Institution
University of Queensland
Education•Brisbane, 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.
Papers published on a yearly basis
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TL;DR: In this paper, a bounded multidimensional model of social entrepreneurship is proposed to consider the unique characteristics of social entrepreneurs and the context within which they must operate, and its implications for social entrepreneurship theory, management practice, and policy directions are discussed.
1,164 citations
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TL;DR: In this paper, the concept of a metafrontier is used to compare the technical efficiencies of firms that may be classified into different groups. And the authors present the basic analytical framework necessary for the definition of a meta-frontier, shows how a meta-frontiers can be estimated using non-parametric and parametric methods, and presents an empirical application using cross-country agricultural sector data.
Abstract: This paper uses the concept of a metafrontier to compare the technical efficiencies of firms that may be classified into different groups. The paper presents the basic analytical framework necessary for the definition of a metafrontier, shows how a metafrontier can be estimated using non-parametric and parametric methods, and presents an empirical application using cross-country agricultural sector data. The paper also explores the issues of technological change, time-varying technical inefficiency, multiple outputs, different efficiency orientations, and firm heterogeneity.
1,162 citations
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Mike A. Nalls1, Cornelis Blauwendraat1, Costanza L. Vallerga2, Karl Heilbron +245 more•Institutions (23)
TL;DR: These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified.
Abstract: Summary Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources).
1,152 citations
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TL;DR: This paper updates the earlier work by Keating et?al.
Abstract: Agricultural systems models worldwide are increasingly being used to explore options and solutions for the food security, climate change adaptation and mitigation and carbon trading problem domains. APSIM (Agricultural Production Systems sIMulator) is one such model that continues to be applied and adapted to this challenging research agenda. From its inception twenty years ago, APSIM has evolved into a framework containing many of the key models required to explore changes in agricultural landscapes with capability ranging from simulation of gene expression through to multi-field farms and beyond.Keating et?al. (2003) described many of the fundamental attributes of APSIM in detail. Much has changed in the last decade, and the APSIM community has been exploring novel scientific domains and utilising software developments in social media, web and mobile applications to provide simulation tools adapted to new demands.This paper updates the earlier work by Keating et?al. (2003) and chronicles the changing external challenges and opportunities being placed on APSIM during the last decade. It also explores and discusses how APSIM has been evolving to a "next generation" framework with improved features and capabilities that allow its use in many diverse topics. APSIM is an agricultural modelling framework used extensively worldwide.It can simulate a wide range of agricultural systems.It begins its third decade evolving into an agro-ecosystem framework.
1,151 citations
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Radboud University Nijmegen1, CSIRO Marine and Atmospheric Research2, Vrije Universiteit Brussel3, Commonwealth Scientific and Industrial Research Organisation4, Forest Research Institute Malaysia5, Griffith University6, University of North Carolina at Wilmington7, University of Queensland8, University of Malaya9, Plymouth Marine Laboratory10
TL;DR: In this article, the authors reviewed the literature with regard to the degree of interlinkage between mangroves and adjacent habitats, a research area which has received increasing attention in the last decade.
1,148 citations
Authors
Showing all 52145 results
Name | H-index | Papers | Citations |
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Graham A. Colditz | 261 | 1542 | 256034 |
George Davey Smith | 224 | 2540 | 248373 |
David J. Hunter | 213 | 1836 | 207050 |
Daniel Levy | 212 | 933 | 194778 |
Christopher J L Murray | 209 | 754 | 310329 |
Matthew Meyerson | 194 | 553 | 243726 |
Luigi Ferrucci | 193 | 1601 | 181199 |
Nicholas G. Martin | 192 | 1770 | 161952 |
Paul M. Thompson | 183 | 2271 | 146736 |
Jie Zhang | 178 | 4857 | 221720 |
Alan D. Lopez | 172 | 863 | 259291 |
Ian J. Deary | 166 | 1795 | 114161 |
Steven N. Blair | 165 | 879 | 132929 |
Carlos Bustamante | 161 | 770 | 106053 |
David W. Johnson | 160 | 2714 | 140778 |