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.

Polo-like Kinase 1: A Potential Therapeutic Option in Combination with Conventional Chemotherapy for the Management of Patients with Triple-Negative Breast Cancer

Abstract: Breast cancers are composed of molecularly distinct subtypes with different clinical outcomes and responses to therapy. To discover potential therapeutic targets for the poor prognosis-associated triple-negative breast cancer (TNBC), gene expression profiling was carried out on a cohort of 130 breast cancer samples. Polo-like kinase 1 (PLK1) was found to be significantly overexpressed in TNBC compared with the other breast cancer subtypes. High PLK1 expression was confirmed by reverse phase protein and tissue microarrays. In triple-negative cell lines, RNAi-mediated PLK1 depletion or inhibition of PLK1 activity with a small molecule (BI-2536) induced an increase in phosphorylated H2AX, G 2 –M arrest, and apoptosis. A soft-agar colony assay showed that PLK1 silencing impaired clonogenic potential of TNBC cell lines. When cells were grown in extracellular matrix gels (Matrigel), and exposed to BI-2536, apoptosis was observed specifically in TNBC cancerous cells, and not in a normal cell line. When administrated as a single agent, the PLK1 inhibitor significantly impaired tumor growth in vivo in two xenografts models established from biopsies of patients with TNBC. Most importantly, the administration of BI-2536, in combination with doxorubicin + cyclophosphamide chemotherapy, led to a faster complete response compared with the chemotherapy treatment alone and prevented relapse, which is the major risk associated with TNBC. Altogether, our observations suggest PLK1 inhibition as an attractive therapeutic approach, in association with conventional chemotherapy, for the management of patients with TNBC. Cancer Res; 73(2); 813–23. ©2012 AACR .

Sulfur derivatives of chitosan for palladium sorption

Abstract: Palladium is efficiently extracted from dilute acidic solutions using chitosan derivatives. Sorption performances are enhanced by modification of chitosan through the grafting of sulfur compounds (thiourea, rubeanic acid), which creates new chelating groups, on chitosan backbone using glutaraldehyde as a linker. A comparison of sorption isotherms and sorption kinetics of these two derivatives with those of glutaraldehyde cross-linked chitosan shows that the rubeanic acid derivative of chitosan is the more efficient for the uptake of palladium from dilute solutions. The chemical modification is suspected of bringing chelating functionalities to the ion exchange resin. Sorption capacity is not influenced by the particle size of rubeanic acid derivative of chitosan. Sorption isotherms are described by the Langmuir equation. Increasing the temperature of the solution has little effect on sorption performances. Sorption kinetics are not greatly influenced by the particle size of the sorbent.

ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples

Abstract: Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-consuming and expensive. Efficient computational methods are therefore needed to prioritize genes within the list of candidates, by exploiting the wealth of information available about the genes in various databases. We propose ProDiGe, a novel algorithm for Prioritization of Disease Genes. ProDiGe implements a novel machine learning strategy based on learning from positive and unlabeled examples, which allows to integrate various sources of information about the genes, to share information about known disease genes across diseases, and to perform genome-wide searches for new disease genes. Experiments on real data show that ProDiGe outperforms state-of-the-art methods for the prioritization of genes in human diseases. ProDiGe implements a new machine learning paradigm for gene prioritization, which could help the identification of new disease genes. It is freely available at http://cbio.ensmp.fr/prodige .

Real time visual traffic lights recognition based on Spot Light Detection and adaptive traffic lights templates

Abstract: This paper introduces a new real-time traffic light recognition system for on-vehicle camera applications. This approach has been tested with good results in urban scenes. Thanks to the use of our generic “Adaptive Templates” it would be possible to recognize different kinds of traffic lights from various countries.