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Institution

University of Vienna

EducationVienna, 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.


Papers
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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
20 Dec 1998-Virology
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

Journal ArticleDOI
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

NameH-indexPapersCitations
Tomas Hökfelt158103395979
Wolfgang Wagner1562342123391
Hans Lassmann15572479933
Stanley J. Korsmeyer151316113691
Charles B. Nemeroff14997990426
Martin A. Nowak14859194394
Barton F. Haynes14491179014
Yi Yang143245692268
Peter Palese13252657882
Gérald Simonneau13058790006
Peter M. Elias12758149825
Erwin F. Wagner12537559688
Anton Zeilinger12563171013
Wolfgang Waltenberger12585475841
Michael Wagner12435154251
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023419
20221,085
20214,479
20204,533
20194,225