Institution
University of California, Irvine
Education•Irvine, California, United States•
About: University of California, Irvine is a education organization based out in Irvine, California, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 47031 authors who have published 113602 publications receiving 5521832 citations. The organization is also known as: UC Irvine & UCI.
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University of Sussex1, Jet Propulsion Laboratory2, California Institute of Technology3, European Space Agency4, Ames Research Center5, University of Edinburgh6, Paris Diderot University7, Imperial College London8, University of Paris-Sud9, Aix-Marseille University10, Cornell University11, University of La Laguna12, Spanish National Research Council13, Complutense University of Madrid14, UK Astronomy Technology Centre15, University of Colorado Boulder16, University of California, Irvine17, Goddard Space Flight Center18, University of Nottingham19, Cardiff University20, University of Padua21, Institut d'Astrophysique de Paris22, University of Cambridge23, University of British Columbia24, European Space Research and Technology Centre25, University of Manchester26, University College London27, University of Lethbridge28, Rutherford Appleton Laboratory29, University of Oxford30, Commonwealth Scientific and Industrial Research Organisation31, University of Hertfordshire32, Harvard University33
TL;DR: The Herschel Multi-tiered Extragalactic Survey (HerMES) is a legacy program designed to map a set of nested fields totalling ∼380deg^2 as mentioned in this paper.
Abstract: The Herschel Multi-tiered Extragalactic Survey (HerMES) is a legacy programme designed to map a set of nested fields totalling ∼380 deg^2. Fields range in size from 0.01 to ∼20 deg^2, using the Herschel-Spectral and Photometric Imaging Receiver (SPIRE) (at 250, 350 and 500 μm) and the Herschel-Photodetector Array Camera and Spectrometer (PACS) (at 100 and 160 μm), with an additional wider component of 270 deg^2 with SPIRE alone. These bands cover the peak of the redshifted thermal spectral energy distribution from interstellar dust and thus capture the reprocessed optical and ultraviolet radiation from star formation that has been absorbed by dust, and are critical for forming a complete multiwavelength understanding of galaxy formation and evolution.
The survey will detect of the order of 100 000 galaxies at 5σ in some of the best-studied fields in the sky. Additionally, HerMES is closely coordinated with the PACS Evolutionary Probe survey. Making maximum use of the full spectrum of ancillary data, from radio to X-ray wavelengths, it is designed to facilitate redshift determination, rapidly identify unusual objects and understand the relationships between thermal emission from dust and other processes. Scientific questions HerMES will be used to answer include the total infrared emission of galaxies, the evolution of the luminosity function, the clustering properties of dusty galaxies and the properties of populations of galaxies which lie below the confusion limit through lensing and statistical techniques.
This paper defines the survey observations and data products, outlines the primary scientific goals of the HerMES team, and reviews some of the early results.
852 citations
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TL;DR: These pilot results suggest that acute, posttrauma propranolol may have a preventive effect on subsequent PTSD.
852 citations
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Scripps Research Institute1, University of California, San Diego2, Massachusetts Institute of Technology3, Tel Aviv University4, University of California, Irvine5, Salk Institute for Biological Studies6, Northwestern University7, University of Washington8, Australian Stem Cell Centre9, Commonwealth Scientific and Industrial Research Organisation10, Monash University, Clayton campus11
TL;DR: The results illustrate the dynamic nature of genomic abnormalities in pluripotent stem cells and the need for frequent genomic monitoring to assure phenotypic stability and clinical safety.
852 citations
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TL;DR: An approach to designing tight-binding ligands with a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches is reported, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.
Abstract: Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.
850 citations
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TL;DR: In this article, the authors focus on problems with the definition and empirical identification of immigrant "first" and "second" generations in the United States These aggregates are decomposed into a typology of distinct generational cohorts (10, 125, 15, 175, 20, 25) defined by age and life stage at migration for the foreign-born, and by parental nativity for the US-born.
Abstract: Many theoretical questions have been raised about the incorporation of children of immigrants: their “coming of age” in the United States, their modes of acculturation, ethnic identity, ethnic group formation, patterns of language use and shift, and social, residential, reproductive, marital, educational, occupational, economic, civic and political trajectories into adulthood All of these are open empirical questions, but each of them presupposes a clear operational definition of what is meant by “second generation” vis-a-vis the “first generation,” and even of something as basic as the ethnicity of first- vs second-generation persons While the import of intergenerational analysis for the study of the long-term impact of immigration is clear, there is no consensus on the meaning and measurement of “generations” This article focuses on problems with the definition and empirical identification of immigrant “first” and “second” generations in the United States These aggregates are decomposed into a typology of distinct generational cohorts (10, 125, 15, 175, 20, 25) defined by age and life stage at migration for the foreign-born, and by parental nativity for the US-born Differences in educational and occupational attainment, language and other aspects of acculturation are then examined to consider whether the practice of “lumping” these generational cohorts together, or “splitting” them into distinctive units of analysis, is empirically supported by available evidence The paper concludes with some thoughts on data needs and methodological considerations in the study of immigrant generations
850 citations
Authors
Showing all 47751 results
Name | H-index | Papers | Citations |
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Daniel Levy | 212 | 933 | 194778 |
Rob Knight | 201 | 1061 | 253207 |
Lewis C. Cantley | 196 | 748 | 169037 |
Dennis W. Dickson | 191 | 1243 | 148488 |
Terrie E. Moffitt | 182 | 594 | 150609 |
Joseph Biederman | 179 | 1012 | 117440 |
John R. Yates | 177 | 1036 | 129029 |
John A. Rogers | 177 | 1341 | 127390 |
Avshalom Caspi | 170 | 524 | 113583 |
Yang Gao | 168 | 2047 | 146301 |
Carl W. Cotman | 165 | 809 | 105323 |
John H. Seinfeld | 165 | 921 | 114911 |
Gregg C. Fonarow | 161 | 1676 | 126516 |
Jerome I. Rotter | 156 | 1071 | 116296 |
David Cella | 156 | 1258 | 106402 |