Institution
University of California, Davis
Education•Davis, California, United States•
About: University of California, Davis is a education organization based out in Davis, California, United States. It is known for research contribution in the topics: Population & Gene. The organization has 78770 authors who have published 180033 publications receiving 8064158 citations. The organization is also known as: UC Davis & UCD.
Topics: Population, Gene, Poison control, Context (language use), Medicine
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Bradley University1, University of Tartu2, National Institutes of Health3, University of California4, Pontifical Catholic University of Chile5, University of Louisville6, University of Latvia7, Pennsylvania State University8, Slovak Academy of Sciences9, University of San Carlos10, University of Malta11, Ghent University12, Clemson University13, Laval University14, University of Buenos Aires15, Osaka University16, Illinois State University17, National Autonomous University of Mexico18, University of Brasília19, University of Western Australia20, University of Lima21, Boğaziçi University22, University of Kassel23, York University24, University of Queensland25, Åbo Akademi University26, Al Akhawayn University27, University of Hawaii at Manoa28, University of Catania29, University of Otago30, University of Dhaka31, Chemnitz University of Technology32, Knox College33, Comenius University in Bratislava34, University of Rijeka35, University of Malaya36, Vilnius University37, American University of Beirut38, Kwangju Health College39, University of Salzburg40, Utrecht University41, National Computerization Agency42, City University of Hong Kong43, University of Idaho44, University of Zimbabwe45, University of Lisbon46, University of Central Lancashire47, Loyola Marymount University48, University of KwaZulu-Natal49, University of Granada50, University of Botswana51, Babeș-Bolyai University52, University of Cyprus53, University of Belgrade54, KPMG55, University of Montpellier56, University of Zurich57, University of Alabama58, Baylor University59, Queen's University Belfast60, University of Ljubljana61, University of Haifa62, University of La Serena63, Florida Atlantic University64, University of California, Davis65, University of Dar es Salaam66, Ramapo College67, Cyprus College68, Middle East Technical University69, Nicolaus Copernicus University in Toruń70, University of the South Pacific71, Vrije Universiteit Brussel72, University at Albany, SUNY73, University of the Aegean74, University of Lethbridge75, University of Vienna76, University of Hong Kong77, Yuan Ze University78, Charles University in Prague79, Chonnam National University80, Indian Institutes of Technology81
TL;DR: The Big Five Inventory (BFI) is a self-report measure designed to assess the high-order personality traits of Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness as discussed by the authors.
Abstract: The Big Five Inventory (BFI) is a self-report measure designed to assess the high-order personality traits of Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. As part of the International Sexuality Description Project, the BFI was translated from English into 28 languages and administered to 17,837 individuals from 56 nations. The resulting cross-cultural data set was used to address three main questions: Does the factor structure of the English BFI fully replicate across cultures? How valid are the BFI trait profiles of individual nations? And how are personality traits distributed throughout the world? The five-dimensional structure was robust across major regions of the world. Trait levels were related in predictable ways to self-esteem, sociosexuality, and national personality profiles. People from the geographic regions of South America and East Asia were significantly different in openness from those inhabiting other world regions. The discussion focuses on limitations of t...
876 citations
TL;DR: In this article, the presence of anthropogenic debris in fishes and shellfish on sale for human consumption was assessed from markets in Makassar, Indonesia, and from California, USA.
Abstract: The ubiquity of anthropogenic debris in hundreds of species of wildlife and the toxicity of chemicals associated with it has begun to raise concerns regarding the presence of anthropogenic debris in seafood. We assessed the presence of anthropogenic debris in fishes and shellfish on sale for human consumption. We sampled from markets in Makassar, Indonesia, and from California, USA. All fish and shellfish were identified to species where possible. Anthropogenic debris was extracted from the digestive tracts of fish and whole shellfish using a 10% KOH solution and quantified under a dissecting microscope. In Indonesia, anthropogenic debris was found in 28% of individual fish and in 55% of all species. Similarly, in the USA, anthropogenic debris was found in 25% of individual fish and in 67% of all species. Anthropogenic debris was also found in 33% of individual shellfish sampled. All of the anthropogenic debris recovered from fish in Indonesia was plastic, whereas anthropogenic debris recovered from fish in the USA was primarily fibers. Variations in debris types likely reflect different sources and waste management strategies between countries. We report some of the first findings of plastic debris in fishes directly sold for human consumption raising concerns regarding human health.
875 citations
TL;DR: Although values for blood urea nitrogen, creatinine, and normalized protein catabolic rate were significantly less among patients who died, these parameters, as well as cholesterol level and diabetes, were not important predictors of death in multivariate analysis.
875 citations
University of Tokyo1, Technische Universität München2, Max Planck Society3, Academia Sinica Institute of Astronomy and Astrophysics4, University of California, Davis5, Subaru6, École Polytechnique Fédérale de Lausanne7, University of Cambridge8, University of California, Los Angeles9, Ludwig Maximilian University of Munich10, Niels Bohr Institute11, Leiden University12, Stanford University13, Kapteyn Astronomical Institute14
TL;DR: In this paper, the authors present a measurement of the Hubble constant and other cosmological parameters from a joint analysis of six gravitationally lensed quasars with measured time delays.
Abstract: We present a measurement of the Hubble constant ($H_{0}$) and other cosmological parameters from a joint analysis of six gravitationally lensed quasars with measured time delays. All lenses except the first are analyzed blindly with respect to the cosmological parameters. In a flat $\Lambda$CDM cosmology, we find $H_{0} = 73.3_{-1.8}^{+1.7}$, a 2.4% precision measurement, in agreement with local measurements of $H_{0}$ from type Ia supernovae calibrated by the distance ladder, but in $3.1\sigma$ tension with $Planck$ observations of the cosmic microwave background (CMB). This method is completely independent of both the supernovae and CMB analyses. A combination of time-delay cosmography and the distance ladder results is in $5.3\sigma$ tension with $Planck$ CMB determinations of $H_{0}$ in flat $\Lambda$CDM. We compute Bayes factors to verify that all lenses give statistically consistent results, showing that we are not underestimating our uncertainties and are able to control our systematics. We explore extensions to flat $\Lambda$CDM using constraints from time-delay cosmography alone, as well as combinations with other cosmological probes, including CMB observations from $Planck$, baryon acoustic oscillations, and type Ia supernovae. Time-delay cosmography improves the precision of the other probes, demonstrating the strong complementarity. Allowing for spatial curvature does not resolve the tension with $Planck$. Using the distance constraints from time-delay cosmography to anchor the type Ia supernova distance scale, we reduce the sensitivity of our $H_0$ inference to cosmological model assumptions. For six different cosmological models, our combined inference on $H_{0}$ ranges from $\sim73$-$78~\mathrm{km~s^{-1}~Mpc^{-1}}$, which is consistent with the local distance ladder constraints.
875 citations
Northern Arizona University1, University of Minnesota2, University of California, Davis3, Woods Hole Oceanographic Institution4, Massachusetts Institute of Technology5, University of Copenhagen6, University of Trento7, Chinese Academy of Sciences8, University of California, San Francisco9, Children's Hospital of Philadelphia10, Pacific Northwest National Laboratory11, North Carolina State University12, University of Montana13, Dalhousie University14, University of British Columbia15, Shedd Aquarium16, University of Colorado Denver17, University of California, San Diego18, Michigan State University19, Stanford University20, Broad Institute21, Harvard University22, Australian National University23, University of Düsseldorf24, Sookmyung Women's University25, San Diego State University26, Howard Hughes Medical Institute27, Max Planck Society28, Cornell University29, University of Washington30, Colorado State University31, Google32, Syracuse University33, Webster University34, United States Department of Agriculture35, University of Arkansas for Medical Sciences36, Colorado School of Mines37, Atlantic Oceanographic and Meteorological Laboratory38, University of Southern Mississippi39, University of California, Merced40, Wageningen University and Research Centre41, University of Arizona42, Environment Agency43, University of Florida44, Merck & Co.45
TL;DR: QIIME 2 provides new features that will drive the next generation of microbiome research, including interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.
Abstract: We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.
875 citations
Authors
Showing all 79538 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Eric S. Lander | 301 | 826 | 525976 |
| Ronald C. Kessler | 274 | 1332 | 328983 |
| George M. Whitesides | 240 | 1739 | 269833 |
| Ronald M. Evans | 199 | 708 | 166722 |
| Virginia M.-Y. Lee | 194 | 993 | 148820 |
| Scott M. Grundy | 187 | 841 | 231821 |
| Julie E. Buring | 186 | 950 | 132967 |
| Patrick O. Brown | 183 | 755 | 200985 |
| Anil K. Jain | 183 | 1016 | 192151 |
| John C. Morris | 183 | 1441 | 168413 |
| Douglas R. Green | 182 | 661 | 145944 |
| John R. Yates | 177 | 1036 | 129029 |
| Barry Halliwell | 173 | 662 | 159518 |
| Roderick T. Bronson | 169 | 679 | 107702 |
| Hongfang Liu | 166 | 2356 | 156290 |