Institution
University of Kiel
Education•Kiel, Germany•
About: University of Kiel is a education organization based out in Kiel, Germany. It is known for research contribution in the topics: Population & Transplantation. The organization has 27816 authors who have published 57114 publications receiving 2061802 citations. The organization is also known as: Christian Albrechts University & Christian-Albrechts-Universität zu Kiel.
Papers published on a yearly basis
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TL;DR: G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested.
Abstract: G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of thet, F, and χ2 test families. In addition, it includes power analyses forz tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.
40,195 citations
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TL;DR: In the new version, procedures to analyze the power of tests based on single-sample tetrachoric correlations, comparisons of dependent correlations, bivariate linear regression, multiple linear regression based on the random predictor model, logistic regression, and Poisson regression are added.
Abstract: G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
20,778 citations
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Queen's University Belfast1, Collège de France2, English Heritage3, University of Arizona4, University of Sheffield5, University of Oxford6, University of Minnesota7, University of Hohenheim8, University of Kiel9, Lawrence Livermore National Laboratory10, University of Bergen11, ETH Zurich12, University of Waikato13, Woods Hole Oceanographic Institution14, Swiss Federal Institute for Forest, Snow and Landscape Research15, Cornell University16, University of Bristol17, University of Glasgow18, University of California, Irvine19, University of New South Wales20
TL;DR: In this paper, Heaton, AG Hogg, KA Hughen, KF Kaiser, B Kromer, SW Manning, RW Reimer, DA Richards, JR Southon, S Talamo, CSM Turney, J van der Plicht, CE Weyhenmeyer
Abstract: Additional co-authors: TJ Heaton, AG Hogg, KA Hughen, KF Kaiser, B Kromer, SW Manning, RW Reimer, DA Richards, JR Southon, S Talamo, CSM Turney, J van der Plicht, CE Weyhenmeyer
13,605 citations
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Wellcome Trust Sanger Institute1, Cambridge University Hospitals NHS Foundation Trust2, Wellcome Trust3, University of British Columbia4, University of Cambridge5, The Breast Cancer Research Foundation6, Oslo University Hospital7, University of Oslo8, University of Münster9, Université libre de Bruxelles10, German Cancer Research Center11, University of Iceland12, Erasmus University Rotterdam13, Paris Descartes University14, French Institute of Health and Medical Research15, University of Paris16, Broad Institute17, University of Bergen18, University of Oviedo19, University of Queensland20, University of Glasgow21, Harvard University22, United States Department of Veterans Affairs23, Netherlands Cancer Institute24, University of Kiel25, Radboud University Nijmegen26, King's College London27, Curie Institute28, University of New South Wales29, Bankstown Lidcombe Hospital30, University of Barcelona31
TL;DR: It is shown that hypermutation localized to small genomic regions, ‘kataegis’, is found in many cancer types, and this results reveal the diversity of mutational processes underlying the development of cancer.
Abstract: All cancers are caused by somatic mutations; however, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single cancer class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, 'kataegis', is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer, with potential implications for understanding of cancer aetiology, prevention and therapy.
7,904 citations
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TL;DR: In this paper, the uncertainty inherent in any observational estimate of the IMF is investigated by studying the scatter introduced by Poisson noise and the dynamical evolution of star clusters, and it is found that this apparent scatter reproduces quite well the observed scatter in power-law index determinations, thus defining the fundamental limit within which any true variation becomes undetectable.
Abstract: A universal initial mass function (IMF) is not intuitive, but so far no convincing evidence for a variable IMF exists. The detection of systematic variations of the IMF with star-forming conditions would be the Rosetta Stone for star formation.
In this contribution an average or Galactic-field IMF is defined, stressing that there is evidence for a change in the power-law index at only two masses: near 0.5 M⊙ and near 0.08 M⊙. Using this supposed universal IMF, the uncertainty inherent in any observational estimate of the IMF is investigated by studying the scatter introduced by Poisson noise and the dynamical evolution of star clusters. It is found that this apparent scatter reproduces quite well the observed scatter in power-law index determinations, thus defining the fundamental limit within which any true variation becomes undetectable. The absence of evidence for a variable IMF means that any true variation of the IMF in well-studied populations must be smaller than this scatter.
Determinations of the power-law indices α are subject to systematic errors arising mostly from unresolved binaries. The systematic bias is quantified here, with the result that the single-star IMFs for young star clusters are systematically steeper by Δα≈0.5 between 0.1 and 1 M⊙ than the Galactic-field IMF, which is populated by, on average, about 5-Gyr-old stars. The MFs in globular clusters appear to be, on average, systematically flatter than the Galactic-field IMF (Piotto & Zoccali; Paresce & De Marchi), and the recent detection of ancient white-dwarf candidates in the Galactic halo and the absence of associated low-mass stars (Ibata et al.; Mendez & Minniti) suggest a radically different IMF for this ancient population. Star formation in higher metallicity environments thus appears to produce relatively more low-mass stars. While still tentative, this is an interesting trend, being consistent with a systematic variation of the IMF as expected from theoretical arguments.
6,784 citations
Authors
Showing all 28103 results
Name | H-index | Papers | Citations |
---|---|---|---|
Günter Klöppel | 93 | 348 | 27963 |
Klaus-Michael Debatin | 93 | 563 | 43403 |
Alfred L. George | 92 | 399 | 30592 |
Michael Krawczak | 92 | 398 | 34290 |
Klaus F. Rabe | 92 | 543 | 37556 |
Pavel Kroupa | 91 | 560 | 34903 |
Daniela Berg | 90 | 571 | 35383 |
Christoph A. Nienaber | 90 | 627 | 38241 |
Ulf Riebesell | 89 | 333 | 25958 |
Hans-Joachim Gabius | 85 | 699 | 28085 |
J. S. Heslop-Harrison | 85 | 398 | 23875 |
John M. Opitz | 85 | 1193 | 40257 |
Matthias Wessling | 84 | 674 | 26409 |
Paul Proost | 84 | 367 | 21723 |
Martin Schrappe | 83 | 355 | 22797 |