> <p>Bone marrow RNA was extracted by Macherey Nagel kit, according to the manufacturer’s instructions. The quality and quantity of mRNA were evaluated using a 2100<br>bioanalyzer with TNA 6000 NanoKits (all Agilent Technologies, Palo Alto, CA, USA). RNA Integrity Numbers superior to 7 were eligible for subsequent reverse transcription into cDNA. RNAseq was performed at the GenomIC plateform Cochin Institute INSERM U1016. After RNA extraction, RNA quality (RNA integrity number) was estimated. 1μg of high-quality total RNA sample (RIN &gt;7) was processed to build up the libraries, using TruSeq Stranded mRNA kit (Illumina) according to manufacturer instructions. Briefly, purified poly-A containing mRNA molecules were fragmented and reverse-transcribed using random primers. Replacement of dTTP by dUTP during second strand synthesis allowed us to achieve strand specificity. Addition of a single A base to the cDNA was followed by ligation of Illumina adapters.<br>Libraries were quantified by qPCR using KAPA Library Quantification Kits for Illumina Libraries (KapaBiosystems, Wilmington, MA). Library profiles were assessed using DNA High Sensitivity LabChip kits on an Agilent Bioanalyzer. Libraries were sequenced on an Illumina Nextseq 500 instrument using 75 base-lengths read V2 chemistry in a paired-end mode. After sequencing, primary analysis based on AOZAN software (ENS, Paris), was applied to demultiplex and control the quality of the raw data (based of FastQC modules / version 0.11.5).</p> <p>The dataset here represents 4 groups of mice, 4 mice per group as follows: HbAA PBS, HbAA heme, HbSS PBS, HbSS heme. </p> <p> </p>
Inter-Chemical Correlation results for the study: HHEARx2018-2532 (Environmental Toxins in Early Life: Shaping Health and Disease in Childhood)
Title: Environmental Toxins in Early Life: Shaping Health and Disease in Childhood <br>Species: Homo sapiens <br>Number of samples: 1147 <br>Number of named analytes: 48 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=69 <br>
Inter-Chemical Correlation results for the study: HHEARx2017-1593 (Role of environmental toxicants in modulating disease severity in children with NAFLD)
Title: Role of environmental toxicants in modulating disease severity in children with NAFLD <br>Species: Homo sapiens <br>Number of samples: 436 <br>Number of named analytes: 7 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=30 <br>
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity: Associated data
<p>This dataset contains raw and processed data from the COvid-19 Multi-omics Blood ATlas (COMBAT) consortium. Data are divided into 26 datasets representing anonymised raw and processed data from deep immune phenotyping of peripheral blood from COVID-19 patients. </p> <p>In addition to the data listed below, some datasets are available through other repositories: </p> <ul> <li> <p>Proteomics data (CBD-KEY-PROTEOMICS) is available at PRIDE</p> <ul> <li> <p>Accession number: PDX023175</p> </li> <li> <p>Contact: Roman Fischer</p> </li> </ul> </li> </ul> <ul> <li> <p>Genetic data and detailed clinical information are available via a data access agreement through EGA</p> <ul> <li> <p>Study accession: EGAS00001005493 </p> </li> </ul> </li> </ul> <p>For further information regarding specific datasets, please contact the individuals listed in Dataset_descriptions.pdf through <a href="mailto:contact@combat.ox.ac.uk">contact@combat.ox.ac.uk</a>. </p>
Project Tycho Level 2 data: Counts of multiple diseases reported in UNITED STATES OF AMERICA, 1888-2014
Project Tycho data include counts of infectious disease cases or deaths per time interval. A count is equivalent to a data point.<p></p><p>Project Tycho level 2 version 1.1.0 data include data counts that have been filtered from the raw data to render standardized data that can be used immediately for analysis. All level 2 data were originally reported in a consistent format and have not been transformed into a standard format by Project Tycho staff, except for smallpox records that included repeated counts for the same location and week, but sometimes with different numbers. These duplicate smallpox records have been averaged into one count for each location and week. Level 2 data include counts for a wide variety of diseases and locations for varying time periods. Because we removed data in an inconsistent format from level 2 data, counts may be missing for certain diseases, locations, or years. For the most complete collection of standardized data, we encourage users to use Project Tycho version 2.0 datasets.</p><p>More detailed methods and additional information about the origin of Projec Tycho level 2 version 1.1.0 data can be found in our original publication in the New England Journal of Medicine: <a href="http://www.nejm.org/doi/full/10.1056/NEJMms1215400">http://www.nejm.org/doi/full/10.1056/NEJMms1215400</a></p><p>Level 2 version 1.1.0 data is represented in a CSV file with 11 columns:</p><ul><li>epi_week: a six digit number that represents the year and epidemiological week for which disease cases or deaths were reported (yyyyww)</li><li>country: a two digit country abbreviation, only including "US" in version 1.1.0</li><li>state: the two digit postal code state abbreviation that represents the state for which a count has been reported</li><li>loc: the name of a state or city for which a count has been reported, capitalized</li><li>loc_type: the type of location (STATE or CITY) for which a count has been reported</li><li>disease: the disease for which a count has been reported, in all capitals</li><li>event: an indicator representing the disease outcome reported, including "CASES" or "DEATHS"</li><li>number: the reported number of cases or deaths</li><li>from_date: the start date of the time interval for which a count was reported, as yyyy-mm-dd</li><li>to_date: the end date of the time interval for which a count was reported, as yyyy-mm-dd</li><li>url: the URL of the source document from which the count was obtained</li></ul><p></p>
Project Tycho Level 1 data: Counts of multiple diseases reported in UNITED STATES OF AMERICA, 1916-2011
<p>Project Tycho data include counts of infectious disease cases or deaths per time interval. A count is equivalent to a data point. Project Tycho level 1 data include data counts that have been standardized for a specific, published, analysis. Standardization of level 1 data included representing various types of data counts into a common format and excluding data counts that are not required for the intended analysis. In addition, external data such as population data may have been integrated with disease data to derive rates or for other applications.</p><p>Version 1.0.0 of level 1 data includes counts at the state level for smallpox, polio, measles, mumps, rubella, hepatitis A, and whooping cough and at the city level for diphtheria. The time period of data varies per disease somewhere between 1916 and 2011. This version includes cases as well as incidence rates per 100,000 population based on historical population estimates. These data have been used by investigators at the University of Pittsburgh to estimate the impact of vaccination programs in the United States, published in the New England Journal of Medicine: <a href="http://www.nejm.org/doi/full/10.1056/NEJMms1215400">http://www.nejm.org/doi/full/10.1056/NEJMms1215400</a>. See this paper for additional methods and detail about the origin of level 1 version 1.0.0 data.</p><p>Level 1 version 1.0.0 data is represented in a CSV file with 7 columns:</p><ul><li>epi_week: a six digit number that represents the year and epidemiological week for which disease cases or deaths were reported (yyyyww)</li><li>state: the two digit postal code state abbreviation that represents the state for which a count has been reported</li><li>loc: the name of a state or city for which a count has been reported, capitalized</li><li>loc_type: the type of location (STATE or CITY) for which a count has been reported</li><li>disease: the disease for which a count has been reported: HEPATITIS A, MEASLES, MUMPS, PERTUSSIS, POLIO, RUBELLA, SMALLPOX, or DIPHTHERIA</li><li>cases: the number of cases reported for the specified disease, epidemiological week, and location</li><li>incidence_per_100000: the number of cases per 100,000 people, computed using historical population counts for cities and states as reported by the US Census Bureau</li></ul><p></p>
Supplemental Material to "Tenacity of Animal Disease Viruses on Wood Surfaces Relevant to Animal Husbandry"
<p>Data set for individual titre reduction of viruses over a period of time in multiple experiments.</p>
Rare Disease analysis in Mondo
<p>To answer the question of 'How many rare diseases are there?' we analyzed terms in Mondo to get a total count of Rare Diseases as defined in Mondo Disease Ontology (Mondo).</p> <p> </p> <p>Methods</p> <p>This analysis was performed on the <a href="http://purl.obolibrary.org/obo/mondo/releases/2019-09-30/mondo.json">Mondo 2019-09-30 release</a>.</p> <p><strong>1. Get all 'Disease' terms from Mondo</strong></p> <p>First we get all the terms in Mondo that are a descendants of <code>MONDO:0000001 'Disease'</code>.</p> <p>There are <code>21633</code> Mondo disease terms.</p> <p><strong>2. Filter terms that are descendants of 'disease susceptibility'</strong></p> <p>We then filter out terms that are descendants of <code>MONDO:0042489 'disease susceptibility'</code>, to avoid counting ambiguous terms that are related to disease susceptibility and not the actual disease itself.</p> <p>This gives us a list of <code>21563</code> Mondo rare disease terms.</p> <p><strong>3. Identify terms that are 'rare'</strong></p> <p>Any disease term in Mondo is considered rare if the term, or its ancestor, has modifier <code>MONDO:0021136 'Rare'</code> in the ontology.</p> <p>There are <code>12914</code> Mondo rare disease terms.</p> <p><strong>4. Consider terms in 'gard_rare' subset</strong></p> <p>There are <code>3176</code> Mondo disease terms that are in <code>gard_rare</code> subset which contains Mondo terms that are yet to be treated as 'rare'.</p> <p>We add these terms to our set of Mondo rare disease terms.</p> <p>This increases the Mondo rare disease term count to <code>13866</code>.</p> <p>But for this analysis, we are interested in terms that are both rare and are leaf nodes in the ontology.</p> <p>After considering only leaf nodes, we get <code>10394</code> as the final count of Mondo rare disease terms.</p> <p> </p> <p>Results</p> <p>all-mondo-disease-terms.tsv: As part of our analysis, we generated a TSV containing 21633 Mondo disease terms, each with annotations that signifies whether the term is a rare disease term and whether that term is a leaf node in the ontology.</p>
IMU data captured unobtrusively and in-the-wild by Parkinson's disease patients and healthy controls
<p><strong>DATASET</strong></p> <p>The dataset contains IMU signals captured in-the-wild via the accelerometer sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). A group of 31 PD patients and 14 Healthy controls contributed accelerometer data using their personal smartphones, for a period spanning many months.Tri-axial acceleration values were recorded automatically whenevera phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article.</p> <p>The data is presented as a list of python dictionaries, stored in a pickle file. Each dictionary in the list, corresponds to one subject and containes the following fields:</p> <p>1. subject_id: scalar<br> A numerical value that uniquely identifies the subject.</p> <p>2. subject_sessions: list of numpy.array<br> A list of numpy arrays of shape (N, 4) that contains the tri-axial accelerometer sessions that the subject contributed. N denotes the total length of the session in samples (which varies from session to session) Column 0 of the array contains the timestamps of the accelerometer samples. Columns 1-3 contain the acceleration values across the x,y,z directions.</p> <p>3. session_datetimes: list of datetime objects <br> A list of datetime objects that denote the capturing date and time of the corresponding entries in the subject_sessions field.</p> <p>4. annotation: dict<br> A dictionary containing the following tremor-related annotation values:<br> * updrs16: scalar int<br> The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject.</p> <p>* updrs20_right: scalar int in range [0, 4]<br> The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs20_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* updrs21_right: scalar int in range [0, 4]<br> The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs21_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* sp_expert: scalar int in range [0, 1]<br> A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article.<br> A '1' value indicates that the subject has tremor.<br> A '0' value indicates that the subject doesn't have tremor.</p> <p>* pd_status: scalar int in range [0, 1]<br> A '1' value indicates that the subject is a PD patient.<br> A '0' value indicates that the subject is a Healthy Control</p> <p>Note: Each annotation value refers to the subject as a whole, and not in any one session.<br> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). 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. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</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>
Exploring the Impact of Physiotherapy on Health Outcomes in Elderly Patients with Chronic Diseases: A Cross-Sectional Analysis
<p>In this cross-sectional analysis, we investigate the transformative impact of physiotherapy on health outcomes among elderly patients grappling with chronic diseases. Physiotherapy emerges as a pivotal intervention, offering multifaceted benefits that extend beyond mere symptom management. Through tailored exercises, mobility enhancements, and targeted pain management strategies, physiotherapy not only mitigates physical limitations but also fosters greater independence and quality of life. By examining a diverse cohort of elderly individuals diagnosed with chronic conditions such as osteoarthritis and cardiovascular diseases, this study underscores the profound role of physiotherapy in promoting functional mobility, reducing healthcare burdens, and enhancing overall well-being among this vulnerable population."</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.