Institution
University of Cambridge
Education•Cambridge, United Kingdom•
About: University of Cambridge is a education organization based out in Cambridge, United Kingdom. It is known for research contribution in the topics: Population & Galaxy. The organization has 118293 authors who have published 282289 publications receiving 14497093 citations. The organization is also known as: Cambridge University & Cambridge.
Topics: Population, Galaxy, Context (language use), Gene, Transplantation
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors proposed a method to predict the distribution of residence-times in large systems using distribution-functions for residence times, which can be used to calculate the efficiencies of reactors and blenders.
1,929 citations
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TL;DR: A class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, as derived from quantum mechanical calculations, are introduced.
Abstract: We introduce a class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, as derived from quantum mechanical calculations. The models do not have a fixed functional form and hence are capable of modeling complex potential energy landscapes. They are systematically improvable with more data. We apply the method to bulk crystals, and test it by calculating properties at high temperatures. Using the interatomic potential to generate the long molecular dynamics trajectories required for such calculations saves orders of magnitude in computational cost.
1,923 citations
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TL;DR: This paper shows how to use the recently developed firefly algorithm to solve non-linear design problems and proposes a few new test functions with either singularity or stochastic components but with known global optimality and thus they can be used to validate new optimisation algorithms.
Abstract: Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimisation problems. In this paper, we show how to use the recently developed firefly algorithm to solve non-linear design problems. For the standard pressure vessel design optimisation, the optimal solution found by FA is far better than the best solution obtained previously in the literature. In addition, we also propose a few new test functions with either singularity or stochastic components but with known global optimality and thus they can be used to validate new optimisation algorithms. Possible topics for further research are also discussed.
1,911 citations
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Broad Institute1, Harvard University2, Monash University3, Kyoto University4, Genentech5, Vanderbilt University6, New York University7, NewYork–Presbyterian Hospital8, Second Military Medical University9, University of Queensland10, University of Toronto11, University of Groningen12, University of Tartu13, Beijing Jiaotong University14, Icahn School of Medicine at Mount Sinai15, Radboud University Nijmegen16, Medisch Spectrum Twente17, Leiden University18, University of Paris19, French Institute of Health and Medical Research20, University of Alabama at Birmingham21, University of Cambridge22, GlaxoSmithKline23, University of Amsterdam24, Hanyang University25, Spanish National Research Council26, Complutense University of Madrid27, Umeå University28, Boston University29, Council on Education for Public Health30, McGill University31, National Health Service32, University of Manchester33, University of Pittsburgh34, University of California, San Francisco35, Karolinska Institutet36, North Shore-LIJ Health System37, University of Chicago38, University of Tokyo39
TL;DR: A genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries provides empirical evidence that the genetics of RA can provide important information for drug discovery, and sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis.
Abstract: A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2, 3, 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci6 and pathway analyses7, 8, 9—as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes—to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.
1,910 citations
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TL;DR: In this article, the authors introduce the current state of development in the application of ferroelectric thin films for electronic devices and discuss the physics relevant for the performance and failure of these devices.
Abstract: This review covers important advances in recent years in the physics of thin-film ferroelectric oxides, the strongest emphasis being on those aspects particular to ferroelectrics in thin-film form. The authors introduce the current state of development in the application of ferroelectric thin films for electronic devices and discuss the physics relevant for the performance and failure of these devices. Following this the review covers the enormous progress that has been made in the first-principles computational approach to understanding ferroelectrics. The authors then discuss in detail the important role that strain plays in determining the properties of epitaxial thin ferroelectric films. Finally, this review ends with a look at the emerging possibilities for nanoscale ferroelectrics, with particular emphasis on ferroelectrics in nonconventional nanoscale geometries.
1,908 citations
Authors
Showing all 119522 results
Name | H-index | Papers | Citations |
---|---|---|---|
Albert Hofman | 267 | 2530 | 321405 |
Zhong Lin Wang | 245 | 2529 | 259003 |
Solomon H. Snyder | 232 | 1222 | 200444 |
Trevor W. Robbins | 231 | 1137 | 164437 |
George Davey Smith | 224 | 2540 | 248373 |
Nicholas J. Wareham | 212 | 1657 | 204896 |
Cyrus Cooper | 204 | 1869 | 206782 |
Eric B. Rimm | 196 | 988 | 147119 |
Martin White | 196 | 2038 | 232387 |
Simon D. M. White | 189 | 795 | 231645 |
Michael Rutter | 188 | 676 | 151592 |
George Efstathiou | 187 | 637 | 156228 |
Mark Hallett | 186 | 1170 | 123741 |
David H. Weinberg | 183 | 700 | 171424 |
Paul G. Richardson | 183 | 1533 | 155912 |