Tesla Model S | EPFL Graph Search

La Model S est une voiture électrique haut de gamme produite par le constructeur automobile américain Tesla Motors. Présentée au public en 2009 au Salon de l'automobile de Francfort, elle est commercialisée aux États-Unis depuis 2012 et dans certains pays d'Europe (dont Norvège, Pays-Bas, Allemagne et France) depuis l'automne 2013. Il s'agit du second modèle de voiture commercialisé par Tesla Motors, après la Tesla Roadster. C'est l'une des voitures électriques de série la plus puissante et avec la plus grande autonomie. Outre la recharge à domicile et sur bornes ordinaires, la Model S bénéficie de bornes de recharges ultra-rapides appelées Superchargeurs. Ces bornes sont déployées par la firme elle-même aux États-Unis, au Canada, en Europe et en Asie. Elles permettent de récupérer jusqu'à en moins de . Au niveau de la sécurité, en Europe, la Model S a obtenu une notation Euro NCAP 2022 avec 5 étoiles : 94 % pour la protection des adultes, 91 % pour la protection des enfants, 85

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Mauro Delorenzi, Darlene Goldstein, Li Guo, Rui Jiang, Jonathan Koh Boon Han, Li Li, Zhen Li, Ying Liu, Jie Liu, Jun Luo, Wen Luo, Lu Meng, Vlad Popovici, Yi Ren, Wei Wang, Li Zhang, Min Zhang, Chen Zhao

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Nature Publishing Group2010

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established In the MAQC II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans In total, >30,000 models were built using many combinations of analytical methods The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis

Nature Publishing Group2010

Resonant optical trapping in hollow photonic crystal cavities and its potential use for bacterial characterisation

Rita Therisod

During the last decade, the development of optofluidic chips has become a large field of research. The integration of nano and microstructures with microfluidics layers allowed for the miniaturisation of a number of tools traditionally used in laboratories and optofluidic systems found their main applications in lab-on-a-chip and sensing platforms. At the same time, optical tweezers have become the tool of choice for the trapping and the manipulation of nano and micrometer-sized objects, that can be inert (dielectric or metallic particles) or biological (proteins, bacteria, viruses,...). In this context, nanostructures are the ideal candidate for tweezers miniaturisation, thanks to their ability to strongly confine light in small volumes and hence to generate intense gradient forces. This work reports on the fabrication of an optofluidic chip based on a two-dimensional photonic crystal cavity and on its use in particle and bacteria differentiation. The cavity has a hollow design that maximises the overlap between the confined field and the trapped objects and it supports Self-Induced Back-Action effects that allow for reducing the trapping power (down to few microwatts) and to simultaneously acquire information of the trapped specimen. The system is excited in an end-fire setup and the detection is carried out by recording the light intensity transmitted through the photonic crystal. The fabrication process, that is entirely carried out in the Institue of Physics and in the Center of MicroNanoTechnology cleanrooms, is first detailed. For the cavity design normally used, typical quality factors of 10000 were obtained. Moreover, SU8 mode adaptors were developed to increase the coupling with lensed fibers and a microfluidic membrane presenting two injection channels is proposed for rapid switch between liquids. The trapping and the differentiation of 250 and 500 nm polystyrene is then presented. The differentiation can be achieved qualitatively by direct observation of the transmitted intensity records and quantitatively by the use of histograms and of statistical moments. Finally, the trapping of seven species of living bacteria is shown and their Gram-type is determined by the analysis of the induced transmission increase. This ability to probe the cell wall of bacteria in a fast, label-free and non-destructive way paves the way for applications in biological and biomedical environments.

EPFL2019

Resonant optical trapping in hollow photonic crystal cavities and its potential use for bacterial characterisation

Rita Therisod

EPFL2019