The prognostic significance of genetic polymorphisms (Methylenetetrahydrofolate Reductase C677T, Methionine Synthase A2756G, Thymidilate Synthase tandem repeat polymorphism) in multimodally treated oesophageal squamous cell carcinoma

The present study retrospectively examined the correlation between the outcome of patients with locally advanced oesophageal squamous cell carcinoma (cT3-4 cN0-1 cM0) after multimodal treatment (radiochemotherapy±surgical resection), and the presence of genetic polymorphisms in genes involved in folate metabolism. In total, 68 patients who took part in a prospective multicentric trial received 5-fluorouracil (FU)-based radiochemotherapy, optionally followed by surgery. DNA was extracted from pretherapeutic tumour biopsies and was subsequently genotyped for common genetic polymorphisms of three genes (MTHFR C677T, MTR A2756G, TS tandem repeat polymorphism) involved in folate metabolism and potentially in sensitivity to 5-FU-based chemotherapy. The genotypes were correlated with tumour response to polychemotherapy, radiochemotherapy and with overall survival. Tumours with the MTR wild-type genotype (2756AA) showed a median survival time of 16 months, whereas tumours with an MTR variant genotype (2756AG/2756GG) showed a median survival time of 42 months (P=0.0463). No prognostic impact could be verified for the genotypes of the MTHFR genes and the TS gene. Among tumours treated with radiochemotherapy and subsequent resection, MTR variant genotype showed higher histopathological response rate than tumours with MTR wild-type genotype (P=0.0442). In contrast, no significant relationship between clinically determined tumour regression after polychemotherapy and polymorphisms of the three genes under analysis was observed. In conclusion, pretherapeutic determination of the MTR A2756G polymorphism may predict survival of multimodally treated oesophageal squamous cell carcinomas. Determination of MTHFR C677T and TS tandem repeat polymorphism has no predictive value

From taxonomies to ontologies: formalizing generalization knowledge for on-demand mapping

© 2015 Cartography and Geographic Information Society Automation of the cartographic design process is central to the delivery of bespoke maps via the web. In this paper, ontological modeling is used to explicitly represent and articulate the knowledge used in this decision-making process. A use case focuses on the visualization of road traffic accident data as a way of illustrating how ontologies provide a framework by which salient and contextual information can be integrated in a meaningful manner. Such systems are in anticipation of web-based services in which the user knows what they need, but do not have the cartographic ability to get what they want

Mining Spatio-temporal Data at Different Levels of Detail

Presented at the 11th AGILE International Conference on Geographic Information Science (AGILE 2008), Girona, Spain, 5-8 May 2008In this paper we propose a methodology for mining very large spatio-temporal datasets. We propose a two-pass strategy for mining and manipulating spatio-temporal datasets at different levels of detail (i.e., granularities). The approach takes advantage of the multi-granular capability of the underlying spatio-temporal model to reduce the amount of data that can be accessed initially. The approach is implemented and applied to real-world spatio-temporal datasets. We show that the technique can deal easily with very large datasets without losing the accuracy of the extracted patterns, as demonstrated in the experimental results.Science Foundation Ireland; Irish Research Council for Science, Engineering & TechnologyConference detailshttp://plone.itc.nl/agile/agile-conference

Prise en charge du patient en hospitalisation ambulatoire pour la réalisation d’un traitement de radiologie interventionnelle (RI)

International audienc

Querying Multigranular Spatio-temporal Objects

The integrated management of both spatial and temporal components of information is crucial in order to extract significant knowledge from datasets concerning phenomena of interest to a large variety of applications. Moreover, multigranularity, i.e., the capability of representing information at different levels of detail, enhances the data modelling flexibility and improves the analysis of information, enabling to zoom-in and zoom-out spatio-temporal datasets. Relying on an existing multigranular spatio-temporal extension of the ODMG data model, in this paper we describe the design of a multigranular spatio-temporal query language. We extend OQL value comparison and object navigation in order to access spatio-temporal objects with attribute values defined at different levels of detail