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11,745 results for “Disorder”
Molecular dynamics simulations of intrinsically disordered proteins p53TAD and Pup
<p>Intrinsically disordered proteins (IDPs) are highly dynamic systems that play an important role in cell signaling processes and their misfunction often causes human disease. Proper understanding of IDP function not only requires the realistic characterization of their three-dimensional conformational ensembles at atomic-level resolution but also of the time scales of interconversion between their conformational substates. Large sets of experimental data are often used in combination with molecular modeling to restrain or bias models to improve agreement with experiment. It is shown here for the N-terminal transactivation domain of p53 (p53TAD) and Pup how the latest advancements in molecular dynamics (MD) simulations methodology produces native conformational ensembles by combining replica exchange with series of microsecond MD simulations. They closely reproduce experimental data at the global conformational ensemble level, in terms of the distribution properties of the radius of gyration tensor, and at the local level, in terms of NMR properties including <sup>15</sup>N spin relaxation, without the need for reweighting. The IDP ensembles were analyzed by graph theory to identify dominant inter-residue contact clusters and characteristic amino-acid contact propensities. These findings indicate that modern MD force fields with residue-specific backbone potentials can produce highly realistic IDP ensembles sampling a hierarchy of nano- and picosecond time scales providing new insights into their biological function.</p>
Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study
<p>We present a methodology for subtyping of persons with a common clinical symptom complex by integrating heterogeneous continuous and categorical data. We illustrate it by clustering women with lower urinary tract symptoms (LUTS), who represent a heterogeneous cohort with overlapping symptoms and multifactorial etiology. Data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN), a multi-center observational study, included self-reported urinary and non-urinary symptoms, bladder diaries, and physical examination data for 545 women. Heterogeneity in these multidimensional data required thorough and non-trivial preprocessing, including scaling by controls and weighting to mitigate data redundancy, while the various data types (continuous and categorical) required novel methodology using a weighted Tanimoto indices approach. Data domains only available on a subset of the cohort were integrated using a semi-supervised clustering approach. Novel contrast criterion for determination of the optimal number of clusters in consensus clustering was introduced and compared with existing criteria. Distinctiveness of the clusters was confirmed by using multiple criteria for cluster quality, and by testing for significantly different variables in pairwise comparisons of the clusters. Cluster dynamics were explored by analyzing longitudinal data at 3- and 12-month follow-up. Five clusters of women with LUTS were identified using the developed methodology. None of the clusters could be characterized by a single symptom, but rather by a distinct combination of symptoms with various levels of severity. Targeted proteomics of serum samples demonstrated that differentially abundant proteins and affected pathways are different across the clusters. The clinical relevance of the identified clusters is discussed and compared with the current conventional approaches to the evaluation of LUTS patients. The rationale and thought process are described for the selection of procedures for data preprocessing, clustering, and cluster evaluation. Suggestions are provided for minimum reporting requirements in publications utilizing clustering methodology with multiple heterogeneous data domains.</p>
Data from: The burden of T. solium cysticercosis and selected neuropsychiatric disorders in Mocuba district, Zambézia province, Mozambique
<p>This dataset is from a study that aimed to evaluate the association of the disease with selected neuropsychiatric disorders and/or symptoms (chronic headache, epileptic seizures/epilepsy and psychosis) in Mocuba district, Mozambique.</p>
Parents' Evaluation of Interaction between Robots and Children with Neurodevelopmental Disorders
<p>This presentation concerns the paper:</p> <p>Andreeva, A., Lekova, A., Simonska, M., Tanev, T. (2022). Parents’ Evaluation of Interaction Between Robots and Children with Neurodevelopmental Disorders. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds) Smart Education and e-Learning - Smart Pedagogy. SEEL-22 2022. Smart Innovation, Systems and Technologies, vol 305. Springer.</p> <p>It presents the evaluation of child-robot interaction in play-like structured speech and language therapy for children with neurodevelopmental disorders. It is organized like a group session. Participants are a speech and language therapist, a child and two robots that assist the therapeutic session: the humanoid robot NAO and its “friend” the emotion-expressive robot EMOSAN. Two engineers who developed and deployed the technical scenarios observe the process and control the robots. Researchers developed an online questionnaire for evaluation of interaction between the child and the robots. Parents of involved children with neurodevelopmental disorders stay in the room and they observe the robot-assisted session. Results show that children are interested in playing and learning with robots. There is a correlation between the type of children’s neurodevelopmental disorder and the use of verbal communication in interaction. According to parents’ answers of the questionnaire, most of the children liked to interact with robots during the play-like structured activities in the speech and language therapy session.</p>
Data from: Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders
<p>This dataset contains supporting data for the publication: Boudriot, E., Schworm, B., Slapakova, L. <em>et al.</em> Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders. <em>Eur Arch Psychiatry Clin Neurosci</em> (2022). https://doi.org/10.1007/s00406-022-01455-z</p> <p> </p>
Universal suppression of superfluid weight by non-magnetic disorder in s-wave superconductors independent of quantum geometry and band dispersion
<p>Motivated by the experimental progress in controlling the properties of the energy bands in superconductors, significant theoretical efforts have been devoted to study the effect of the quantum geometry and the flatness of the dispersion on the superfluid weight. In conventional superconductors, where the energy bands are wide and the Fermi energy is large, the contribution due to the quantum geometry is negligible, but in the opposite limit of flat-band superconductors the superfluid weight originates purely from the quantum geometry of Bloch wave functions.</p> <p>Here, we study how the energy band dispersion and the quantum geometry affect the disorder-induced suppression of the superfluid weight. In particular, we consider non-magnetic disorder and s-wave superconductivity. Surprisingly, we find that the disorder-dependence of the superfluid weight is universal across a variety of models, and independent of the quantum geometry and the flatness of the dispersion. Our results suggest that a flat-band superconductor is as resilient to disorder as a conventional superconductor.</p> <p>The provided repository contains all code and data to reproduce the results of this study.</p>
Trajectories and Code from "Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states" Zhu et al. 2022
<p>Trajectories, GROMACS input files, and analysis code from the manuscript "Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states" Zhu et al. 2022 (Nature Communications, In Press)</p> <p>https://www.biorxiv.org/content/10.1101/2021.12.23.474012v1.abstract</p>
Elution profiles and protein interaction data accompanying "Ancient eukaryotic protein interactions illuminate modern genetic disorders"
<div> <p> </p> <table> <tbody> <tr> <td> <h2><strong>DESCRIPTION</strong></h2> </td> <td> <h2><strong>FILENAME</strong></h2> </td> <td> <h2><strong>LOCATION</strong></h2> </td> </tr> <tr> <td> <p>LECA 10K OG set</p> </td> <td> <p>leca_ogs_annotated.xlsx</p> </td> <td> <p>Paper, Table S1</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Summary of biological resources</p> </td> <td> <p>resource_summary.xlsx</p> </td> <td> <p>Paper, Table S2</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>LECA interactome (complexes)</p> </td> <td> <p>leca_ppis_fdr10_clustered_annotated.xlsx</p> </td> <td> <p>Paper, Table S3</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS - ref proteomes</p> </td> <td> <p>cfms_ref_proteomes.xlsx</p> </td> <td> <p>Paper, Table S4</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>ML - top algorithms</p> </td> <td> <p>tpot_top_algorithms.xlsx</p> </td> <td> <p>Paper, Table S5</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>LECA interactome (pairwise)</p> </td> <td> <p>leca_ppis_fdr10_pairwise.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>UniProt Subcellular Localization IDs</p> </td> <td> <p>uniprot_localization_codes.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Dollo parsimony - ref proteomes</p> </td> <td> <p>dollo_parsimony_ref_proteomes.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Dollo parsimony - input trait matrix</p> </td> <td> <p>dollo_parsimony_count_matrix.tsv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS - raw elution profiles</p> </td> <td> <p>amorphea_raw_elution_vectors.csv</p> <p>excavata_raw_elution_vectors.csv</p> <p>tsar_raw_elution_vectors.csv</p> <p>archaeplastida_raw_elution_vectors.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS - normalized elution profiles</p> </td> <td> <p>amorphea_norm_elution_vectors.csv</p> <p>excavata_norm_elution_vectors.csv</p> <p>tsar_norm_elution_vectors.csv</p> <p>archaeplastida_norm_elution_vectors.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS/APMS - complete feature matrix</p> </td> <td> <p>feature_matrix.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>ML - top features</p> </td> <td> <p>linearsvc_top_100_features.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>OMIM disease propagation, statistics</p> </td> <td> <p>omim_disease_propagation_stats.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>OMIM disease propagation, top 20 hits per disease</p> </td> <td> <p>omim_disease_propagation_top20hits_per_disease.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Curated OMIM gene-disease relationships for LECA OGs</p> </td> <td> <p>omim_disease_network.tsv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td>Curated OMIM gene-disease relationships for human UniProt IDs</td> <td>omim_disease_groups.csv</td> <td>Zenodo</td> </tr> </tbody> </table> </div> <p> </p>
AProtocol for Managing Orthodontic Complications in Patients with Thalassemia and Haemoglobin Disorder; Article Review
<p>Hemoglobinopathies are known as haemoglobin production disorders. Familial disorders caused by thalassemia or sickle cell anaemia are autosomal recessive disorders affecting haemoglobin production and structure. Objectives:This article review aimed to clarify protocols for managing orthodontic complications in patients with thalassemia and haemoglobin disorder. The article explains how to deal with common dental and orthodontic issues that patients with these conditions may experience.It provides guidance on diagnosing and treating these issues to ensure the best possible outcomes for these patients. Conclusion:Hemoglobinopathies can cause dental diseases, which can be particularly concerning for children with this condition. While specialists are responsible for treating dental diseases, prevention is the best approach. Physicians with adequate knowledge of the diseases can address this issue safely and effectively. Paying attention to this matter is essential to ensure the best possible patient outcomes.</p>
Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface
<h3>Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface</h3> <p>We ran simulations for four different systems:</p> <ul> <li>WT: A1-LCD WT (N=137), wild-type (WT) sequence of the low-complexity domain (LCD) of the heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), with electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T260: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T290: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=290K</li> <li>HP: homopolymer consisting of prolines (N=137), at temperature T=550</li> </ul> <p>For every system, we ran five independent simulations over 5µs (1000 frames) and used the last 900 frames (4.5µs) for our analysis.</p> <p>This data repository consists of<br> (1) folders containing the data for every seperate run (*_i, i=1,2,3,4,5) in simulation units<br> (2) folders containing the averaged data of all five runs (*_AVG), converted to SI units<br> (3) a droplet folder, containing the data (square radius of gyration and asphericity) for the whole droplet (for all four systems, all five runs)<br>Units are also clarified in each file's header.</p> <p>The simulation units can be converted to SI units via:</p> <ul> <li>Distance: D = 0.45nm</li> <li>Mass: M = 57.05amu</li> <li>Energy: epsilon = 0.2 kcal/mol</li> </ul> <p> </p> <p>Details for (1) and (2):<br>Each folder (*_i, i=1,2,3,4,5, and *_AVG) contains the following subfolders and files:</p> <p><strong>Ree:</strong></p> <ul> <li>distribCos2_all.dat: distribution of cos^2(θ_{ee}) of the whole chains, where θ_{ee} is the angle between the polymer's center r_c and the chain’s end-to-end vector Ree [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_{ee,s}) of segment seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_{ee,s}) of all segments seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos.dat: distribution of the polymer end positions (whole chains) [Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos_segment_i.dat: distribution of the polymer segment end positions of seg_i</li> <li>distribPolymerEndPos_segment_all.dat: distribution of the polymer segment end positions of all segments</li> <li>distribPolymerRee2_all.dat: distribution of Ree^2 (whole chains), binned via polymer center position r_c</li> <li>distribPolymerRee_segment_i.dat: distribution of Ree^2 of segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerRee_segments_all.dat: distribution of Ree^2 of all segments, binned via segment center position r_{c,s}</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all polymer segments, binned via segment center position r_{c,s}</li> </ul> <p><strong>Rg:</strong></p> <ul> <li>distribCos2_all.dat: distribution of cos^2(θ) of the whole chains, where θ is the angle between the polymer's center r_c and the eigenvector belonging to the largest eigenvalue of the chain’s gyration tensor [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_s) of segment seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_s) of all segments seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribMonomerRg_all.dat: distribution of monomer weighted Rg^2 (whole chains), referred to as R_{g,mono}^2 (following Farag et. al) [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_all.dat: distribution of Rg^2 (whole chains), binned via polymer center position r_c [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_segment_i.dat: distribution of Rg^2 of segment seg_i, referred to as R_{g,s}^2, binned via segment center position r_{c,s} [Fig. S4a, Fig. S7a, Fig. S10a, Fig. S13a]</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all segments, binned via segment center position r_{c,s}</li> </ul> <p><strong>resDist:</strong></p> <ul> <li>distribPolymerRee2_base_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRee2_center_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRg2_base_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> </ul> <p>distribPolymerRg2_center_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14] </p> <p> </p> <p>Details for (3):<br>The folder '<strong>droplet</strong>' contains four system folders (HP, WT, WT_noEL_T260, WT_noEL_T290). Each of those folders contains the following files:</p> <ul> <li>runX_cluster_Rg2_Rg2Normal_kappa2.dat: for every run X, one finds the time evolution (in simulation units, with 1e8 timesteps = 1µs) of the square radius of gyration Rg2 of the full droplet, its x-, y- and z-components, its three eigenvalues and the droplet asphericity A (referred to as kappa2 in the header) [Fig.S1c, Fig.S1d]</li> <li>AVG_cluster_Rg2_Rg2Normal_kappa2.dat: average of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> <li>STD_cluster_Rg2_Rg2Normal_kappa2.dat: standard deviation of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> </ul>
Fig. 1 in Extinction Disorders The Species Composition Of Metacommunities
Fig. 1. Changes of nestedness in relation to the exclusion of the rarest species from the metacommunity. A = orthopterans, B = butterflies, C = beetles, D = birds, D(st) is the standardized difference between the average temperature of 100 random matrices and the actual matrix temperature, divided by the standard deviation of the random matrices. Triangles indicate non-significant nestedness
Video-Audio Neural Network Ensemble For Comprehensive Screening Of Autism Spectrum Disorder in Young Children (Openpose ADOS Dataset)
<p>Here, we share a de-identify subsample of the data used in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308388">research article</a>, that will allow interested scientists to test the <a href="https://github.com/AutismBrainBehavior/Video-Neural-Network-ASD-screening">shared code</a>, as well as, develop alternatives for achieving better prediction accuracy. We have prepared a subsample of pose estimation videos for the first 10 minutes of ADOS examination videos for each of the 160 children including in the current study (80 ASD and 80 TD, 80 Training set and 80 Testing set).</p> <p>With this subset of the full dataset, our trained model achieved an accuracy of 68.75% over 80 videos (40 ASD & 40 TD) by training the Visual Geometry Group 16 Long short term memory recurrent neural network (VGG16 LSTM RNN) over 80 training videos (40 ASD & 40 TD) at 64 batch size and 120 epochs.</p>
Normalized and batch-corrected concentration of 43 immune markers from 248 subjects with stress-related mental disorders and 36 healthy controls
<h1>Abstract</h1> <p>In a subset of patients with mental disorders, such as depression, low-grade inflammation and altered immune marker concentrations are observed. However, these immune alterations are often assessed by only one data type and small markers panels. Here, we used a transdiagnostic approach and combined data from two cohorts to define subgroups of depression symptoms across the diagnostic spectrum through a large-scale multi-omics clustering approach in 237 individuals. The method incorporated age, body mass index (BMI), 43 plasma immune markers and RNA-seq data from peripheral mononuclear blood cells (PBMCs). Our initial clustering revealed four clusters, including two immune-related depression symptom clusters characterized by elevated BMI, higher depression severity and elevated levels of immune markers such as interleukin-1 receptor antagonist (IL-1RA), C-reactive protein (CRP) and C-C motif chemokine 2 (CCL2 or MCP-1). In contrast, the RNA-seq data mostly differentiated a cluster with low depression severity, enriched in brain related gene sets. This cluster was also distinguished by electrocardiography data, while structural imaging data revealed differences in ventricle volumes across the clusters. Incorporating predicted cell type proportions into the clustering resulted in three clusters, with one showing elevated immune marker concentrations. The cell type proportion and genes related to cell types were most pronounced in an intermediate depression symptoms cluster, suggesting that RNA-seq and immune markers measure different aspects of immune dysregulation. Lastly, we found a dysregulation of the SERPINF1/VEGF-A pathway that was specific to dendritic cells by integrating immune marker and RNA-seq data. This shows the advantages of combining different data modalities and highlights possible markers for further stratification research of depression symptoms.</p> <h1>Methods</h1> <p>The normalized and batch-corrected concentration of 43 immune markers from 237 subjects with stress-related mental disorders and 36 healthy controls was determined in plasma. This data was used in the initial analysis. Additionally, the same measurements are provided for 11 subjects with stress-related mental disorders used in a replication analysis.</p> <p>- blood was collected in the morning under fasted conditions and plasma stored at -80°C until further processing<br>- samples were randomized into 96 well plates<br>- immune marker concentration was measured with the Meso Scale Diagnostics V-PLEX Human Biomarker 54-Plex Kit and the MESO QuickPlex SQ 120 imager according to the manufacturer's instructions<br>- additionally, high-sensitivity C-reactive protein (Tecan Group Ltd.), cortisol (Tecan Group Ltd.), interleukin (IL)-6 (Thermo Fisher Scientific), IL-6 soluble receptor (Thermo Fisher Scientific) and IL-13 (Thermo Fisher Scientific) was measured via ELISA according to the manufacturer's instructions<br>- values below the detection limit in markers measured with ELISA were set to zero and values above the detection limit to the upper limit<br>- the data was quantile-normalized (values were ranked and mapped to the quantiles of a standard normal distribution)<br>- the normalized concentration was corrected for the biobank storage position (batch_variable) via a linear model and the residuals reported as the concentration</p> <p>The following markers were measured:<br>fibroblast growth factor 2 (FGF2 or bFGF), cortisol, C-C motif chemokine 11 (CCL11 or eotaxin), CCL26 (eotaxin-3), vascular endothelial growth factor receptor 1 (VEGFR1 or Flt-1), hsCRP, intercellular adhesion molecule (ICAM)-1, interferon (IFN)-gamma, IL-1alpha, IL-1 receptor antagonist (IL-1RA), IL-10, IL-12/IL-23p40, IL-12p70, IL-13, IL-15, IL-16, IL-17A, IL-17B, IL-2, IL-27, IL-31, IL-5, IL-6 high sensitivity (IL-6HS), IL-7, IL-8HS, C-X-C motif chemokine 10 (CXCL10 or IP-10), CCL2 (MCP-1), CCL13 (MCP-4), CCL22 (MDC), CCL3 (MIP-1alpha), CCL4 (MIP-1beta), placental growth factor (PlGF), serum amyloid A (SAA), sIL-6R, CCL17 (TARC), angiopoietin-1 receptor (Tie-2), tumor necrosis factor (TNF or TNF-alpha), lymphotoxin-alpha (LT-alpha or TNF-beta), thymic stromal lymphopoietin (TSLP), vascular cell adhesion protein 1 (VCAM-1), vascular endothelial growth factor (VEGF)-A-HS, VEGF-C, VEGF-D</p> <p>The data is provided as a tab separated file.</p>
Common Data Elements for Disorders of Consciousness - Version 1.1
<p>In 2020, the Neurocritical Care Society’s <em>Curing Coma Campaign</em> launched an international initiative to create common data elements (CDEs) for disorders of consciousness (DoC). This CDE initiative is motivated by the recognition that ongoing progress in our field depends on the development of harmonized and uniform data elements. We formed multidisciplinary Work Groups with expertise in 1) Behavioral Phenotyping; 2) Hospital Course/Confounders/Medications; 3) Neuroimaging; 4) Electrophysiology; 5) Biospecimens; 6) Physiologic Data/Big Data; 7) Therapeutic Interventions; 8) Outcomes/Endpoints; and 9) Goals of Care/Family Data. Here, we disseminate the initial recommendations of this CDE development process and version 1.0 of the case report forms (CRFs) with CDEs that can be used in DoC studies. We aim for these CDEs to support progress in the field of DoC research and to facilitate multi-institutional collaboration. </p> <p>We welcome feedback and are committed to revising the CDEs and CRFs to ensure that they reflect developments in our field. To provide feedback about the current CDEs and CRFs, and to make recommendations about updates for future versions, please email <a href="mailto:cde.curingcoma@gmail.com">cde.curingcoma@gmail.com</a>.</p>
Supplementary file 48 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 48: Proportion of patients on methylphenidate with agitation</p>
Supplementary file 51 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 51: Proportion of patients on methylphenidate with sleep disorder</p>
Supplementary file 47 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 47: Proportion of patients on methylphenidate with isolation</p>
Supplementary file ROBINS-I Risk of Bias table in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies.
<p>ROBINS-I Risk og Bias Table</p>
Supplementary file 19 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 19 Proportion of patients on methylphenidate with asthenia </p>
Supplementary file 13 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 13: Proportion of patients on methylphenidate with tachycardia </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.