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Lee A.Y.,McGill University | Perreault R.,Universitedu Quebeca Montreal | Harel S.,Universitedu Quebeca Montreal | Boulier E.L.,Universitedu Quebeca Montreal | And 5 more authors.

Background: The symptoms of numerous diseases result from genetic mutations that disrupt the homeostasis maintained by the appropriate integration of signaling gene activities. The relationships between signaling genes suggest avenues through which homeostasis can be restored and disease symptoms subsequently reduced. Specifically, disease symptoms caused by loss-of-function mutations in a particular gene may be reduced by concomitant perturbations in genes with antagonistic activities. Methodology/Principal Findings: Here we use network-neighborhood analyses to predict genetic interactions in Caenorhabditis elegans towards mapping antagonisms and synergisms between genes in an animal model. Most of the predicted interactions are novel, and the experimental validation establishes that our approach provides a gain in accuracy compared to previous efforts. In particular, we identified genetic interactors of gdi-1, the orthologue of GDI1, a gene associated with mental retardation in human. Interestingly, some gdi-1 interactors have human orthologues with known neurological functions, and upon validation of the interactions in mammalian systems, these orthologues would be potential therapeutic targets for GDI1-associated neurological disorders. We also observed the conservation of a gdi-1 interaction between different cellular systems in C. elegans, suggesting the involvement of GDI1 in human muscle degeneration. Conclusions/Significance: We developed a novel predictor of genetic interactions that may have the ability to significantly streamline the identification of therapeutic targets for monogenic disorders involving genes conserved between human and C. elegans. © 2010 Lee et al. Source

Codjia C.,Universitedu Quebeca Montreal | Cavayas F.,University of Montreal | Desjardins R.,Universitedu Quebeca Montreal
Canadian Journal of Remote Sensing

The advent of high-resolution radar images, in particular those of RADARSAT-2, reinforces the potential for the survey of our environment. The analysis and interpretation of those images, however, remains a challenge, particularly regarding the urban environment. In this type of environment, the complexity of responses of the objects combined with the variability of backscatter for similar objects viewed under different angles can prove confusing and complicates the visual interpretation or the development of classification/segmentation algorithms. The purpose of this article is to present the results of an analysis of the cardinal effects on RADARSAT-2 images and to examine the feasibility of an algorithm to compensate for these effects. A radar simulation from the digital surface model of the environment allowed us to extract the building backscatter zones and to analyze the related backscatter. Thus, we were able to devise a strategy of compensation of cardinal effects solely based on the responses of the objects according to their orientation from the plane of illumination through the radar's beam. It appeared that a compensation algorithm based on the radar cross section was appropriate. Some examples of the application of this algorithm on HH polarized RADARSAT-2 images are presented as well. Application of this algorithm will allow considerable gains with regard to certain forms of automation (classification and segmentation) at the level of radar imagery thus generating a higher level of quality in regard to visual interpretation. © 2012 CASI. Source

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