scispace - formally typeset
Search or ask a question
Institution

University of Cambridge

EducationCambridge, 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.


Papers
More filters
Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
Yukinori Okada1, Yukinori Okada2, Di Wu3, Di Wu2, Di Wu1, Gosia Trynka1, Gosia Trynka2, Towfique Raj1, Towfique Raj2, Chikashi Terao4, Katsunori Ikari, Yuta Kochi, Koichiro Ohmura4, Akari Suzuki, Shinji Yoshida, Robert R. Graham5, A. Manoharan5, Ward Ortmann5, Tushar Bhangale5, Joshua C. Denny6, Robert J. Carroll6, Anne E. Eyler6, Jeff Greenberg7, Joel M. Kremer, Dimitrios A. Pappas8, Lei Jiang9, Jian Yin9, Lingying Ye9, Ding Feng Su9, Jian Yang10, Gang Xie11, E.C. Keystone11, Harm-Jan Westra12, Tõnu Esko13, Tõnu Esko2, Tõnu Esko1, Andres Metspalu13, Xuezhong Zhou14, Namrata Gupta1, Daniel B. Mirel1, Eli A. Stahl15, Dorothee Diogo2, Dorothee Diogo1, Jing Cui2, Jing Cui1, Katherine P. Liao1, Katherine P. Liao2, Michael H. Guo2, Michael H. Guo1, Keiko Myouzen, Takahisa Kawaguchi4, Marieke J H Coenen16, Piet L. C. M. van Riel16, Mart A F J van de Laar17, Henk-Jan Guchelaar18, Tom W J Huizinga18, Philippe Dieudé19, Xavier Mariette20, S. Louis Bridges21, Alexandra Zhernakova18, Alexandra Zhernakova12, René E. M. Toes18, Paul P. Tak22, Paul P. Tak23, Paul P. Tak24, Corinne Miceli-Richard20, So Young Bang25, Hye Soon Lee25, Javier Martin26, Miguel A. Gonzalez-Gay, Luis Rodriguez-Rodriguez27, Solbritt Rantapää-Dahlqvist28, Lisbeth Ärlestig28, Hyon K. Choi29, Hyon K. Choi2, Yoichiro Kamatani30, Pilar Galan19, Mark Lathrop31, Steve Eyre32, Steve Eyre33, John Bowes33, John Bowes32, Anne Barton33, Niek de Vries24, Larry W. Moreland34, Lindsey A. Criswell35, Elizabeth W. Karlson2, Atsuo Taniguchi, Ryo Yamada4, Michiaki Kubo, Jun Liu2, Sang Cheol Bae25, Jane Worthington33, Jane Worthington32, Leonid Padyukov36, Lars Klareskog36, Peter K. Gregersen37, Soumya Raychaudhuri2, Soumya Raychaudhuri1, Barbara E. Stranger38, Philip L. De Jager1, Philip L. De Jager2, Lude Franke12, Peter M. Visscher10, Matthew A. Brown10, Hisashi Yamanaka, Tsuneyo Mimori4, Atsushi Takahashi, Huji Xu9, Timothy W. Behrens5, Katherine A. Siminovitch11, Shigeki Momohara, Fumihiko Matsuda4, Kazuhiko Yamamoto39, Robert M. Plenge1, Robert M. Plenge2 
20 Feb 2014-Nature
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

Journal ArticleDOI
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

NameH-indexPapersCitations
Albert Hofman2672530321405
Zhong Lin Wang2452529259003
Solomon H. Snyder2321222200444
Trevor W. Robbins2311137164437
George Davey Smith2242540248373
Nicholas J. Wareham2121657204896
Cyrus Cooper2041869206782
Eric B. Rimm196988147119
Martin White1962038232387
Simon D. M. White189795231645
Michael Rutter188676151592
George Efstathiou187637156228
Mark Hallett1861170123741
David H. Weinberg183700171424
Paul G. Richardson1831533155912
Network Information
Related Institutions (5)
University of Oxford
258.1K papers, 12.9M citations

98% related

University College London
210.6K papers, 9.8M citations

97% related

Imperial College London
209.1K papers, 9.3M citations

97% related

McGill University
162.5K papers, 6.9M citations

94% related

University of Toronto
294.9K papers, 13.5M citations

93% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023466
20222,049
202115,692
202015,352
201913,664
201812,549