Institution
University of Western Australia
Education•Perth, Western Australia, Australia•
About: University of Western Australia is a education organization based out in Perth, Western Australia, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 29613 authors who have published 87405 publications receiving 3064466 citations. The organization is also known as: UWA & University of WA.
Papers published on a yearly basis
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TL;DR: Improved P-use efficiency can be achieved by plants that have overall lower P concentrations, and by optimal distribution and redistribution of P in the plant allowing maximum growth and biomass allocation to harvestable plant parts.
Abstract: Limitation of grain crop productivity by phosphorus (P) is widespread and will probably increase in the future. Enhanced P efficiency can be achieved by improved uptake of phosphate from soil (P-acquisition efficiency) and by improved productivity per unit P taken up (P-use efficiency). This review focuses on improved P-use efficiency, which can be achieved by plants that have overall lower P concentrations, and by optimal distribution and redistribution of P in the plant allowing maximum growth and biomass allocation to harvestable plant parts. Significant decreases in plant P pools may be possible, for example, through reductions of superfluous ribosomal RNA and replacement of phospholipids by sulfolipids and galactolipids. Improvements in P distribution within the plant may be possible by increased remobilization from tissues that no longer need it (e.g. senescing leaves) and reduced partitioning of P to developing grains. Such changes would prolong and enhance the productive use of P in photosynthesis and have nutritional and environmental benefits. Research considering physiological, metabolic, molecular biological, genetic and phylogenetic aspects of P-use efficiency is urgently needed to allow significant progress to be made in our understanding of this complex trait.
655 citations
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TL;DR: The role of modelling in economic evaluation is explored by discussing, with examples, the uses of models and some suggestions for good modelling practice are made.
Abstract: The role of modelling in economic evaluation is explored by discussing, with examples, the uses of models. The expanded use of pragmatic clinical trials as an alternative to models is discussed. Some suggestions for good modelling practice are made.
655 citations
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Sahlgrenska University Hospital1, National Institutes of Health2, University of Paris3, Children's Hospital Oakland Research Institute4, University of Glasgow5, Aarhus University6, Centro Nacional de Investigaciones Cardiovasculares7, Medical University of Vienna8, University of Amsterdam9, University of California, Los Angeles10, University of Western Ontario11, Monash University12, University of Copenhagen13, University of Western Australia14, Royal Perth Hospital15, French Institute of Health and Medical Research16, Oregon Health & Science University17, University of Bristol18, University of Cambridge19, Trinity College, Dublin20, University of Texas Southwestern Medical Center21, Charité22, Utrecht University23, University of the Witwatersrand24, Imperial College London25, Technische Universität München26, University of Helsinki27, University of Groningen28, Hacettepe University29, University of Milan30, Columbia University31
TL;DR: In this paper, the authors proposed a method to solve the problem of the problem: this paper ] of "uniformity" of the distribution of data points in the data set.
Abstract: Abstract
655 citations
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Nasim Mavaddat1, Kyriaki Michailidou2, Kyriaki Michailidou1, Joe Dennis1 +307 more•Institutions (105)
TL;DR: This PRS, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset is developed and empirically validated and is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.
Abstract: Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.
653 citations
Authors
Showing all 29972 results
Name | H-index | Papers | Citations |
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Nicholas G. Martin | 192 | 1770 | 161952 |
Cornelia M. van Duijn | 183 | 1030 | 146009 |
Kay-Tee Khaw | 174 | 1389 | 138782 |
Steven N. Blair | 165 | 879 | 132929 |
David W. Bates | 159 | 1239 | 116698 |
Mark E. Cooper | 158 | 1463 | 124887 |
David Cameron | 154 | 1586 | 126067 |
Stephen T. Holgate | 142 | 870 | 82345 |
Jeremy K. Nicholson | 141 | 773 | 80275 |
Xin Chen | 139 | 1008 | 113088 |
Graeme J. Hankey | 137 | 844 | 143373 |
David Stuart | 136 | 1665 | 103759 |
Joachim Heinrich | 136 | 1309 | 76887 |
Carlos M. Duarte | 132 | 1173 | 86672 |
David Smith | 129 | 2184 | 100917 |