8,290 research outputs found
Torque prediction using the flux-MMF diagram in AC, DC, and reluctance motors
No full textThis paper uses the flux-MMF diagram to compare and contrast the torque production mechanism in seven common types of electric motor. The flux-MMF diagram is a generalized version of the flux-linkage versus current (ψ-i) diagram for switched-reluctance motors. It is illustrated for switched-reluctance, synchronous-reluctance, induction, brushless AC, brushless DC, interior PM and commutator motors. The calculated flux-MMF diagrams for motors with the same electromagnetic volume, airgap, slotfill, and total copper loss are shown and are used to compare the low-speed torque and torque ripple performance. The motor designs used were reasonably optimized using a combination of commercially available motor CAD packages and finite-element analysis
A Bivariate Genome-Wide Approach to Metabolic Syndrome STAMPEED Consortium
No full textOBJECTIVE The metabolic syndrome (MetS) is defined as concomitant disorders of lipid and glucose metabolism, central obesity, and high blood pressure, with an increased risk of type 2 diabetes and cardiovascular disease. This study tests whether common genetic variants with pleiotropic effects account for some of the correlated architecture among five metabolic phenotypes that define MetS. RESEARCH DESIGN AND METHODS Seven studies of the STAMPEED consortium, comprising 22,161 participants of European ancestry, underwent genome-wide association analyses of metabolic traits using a panel of ∼2.5 million imputed single nucleotide polymorphisms (SNPs). Phenotypes were defined by the National Cholesterol Education Program (NCEP) criteria for MetS in pairwise combinations. Individuals exceeding the NCEP thresholds for both traits of a pair were considered affected. RESULTS Twenty-nine common variants were associated with MetS or a pair of traits. Variants in the genes LPL, CETP, APOA5 (and its cluster), GCKR (and its cluster), LIPC, TRIB1, LOC100128354/MTNR1B, ABCB11, and LOC100129150 were further tested for their association with individual qualitative and quantitative traits. None of the 16 top SNPs (one per gene) associated simultaneously with more than two individual traits. Of them 11 variants showed nominal associations with MetS per se. The effects of 16 top SNPs on the quantitative traits were relatively small, together explaining from ∼9% of the variance in triglycerides, 5.8% of high-density lipoprotein cholesterol, 3.6% of fasting glucose, and 1.4% of systolic blood pressure. CONCLUSIONS Qualitative and quantitative pleiotropic tests on pairs of traits indicate that a small portion of the covariation in these traits can be explained by the reported common genetic variants
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
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
The SH2B1 obesity locus and abnormal glucose homeostasis:lack of evidence for association from a meta-analysis in individuals of European ancestry
No full textBACKGROUND/AIMS: The development of type 2 diabetes (T2D) is influenced both by environmental and by genetic determinants. Obesity is an important risk factor for T2D, mostly mediated by obesity-related insulin resistance. Obesity and insulin resistance are also modulated by the genetic milieu; thus, genes affecting risk of obesity and insulin resistance might also modulate risk of T2D. Recently, 32 loci have been associated with body mass index (BMI) by genome-wide studies, including one locus on chromosome 16p11 containing the SH2B1 gene. Animal studies have suggested that SH2B1 is a physiological enhancer of the insulin receptor and humans with rare deletions or mutations at SH2B1 are obese with a disproportionately high insulin resistance. Thus, the role of SH2B1 in both obesity and insulin resistance makes it a strong candidate for T2D. However, published data on the role of SH2B1 variability on the risk for T2D are conflicting, ranging from no effect at all to a robust association.METHODS: The SH2B1 tag SNP rs4788102 (SNP, single nucleotide polymorphism) was genotyped in 6978 individuals from six studies for abnormal glucose homeostasis (AGH), including impaired fasting glucose, impaired glucose tolerance or T2D, from the GENetics of Type 2 Diabetes in Italy and the United States (GENIUS T2D) consortium. Data from these studies were then meta-analyzed, in a Bayesian fashion, with those from DIAGRAM+ (n = 47,117) and four other published studies (n = 39,448).RESULTS: Variability at the SH2B1 obesity locus was not associated with AGH either in the GENIUS consortium (overall odds ratio (OR) = 0.96; 0.89-1.04) or in the meta-analysis (OR = 1.01; 0.98-1.05).CONCLUSION: Our data exclude a role for the SH2B1 obesity locus in the modulation of AGH.</p
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
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