The 8 bits 100 MS/s Pipeline ADC for the INNOTEP Project – TWEPP-09

This paper describes the Analog to Digital Converter developed for the front end electronic of the IN2P3 INNOTEP project by the “pole microelectronique Rhone-Auvergne”. (Collaboration between LPC Clermont-Ferrand and IPNL Lyon). This ADC is a 4 stages 2.5 bits per stage pipe line with open loops track and holds and amplifiers. It runs at 100MSamples/s and has 8 bits resolution. The stages used two lines, the gain line and the comparison line, with most operators running in current. The main idea of this current line is to make a first step toward an all in current structure. Currently, this ADC is designed with a 0,35μm SiGe technology

Contribution of HEP electronics techniques to the medical imaging field

présenté par P.-E. Vert, proceedings sous forme de CD Imagerie Médical

Global alignment of protein-protein interaction networks by graph matching methods

Aligning protein-protein interaction (PPI) networks of different species has drawn a considerable interest recently. This problem is important to investigate evolutionary conserved pathways or protein complexes across species, and to help in the identification of functional orthologs through the detection of conserved interactions. It is however a difficult combinatorial problem, for which only heuristic methods have been proposed so far. We reformulate the PPI alignment as a graph matching problem, and investigate how state-of-the-art graph matching algorithms can be used for that purpose. We differentiate between two alignment problems, depending on whether strict constraints on protein matches are given, based on sequence similarity, or whether the goal is instead to find an optimal compromise between sequence similarity and interaction conservation in the alignment. We propose new methods for both cases, and assess their performance on the alignment of the yeast and fly PPI networks. The new methods consistently outperform state-of-the-art algorithms, retrieving in particular 78% more conserved interactions than IsoRank for a given level of sequence similarity. Availability:http://cbio.ensmp.fr/proj/graphm\_ppi/, additional data and codes are available upon request. Contact: [email protected]: Preprint versio

SIRENE: Supervised Inference of Regulatory Networks

Living cells are the product of gene expression programs that involve the regulated transcription of thousands of genes. The elucidation of transcriptional regulatory networks in thus needed to understand the cell's working mechanism, and can for example be useful for the discovery of novel therapeutic targets. Although several methods have been proposed to infer gene regulatory networks from gene expression data, a recent comparison on a large-scale benchmark experiment revealed that most current methods only predict a limited number of known regulations at a reasonable precision level. We propose SIRENE, a new method for the inference of gene regulatory networks from a compendium of expression data. The method decomposes the problem of gene regulatory network inference into a large number of local binary classification problems, that focus on separating target genes from non-targets for each TF. SIRENE is thus conceptually simple and computationally efficient. We test it on a benchmark experiment aimed at predicting regulations in E. coli, and show that it retrieves of the order of 6 times more known regulations than other state-of-the-art inference methods

Large-scale functional RNAi screen in C. elegans identifies genes that regulate the dysfunction of mutant polyglutamine neurons

<p>Abstract</p> <p>Background</p> <p>A central goal in Huntington's disease (HD) research is to identify and prioritize candidate targets for neuroprotective intervention, which requires genome-scale information on the modifiers of early-stage neuron injury in HD.</p> <p>Results</p> <p>Here, we performed a large-scale RNA interference screen in <it>C. elegans </it>strains that express N-terminal huntingtin (htt) in touch receptor neurons. These neurons control the response to light touch. Their function is strongly impaired by expanded polyglutamines (128Q) as shown by the nearly complete loss of touch response in adult animals, providing an <it>in vivo </it>model in which to manipulate the early phases of expanded-polyQ neurotoxicity. In total, 6034 genes were examined, revealing 662 gene inactivations that either reduce or aggravate defective touch response in 128Q animals. Several genes were previously implicated in HD or neurodegenerative disease, suggesting that this screen has effectively identified candidate targets for HD. Network-based analysis emphasized a subset of high-confidence modifier genes in pathways of interest in HD including metabolic, neurodevelopmental and pro-survival pathways. Finally, 49 modifiers of 128Q-neuron dysfunction that are dysregulated in the striatum of either R/2 or CHL2 HD mice, or both, were identified.</p> <p>Conclusions</p> <p>Collectively, these results highlight the relevance to HD pathogenesis, providing novel information on the potential therapeutic targets for neuroprotection in HD.</p

Glutathione-triggered disassembly of isothermally responsive polymer nanoparticles obtained by nanoprecipitation of hydrophilic polymers

The encapsulation and selective delivery of therapeutic compounds within polymeric nanoparticles offers hope for the treatment of a variety of diseases. Traditional approaches to trigger selective cargo release typically rely on polymer degradation which is not always sensitive to the biological location of a material. In this report, we prepare nanoparticles from thermoresponsive polymers with a ‘solubility release catch’ at the chain-end. This release catch is exclusively activated in the presence of intracellular glutathione, triggering an ‘isothermal’ response and promoting a change in polymer solubility. This solubility switch leads to specific and rapid nanoparticle disassembly, release of encapsulated cargo and produces completely soluble polymeric side-products

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/

Classification of microarray data using gene networks

BACKGROUND: Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks in order to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. RESULTS: We propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classification algorithms of expression profiles, resulting in classifiers with biological relevance. We illustrate the method with the analysis of a set of expression profiles from irradiated and non-irradiated yeast strains. CONCLUSION: Including a priori knowledge of a gene network for the analysis of gene expression data leads to good classification performance and improved interpretability of the results

Modeling recursive RNA interference.

An important application of the RNA interference (RNAi) pathway is its use as a small RNA-based regulatory system commonly exploited to suppress expression of target genes to test their function in vivo. In several published experiments, RNAi has been used to inactivate components of the RNAi pathway itself, a procedure termed recursive RNAi in this report. The theoretical basis of recursive RNAi is unclear since the procedure could potentially be self-defeating, and in practice the effectiveness of recursive RNAi in published experiments is highly variable. A mathematical model for recursive RNAi was developed and used to investigate the range of conditions under which the procedure should be effective. The model predicts that the effectiveness of recursive RNAi is strongly dependent on the efficacy of RNAi at knocking down target gene expression. This efficacy is known to vary highly between different cell types, and comparison of the model predictions to published experimental data suggests that variation in RNAi efficacy may be the main cause of discrepancies between published recursive RNAi experiments in different organisms. The model suggests potential ways to optimize the effectiveness of recursive RNAi both for screening of RNAi components as well as for improved temporal control of gene expression in switch off-switch on experiments

Modelling regional land change scenarios to assess land abandonment and reforestation dynamics in the Pyrenees (France)

International audienceOver the last decades and centuries, European mountain landscapes have experienced substantial transformations. Natural and anthropogenic LULC changes (land use and land cover changes), especially agro-pastoral activities, have directed influenced the spatial organization and composition of European mountain landscapes. For the past 60 years, natural reforestation has been occurring due to a decline in both agricultural production activities and rural population. Stakeholders, to better anticipate future changes, need spatially and temporally explicit models to identiy areas at risk of land change and possible abandonment. This paper presents an integrated approach combining forecasting scenarios and a LULC changes simulation model to assess where LULC changes may occur in the Pyrenees Mountains, based on historical LULC trands and a range of future socio-economic drivers. The proposed methodology considers local specificities of Pyrenan valleys, sub-regional climate and topographical properties, and regional economic policies. Results indicate that some regions are projected to face strong abandonment, regardless of scenario conditions. Overall, high rates of change are associated with administrative regions where land productivity is highly dependent on socio-economic drivers and climatic and environmental conditions limit intensive (agricultural and/or pastoral) production and profitability. The combination of the results for the four scenarios allows assessements of where encroachment (e.g. colonization by shrublands) and reforestation are the most probable. This assessment intends to provide insight into the potential future development of the Pyrenees to help identify areas that are the most sensitive to change and to guide decision makers to help their management decisions