Mines ParisTech

About: Mines ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Microstructure. The organization has 6564 authors who have published 11676 publications receiving 359898 citations. The organization is also known as: École nationale supérieure des mines de Paris & École des mines de Paris.

Photoluminescent diamond nanoparticles for cell labeling: study of the uptake mechanism in mammalian cells.

Abstract: Diamond nanoparticles (nanodiamonds) have been recently proposed as new labels for cellular imaging. For small nanodiamonds (size <40 nm), resonant laser scattering and Raman scattering cross sections are too small to allow single nanoparticle observation. Nanodiamonds can, however, be rendered photoluminescent with a perfect photostability at room temperature. Such a remarkable property allows easier single-particle tracking over long time scales. In this work, we use photoluminescent nanodiamonds of size <50 nm for intracellular labeling and investigate the mechanism of their uptake by living cells. By blocking selectively different uptake processes, we show that nanodiamonds enter cells mainly by endocytosis, and converging data indicate that it is clathrin-mediated. We also examine nanodiamond intracellular localization in endocytic vesicles using immunofluorescence and transmission electron microscopy. We find a high degree of colocalization between vesicles and the biggest nanoparticles or aggregate...

An accurate and interpretable model for siRNA efficacy prediction

Abstract: The use of exogenous small interfering RNAs (siRNAs) for gene silencing has quickly become a widespread molecular tool providing a powerful means for gene functional study and new drug target identification. Although considerable progress has been made recently in understanding how the RNAi pathway mediates gene silencing, the design of potent siRNAs remains challenging. We propose a simple linear model combining basic features of siRNA sequences for siRNA efficacy prediction. Trained and tested on a large dataset of siRNA sequences made recently available, it performs as well as more complex state-of-the-art models in terms of potency prediction accuracy, with the advantage of being directly interpretable. The analysis of this linear model allows us to detect and quantify the effect of nucleotide preferences at particular positions, including previously known and new observations. We also detect and quantify a strong propensity of potent siRNAs to contain short asymmetric motifs in their sequence, and show that, surprisingly, these motifs alone contain at least as much relevant information for potency prediction as the nucleotide preferences for particular positions. The model proposed for prediction of siRNA potency is as accurate as a state-of-the-art nonlinear model and is easily interpretable in terms of biological features. It is freely available on the web at http://cbio.ensmp.fr/dsir

Conditional Prediction Intervals of Wind Power Generation

Abstract: A generic method for the providing of prediction intervals of wind power generation is described. Prediction intervals complement the more common wind power point forecasts, by giving a range of potential outcomes for a given probability, their so-called nominal coverage rate. Ideally they inform of the situation-specific uncertainty of point forecasts. In order to avoid a restrictive assumption on the shape of forecast error distributions, focus is given to an empirical and nonparametric approach named adapted resampling. This approach employs a fuzzy inference model that permits to integrate expertise on the characteristics of prediction errors for providing conditional interval forecasts. By simultaneously generating prediction intervals with various nominal coverage rates, one obtains full predictive distributions of wind generation. Adapted resampling is applied here to the case of an onshore Danish wind farm, for which three point forecasting methods are considered as input. The probabilistic forecasts generated are evaluated based on their reliability and sharpness, while compared to forecasts based on quantile regression and the climatology benchmark. The operational application of adapted resampling to the case of a large number of wind farms in Europe and Australia among others is finally discussed.