Tesla Model S | EPFL Graph Search

The Tesla Model S is an electric full-size car built by Tesla, Inc. since 2012. The Model S features a battery-powered dual-motor, all-wheel drive layout, although earlier versions featured a rear-motor and rear-wheel drive layout. It has a liftback body style. Development of the Model S began prior to 2007, under the codename "WhiteStar". The Model S was officially announced on June 30, 2008, and a prototype vehicle was unveiled in March 2009. The Model S debuted on June 22, 2012. A revised, dual-motor, all-wheel-drive version, known as the 60D, debuted on October 9, 2014. The 60D was followed by the 70D, which made dual-motor and all-wheel drive the standard, followed by the 85D, P85D, and P90D. Along with these updates, Tesla offered the Autopilot driving assistance system. The first major design refresh came in April 2016 when the Model S was updated with a new front hood design. In October of the same year, hardware became standard that supports Tesla's

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