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11,745 results for “Disorder”
No relationship between frontal alpha asymmetry and depressive disorders in a multiverse analysis of five studies
<p>For decades, the frontal alpha asymmetry (FAA) – a disproportion in EEG alpha oscillations power between right and left frontal channels – has been one of the most popular measures of depressive disorders (DD) in electrophysiology studies. Patients with DD often manifest a left-sided FAA: relatively higher alpha power in the left versus right frontal lobe. Recently, however, multiple studies failed to confirm this effect, questioning its reproducibility. Our purpose is to thoroughly test the validity of FAA in depression by conducting a multiverse analysis – running many related analyses and testing the sensitivity of the effect to changes in the analytical approach – on data from five independent studies. Only 13 of the 270 analyses revealed significant results. We conclude the paper by discussing theoretical assumptions underlying the FAA and suggest a list of guidelines for improving and expanding the EEG data analysis in future FAA studies.</p>
Supplementary tables in article entitled "Global, regional, and national trends in disability adjusted life years of musculoskeletal disorders in different sexes and ages, 1990-2019"
<p>Table S1. The DALYs and ASDR of musculoskeletal disorders in 1990 and 2019 at national level, by sex and age group.</p> <p>Table S3. The DALYs and ASDR of musculoskeletal disorders in 1990 and 2019 at global and regional level, by sex, age group and cause.</p> <p>Table S4. The DALYs and ASDR of musculoskeletal disorders in 1990 and 2019 at national level, by sex, age group and cause.</p>
Twitter Mental Disorder Tweets and Musics Dataset
<p>https://www.kaggle.com/datasets/rrmartin/twitter-mental-disorder-tweets-and-musics?resource=download</p>
Conformational ensembles used in "Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022"
<p>Ensemble of intrinsically disordered proteins used in: <em>"Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022"</em>.</p> <p>Ensembles are produced with Flexible-meccano and Langevin simulations with CALVADOS for:</p> <ul> <li>Hst5</li> <li>RS</li> <li>DSS1</li> <li>Sic1</li> <li>ProTa</li> <li>NHE6cmdd</li> <li>A1</li> <li>aSyn</li> <li>ANAC046</li> <li>GHR-ICD</li> <li>Tau</li> </ul>
Dataset for Disordered dolomite as an unusual biomineralization product found in the center of a natural Cassis pearl
<p>Natural pearls are produced without human intervention, mainly due to various irritations from the surrounding environment to their mantle tissues. Pearls usually possess similar mineral compositions to the host shells, which means they are also dominated by aragonite and calcite. In this study, we report a natural pearl from a <em>Cassis</em> species mollusk containing granular central structures. Raman spectroscopy, laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS), energy dispersive X-ray spectroscopy (EDS) coupled with scanning electron microscope (SEM), and X-ray diffraction (XRD) analyses were carried out in order to characterize the mineral composition in the center region of this pearl. Our results showed that this pearl's center was made of mostly disordered dolomite (Ca<sub>0.53</sub>Mg<sub>0.47</sub>CO<sub>3</sub>) mixing with small amount of aragonite and high magnesium-calcite. To the best of our knowledge, this is the first time disordered dolomite was conclusively identified inside of a natural pearl and such information expanded our knowledge on internal growth structures and formation of natural pearls.</p>
Spectral response of disorder-free localized lattice gauge theories
<p>Raw data for all figures in the manuscript "Spectral response of disorder-free localized lattice gauge theories"</p>
League tables for the NMA of temporomandibular disorders
<p>sheet 1 for pain; sheet 2 for physical functioning; sheet 3 for adverse events</p>
LAMMPS trajectories for "The disordered N-terminal tail of SARS CoV-2 Nucleocapsid protein forms a dynamic complex with RNA"
<p><strong>LAMMPS trajectory data for:</strong><br> <strong>The disordered N-terminal tail of SARS CoV-2 Nucleocapsid protein forms a dynamic complex with RNA</strong><br> Jasmine Cubuk<sup>1,2</sup>, Jhullian J. Alston<sup>1,2</sup>, J. Jeremías Incicco<sup>1,2</sup>, Alex S. Holehouse<sup>1,2</sup>, Kathleen B Hall<sup>1</sup>, Melissa D. Stuchell-Brereton<sup>1,2</sup>, Andrea Soranno<sup>1,2,*</sup></p> <p><sup>1</sup>Department of Biochemistry and Molecular Biophysics, Washington University in St Louis, 660 St Euclid Ave, 63110, Saint Louis, MO, USA</p> <p><sup>2</sup>Center for Biomolecular Condensates, Washington University in St Louis, 1 Brookings Drive, 63130, Saint Louis, MO, USA</p> <p><strong>Corresponding author: </strong>Andrea Soranno (soranno@wustl.edu)<br> <strong>Questions about simulations: </strong>Alex Holehouse (alex.holehouse@wustl.edu)</p> <p><strong>Preprint link: </strong><a href="https://doi.org/10.1101/2023.02.10.527914">https://doi.org/10.1101/2023.02.10.527914</a></p> <p><strong>GitHub link to simulation info:</strong> <a href="https://github.com/holehouse-lab/supportingdata/tree/master/2023/cubuk_2023">https://github.com/holehouse-lab/supportingdata/tree/master/2023/cubuk_2023</a></p> <p><strong>Funding: </strong>This research was supported by the NIH National Institute on Allergic and Infectious Diseases with R01AI163142 (to A.S., A.S.H.) and by the NIH National Cancer Institute F99CA264413 (to J.J.A.).</p> <p> </p>
Estradiol Enhances the Development of Addition-Like Features in a Female Rat Model of Opioid Use Disorder
<p>This dataset indicates that, as with findings with psychostimulants and alcohol, estradiol enhances vulnerability in females to developing opioid addiction-like features and serious opioid-related health complications in a rat model of opioid use disorder.</p>
Data for "Models of orientational disorder in hybrid organic-inorganic piezoelectric materials"
<p>Data for "Models of orientational disorder in hybrid organic-inorganic piezoelectric materials"</p>
Effects of disorder on deformation and failure of brittle porous materials: dataset
<p><strong>Data from:</strong></p> <p>[1] Ritter, J.; Shegufta, S. & Zaiser, M. "Effects of disorder on deformation and failure of brittle porous materials" <em>arXiv, </em><strong>2023</strong></p> <p> </p> <p><strong>Abstract:</strong></p> <p>from [1]</p> <p>The mechanical behavior of porous materials depends strongly on porosity and pore geometry, but also on morphological parameters characterizing the spatial arrangement of pores. Here we use bond-based peridynamics to study effects of disorder on the deformation and failure behavior of brittle porous solids both in the quasi-static limit and in case of dynamic loading scenarios. We show that structural disorder, which has a strong influence on stiffness, strength and toughness in the quasi-static limit, becomes less relevant under dynamic loading conditions. </p> <p> </p> <p><strong>Contact:</strong></p> <p>Jonas Ritter<br> Institute of Materials Simulation<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Dr.-Mack-Str. 77<br> 90762 Fürth<br> Germany</p> <p> </p> <p><strong>Software:</strong><br> <a href="https://github.com/peridigm/peridigm">Peridigm</a></p> <p> </p> <p><strong>Content:</strong></p> <p>The dataset consists of the input geometries as well as the combined results (values for each simulation and mean values and standard deviation of each parameter set). Each folder contains a README.md that lists the content and provides description of the data and further metadata.</p>
Assessment of Temporomandibular Joint for Occurrence and Severity of Disorders in Adult Cases with Unilateral Cleft Lip and Palate and Non-Cleft Class I Using Helkimo Index
<p>master charts</p>
Excessive checking in Obsessive Compulsive Disorder: neurochemical correlates revealed by 7-Tesla magnetic resonance spectroscopy
<p>This link contains datasets analysed in the article titled "Excessive checking in Obsessive Compulsive Disorder: neurochemical correlates revealed by 7 Tesla magnetic resonance spectroscopy". The anonymised behavioural, clinical and MRS data are shared here. Whereas the structural data which could not be anonymised can be requested <a href="https://doi.org/10.5281/zenodo.7832207">here</a> with restricted access.</p>
Cortical glutamate and GABA are related to compulsive behaviour in individuals with obsessive compulsive disorder and healthy controls
<p>This link contains the Data Source files, the code, and raw MRS data analysed in <a href="https://www.researchsquare.com/article/rs-2152573/v1">this article</a>. </p>
Salt Induced Transitions in Structural Ensemble of Intrinsically Disordered Proteins
<p>Simulation data and the corrosponding analysis script for the work "<strong>Salt Induced Transitions in Conformational Ensemble of Intrinsically Disordered Proteins</strong> " by <em>Hiranmay Maity, Lipika Baidya </em>and<em> Govardhan reddy</em> are deposited here. </p> <p>Analysis Scripts:</p> <p>The scripts for analysing the simulation data are in analysis_script.zip. The folder contains:</p> <ul> <li> <p>autocorrelation.c : code for calculating end_to_end distance autocorrelation function with time in C.</p> </li> <li> <p>average_property.cpp: code for calculating average property such as radius of gyration (R<sub>g</sub>) from trajectory files in C++.</p> </li> <li> <p>calculate_saxs_kratky.c: code for calculating scattering profile (SAXS and Kratky) from simulation data in C.</p> </li> <li> <p>compute_contact_map.cpp: code for calculating contact map in C.</p> </li> <li> <p>probablity_distribution.c: code for calculating probablity distribution of Rg in C.</p> </li> <li> <p>structure_factor.c: code for calculating structure factor in C.</p> </li> </ul>
Baseline EEG in the first year of life: insights into the development of autism spectrum disorder and language impairments
<p>The dataset includes power values obtained from baseline EEG recorded in infants. Specifically, for each infant the Power Spectral Density (PSD) was estimated for epochs of 30 s (at least 2 epochs) using the Welch’s method (Hamming window of 10s with 50% overlap) and successively log transformed as follow: log PSD = log10(1+PSD). The power in the following bands was computed: delta (2-4 Hz), theta (4-6 Hz), low-alpha (6-9-Hz), high-alpha (9-13 Hz), beta (13-30 Hz) and gamma (30-45 Hz). Two clusters of 6 frontal electrodes were selected (frontal left cluster and frontal right cluster) and power values were then averaged across electrodes within each cluster. Thus, the database contains for each subjects and frequency band two values of power one related to the frontal left cluster and the other one related to frontal right cluster.</p> <p>Each raw in the database corresponds to a subject and each column to a different variable.</p> <p>Column A, Subject code;</p> <p>Column B, Group: 0 = typically developing (TD) infant; 1 = infant higher likelihood of developing language and learning impairments (HL-LLI); 2 = infant at higher likelihood of developing autism spectrum disorder (HL-ASD);</p> <p>Column C, Diagnosis_group: 0 = children from the original TD group (without familiarity for neither ASD nor LLI) who at 3 years of age scored above the 30th percentile in the parent-administered Language Development Survey (LDS) (Rescorla and Alley, 2001); 1 = children diagnosed with Developmental Language Disorders/Developmental Dyslexia (DLD/DD); 2 = children diagnosed with ASD;</p> <p>Column D, Time_point: 1 = data related to the EEG recording performed at six months of age; 2 = data related to the EEG recording performed at twelve months of age;</p> <p>Columns from E to P: power values obtained in the frontal left (F_sx) and right (F_dx) clusters for each frequency band.</p> <p>All details about subjects, groups, data acquisition and preprocessing are available in the following manuscript: Piazza, C., Dondena, C., Riboldi, E. M., Riva, V., & Cantiani, C. (2023). Baseline EEG in the first year of life: Preliminary insights into the development of autism spectrum disorder and language impairments. <em>iScience</em>, <em>26</em>(7).</p> <p>Matlab .m files related to the preprocessing steps and the power value extraction are also available.</p>
Movies: CellSim3D simulation of epithelial growth with and without disorder (pinning)
<p>The movies show growing epithelial tissue starting from a line geometry for both weak and strong cell-cell adhesion. Cells are shown in blue and pinning centers, or disorder, when present in red. The pinning centers are stationary. The movies show the following cases:</p> <ul> <li>line_geometry_strong_cell_cell_adhesion_with_disorder.avi: Strong cell-cell adhesion with disorder present</li> <li>line_geometry_strong_cell_cell_adhesion_no_disorder.avi: Strong cell-cell adhesion, no disorder</li> <li>line_geometry_weak_cell_cell_adhesion_with_disorder.avi: Weak cell-cell adhesion with disorder present</li> <li>line_geometry_weak_cell_cell_adhesion_no_disorder.avi: : Weak cell-cell adhesion, no disorder</li> </ul> <p><strong>Notes:</strong></p> <ul> <li>The simulations were done with the CellSim3D GPU accelerated simulation code and model, link to the source code is below.</li> <li>Details of the CellSim3D model are provided as Supplementary Information in the two references below.</li> </ul> <p> </p> <p><strong>References:</strong></p> <ul> <li><em>Effect of substrate heterogeneity and topology on epithelial tissue growth dynamics.</em> Mazarei, Mahmood; Åström, Jan; Westerholm, Jan; <a href="https://www.researchgate.net/profile/Mikko-Karttunen-2">Karttunen, Mikko</a>. Submitted (2023). Preprint: <a href="https://arxiv.org/abs/2303.10850">https://arxiv.org/abs/2303.10850</a></li> <li><em>In Silico Testing of the Universality of Epithelial Tissue Growth.</em> Mazarei, Mahmood; Åström, Jan; Westerholm, Jan; <a href="https://www.researchgate.net/profile/Mikko-Karttunen-2">Karttunen, Mikko</a>. Phys. Rev. E 106, L062402, (2022). DOI link: <a href="https://doi.org/10.1103/PhysRevE.106.L062402">https://doi.org/10.1103/PhysRevE.106.L062402</a> Preprint: <a href="https://arxiv.org/abs/2203.15883">https://arxiv.org/abs/2203.15883</a></li> <li>Github: <a href="https://github.com/SoftSimu/CellSim3D">CellSim3D: GPU Accelerated 3D Cell Simulator</a></li> </ul> <p> </p>
Online Mendelian Inheritance in Man (OMIM): Comprehensive genetic disorder database for inherited phenotypes and genes
<p><strong>ABSTRACT: </strong></p> <p>Online Mendelian Inheritance in Man (OMIM) is a comprehensive and authoritative research resource that provides curated descriptions of human genes and phenotypes, along with their intricate relationships. I have compiled a dataset that specifically focuses on genes from the Online Mendelian Inheritance in Man database. The dataset includes the unique identifiers (MIM IDs) and names of these OMIM genes.</p> <p><strong>Instructions:</strong></p> <p>Dataset was downloaded and uploaded to excel to have unnecessary columns removed. Then dataset was imported to Jupyter Notebook and further cleaned of the nullified values and unrelated gene types such as phenotypes and etc. A new column with the respective links was added to make a total of 4 columns with one type: genes. Dataset was uploaded to the drg-depot cheaha.</p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "DRG_DEPOT" summer 2023 team project.</p> <p><strong>Acknowledgements:</strong></p> <p>Joanna S. Amberger, Carol A. Bocchini, François Schiettecatte, Alan F. Scott, Ada Hamosh</p> <p><em>Nucleic Acids Research</em>, Volume 43, Issue D1, 28 January 2015, Pages D789–D798, DOI: <a href="https://doi.org/10.1093/nar/gku1205">https://doi.org/10.1093/nar/gku1205</a></p> <p><strong>OMIM Access to Gene Information:</strong> https://omim.org/search/advanced/entry</p> <p>U-BRITE Last Updated: July 5, 2023</p>
Temporal changes of gene expression in health and mental disorders. Supporting Data
<p>This dataset provides data necessary to replicate the results of the time-perturbation analysis of gene expression in health and mental disorders. The corresponding code is available in the GitHub repository <a href="https://github.com/susieavagyan/BrainExp-TemporalDynamics">https://github.com/susieavagyan/BrainExp-TemporalDynamics</a>․</p> <p>The content of the data archive is following:</p> <ul> <li><strong>"GTEX_unzipped"</strong> folder contains GTEx single-tissue cis-eQTL dataset (v8)</li> <li><strong>"pheno"</strong> folder contains sample phenotype data for datasets used in the analysis </li> <li><strong>"scRNA-Ruzicka_data"</strong> folder contains gene signatures for neuronally associated processes and brian cell populations from Ruzicka, W. Brad, Shahin Mohammadi, Jose Davila-Velderrain, Sivan Subburaju, Daniel Reed Tso, Makayla Hourihan, and Manolis Kellis. 2020. “Single-Cell Dissection of Schizophrenia Reveals Neurodevelopmental-Synaptic Axis and Transcriptional Resilience.” MedRxiv, January, 2020.11.06.20225342. https://doi.org/10.1101/2020.11.06.20225342 paper.</li> <li><strong>"v5_results.SOM - Results"</strong> folder contains the transcriptome analysis results generated with oposSOM pipeline </li> <li><strong>"BM.table.Rdata"</strong> file contains gene symbol ENSEMBL ID conversion table required for eQTL analysis </li> <li><strong>"Brain_development_v3.Rdata"</strong> file contains combined raw gene expression data of datasets used in this analysis</li> <li><strong>"som_input_v5.Rdata"</strong> file contains batch adjusted and normalized gene expression matrix that was used as an input for oposSOM pipeline.</li> </ul>
Strategy of selection and optimization of single domain antibodies targeting the PHF6 linear peptide within the Tau intrinsically disordered protein
<p>Dataset pertaining to Strategy of selection and optimization of single domain antibodies targeting the PHF6 linear peptide within the Tau intrinsically disordered protein</p>
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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.