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Institution

University of Lausanne

EducationLausanne, 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.


Papers
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Journal ArticleDOI
28 Sep 1989-Nature
TL;DR: In this article, an epitope contained within amino acids 249-260 of the Plasmodium berghei circumsporozoite protein was identified by H-2Kd-restricted cytotoxic T cells.
Abstract: PROTECTIVE immunity against malaria is induced by vaccination of hosts with irradiation-attenuated sporozoites. This immunity is mediated in part by neutralizing antibodies that are directed mainly against the repeat domain of the circumsporozoite protein1–4. Early experiments showed, however, that B-cell-depleted mice that are immunized with sporozoites can resist challenge, indicating that T-cell effector mechanisms may also have a role in protection5. This idea was supported by the recent observation that protective immunity also requires T-cells expressing the CDS antigen (CD8+ T cells) 6,7, whose target is probably the developing liver-stage parasites8–10. Moreover, an oral Salmonella vaccine that expresses the circumsporozoite protein is able to protect against murine malaria in the absence of antibodies11. Here we report the identification of an epitope contained within amino acids 249–260 of the Plasmodium berghei circumsporozoite protein that is recognized by H–2Kd-restricted cytotoxic T cells12,13. Passive transfer into mice of cytotoxic-T-cell clones that recognize this epitope conferred a high degree of protection against challenge. These results provide the first direct evidence that CD8+ T cells that are specific for a defined epitope can confer protection against a parasitic infection.

508 citations

Journal ArticleDOI
TL;DR: A dynamic mean field model is derived that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects, and it is demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization.
Abstract: Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.

508 citations

Journal ArticleDOI
TL;DR: Six issues are discussed in a methodological framework for generalized regression: links with ecological theory, optimal use of existing data and artificially generated data, incorporating spatial context, integrating ecological and environmental interactions, and assessing prediction errors and uncertainties.
Abstract: Summary 1. Biogeographical models of species’ distributions are essential tools for assessing impacts of changing environmental conditions on natural communities and ecosystems. Practitioners need more reliable predictions to integrate into conservation planning (e.g. reserve design and management). 2. Most models still largely ignore or inappropriately take into account important features of species’ distributions, such as spatial autocorrelation, dispersal and migration, biotic and environmental interactions. Whether distributions of natural communities or ecosystems are better modelled by assembling individual species’ predictions in a bottom-up approach or modelled as collective entities is another important issue. An international workshop was organized to address these issues. 3. We discuss more specifically six issues in a methodological framework for generalized regression: (i) links with ecological theory; (ii) optimal use of existing data and artificially generated data; (iii) incorporating spatial context; (iv) integrating ecological and environmental interactions; (v) assessing prediction errors and uncertainties; and (vi) predicting distributions of communities or collective properties of biodiversity. 4. Synthesis and applications. Better predictions of the effects of impacts on biological communities and ecosystems can emerge only from more robust species’ distribution models and better documentation of the uncertainty associated with these models. An improved understanding of causes of species’ distributions, especially at their range limits, as well as of ecological assembly rules and ecosystem functioning, is necessary if further progress is to be made. A better collaborative effort between theoretical and functional ecologists, ecological modellers and statisticians is required to reach these goals.

506 citations

Journal ArticleDOI
TL;DR: Patient derived xenografts (PDXs) have emerged as an important platform to elucidate new treatments and biomarkers in oncology as mentioned in this paper, and the ability of PDX models to predict clinical outcomes is being improved through mouse humanization strategies and the implementation of co-clinical trials, within which patients and PDXs reciprocally inform therapeutic decisions.
Abstract: Patient-derived xenografts (PDXs) have emerged as an important platform to elucidate new treatments and biomarkers in oncology. PDX models are used to address clinically relevant questions, including the contribution of tumour heterogeneity to therapeutic responsiveness, the patterns of cancer evolutionary dynamics during tumour progression and under drug pressure, and the mechanisms of resistance to treatment. The ability of PDX models to predict clinical outcomes is being improved through mouse humanization strategies and the implementation of co-clinical trials, within which patients and PDXs reciprocally inform therapeutic decisions. This Opinion article discusses aspects of PDX modelling that are relevant to these questions and highlights the merits of shared PDX resources to advance cancer medicine from the perspective of EurOPDX, an international initiative devoted to PDX-based research.

506 citations


Authors

Showing all 20911 results

NameH-indexPapersCitations
Peer Bork206697245427
Aaron R. Folsom1811118134044
Kari Alitalo174817114231
Ralph A. DeFronzo160759132993
Johan Auwerx15865395779
Silvia Franceschi1551340112504
Matthias Egger152901184176
Bart Staels15282486638
Fernando Rivadeneira14662886582
Christopher George Tully1421843111669
Richard S. J. Frackowiak142309100726
Peter Timothy Cox140126795584
Jürg Tschopp14032886900
Stylianos E. Antonarakis13874693605
Michael Weller134110591874
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023249
2022635
20213,969
20203,508
20193,091
20182,776