8,147 research outputs found
Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning
No full textData availability: Source data are not publicly available but non-commercial academic researcher requests may be made to the chief investigators of the seven source studies, subject to data access agreements and conditions that preserve participant anonymity. The underlying SuStaIn model code is publicly available at https://github.com/ucl-pond/pySuStaIn.68 .Supplementary data: available online at: https://academic.oup.com/braincomms/article/5/2/fcad048/7067775#398676040 .Copyright © The Author(s) 2023. To better understand the pathological and phenotypic heterogeneity of progressive supranuclear palsy and the links between the two, we applied a novel unsupervised machine learning algorithm (Subtype and Stage Inference) to the largest MRI data set to date of people with clinically diagnosed progressive supranuclear palsy (including progressive supranuclear palsy–Richardson and variant progressive supranuclear palsy syndromes).
Our cohort is comprised of 426 progressive supranuclear palsy cases, of which 367 had at least one follow-up scan, and 290 controls. Of the progressive supranuclear palsy cases, 357 were clinically diagnosed with progressive supranuclear palsy–Richardson, 52 with a progressive supranuclear palsy–cortical variant (progressive supranuclear palsy–frontal, progressive supranuclear palsy–speech/language, or progressive supranuclear palsy–corticobasal), and 17 with a progressive supranuclear palsy–subcortical variant (progressive supranuclear palsy–parkinsonism or progressive supranuclear palsy–progressive gait freezing). Subtype and Stage Inference was applied to volumetric MRI features extracted from baseline structural (T1-weighted) MRI scans and then used to subtype and stage follow-up scans. The subtypes and stages at follow-up were used to validate the longitudinal consistency of subtype and stage assignments. We further compared the clinical phenotypes of each subtype to gain insight into the relationship between progressive supranuclear palsy pathology, atrophy patterns, and clinical presentation.
The data supported two subtypes, each with a distinct progression of atrophy: a ‘subcortical’ subtype, in which early atrophy was most prominent in the brainstem, ventral diencephalon, superior cerebellar peduncles, and the dentate nucleus, and a ‘cortical’ subtype, in which there was early atrophy in the frontal lobes and the insula alongside brainstem atrophy. There was a strong association between clinical diagnosis and the Subtype and Stage Inference subtype with 82% of progressive supranuclear palsy–subcortical cases and 81% of progressive supranuclear palsy–Richardson cases assigned to the subcortical subtype and 82% of progressive supranuclear palsy–cortical cases assigned to the cortical subtype. The increasing stage was associated with worsening clinical scores, whilst the ‘subcortical’ subtype was associated with worse clinical severity scores compared to the ‘cortical subtype’ (progressive supranuclear palsy rating scale and Unified Parkinson’s Disease Rating Scale). Validation experiments showed that subtype assignment was longitudinally stable (95% of scans were assigned to the same subtype at follow-up) and individual staging was longitudinally consistent with 90% remaining at the same stage or progressing to a later stage at follow-up.
In summary, we applied Subtype and Stage Inference to structural MRI data and empirically identified two distinct subtypes of spatiotemporal atrophy in progressive supranuclear palsy. These image-based subtypes were differentially enriched for progressive supranuclear palsy clinical syndromes and showed different clinical characteristics. Being able to accurately subtype and stage progressive supranuclear palsy patients at baseline has important implications for screening patients on entry to clinical trials, as well as tracking disease progression.W.J.S. is supported by a Wellcome Trust Clinical PhD fellowship (220582/Z/20/Z). C.S. is supported by the UK Research and Innovation Medical Research Council (MR/S03546X/1). M.B. is supported by a fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517), and the UK Dementia Research Institute. D.M.C. is supported by the UK Dementia Research Institute, as well as Alzheimer’s Research UK (ARUK-PG2017-1946), and the University College London/University College London Hospitals, National Institute for Health and Care Research Biomedical Research Centre. H.H. is supported by the National Institutes of Health (R01AG038791, U19AG063911). A.L.Y. is supported by a Skills Development Fellowship from the Medical Research Council (MR/T027800/1). N.P.O. is a UK Research and Innovation Future Leaders Fellow (MR/S03546X/1). L.V.V. is supported by the National Institutes of Health (R01AG038791, K23AG073514) and the Alzheimer’s Association. D.C.A. is supported by the Engineering and Physical Sciences Research Council (EP/M020533/1), Medical Research Council (MR/T046422/1), and Wellcome Trust (UNS113739). J.B.R. is supported by the Wellcome Trust (220258), National Institute for Health and Care Research Cambridge Biomedical Research Centre (BRC-1215-20014), PSP Association, Evelyn Trust, and Medical Research Council (SUAG051 R101400). H.R.M. is supported by Parkinson’s UK, Cure Parkinson’s Trust, PSP Association, CBD Solutions, Drake Foundation, Medical Research Council, and the Michael J Fox Foundation. A.L.B. is supported by the National Institutes of Health (U19AG063911, R01AG038791, R01AG073482, and U24AG057437), the Rainwater Charitable Foundation, the Bluefield Project to Cure FTD, and the Alzheimer’s Association and the Association for Frontotemporal Degeneration. J.D.R. is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from a Medical Research Council Clinician Scientist Fellowship (MR/M008525/1) and the National Institute for Health and Care Research Rare Disease Translational Research Collaboration (BRC149/NS/MH). P.A.W. is supported by a Medical Research Council Skills Development Fellowship (MR/T027770/1).
The Dementia Research Centre is supported by Alzheimer’s Research UK, Alzheimer’s Society, Brain Research UK, and The Wolfson Foundation. This work was supported by the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility, and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd., funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK. The PROSPECT study is funded by the PSP Association and CBD Solutions. The 4-Repeat Tauopathy Neuroimaging Initiative (4RTNI) and FTLDNI are funded by the National Institutes of Health Grant (R01 AG038791) and through generous contributions from the Tau Research Consortium. Both are coordinated through the University of California, San Francisco, Memory and Aging Center. 4RTNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California
SHui open data research platform
No full textData collected and revised by individual instutions of the Shui-Consortium. Publication by the EU-China Consortium SHui.For each data-file, the author (institution) of the file is given as “operator”.-- At project end, June 30th, 2022.-- For each data-file, the author/data owner for citation is given as “operator” and “contact”.-- Plot data as .csv; catchment data ad libitum.Spatial situation data: Plot data and catchment data available; country, latitude, and longitude coordinates given.-- Temporal situation data: Long-term and single-season data available. Start and end date for each data file given.CC BY-SA. No embargo. The release on the Shui download site and CSIC repository implies expiration of any embargo delivered by the data owner.Project Co-ordinators: Dr. Jose Alfonso Gómez Calero (Instituto de Agricultura Sostenible (IAS-CISC), Dr. Weifeng Xu (Fujian Agriculture and Forest University, FAFU).This data set contains data from the SHui open-data platform for sharing long-term agricultural experiments aimed to optimizing yield and soil and water. Data and additional material are available under https://shui.boku.ac.at/shui/public/startAlphanumeric data measured at hydrologic and agronomical experiments (e.g., plant development, soil properties, hydrology, erosion, management).Further information on the data, project, partners, and publications under https://www.shui-eu.org/EU-China Consortium SHui: European Union Project 773903 and Chinese MOST.Peer reviewe
Research on the evolutionary game of safety behavior of EPC consortium members based on prospect theory
No full textIn recent years, the consortium, composed of the design enterprise, construction enterprise, and supplier, has witnessed rapid development in implementing the EPC project model in China. However, there are conflicts of interest and ambiguous safety management among EPC consortium members, leading to accidents. This study aims to explore the decision-making process regarding different safety behavior strategies among EPC consortium members. Given the uncertainty of the EPC project environment, consortium members show risk preferences and perceptual biases. Prospect theory reveals the irrational elements in the decision-making process. Therefore, this study incorporates prospect theory, constructs a safety behavior perception payoff matrix and an evolutionary game model involving design enterprise, construction enterprise, and supplier as the primary stakeholders, analyzes the interactive mechanisms of safety behavior among the three parties, and uses MATLAB simulation to explain the evolutionary path of behavioral strategies under varying parameters. The consequence indicates that the optimal strategy’s stable state in the safety behavior evolution system of EPC consortium members is affected by the players’ cognitive biases and risk preferences, and the costs of safety input, punishment intensity, and accident losses. This study mainly provides theoretical evidence and decision support for safety control activities among EPC consortium members
Enrichment and characterization of a bacteria consortium capable of heterotrophic nitrification and aerobic denitrification at low temperature
No full textNitrogen removal in wastewater treatment plants is usually severely inhibited under cold temperature. The present study proposes bioaugmentation using psychrotolerant heterotrophic nitrification-aerobic denitrification consortium to enhance nitrogen removal at low temperature. A functional consortium has been successfully enriched by stepped increase in DO concentration. Using this consortium, the specific removal rates of ammonia and nitrate at 10 degrees C reached as high as 3.1 mg N/(g SS h) and 9.6 mg N/ (g SS h), respectively. PCR-DGGE and clone library analysis both indicated a significant reduction in bacterial diversity during enrichment. Phylogenetic analysis based on nearly full-length 16S rRNA genes showed that Alphaproteobacteria. Deltaproteobacteria and particularly Bacteroidetes declined while Gammaproteobacteria (all clustered into Pseudomonas sp.) and Betaproteobacteria (mainly Rhodoferax ferrireducens) became dominant in the enriched consortium. It is likely that Pseudomonas spp. played a major role in nitrification and denitrification, while R. ferrireducens and its relatives utilized nitrate as both electron acceptor and nitrogen source. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.</p
Arabic Treebank : Part 2 v 3.1
No full textArabic Treebank: Part 2 (ATB2) v 3.1 , Linguistic Data Consortium (LDC) catalog number LDC2011T09 and isbn 1-58563-590-1, was developed at LDC. It consists of 501 newswire stories from Ummah Press with part-of-speech (POS), morphology, gloss and syntactic treebank annotation in accordance with the Penn Arabic Treebank (PATB) Guidelines developed in 2008 and 2009
Publisher Correction: Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes (Nature Genetics, (2018), 50, 4, (524-537), 10.1038/s41588-018-0058-3)
No full textIn the HTML version of this article initially published, the author groups ‘AFGen Consortium’, ‘Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium’, ‘International Genomics of Blood Pressure (iGEN-BP) Consortium’, ‘INVENT Consortium’, ‘STARNET’, ‘BioBank Japan Cooperative Hospital Group’, ‘COMPASS Consortium’, ‘EPIC-CVD Consortium’, ‘EPIC-InterAct Consortium’, ‘International Stroke Genetics Consortium (ISGC)’, ‘METASTROKE Consortium’, ‘Neurology Working Group of the CHARGE Consortium’, ‘NINDS Stroke Genetics Network (SiGN)’, ‘UK Young Lacunar DNA Study’ and ‘MEGASTROKE Consortium’ appeared at the end of the author list but should have appeared earlier in the list. In addition, the author group ‘MEGASTROKE Consortium’ was duplicated, and its members were not displayed in the ‘Author information’ section. The errors have been corrected in the HTML version of the article
Author Correction: Expanded encyclopaedias of DNA elements in the human and mouse genomes
No full textOnline Correction for: https://doi.org/10.1038/s41586-020-2493-4 | Erratum for https://bura.brunel.ac.uk/handle/2438/21299In the version of this article initially published, two members of the ENCODE Project Consortium were missing from the author list. Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) are now included in the author list. These errors have been corrected in the online version of the article : 'Expanded encyclopaedias of DNA elements in the human and mouse genomes'.https://www.nature.com/articles/s41586-021-04226-3https://www.nature.com/articles/s41586-021-04226-
Author Correction: Perspectives on ENCODE (Nature, (2020), 583, 7818, (693-698), 10.1038/s41586-020-2449-8)
No full textThe Original Article (https://doi.org/10.1038/s41586-020-2449-8) was published on 29 July 2020.Copyright © The Authors 2022. In this Article, the authors Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) were mistakenly omitted from the ENCODE Project Consortium author list. The original Article has been corrected online
Comparing consortial repositories: a model-driven analysis
No full textThis study aims to provide a comparative assessment of different repository consortia as a reference to inform future work in the area. A review of the literature was used to identify repository consortia, and their features were compared. Three models of consortial repositories were derived from this comparison, based on their structure and aims. The consortial models were based around either: creating a shared repository for the members, developing a repository software platform or creating a metadata harvesting service to aggregate content. Using case studies of each type of repository consortium, each model was assessed in terms of its particular strengths and weaknesses. These strengths were then compared across the models to enable those considering a consortial repository project to assess which model, or combination of models, would best address their needs and to aid in project planning
Genome-wide association study identifies eight loci associated with blood pressure
No full textElevated blood pressure is a common, heritable cause of cardiovascular disease worldwide. To date, identification of common genetic variants influencing blood pressure has proven challenging. We tested 2.5 million genotyped and imputed SNPs for association with systolic and diastolic blood pressure in 34,433 subjects of European ancestry from the Global BPgen consortium and followed up findings with direct genotyping (N ≤ 71,225 European ancestry, N ≤ 12,889 Indian Asian ancestry) and <i>in silico</i> comparison (CHARGE consortium, N= 29,136). We identified association between systolic or diastolic blood pressure and common variants in eight regions near the CYP17A1 (P = 7 × 10<sup>−24</sup>), CYP1A2 (P = 1 × 10<sup>−23</sup>), FGF5 (P = 1 × 10<sup>−21</sup>), SH2B3 (P = 3 × 10<sup>−18</sup>), MTHFR (P = 2 × 10<sup>−13</sup>), c10orf107 (P = 1 × 10<sup>−9</sup>), ZNF652(P = 5 × 10<sup>−9</sup>) and PLCD3 (P = 1 × 10<sup>−8</sup>) genes. All variants associated with continuous blood pressure were associated with dichotomous hypertension. These associations between common variants and blood pressure and hypertension offer mechanistic insights into the regulation of blood pressure and may point to novel targets for interventions to prevent cardiovascular disease
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