Institution
University of Kansas
Education•Lawrence, Kansas, United States•
About: University of Kansas is a education organization based out in Lawrence, Kansas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 38183 authors who have published 81381 publications receiving 2986312 citations. The organization is also known as: KU & Univ of Kansas.
Topics: Population, Poison control, Large Hadron Collider, Health care, Cancer
Papers published on a yearly basis
Papers
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TL;DR: This article disentangle conflicting definitions of moderated mediation and describes approaches for estimating and testing a variety of hypotheses involving conditional indirect effects, showing that the indirect effect of intrinsic student interest on mathematics performance through teacher perceptions of talent is moderated by student math self-concept.
Abstract: This article provides researchers with a guide to properly construe and conduct analyses of conditional indirect effects, commonly known as moderated mediation effects. We disentangle conflicting definitions of moderated mediation and describe approaches for estimating and testing a variety of hypotheses involving conditional indirect effects. We introduce standard errors for hypothesis testing and construction of confidence intervals in large samples but advocate that researchers use bootstrapping whenever possible. We also describe methods for probing significant conditional indirect effects by employing direct extensions of the simple slopes method and Johnson-Neyman technique for probing significant interactions. Finally, we provide an SPSS macro to facilitate the implementation of the recommended asymptotic and bootstrapping methods. We illustrate the application of these methods with an example drawn from the Michigan Study of Adolescent Life Transitions, showing that the indirect effect of intrinsic student interest on mathematics performance through teacher perceptions of talent is moderated by student math self-concept.
7,973 citations
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University of Melbourne1, Stony Brook University2, City University of New York3, Princeton University4, University of Lausanne5, University of California, Berkeley6, University of Alaska Fairbanks7, National Institute of Water and Atmospheric Research8, Commonwealth Scientific and Industrial Research Organisation9, University of São Paulo10, University of Missouri11, Consejo Nacional de Ciencia y Tecnología12, University of Kansas13, Landcare Research14, AT&T15, McGill University16, James Cook University17, Swiss Federal Institute for Forest, Snow and Landscape Research18
TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
Abstract: Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.
7,589 citations
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TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Abstract: Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
7,185 citations
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Medical University of Vienna1, Boston University2, Arthritis Research UK3, Johns Hopkins University4, University of California, San Francisco5, Humboldt University of Berlin6, University of Toronto7, National Jewish Health8, Brigham and Women's Hospital9, Paris Descartes University10, University of Leeds11, Catholic University of the Sacred Heart12, Erasmus University Rotterdam13, University of Colorado Denver14, Leiden University15, University of California, San Diego16, University of Massachusetts Medical School17, University of Michigan18, University of Washington19, McGill University Health Centre20, University of Pittsburgh21, Ministry of Health (New Zealand)22, New York University23, University of Manchester24, University of Amsterdam25, University of Kansas26, Women's College Hospital27
TL;DR: This new classification system redefines the current paradigm of RA by focusing on features at earlier stages of disease that are associated with persistent and/or erosive disease, rather than defining the disease by its late-stage features.
Abstract: Objective The 1987 American College of Rheumatology (ACR; formerly the American Rheumatism Association) classifi cation criteria for rheumatoid arthritis (RA) have been criticised for their lack of sensitivity in early disease. This work was undertaken to develop new classifi cation criteria for RA. Methods A joint working group from the ACR and the European League Against Rheumatism developed, in three phases, a new approach to classifying RA. The work focused on identifying, among patients newly presenting with undifferentiated infl ammatory synovitis, factors that best discriminated between those who were and those who were not at high risk for persistent and/ or erosive disease—this being the appropriate current paradigm underlying the disease construct ‘RA’. Results In the new criteria set, classifi cation as ‘defi nite RA’ is based on the confi rmed presence of synovitis in at least one joint, absence of an alternative diagnosis better explaining the synovitis, and achievement of a total score of 6 or greater (of a possible 10) from the individual scores in four domains: number and site of involved joints (range 0–5), serological abnormality (range 0–3), elevated acute-phase response (range 0–1) and symptom duration (two levels; range 0–1). Conclusion This new classifi cation system redefi nes the current paradigm of RA by focusing on features at earlier stages of disease that are associated with persistent and/or erosive disease, rather than defi ning the disease by its late-stage features. This will refocus attention on the important need for earlier diagnosis and institution of effective disease-suppressing therapy to prevent or minimise the occurrence of the undesirable sequelae that currently comprise the paradigm underlying the disease construct ‘RA’.
7,120 citations
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University of Leeds1, University of Cambridge2, Royal Society for the Protection of Birds3, Macquarie University4, Durham University5, University of the Witwatersrand6, Conservation International7, Stellenbosch University8, World Conservation Monitoring Centre9, National Autonomous University of Mexico10, University of Kansas11, James Cook University12
TL;DR: Estimates of extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.
Abstract: Climate change over the past approximately 30 years has produced numerous shifts in the distributions and abundances of species and has been implicated in one species-level extinction. Using projections of species' distributions for future climate scenarios, we assess extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface. Exploring three approaches in which the estimated probability of extinction shows a power-law relationship with geographical range size, we predict, on the basis of mid-range climate-warming scenarios for 2050, that 15-37% of species in our sample of regions and taxa will be 'committed to extinction'. When the average of the three methods and two dispersal scenarios is taken, minimal climate-warming scenarios produce lower projections of species committed to extinction ( approximately 18%) than mid-range ( approximately 24%) and maximum-change ( approximately 35%) scenarios. These estimates show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.
7,089 citations
Authors
Showing all 38401 results
Name | H-index | Papers | Citations |
---|---|---|---|
Gordon H. Guyatt | 231 | 1620 | 228631 |
Krzysztof Matyjaszewski | 169 | 1431 | 128585 |
Wei Li | 158 | 1855 | 124748 |
David Tilman | 158 | 340 | 149473 |
Tomas Hökfelt | 158 | 1033 | 95979 |
Pete Smith | 156 | 2464 | 138819 |
Daniel J. Rader | 155 | 1026 | 107408 |
Melody A. Swartz | 148 | 1304 | 103753 |
Kevin Murphy | 146 | 728 | 120475 |
Carlo Rovelli | 146 | 1502 | 103550 |
Stephen Sanders | 145 | 1385 | 105943 |
Marco Zanetti | 145 | 1439 | 104610 |
Andrei Gritsan | 143 | 1531 | 135398 |
Gunther Roland | 141 | 1471 | 100681 |
Joseph T. Hupp | 141 | 731 | 82647 |