Institution
University of Lausanne
Education•Lausanne, Switzerland•
About: University of Lausanne is a education organization based out in Lausanne, Switzerland. It is known for research contribution in the topics: Population & Poison control. The organization has 20508 authors who have published 46458 publications receiving 1996655 citations. The organization is also known as: Université de Lausanne & UNIL.
Topics: Population, Poison control, Immune system, Cytotoxic T cell, T cell
Papers published on a yearly basis
Papers
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TL;DR: Because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases.
Abstract: Metabolomics, which is the profiling of metabolites in biofluids, cells and tissues, is routinely applied as a tool for biomarker discovery. Owing to innovative developments in informatics and analytical technologies, and the integration of orthogonal biological approaches, it is now possible to expand metabolomic analyses to understand the systems-level effects of metabolites. Moreover, because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases.
1,440 citations
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TL;DR: In this article, the authors present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models.
Abstract: Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity – which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error – renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.
1,438 citations
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TL;DR: In this article, a life-designing model for career intervention endorses five presuppositions about people and their work lives: contextual possibilities, dynamic processes, non-linear progression, multiple perspectives, and personal patterns.
1,428 citations
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Memorial Sloan Kettering Cancer Center1, University of Lausanne2, University of North Carolina at Chapel Hill3, Rutgers University4, Harvard University5, University of Southern California6, Broad Institute7, Washington University in St. Louis8, Buck Institute for Research on Aging9, University of British Columbia10, Van Andel Institute11, The Chinese University of Hong Kong12, University of Utah13, Stanford University14, University of California, San Francisco15, United States Department of Veterans Affairs16, University of Pittsburgh17, University of Texas MD Anderson Cancer Center18, BC Cancer Agency19
TL;DR: This multidimensional molecular atlas sheds new light on the genetic bases of ILC and provides potential clinical options, suggesting differential modulation of ER activity in I LC and IDC.
1,414 citations
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TL;DR: In this paper, a multivariate approach to the study of geographic species dis- tribution which does not require absence data is proposed, based on Hutchinson's concept of the ecological niche, which compares the distribution of localities where the focal species was observed to a reference set describing the whole study area.
Abstract: We propose a multivariate approach to the study of geographic species dis- tribution which does not require absence data. Building on Hutchinson's concept of the ecological niche, this factor analysis compares, in the multidimensional space of ecological variables, the distribution of the localities where the focal species was observed to a reference set describing the whole study area. The first factor extracted maximizes the marginality of the focal species, defined as the ecological distance between the species optimum and the mean habitat within the reference area. The other factors maximize the specialization of this focal species, defined as the ratio of the ecological variance in mean habitat to that observed for the focal species. Eigenvectors and eigenvalues are readily interpreted and can be used to build habitat-suitability maps. This approach is recommended in situations where absence data are not available (many data banks), unreliable (most cryptic or rare species), or meaningless (invaders). We provide an illustration and validation of the method for the alpine ibex, a species reintroduced in Switzerland which presumably has not yet recolonized its entire range.
1,413 citations
Authors
Showing all 20911 results
Name | H-index | Papers | Citations |
---|---|---|---|
Peer Bork | 206 | 697 | 245427 |
Aaron R. Folsom | 181 | 1118 | 134044 |
Kari Alitalo | 174 | 817 | 114231 |
Ralph A. DeFronzo | 160 | 759 | 132993 |
Johan Auwerx | 158 | 653 | 95779 |
Silvia Franceschi | 155 | 1340 | 112504 |
Matthias Egger | 152 | 901 | 184176 |
Bart Staels | 152 | 824 | 86638 |
Fernando Rivadeneira | 146 | 628 | 86582 |
Christopher George Tully | 142 | 1843 | 111669 |
Richard S. J. Frackowiak | 142 | 309 | 100726 |
Peter Timothy Cox | 140 | 1267 | 95584 |
Jürg Tschopp | 140 | 328 | 86900 |
Stylianos E. Antonarakis | 138 | 746 | 93605 |
Michael Weller | 134 | 1105 | 91874 |