Un modelo sintáctico para la representación, segmentación y reconocimiento de símbolos texturados en documentos gráficos

Cette thèse s'inscrit dans le domaine de l'analyse de symboles graphiques texturés, définis comme des textures structurées, c'est-à-dire par un ou plusieurs texels distribués dans l'espace en suivant des règles de disposition. Dans notre modèle, ces texels sont des polygones fermés ou des segments de droite. Les documents à analyser sont modélisés au moyen d'un graphe de régions adjacentes, dont les nÏuds représentent les polygones et les arcs leurs relations spatiales. Nous proposons des solutions pour segmenter les textures structurées au moyen d'un regroupement hiérarchique de formes et de règles de disposition similaire. Les représentants des regroupements, un polygone et un voisinage moyen calculés à partir des éléments de ce regroupement, permettent d'inférer automatiquement une grammaire. Celle-ci, incluant une prise en charge des erreurs, est utilisée pour appliquer une analyse syntaxique au graphe et ainsi reconnaître les symboles texturés.This work focuses on the textured graphical symbols analysis, that means symbols formed by a structured texture. Structural textures are defined by one or more texels placed following placement rules. In our model those texels are polygons or line segments. An input document is modelized by means of a Region Adjacency Graph, where the nodes represent polygonals and the edges their spatial relations. Some problems are solved. First the segmentation of structural textures by means of a hierarchical clustering of similar polygons and placement rules. Then the modelization of textured symbols by means of a graph grammar with error productions and the recognition of those symbols parsing with the grammar rules over an input graph. The grammar is automatically inferred from the representatives of the different found clusters, computed as the mean shape and the mean placement rule of all the elements forming the clusterNANCY1-SCD Sciences & Techniques (545782101) / SudocSudocFranceF

Polymorphisms at phase I-metabolizing enzyme and hormone receptor loci influence the response to anti-TNF therapy in rheumatoid arthritis patients

The aim of this case-control study was to evaluate whether 47 single-nucleotide polymorphisms (SNPs) in steroid hormone-related genes are associated with the risk of RA and anti-TNF drug response. We conducted a case-control study in 3 European populations including 2936 RA patients and 2197 healthy controls. Of those, a total of 1985 RA patients were treated with anti-TNF blockers. The association of potentially interesting markers in the discovery population was validated through meta-analysis with data from DREAM and DANBIO registries. Although none of the selected variants had a relevant role in modulating RA risk, the meta-analysis of the linear regression data with those from the DREAM and DANBIO registries showed a significant correlation of the CYP3A4rs11773597 and CYP2C9rs1799853 variants with changes in DAS28 after the administration of anti-TNF drugs (P = 0.00074 and P = 0.006, respectively). An overall haplotype analysis also showed that the ESR2GGG haplotype significantly associated with a reduced chance of having poor response to anti-TNF drugs (P = 0.0009). Finally, a ROC curve analysis confirmed that a model built with eight steroid hormone-related variants significantly improved the ability to predict drug response compared with the reference model including demographic and clinical variables (AUC = 0.633 vs. AUC = 0.556; PLR_test = 1.52 x 10(-6)). These data together with those reporting that the CYP3A4 and ESR2 SNPs correlate with the expression of TRIM4 and ESR2 mRNAs in PBMCs (ranging from P = 1.98 x 10(-)(6) to P = 2.0 x 10(-35)), and that the CYP2C9rs1799853 SNP modulates the efficiency of multiple drugs, suggest that steroid hormone-related genes may have a role in determining the response to anti-TNF drugs.KEY POINTS* Polymorphisms within the CYP3A4 and CYP2C9 loci correlate with changes in DAS28 after treatment with anti-TNF drugs.* A haplotype including eQTL SNPs within the ESR2 gene associates with better response to anti-TNF drugs.* A genetic model built with eight steroid hormone-related variants significantly improved the ability to predict drug response

Development and validation of a score to predict postoperative respiratory failure in a multicentre European cohort : A prospective, observational study

BACKGROUND Postoperative respiratory failure (PRF) is the most frequent respiratory complication following surgery. OBJECTIVE The objective of this study was to build a clinically useful predictive model for the development of PRF. DESIGN A prospective observational study of a multicentre cohort. SETTING Sixty-three hospitals across Europe. PATIENTS Patients undergoing any surgical procedure under general or regional anaesthesia during 7-day recruitment periods. MAIN OUTCOME MEASURES Development of PRF within 5 days of surgery. PRF was defined by a partial pressure of oxygen in arterial blood (PaO2) less than 8 kPa or new onset oxyhaemoglobin saturation measured by pulse oximetry (SpO(2)) less than 90% whilst breathing room air that required conventional oxygen therapy, noninvasive or invasive mechanical ventilation. RESULTS PRF developed in 224 patients (4.2% of the 5384 patients studied). In-hospital mortality [95% confidence interval (95% CI)] was higher in patients who developed PRF [10.3% (6.3 to 14.3) vs. 0.4% (0.2 to 0.6)]. Regression modelling identified a predictive PRF score that includes seven independent risk factors: low preoperative SpO(2); at least one preoperative respiratory symptom; preoperative chronic liver disease; history of congestive heart failure; open intrathoracic or upper abdominal surgery; surgical procedure lasting at least 2 h; and emergency surgery. The area under the receiver operating characteristic curve (c-statistic) was 0.82 (95% CI 0.79 to 0.85) and the Hosmer-Lemeshow goodness-of-fit statistic was 7.08 (P = 0.253). CONCLUSION A risk score based on seven objective, easily assessed factors was able to predict which patients would develop PRF. The score could potentially facilitate preoperative risk assessment and management and provide a basis for testing interventions to improve outcomes. The study was registered at ClinicalTrials.gov (identifier NCT01346709)