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11,745 results for “Disorder”
PhasAGE Expert Seminar- Intrinsic Protein Disorder and Conditional Folding in AlphaFoldDB
<p>The PhasAGE <strong>Expert Seminars</strong> consist of a series of talks with speakers from PhasAGE partner’s institutions to promote a successful transfer of knowledge about PhasAGE topics – biomolecular phase separation, aging and age-related diseases.</p>
Supplementary material: Negative valence in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium
<p>The ridge plots attached here accompany the supplement to the manuscript <em>Negative valence in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium</em> (Dzinalija et al., 2024)<em>. </em>These are full results of Figures 1, 2C, and 3C of the manuscript and Figures S3, S5, S7 and S9 of the supplement depicting whole-brain Bayesian multilevel models run using the Regional Bayesian Analysis toolbox (RBA; Chen et al., 2019). Whole-brain analyses were parcelated into the Schaefer-Yeo 7-network 200-parcel cortical atlas (Schaefer et al., 2018) and the Melbourne 32-region subcortical atlas (Tian et al., 2020). Results are presented according to contrast of interests: [Negative > Neutral], [OCD > Neutral], [Threat > Neutral], and [OCD > Threat], and effects of interest: [Diagnosis = OCD or HC], [MED = medication], [AO = age of onset], [YBOCS = OCD severity], and [Intercept = task effect]. </p> <p>P+ values denote the probability that there is increased brain activation in a given region of the Schaefer 200-parcel 7-network cortical atlas and Melbourne 32-region subcortical atlas. We used the guidelines proposed by Chen et al. (2019) to infer credibility of evidence, namely taking a positive posterior probability (P+) of <0.10 or >0.90 as indication of moderate evidence and, <0.05 or >0.95 or <0.025 or >0.975 as strong or very strong evidence, respectively. To interpret the pairwise comparisons presented as Group1-vs-Group2, posterior distributions to the right of the green no-effect line represent regions in which individuals in Group 1 show credible evidence for higher activation than individuals in Group 2. Regions with posterior distributions to the left of this line show credible evidence for higher activity in Group 2 than in Group 1. (Darker) red color represents regions in which individuals in Group 1 show moderate-to-very-strong evidence for higher activation than Group 2. (Darker) blue color represents regions in which Group 2 show moderate-to-very-strong evidence for higher activation than Group 1. Grey color represents regions in which there is no strong evidence of a difference between Group 1 and Group 2. Schaefer-Yeo 200-parcel atlas name abbreviations can be retrieved <a href="https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations">via this link.</a></p>
Dautan et al 2024 " Gut-Initiated Alpha Synuclein Fibrils Drive Parkinson's Disease Phenotypes: Temporal Mapping of non-Motor Symptoms and REM Sleep Behavior Disorder"
<p><span>Parkinson’s disease (PD) is characterized by progressive motor as well as less recognized non-motor symptoms that arise often years before motor manifestation, including sleep and gastrointestinal disturbances. Despite the heavy burden on the patient’s quality of life, these non-motor manifestations are poorly understood. To elucidate the temporal dynamics of the disease, we employed a mice model involving injection of alpha-synuclein (αSyn) pre-formed fibrils (PFF) in the duodenum and antrum as a gut-brain model of Parkinsonism. Using anatomical mapping of αSyn PFF propagation and behavioral and physiological characterizations, we unveil a correlation between post-injection time the temporal dynamics of αSyn propagation and non-motor/motor manifestations of the disease. We highlight the concurrent presence of aggregates in key brain regions, expressing acetylcholine or dopamine and their functions in sleep duration, wakefulness, and particularly REM-associated atonia corresponging to REM behavioral disorder-like symptoms. This study presents a novel and in-depth exploration into the multifaceted nature of PD, unraveling the complex connections between α-synucleinopathies, gut-brain connectivity, and the emergence of non-motor phenotypes.</span></p>
Movement of plastic balls in a long-vibrating cylinder: from disorder to structure formation with examples of its instability
<p>Many plastic balls, made from PolyOxyMethylene (POM) and of three different diameters 2, 3 and 4 mm, were used to observe the formation and stability of the structure in ball beds when, after pouring into a plexiglass cylinder, they were subjected to long-term vertical vibrations with frequency 100 Hz. The vibration table Vibrax (Renfert GmbH, Germany), used in the experiment and shown in Fig. 1, can function in two modes: sinusoidal (s) and nonsinusoidal (ns). The vibration table can act at four power levels of the vibrations, selected by an operator using the right knob of the table (see Fig. 1). In the presented series of 43 vibration experiments, always the highest power level was used. </p> <p>Locations of surface balls on all sides of a vibrated cylindrical bed were simultaneously recorded on one video frame thanks to the use of two perpendicular mirrors (see Fig. 1), which enables observation of four images: one of the real cylinder and three of its mirror reflections. An explanation of the scene, as seen by the recording camera, is given in the scheme in Fig. 2. Video names were given in a standard form to inform the user about the most important parameters (more in README.txt).</p> <p> </p>
Supplementary file 30 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 30: Proportion of participants on methylphenidate with asthenia and fatigue </p>
Supplementary file 37 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 37: Proportion of participants on methylphenidate with restlessness and agitation</p>
Supplementary file 66 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 66: Proportion of participants on methylphenidate with gastrointestinal adverse events</p>
Supplementary file 55 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 55: Proportion of participants on methylphenidate with upper respiratory tract infection</p>
Supplementary file 11 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 11: Proportion of participants on methylphenidate with respiratory, thoracic and mediastinal disorders</p>
Supplementary file 22 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 22: Proportion of participants on methylphenidate with difficulty falling asleep</p>
Supplementary file 33 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 33: Proportion of participants on methylphenidate with involuntary movements </p>
Supplementary file 41 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 41: Proportion of participants on methylphenidate with taciturnity (‘talking too little’)</p>
Supplementary file 32 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 32 Proportion of participants on methylphenidate with increased need to sleep</p>
Supplementary file 44 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 44: Proportion of participants on methylphenidate with ’zombie-like’ demeanour</p>
Supplementary file 43 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 43: Proportion of participants on methylphenidate with isolation and lack of interest in others</p>
[Saliency4ASD] A dataset of eye movements for the children with autism spectrum disorder
<p>Social difficulties are the hallmark features of Autism Spectrum Disorder (ASD) and can lead to atypical visual attention towards stimuli. Eye movements encode rich information about attention and psychological factors of an individual, which could help to characterize the traits of ASD. Learning atypical eye movements of the individuals with ASD towards various stimuli is important and has many application scenarios. However, due to the lack of open datasets, research in this sense is still limited. In this work, we present an open dataset of eye movements of children with Autism Spectrum Disorder. It consists of 300 natural scene images and the corresponding eye movement data collected from 14 children with ASD and 14 healthy controls. In particular, fixation maps and scanpaths are available in the dataset. Based on this dataset, researchers could analyze the visual traits of children with ASD and design specialized visual attention models to promote research in related fields, as well as design specialized models to identify the individuals with ASD</p>
Debunking neuromyths: Pre‐service teachers' insights on autism spectrum disorder.
<p>This database corresponds to the results of the paper:</p> <p>Lacruz-Pérez, I., Pastor-Cerezuela, G., Caurín-Alonso, C., Morales-Hernández, A.J. & Tárraga-Mínguez, R. (in press). Debunking neuromyths: Pre‐service teachers' insights on autism spectrum disorder. <em>Journal of Research in Special Educational Needs. </em></p>
PhasAGE Training School 1 - Computational prediction of intrinsic disorder in proteins-DisProt - PRACTICAL
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
PhasAGE Training School 1 - Computational prediction of intrinsic disorder in proteins-MobiDB - PRACTICAL
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
Epidemiology and Disease Burden of Neurocritical Disorders: A Cohort Study - Data Sharing
<p>Dataset of a Neurocritical Brazil cohort study whose summary is described below.</p> <p><strong>Abstract</strong></p> <p><strong>Objective:</strong> To describe a cohort of neurocritical patients and their differences based on primary neurological diagnoses and identify predictors of mortality and unfavorable outcome along with the disease burden of each neurological condition on intensive care unit (ICU) admission. <strong>Methods:</strong> Prospective cohort study including patients admitted to 36 ICUs in Brazil and followed up for 30 days. <strong>Results:</strong> Of 4245 patients admitted to the participating ICUs during the study period, 1194 (28.1%) were neurocritical patients and were included in the study. Neurocritical patients had a mean mortality rate 1.7 times higher than non-neurocritical patients admitted to the same ICUs (17.21% versus 10.1%, respectively). The most frequent primary neurological diagnoses on ICU admission were postoperative care of elective neurosurgery, traumatic brain injury, ischemic stroke, and encephalopathy. The estimated total disability-adjusted life-years (DALYs) were 4482.94 in the overall cohort, and the diagnosis with the highest DALYs was traumatic brain injury (1634.42). DALYs were significantly impacted by the patients’ primary neurological diagnosis, sex, age group, and number of secondary neurological injuries. <strong>Conclusion:</strong> We accurately described the epidemiology of neurocritical patients and estimated their overall and relative disease burden. The findings of this study are important to direct policies regarding education, prevention, and treatment of severe neurocritical diseases.</p> <p> </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.