Institution
University of Vienna
Education•Vienna, Austria•
About: University of Vienna is a education organization based out in Vienna, Austria. It is known for research contribution in the topics: Population & Stars. The organization has 44686 authors who have published 95840 publications receiving 2907492 citations.
Topics: Population, Stars, Galaxy, Transplantation, Crystal structure
Papers published on a yearly basis
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Johns Hopkins University School of Medicine1, Harvard University2, University of Alberta3, University of Basel4, University of California, Los Angeles5, Catholic University of Leuven6, University of Pittsburgh7, Vanderbilt University8, University of Leicester9, University of Helsinki10, University of Iowa11, Yale University12, University of Texas Health Science Center at Houston13, Nagoya City University14, University of North Carolina at Chapel Hill15, University of Vienna16, University of Barcelona17, Cornell University18, Rockyview General Hospital19
TL;DR: This article presents international consensus criteria for and classification of AbAR developed based on discussions held at the Sixth Banff Conference on Allograft Pathology in 2001, to be revisited as additional data accumulate in this important area of renal transplantation.
1,018 citations
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TL;DR: The powerful visualization tools of geometric morphometrics and the typically large amount of shape variables give rise to a specific exploratory style of analysis, allowing the identification and quantification of previously unknown shape features.
Abstract: Geometric morphometrics is the statistical analysis of form based on Cartesian landmark coordinates. After separating shape from overall size, position, and orientation of the landmark configurations, the resulting Procrustes shape coordinates can be used for statistical analysis. Kendall shape space, the mathematical space induced by the shape coordinates, is a metric space that can be approximated locally by a Euclidean tangent space. Thus, notions of distance (similarity) between shapes or of the length and direction of developmental and evolutionary trajectories can be meaningfully assessed in this space. Results of statistical techniques that preserve these convenient properties—such as principal component analysis, multivariate regression, or partial least squares analysis—can be visualized as actual shapes or shape deformations. The Procrustes distance between a shape and its relabeled reflection is a measure of bilateral asymmetry. Shape space can be extended to form space by augmenting the shape coordinates with the natural logarithm of Centroid Size, a measure of size in geometric morphometrics that is uncorrelated with shape for small isotropic landmark variation. The thin-plate spline interpolation function is the standard tool to compute deformation grids and 3D visualizations. It is also central to the estimation of missing landmarks and to the semilandmark algorithm, which permits to include outlines and surfaces in geometric morphometric analysis. The powerful visualization tools of geometric morphometrics and the typically large amount of shape variables give rise to a specific exploratory style of analysis, allowing the identification and quantification of previously unknown shape features.
1,017 citations
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University of Iceland1, University of Manchester2, Charité3, University of California, San Diego4, University of Amsterdam5, Netherlands Bioinformatics Centre6, Chalmers University of Technology7, University of Virginia8, University of Sheffield9, Central Manchester University Hospitals NHS Foundation Trust10, University of Vienna11, University of North Texas12, California Institute of Technology13, European Bioinformatics Institute14, Babraham Institute15, University of Warwick16, University of Edinburgh17, Institute for Systems Biology18, University of Luxembourg19, Jacobs University Bremen20, Russian Academy of Sciences21, VU University Amsterdam22, Virginia Bioinformatics Institute23
TL;DR: Recon 2, a community-driven, consensus 'metabolic reconstruction', is described, which is the most comprehensive representation of human metabolism that is applicable to computational modeling and has improved topological and functional features.
Abstract: Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.
1,002 citations
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TL;DR: In this paper, a viable transfectant influenza A virus (delNS1) which lacks the NS1 gene has been generated through the use of reverse genetics, and it has been shown that the NS 1 protein plays a crucial role in inhibiting interferon-mediated antiviral responses of the host.
998 citations
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TL;DR: In this article, the authors simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics, construct co-occurrence networks, and evaluate how well networks reveal the underlying interactions, and how experimental and ecological parameters can affect network inference and interpretation.
Abstract: Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics, construct co-occurrence networks, and evaluate how well networks reveal the underlying interactions, and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of “hub” species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.
997 citations
Authors
Showing all 45262 results
Name | H-index | Papers | Citations |
---|---|---|---|
Tomas Hökfelt | 158 | 1033 | 95979 |
Wolfgang Wagner | 156 | 2342 | 123391 |
Hans Lassmann | 155 | 724 | 79933 |
Stanley J. Korsmeyer | 151 | 316 | 113691 |
Charles B. Nemeroff | 149 | 979 | 90426 |
Martin A. Nowak | 148 | 591 | 94394 |
Barton F. Haynes | 144 | 911 | 79014 |
Yi Yang | 143 | 2456 | 92268 |
Peter Palese | 132 | 526 | 57882 |
Gérald Simonneau | 130 | 587 | 90006 |
Peter M. Elias | 127 | 581 | 49825 |
Erwin F. Wagner | 125 | 375 | 59688 |
Anton Zeilinger | 125 | 631 | 71013 |
Wolfgang Waltenberger | 125 | 854 | 75841 |
Michael Wagner | 124 | 351 | 54251 |