Glycosidation avec protection minimale en solution et sur support solide

Les glycosides sont reconnus pour leur potentiel pharmaceutique tels que les antibiotiques, les agents anticancéreux et antiviraux. Ils sont impliqués dans plusieurs processus biologiques entre autres la reconnaissance cellulaire, l’inflammation, la réponse immunitaire, la croissance, le transport cellulaire, l’adhésion cellulaire et les groupes sanguin. Notre groupe excelle dans la glycosidation stéréocontrôlée avec un minimum de protection suivant le concept d’activation à distance d’aglycones hétérocycliques anomériques. La présence d’une quantité sous stoechiométrique d’acide de Lewis, les (2-pyridyl)-β-D-glycosides déprotégés sont d’excellents donneurs permettant de haute sélectivité pour l’anomère- α-D de glycosides simples et complexes. Inversement, (2-pyridyl)-α-D-glycosides donnent les β-D-glycosides avec de bonne sélectivité. Des exemples de formation stéréocontrôlée de glycosides sont présentés dans cette thèse avec des accepteurs tels que les phénols, les stéroïdes, les terpènes et les acides hydroxyaminés. Cette méthodologie de glycosidation a été appliquée sur support solide.

Elementary School Principals in Low-Socioeconomic-Status Schools: A University-Based Research Program Designed to Support Mandated Reform

This paper presents a reform initiative, the Supporting Montreal Schools Program (SMSP), created by the government of Quebec to assist 184 low socio-economic-status schools in Montreal implement seven reform strategies prescribed by the government. On a regular basis, the professional team of the SMSP engages in reflection and research with universities concerning one or more of the strategies they are charged with helping schools implement or the functioning of the SMSP more generally. The present research programme is part of the team’s ongoing reflection on a component of Strategy 4: professional development of school administrators and the school team. In this paper, we detail results from this initial and subsequent studies on the work of principals in low-performing schools. We also describe our collaborative relationship with the SMSP team, discuss the effectiveness of the SMSP in promoting the implementation of the seven governmentmandated strategies and critique the utility of our partnership with the SMSP and our use of that programme as a vehicle for linking research to practice.

Romantic attachment and perception of partner support to explain psychological aggression perpetrated in couples seeking couples therapy

Essai doctoral présenté à la Faculté des études supérieures en vue de l’obtention du grade de Docteur en psychologie (D.Psy.), option clinique

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.