Le religieux entre science et cité. Penser avec Pierre Gisel

Fin observateur des mutations du religieux au cours des trente dernières années, Pierre Gisel fait l'objet, dans ce livre, d'analyses sur les enjeux qui marquent ses recherches fondamentales : la mise à l'épreuve des religions par leurs confrontations avec la science, la culture et le politique. La pensée de Pierre Gisel est l'une des rares à prendre en compte les défis que pose la sécularisation aux postulats des traditions religieuses en général, et au christianisme en particulier. Le statut des textes sacrés, la légitimité des institutions religieuses, les généalogies des monothéismes : autant d'objets auxquels Pierre Gisel adresse des questions exigeantes et « théologiquement incorrectes », pour le meilleur des débats actuels sur les transformations et les valeurs de nos sociétés. Dans ce livre, sociologues, philosophes, théologiens, historiens, journalistes ainsi qu'une représentante politique rendent compte de la réception qu'ils accordent aux travaux de Pierre Gisel, pour les prolonger, les déplacer et les critiquer, tout en soulignant leur incontestable pertinence.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).