Institution
University of Exeter
Education•Exeter, United Kingdom•
About: University of Exeter is a education organization based out in Exeter, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 15820 authors who have published 50650 publications receiving 1793046 citations. The organization is also known as: Exeter University & University of the South West of England.
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TL;DR: In this article, an updated review of repetitive negative thinking as a transdiagnostic process is presented, where it is shown that elevated levels of negative thinking are present across a large range of Axis I disorders and appear causally involved in the maintenance of emotional problems.
Abstract: The current paper provides an updated review of repetitive negative thinking as a transdiagnostic process. It is shown that elevated levels of repetitive negative thinking are present across a large range of Axis I disorders and appear to be causally involved in the maintenance of emotional problems. As direct comparisons of repetitive negative thinking between different disorders (e.g., GAD–type worry and depressive rumination) have generally revealed more similarities than differences, it is argued that repetitive negative thinking is characterized by the same process across disorders, which is applied to a disorder–specific content. On the other hand, there is some evidence that—within given disorders—repetitive negative thinking can be reliably distinguished from other forms of recurrent cognitions, such as obsessions, intrusive memories or functional forms of repeated thinking. An agenda for future research on repetitive negative thinking from a transdiagnostic perspective is presented.
745 citations
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Imperial College London1, Institut national de la recherche agronomique2, University of Exeter3, Max Planck Society4, John Innes Centre5, University of Oxford6, University of Reading7, Cornell University8, United States Department of Agriculture9, University of Copenhagen10, University of Zurich11, Technical University of Madrid12
TL;DR: A group of papers analyzes pathogen genomes to find the roots of virulence, opportunism, and life-style determinants in plant pathogens, suggesting that most effectors represent species-specific adaptations.
Abstract: Powdery mildews are phytopathogens whose growth and reproduction are entirely dependent on living plant cells. The molecular basis of this life-style, obligate biotrophy, remains unknown. We present the genome analysis of barley powdery mildew, Blumeria graminis f.sp. hordei (Blumeria), as well as a comparison with the analysis of two powdery mildews pathogenic on dicotyledonous plants. These genomes display massive retrotransposon proliferation, genome-size expansion, and gene losses. The missing genes encode enzymes of primary and secondary metabolism, carbohydrate-active enzymes, and transporters, probably reflecting their redundancy in an exclusively biotrophic life-style. Among the 248 candidate effectors of pathogenesis identified in the Blumeria genome, very few (less than 10) define a core set conserved in all three mildews, suggesting that most effectors represent species-specific adaptations.
744 citations
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TL;DR: Carry-over effects are likely to be far more widespread than currently indicated, and they could feasibly be responsible for a large amount of the observed variation in performance among individuals, and warrant a wealth of new research designed specifically to decompose components of variation in fitness attributes related to processes across and within seasons.
Abstract: 1. Carry-over effects occur when processes in one season influence the success of an individual in the following season. This phenomenon has the potential to explain a large amount of variation in individual fitness, but so far has only been described in a limited number of species. This is largely due to difficulties associated with tracking individuals between periods of the annual cycle, but also because of a lack of research specifically designed to examine hypotheses related to carry-over effects. 2. We review the known mechanisms that drive carry-over effects, most notably macronutrient supply, and highlight the types of life histories and ecological situations where we would expect them to most often occur. We also identify a number of other potential mechanisms that require investigation, including micronutrients such as antioxidants. 3. We propose a series of experiments designed to estimate the relative contributions of extrinsic and intrinsic quality effects in the pre-breeding season, which in turn will allow an accurate estimation of the magnitude of carry-over effects. To date this has proven immensely difficult, and we hope that the experimental frameworks described here will stimulate new avenues of research vital to advancing our understanding of how carry-over effects can shape animal life histories. 4. We also explore the potential of state-dependent modelling as a tool for investigating carry-over effects, most notably for its ability to calculate optimal rates of acquisition of a multitude of resources over the course of the annual cycle, and also because it allows us to vary the strength of density-dependent relationships which can alter the magnitude of carry-over effects in either a synergistic or agonistic fashion. 5. In conclusion carry-over effects are likely to be far more widespread than currently indicated, and they are likely to be driven by a multitude of factors including both macro- and micronutrients. For this reason they could feasibly be responsible for a large amount of the observed variation in performance among individuals, and consequently warrant a wealth of new research designed specifically to decompose components of variation in fitness attributes related to processes across and within seasons.
743 citations
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TL;DR: Methods for exploring spatial variation in disease risk, spatial and space-time clustering, and the raised incidence of disease around suspected point sources of pollution are examined.
Abstract: This paper reviews a number of methods for the exploration and modelling of spatial point patterns with particular reference to geographical epidemiology (the geographical incidence of disease). Such methods go well beyond the conventional ‘nearest-neighbour’ and ‘quadrat’ analyses which have little to offer in an epidemiological context because they fail to allow for spatial variation in population density. Correction for this is essential if the aim is to assess the evidence for ‘clustering’ of cases of disease. We examine methods for exploring spatial variation in disease risk, spatial and space-time clustering, and we consider methods for modelling the raised incidence of disease around suspected point sources of pollution. All methods are illustrated by reference to recent case studies including child cancer incidence, Burkitt’s lymphoma, cancer of the larynx and childhood asthma. An Appendix considers a range of possible software environments within which to apply these methods. The links to modern geographical information systems are discussed.
743 citations
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16 Mar 1989TL;DR: The GLIM 3 directives system defined structures in GLIM datasets and macros are discussed in this paper, where the authors introduce the GLIM3 directives system and discuss the use of regression models for calibration fatorial designs midding data.
Abstract: Part 1 Introducing GLIM 3: getting started in GLIM 3. Part 2 Statistical modelling and statistical inference: the Bernoulli distribution for binary data types of variables definition of a statistical model model criticism likelihood-based confidence intervals. Part 3 Normal regression and analysis of variance: the normal distribution and the Box-Cox transformation family link functions and transformations regression models for prediction the use of regression models for calibration fatorial designs midding data. Part 4 Binomial response data: binary responses transformations and link functions contingency table construction from binary data multidimensional contingency tables with a binary response. Part 5: multinomial and Poisson response data. Part 6 Survival data: probability plotting with censored data - the Kaplan-Meier estimator the Weibull distribution the Cox proportional hazards model and the piecewise exponential distribution the logistic and log logistic distributions time-dependent explanatory variables. Appendices: discussion GLIM directives system defined structures in GLIM datasets and macros.
742 citations
Authors
Showing all 16338 results
Name | H-index | Papers | Citations |
---|---|---|---|
Frank B. Hu | 250 | 1675 | 253464 |
John C. Morris | 183 | 1441 | 168413 |
David W. Johnson | 160 | 2714 | 140778 |
Kevin J. Gaston | 150 | 750 | 85635 |
Andrew T. Hattersley | 146 | 768 | 106949 |
Timothy M. Frayling | 133 | 500 | 100344 |
Joel N. Hirschhorn | 133 | 431 | 101061 |
Jonathan D. G. Jones | 129 | 417 | 80908 |
Graeme I. Bell | 127 | 531 | 61011 |
Mark D. Griffiths | 124 | 1238 | 61335 |
Tao Zhang | 123 | 2772 | 83866 |
Brinick Simmons | 122 | 691 | 69350 |
Edzard Ernst | 120 | 1326 | 55266 |
Michael Stumvoll | 119 | 655 | 69891 |
Peter McGuffin | 117 | 624 | 62968 |