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Institution

University of Queensland

EducationBrisbane, Queensland, Australia
About: University of Queensland is a education organization based out in Brisbane, Queensland, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 51138 authors who have published 155721 publications receiving 5717659 citations. The organization is also known as: UQ & The University of Queensland.


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Journal ArticleDOI
TL;DR: DREME is much faster than many commonly used algorithms, scales linearly in dataset size, finds multiple, non-redundant motifs and reports a reliable measure of statistical significance for each motif found.
Abstract: Motivation: Transcription factor (TF) ChIP-seq datasets have particular characteristics that provide unique challenges and opportunities for motif discovery. Most existing motif discovery algorithms do not scale well to such large datasets, or fail to report many motifs associated with cofactors of the ChIP-ed TF. Results: We present DREME, a motif discovery algorithm specifically designed to find the short, core DNA-binding motifs of eukaryotic TFs, and optimized to analyze very large ChIP-seq datasets in minutes. Using DREME, we discover the binding motifs of the the ChIP-ed TF and many cofactors in mouse ES cell (mESC), mouse erythrocyte and human cell line ChIP-seq datasets. For example, in mESC ChIP-seq data for the TF Esrrb, we discover the binding motifs for eight cofactor TFs important in the maintenance of pluripotency. Several other commonly used algorithms find at most two cofactor motifs in this same dataset. DREME can also perform discriminative motif discovery, and we use this feature to provide evidence that Sox2 and Oct4 do not bind in mES cells as an obligate heterodimer. DREME is much faster than many commonly used algorithms, scales linearly in dataset size, finds multiple, non-redundant motifs and reports a reliable measure of statistical significance for each motif found. DREME is available as part of the MEME Suite of motif-based sequence analysis tools (http://meme.nbcr.net).

963 citations

Journal ArticleDOI
TL;DR: This Consensus Statement documents the central role and global importance of microorganisms in climate change biology and puts humanity on notice that the impact of climate change will depend heavily on responses of micro organisms, which are essential for achieving an environmentally sustainable future.
Abstract: In the Anthropocene, in which we now live, climate change is impacting most life on Earth. Microorganisms support the existence of all higher trophic life forms. To understand how humans and other life forms on Earth (including those we are yet to discover) can withstand anthropogenic climate change, it is vital to incorporate knowledge of the microbial 'unseen majority'. We must learn not just how microorganisms affect climate change (including production and consumption of greenhouse gases) but also how they will be affected by climate change and other human activities. This Consensus Statement documents the central role and global importance of microorganisms in climate change biology. It also puts humanity on notice that the impact of climate change will depend heavily on responses of microorganisms, which are essential for achieving an environmentally sustainable future.

963 citations

Journal ArticleDOI
Ali H. Mokdad1, Katherine Ballestros1, Michelle Echko1, Scott D Glenn1, Helen E Olsen1, Erin C Mullany1, Alexander Lee1, Abdur Rahman Khan2, Alireza Ahmadi3, Alireza Ahmadi4, Alize J. Ferrari5, Alize J. Ferrari6, Alize J. Ferrari1, Amir Kasaeian7, Andrea Werdecker, Austin Carter1, Ben Zipkin1, Benn Sartorius8, Benn Sartorius9, Berrin Serdar10, Bryan L. Sykes11, Christopher Troeger1, Christina Fitzmaurice12, Christina Fitzmaurice1, Colin D. Rehm13, Damian Santomauro5, Damian Santomauro1, Damian Santomauro6, Daniel Kim14, Danny V. Colombara1, David C. Schwebel15, Derrick Tsoi1, Dhaval Kolte16, Elaine O. Nsoesie1, Emma Nichols1, Eyal Oren17, Fiona J Charlson1, Fiona J Charlson6, Fiona J Charlson5, George C Patton18, Gregory A. Roth1, H. Dean Hosgood19, Harvey Whiteford5, Harvey Whiteford1, Harvey Whiteford6, Hmwe H Kyu1, Holly E. Erskine1, Holly E. Erskine5, Holly E. Erskine6, Hsiang Huang20, Ira Martopullo1, Jasvinder A. Singh15, Jean B. Nachega21, Jean B. Nachega22, Jean B. Nachega23, Juan Sanabria24, Juan Sanabria25, Kaja Abbas26, Kanyin Ong1, Karen M. Tabb27, Kristopher J. Krohn1, Leslie Cornaby1, Louisa Degenhardt1, Louisa Degenhardt28, Mark Moses1, Maryam S. Farvid29, Max Griswold1, Michael H. Criqui30, Michelle L. Bell31, Minh Nguyen1, Mitch T Wallin32, Mitch T Wallin33, Mojde Mirarefin1, Mostafa Qorbani, Mustafa Z. Younis34, Nancy Fullman1, Patrick Liu1, Paul S Briant1, Philimon Gona35, Rasmus Havmoller3, Ricky Leung36, Ruth W Kimokoti37, Shahrzad Bazargan-Hejazi38, Shahrzad Bazargan-Hejazi39, Simon I. Hay1, Simon I. Hay40, Simon Yadgir1, Stan Biryukov1, Stein Emil Vollset1, Stein Emil Vollset41, Tahiya Alam1, Tahvi Frank1, Talha Farid2, Ted R. Miller42, Ted R. Miller43, Theo Vos1, Till Bärnighausen44, Till Bärnighausen29, Tsegaye Telwelde Gebrehiwot45, Yuichiro Yano46, Ziyad Al-Aly47, Alem Mehari48, Alexis J. Handal49, Amit Kandel50, Ben Anderson51, Brian J. Biroscak31, Brian J. Biroscak52, Dariush Mozaffarian53, E. Ray Dorsey54, Eric L. Ding29, Eun-Kee Park55, Gregory R. Wagner29, Guoqing Hu56, Honglei Chen57, Jacob E. Sunshine51, Jagdish Khubchandani58, Janet L Leasher59, Janni Leung5, Janni Leung51, Joshua A. Salomon29, Jürgen Unützer51, Leah E. Cahill60, Leah E. Cahill29, Leslie T. Cooper61, Masako Horino, Michael Brauer1, Michael Brauer62, Nicholas J K Breitborde63, Peter J. Hotez64, Roman Topor-Madry65, Roman Topor-Madry66, Samir Soneji67, Saverio Stranges68, Spencer L. James1, Stephen M. Amrock69, Sudha Jayaraman70, Tejas V. Patel, Tomi Akinyemiju15, Vegard Skirbekk41, Vegard Skirbekk71, Yohannes Kinfu72, Zulfiqar A Bhutta73, Jost B. Jonas44, Christopher J L Murray1 
Institute for Health Metrics and Evaluation1, University of Louisville2, Karolinska Institutet3, Kermanshah University of Medical Sciences4, University of Queensland5, Centre for Mental Health6, Tehran University of Medical Sciences7, South African Medical Research Council8, University of KwaZulu-Natal9, University of Colorado Boulder10, University of California, Irvine11, Fred Hutchinson Cancer Research Center12, Montefiore Medical Center13, Northeastern University14, University of Alabama at Birmingham15, Brown University16, San Diego State University17, University of Melbourne18, Albert Einstein College of Medicine19, Cambridge Health Alliance20, University of Pittsburgh21, University of Cape Town22, Johns Hopkins University23, Case Western Reserve University24, Marshall University25, University of London26, University of Illinois at Urbana–Champaign27, National Drug and Alcohol Research Centre28, Harvard University29, University of California, San Diego30, Yale University31, Veterans Health Administration32, Georgetown University33, Jackson State University34, University of Massachusetts Boston35, State University of New York System36, Simmons College37, University of California, Los Angeles38, Charles R. Drew University of Medicine and Science39, University of Oxford40, Norwegian Institute of Public Health41, Pacific Institute42, Curtin University43, Heidelberg University44, Jimma University45, Northwestern University46, Washington University in St. Louis47, Howard University48, University of New Mexico49, University at Buffalo50, University of Washington51, University of South Florida52, Tufts University53, University of Rochester Medical Center54, Kosin University55, Central South University56, Michigan State University57, Ball State University58, Nova Southeastern University59, Dalhousie University60, Mayo Clinic61, University of British Columbia62, Ohio State University63, Baylor University64, Wrocław Medical University65, Jagiellonian University Medical College66, Dartmouth College67, University of Western Ontario68, Oregon Health & Science University69, Virginia Commonwealth University70, Columbia University71, University of Canberra72, Aga Khan University73
10 Apr 2018-JAMA
TL;DR: There are wide differences in the burden of disease at the state level and specific diseases and risk factors, such as drug use disorders, high BMI, poor diet, high fasting plasma glucose level, and alcohol use disorders are increasing and warrant increased attention.
Abstract: Introduction Several studies have measured health outcomes in the United States, but none have provided a comprehensive assessment of patterns of health by state. Objective To use the results of the Global Burden of Disease Study (GBD) to report trends in the burden of diseases, injuries, and risk factors at the state level from 1990 to 2016. Design and Setting A systematic analysis of published studies and available data sources estimates the burden of disease by age, sex, geography, and year. Main Outcomes and Measures Prevalence, incidence, mortality, life expectancy, healthy life expectancy (HALE), years of life lost (YLLs) due to premature mortality, years lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 333 causes and 84 risk factors with 95% uncertainty intervals (UIs) were computed. Results Between 1990 and 2016, overall death rates in the United States declined from 745.2 (95% UI, 740.6 to 749.8) per 100 000 persons to 578.0 (95% UI, 569.4 to 587.1) per 100 000 persons. The probability of death among adults aged 20 to 55 years declined in 31 states and Washington, DC from 1990 to 2016. In 2016, Hawaii had the highest life expectancy at birth (81.3 years) and Mississippi had the lowest (74.7 years), a 6.6-year difference. Minnesota had the highest HALE at birth (70.3 years), and West Virginia had the lowest (63.8 years), a 6.5-year difference. The leading causes of DALYs in the United States for 1990 and 2016 were ischemic heart disease and lung cancer, while the third leading cause in 1990 was low back pain, and the third leading cause in 2016 was chronic obstructive pulmonary disease. Opioid use disorders moved from the 11th leading cause of DALYs in 1990 to the 7th leading cause in 2016, representing a 74.5% (95% UI, 42.8% to 93.9%) change. In 2016, each of the following 6 risks individually accounted for more than 5% of risk-attributable DALYs: tobacco consumption, high body mass index (BMI), poor diet, alcohol and drug use, high fasting plasma glucose, and high blood pressure. Across all US states, the top risk factors in terms of attributable DALYs were due to 1 of the 3 following causes: tobacco consumption (32 states), high BMI (10 states), or alcohol and drug use (8 states). Conclusions and Relevance There are wide differences in the burden of disease at the state level. Specific diseases and risk factors, such as drug use disorders, high BMI, poor diet, high fasting plasma glucose level, and alcohol use disorders are increasing and warrant increased attention. These data can be used to inform national health priorities for research, clinical care, and policy.

962 citations

Journal ArticleDOI
TL;DR: Graphene was doped with both boron and nitrogen at well-defined doping sites to induce a synergistic effect that boosts its catalytic activity for oxygen reduction.
Abstract: Don't be a dope: be a double dope! Graphene was doped with both boron and nitrogen at well-defined doping sites to induce a synergistic effect that boosts its catalytic activity for oxygen reduction (see structure). The excellent catalytic performance of the new metal-free catalyst is comparable to that of commercial Pt/C.

962 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a tripartite model for social identity, which can be represented in terms of three factors: centrality, ingroup affect, and ingroup ties, and examined the efficacy of this model in five studies involving a total of 1078 respondents, one nonstudent sample, and three group memberships.
Abstract: Despite the importance of the social identification construct in research and theory on group processes and intergroup relations, the issue of its dimensionality remains unresolved. It is proposed that social identity can be represented in terms of three factors: centrality; ingroup affect; and ingroup ties. I examined the efficacy of this model in five studies involving a total of 1078 respondents, one nonstudent sample, and three group memberships (university, gender, and nationality). Results of confirmatory factor analyses support the acceptability of the tripartite model, which fits the data significantly better than one- or two-dimensional (cognition/affect) alternatives. Correlations with theoretically relevant variables provide support for the convergent and discriminant validity of the three factors. Advantages and implications of the three-factor model are considered, with particular reference to social identity theory.

961 citations


Authors

Showing all 52145 results

NameH-indexPapersCitations
Graham A. Colditz2611542256034
George Davey Smith2242540248373
David J. Hunter2131836207050
Daniel Levy212933194778
Christopher J L Murray209754310329
Matthew Meyerson194553243726
Luigi Ferrucci1931601181199
Nicholas G. Martin1921770161952
Paul M. Thompson1832271146736
Jie Zhang1784857221720
Alan D. Lopez172863259291
Ian J. Deary1661795114161
Steven N. Blair165879132929
Carlos Bustamante161770106053
David W. Johnson1602714140778
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023507
20221,728
202111,678
202010,832
20199,671
20189,015