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Institution

Bielefeld University

EducationBielefeld, Nordrhein-Westfalen, Germany
About: Bielefeld University is a education organization based out in Bielefeld, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Population & Quantum chromodynamics. The organization has 10123 authors who have published 26576 publications receiving 728250 citations. The organization is also known as: University of Bielefeld & UNIVERSITAET BIELEFELD.


Papers
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Journal ArticleDOI
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016.

10,401 citations

Journal ArticleDOI
TL;DR: This work reveals that it is in general not optimal to regard the information to be multicast as a "fluid" which can simply be routed or replicated, and by employing coding at the nodes, which the work refers to as network coding, bandwidth can in general be saved.
Abstract: We introduce a new class of problems called network information flow which is inspired by computer network applications. Consider a point-to-point communication network on which a number of information sources are to be multicast to certain sets of destinations. We assume that the information sources are mutually independent. The problem is to characterize the admissible coding rate region. This model subsumes all previously studied models along the same line. We study the problem with one information source, and we have obtained a simple characterization of the admissible coding rate region. Our result can be regarded as the max-flow min-cut theorem for network information flow. Contrary to one's intuition, our work reveals that it is in general not optimal to regard the information to be multicast as a "fluid" which can simply be routed or replicated. Rather, by employing coding at the nodes, which we refer to as network coding, bandwidth can in general be saved. This finding may have significant impact on future design of switching systems.

8,533 citations

Journal ArticleDOI
TL;DR: In this article, the authors make a case for the importance of reporting variance explained (R2) as a relevant summarizing statistic of mixed-effects models, which is rare, even though R2 is routinely reported for linear models and also generalized linear models (GLM).
Abstract: Summary The use of both linear and generalized linear mixed-effects models (LMMs and GLMMs) has become popular not only in social and medical sciences, but also in biological sciences, especially in the field of ecology and evolution. Information criteria, such as Akaike Information Criterion (AIC), are usually presented as model comparison tools for mixed-effects models. The presentation of ‘variance explained’ (R2) as a relevant summarizing statistic of mixed-effects models, however, is rare, even though R2 is routinely reported for linear models (LMs) and also generalized linear models (GLMs). R2 has the extremely useful property of providing an absolute value for the goodness-of-fit of a model, which cannot be given by the information criteria. As a summary statistic that describes the amount of variance explained, R2 can also be a quantity of biological interest. One reason for the under-appreciation of R2 for mixed-effects models lies in the fact that R2 can be defined in a number of ways. Furthermore, most definitions of R2 for mixed-effects have theoretical problems (e.g. decreased or negative R2 values in larger models) and/or their use is hindered by practical difficulties (e.g. implementation). Here, we make a case for the importance of reporting R2 for mixed-effects models. We first provide the common definitions of R2 for LMs and GLMs and discuss the key problems associated with calculating R2 for mixed-effects models. We then recommend a general and simple method for calculating two types of R2 (marginal and conditional R2) for both LMMs and GLMMs, which are less susceptible to common problems. This method is illustrated by examples and can be widely employed by researchers in any fields of research, regardless of software packages used for fitting mixed-effects models. The proposed method has the potential to facilitate the presentation of R2 for a wide range of circumstances.

7,749 citations

Journal ArticleDOI
TL;DR: In this paper, a new vector strategy for the insertion of foreign genes into the genomes of gram negative bacteria not closely related to Escherichia coli was developed, which can utilize any gram negative bacterium as a recipient for conjugative DNA transfer.
Abstract: We have developed a new vector strategy for the insertion of foreign genes into the genomes of gram negative bacteria not closely related to Escherichia coli. The system consists of two components: special E. coli donor strains and derivatives of E. coli vector plasmids. The donor strains (called mobilizing strains) carry the transfer genes of the broad host range IncP–type plasmid RP4 integrated in their chromosomes. They can utilize any gram negative bacterium as a recipient for conjugative DNA transfer. The vector plasmids contain the P–type specific recognition site for mobilization (Mob site) and can be mobilized with high frequency from the donor strains. The mobilizable vectors are derived from the commonly used E. coli vectors pACYC184, pACYC177, and pBR325, and are unable to replicate in strains outside the enteric bacterial group. Therefore, they are widely applicable as transposon carrier replicons for random transposon insertion mutagenesis in any strain into which they can be mobilized but not stably maintained. The vectors are especially useful for site–directed transposon mutagenesis and for site–specific gene transfer in a wide variety of gram negative organisms.

7,278 citations

Journal ArticleDOI
TL;DR: The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) as discussed by the authors provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution.

5,668 citations


Authors

Showing all 10375 results

NameH-indexPapersCitations
Stefan Grimme113680105087
Alfred Pühler10265845871
James Barber10264242397
Swagata Mukherjee101104846234
Hans-Joachim Werner9831748508
Krzysztof Redlich9860932693
Graham C. Walker9338136875
Christian Meyer93108138149
Muhammad Farooq92134137533
Jean Willy Andre Cleymans9054227685
Bernhard T. Baune9060850706
Martin Wikelski8942025821
Niklas Luhmann8542142743
Achim Müller8592635874
Oliver T. Wolf8333724211
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023150
2022511
20211,696
20201,655
20191,410
20181,299