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    Tourism specialization across qualities in Europe

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    Germline SUFU mutation carriers and medulloblastoma: clinical characteristics, cancer risk, and prognosis

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    International audienceAbstract Background Germline mutations of suppressor of fused homolog (SUFU) predispose to sonic hedgehog (SHH) medulloblastoma. Germline SUFU mutations have been reported in nevoid basal cell carcinoma syndrome (NBCCS), but little is known about the cancer risk and clinical spectrum. Methods We performed a retrospective review of all patients with medulloblastoma and a germline SUFU mutation in France. Results Twenty-two patients from 17 families were identified with medulloblastoma and a germline SUFU mutation (median age at diagnosis: 16.5 mo). Macrocrania was present in 20 patients, but only 5 met the diagnostic criteria for NBCCS. Despite treatment with surgery and chemotherapy, to avoid radiotherapy in all patients except one, the outcome was worse than expected for SHH medulloblastoma, due to the high incidence of local relapses (8/22 patients) and second malignancies (n = 6 in 4/22 patients). The 5-year progression-free survival and overall survival rates were 42% and 66%. Mutations were inherited in 79% of patients, and 34 additional SUFU mutation carriers were identified within 14 families. Medulloblastoma penetrance was incomplete, but higher than in Patched 1 (PTCH1) mutation carriers. Besides medulloblastoma, 19 other tumors were recorded among the 56 SUFU mutation carriers, including basal cell carcinoma (BCC) in 2 patients and meningioma in 3 patients. Conclusion Germline SUFU mutations strongly predispose to medulloblastoma in the first years of life, with worse prognosis than usually observed for SHH medulloblastoma. The clinical spectrum differs between SUFU and PTCH1 mutation carriers, and BCC incidence is much lower in SUFU mutation carriers. The optimal treatment of SUFU mutation–associated medulloblastoma has not been defined

    Representation, Analysis and Recognition of 3D Humans: A Survey

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    International audienceComputer Vision and Multimedia solutions are now offering an increasing number of applications ready for use by end users in everyday life. Many of these applications are centered of detection, representation, and analysis of face and body. Methods based on 2D images and videos are the most widespread, but there is a recent trend that successfully extends the study to 3D human data as acquired by a new generation of 3D acquisition devices. Based on these premises, in this survey, we provide an overview on the newly designed techniques that exploit 3D human data and also prospect the most promising current and future research directions. In particular, we first propose a taxonomy of the representation methods, distinguishing between spatial and temporal modeling of the data. Then, we focus on the analysis and recognition of 3D humans from 3D static and dynamic data, considering many applications for body and face

    Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks with Stochastic Energy Harvesting

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    International audienceWe address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks. We consider the case where two types of sensors are deployed: the first consists of expensive, high quality sensors; and the second, of cheap low quality sensors, which are activated only if the intensity of the spatial field exceeds a pre-defined activation threshold (e.g., wind sensors). In addition, these sensors are powered by means of energy harvesting and their time varying energy status impacts on the accuracy of the measurement that may be obtained. We then address the following two important problems: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from both networks; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. To overcome this problem, we solve the first problem by developing a low complexity algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy method which solves the combinatorial optimization problem in an efficient manner. We present a comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using both synthetic and real insurance storm surge database known as the Extreme Wind Storms Catalogue

    On estimation in a spatial functional linear regression model with derivatives

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    International audienceThis paper deals with functional linear regression for spatial data. We study the asymptotic properties of an estimator of a linear model where a spatial scalar response variable is related to a spatial functional explanatory variable and to its derivative. Convergence results with rate of this estimator are derived

    L'efficacité des règles budgétaires dans les provinces canadiennes

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    Cet article s’inscrit dans le débat sur les règles budgétaires, qui occupe une place centrale dans les questions de politiques publiques étant donné les déficits hérités de la grande récession. La diversité des règles dans le cas canadien offre une expérience quasi naturelle de leur effet. Nous définissons une nomenclature des règles d’équilibre budgétaire en vigueur dans les dix provinces canadiennes, puis examinons les performances des diverses règles existantes, via une analyse économétrique sur la période post 1980. Le problème de l’endogénéité de l’adoption des règles étant intégré, les résultats confirment que l’existence de règles contraignantes contribue à réduire le niveau de dette nette

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