Institution
University of Hawaii at Manoa
Education•Honolulu, Hawaii, United States•
About: University of Hawaii at Manoa is a education organization based out in Honolulu, Hawaii, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 13693 authors who have published 25161 publications receiving 1023924 citations.
Papers published on a yearly basis
Papers
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TL;DR: It is suggested that NK-cell responses may be negatively regulated when NK cells encounter target cells expressing cognate ligands of Tim-3, which is a maturation marker on NK cells.
364 citations
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Harvard University1, University of Exeter2, University of Cambridge3, University of Massachusetts Amherst4, University of Toledo5, University of Texas at Austin6, California Institute of Technology7, Herzberg Institute of Astrophysics8, University of Victoria9, University of Hawaii at Manoa10, European Space Agency11, Dublin Institute for Advanced Studies12, University of New South Wales13
TL;DR: In this article, a global study of low mass, young stellar object (YSO) surface densities in nearby star forming regions based on a comprehensive collection of Spitzer Space Telescope surveys is presented.
Abstract: We present a global study of low mass, young stellar object (YSO) surface densities (�) in nearby (< 500 pc) star forming regions based on a comprehensive collection of Spitzer Space Telescope surveys. We show that the distribution of YSO surface densities in the solar neighbourhood is a smooth distribution, being adequately described by a lognormal function from a few to 10 3 YSOs per pc 2 , with a peak at � 22 stars pc
363 citations
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James Cook University1, Australian Institute of Marine Science2, Dalhousie University3, Lancaster University4, Wildlife Conservation Society5, University of Queensland6, Macquarie University7, University of Montpellier8, Conservation International9, Stanford University10, University of Hawaii at Manoa11, University of Washington12, Institut de recherche pour le développement13, University of Nottingham14, National Oceanic and Atmospheric Administration15, University of Perpignan16, University of New Caledonia17, University of Tasmania18, University of California, San Diego19, The Nature Conservancy20, University of Western Australia21, University of California, Santa Barbara22, University of Puerto Rico23, University of Technology, Sydney24, Blue Ventures25, Leibniz Center for Tropical Marine Ecology26, University of British Columbia27
TL;DR: This paper identified 15 bright spots and 35 dark spots among more than 2,500 reefs worldwide and developed a Bayesian hierarchical model to generate expectations of how standing stocks of reef fish biomass are related to 18 socioeconomic drivers and environmental conditions.
Abstract: Ongoing declines in the structure and function of the world’s coral reefs require novel approaches to sustain these ecosystems and the millions of people who depend on them3. A presently unexplored approach that draws on theory and practice in human health and rural development is to systematically identify and learn from the ‘outliers’—places where ecosystems are substantially better (‘bright spots’) or worse (‘dark spots’) than expected, given the environmental conditions and socioeconomic drivers they are exposed to. Here we compile data from more than 2,500 reefs worldwide and develop a Bayesian hierarchical model to generate expectations of how standing stocks of reef fish biomass are related to 18 socioeconomic drivers and environmental conditions. We identify 15 bright spots and 35 dark spots among our global survey of coral reefs, defined as sites that have biomass levels more than two standard deviations from expectations. Importantly, bright spots are not simply comprised of remote areas with low fishing pressure; they include localities where human populations and use of ecosystem resources is high, potentially providing insights into how communities have successfully confronted strong drivers of change. Conversely, dark spots are not necessarily the sites with the lowest absolute biomass and even include some remote, uninhabited locations often considered near pristine6. We surveyed local experts about social, institutional, and environmental conditions at these sites to reveal that bright spots are characterized by strong sociocultural institutions such as customary taboos and marine tenure, high levels of local engagement in management, high dependence on marine resources, and beneficial environmental conditions such as deep-water refuges. Alternatively, dark spots are characterized by intensive capture and storage technology and a recent history of environmental shocks. Our results suggest that investments in strengthening fisheries governance, particularly aspects such as participation and property rights, could facilitate innovative conservation actions that help communities defy expectations of global reef degradation.
363 citations
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TL;DR: This paper found that the veracity effect stems from a truth bias, and suggest that the single best predictor of detection accuracy may be the message being judged, and that altering the truth-lie base rate affected accuracy.
Abstract: Deception research has consistently shown that accuracy rates tend to be just over fifty percent when accuracy rates are averaged across truthful and deceptive messages and when an equal number of truths and lies are judged. Breaking accuracy rates down by truths and lies, however, leads to a radically different conclusion. Across three studies, a large and consistent veracity effect was evident. Truths are most often correctly identified as honest, but errors predominate when lies are judged. Truth accuracy is substantially greater than chance, but the detection of lies was often significantly below chance. Also, consistent with the veracity effect, altering the truth‐lie base rate affected accuracy. Accuracy was a positive linear function of the ratio of truthful messages to total messages. The results show that this veracity effect stems from a truth‐bias, and suggest that the single best predictor of detection accuracy may be the veracity of message being judged. The internal consistency and paralleli...
363 citations
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Arizona State University1, Cornell University2, University of Hawaii at Manoa3, California Institute of Technology4, University of Colorado Boulder5, United States Geological Survey6, Planetary Science Institute7, University of Tennessee8, University of Southern California9, Goddard Space Flight Center10
TL;DR: The Thermal Emission Imaging System (THEMIS) on Mars Odyssey has produced infrared to visible wavelength images of the martian surface that show lithologically distinct layers with variable thickness, implying temporal changes in the processes or environments during or after their formation.
Abstract: The Thermal Emission Imaging System (THEMIS) on Mars Odyssey has produced infrared to visible wavelength images of the martian surface that show lithologically distinct layers with variable thickness, implying temporal changes in the processes or environments during or after their formation. Kilometer-scale exposures of bedrock are observed; elsewhere airfall dust completely mantles the surface over thousands of square kilometers. Mars has compositional variations at 100-meter scales, for example, an exposure of olivine-rich basalt in the walls of Ganges Chasma. Thermally distinct ejecta facies occur around some craters with variations associated with crater age. Polar observations have identified temporal patches of water frost in the north polar cap. No thermal signatures associated with endogenic heat sources have been identified.
363 citations
Authors
Showing all 13867 results
Name | H-index | Papers | Citations |
---|---|---|---|
Pulickel M. Ajayan | 176 | 1223 | 136241 |
Steven N. Blair | 165 | 879 | 132929 |
Qiang Zhang | 161 | 1137 | 100950 |
Jack M. Guralnik | 148 | 453 | 83701 |
Thomas J. Smith | 140 | 1775 | 113919 |
James A. Richardson | 136 | 363 | 75778 |
Donna Neuberg | 135 | 810 | 72653 |
Jian Zhou | 128 | 3007 | 91402 |
Eric F. Bell | 128 | 631 | 72542 |
Jorge Luis Rodriguez | 128 | 834 | 73567 |
Bin Wang | 126 | 2226 | 74364 |
Nicholas J. Schork | 125 | 587 | 62131 |
Matthew Jones | 125 | 1161 | 96909 |
Anthony F. Jorm | 124 | 798 | 67120 |
Adam G. Riess | 118 | 363 | 117310 |