Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Role of fibroblast growth factor (FGF) signaling in the neuroendocrine control of human reproduction.

Fibroblast growth factor (FGF) signaling is critical for a broad range of developmental processes. In 2003, Fibroblast growth factor receptor 1 (FGFR1) was discovered as a novel locus causing both forms of isolate GnRH Deficiency, Kallmann syndrome [KS with anosmia] and normosmic idiopathic hypogonadotropic hypogonadism [nIHH] eventually accounting for approximately 10% of gonadotropin-releasing hormone (GnRH) deficiency cases. Such cases are characterized by a broad spectrum of reproductive phenotypes from severe congenital forms of GnRH deficiency to reversal of HH. Additionally, the variable expressivity of both reproductive and non-reproductive phenotypes among patients and family members harboring the identical FGFR1 mutations has pointed to a more complex, oligogenic model for GnRH deficiency. Further, reversal of HH in patients carrying FGFR1 mutations suggests potential gene-environment interactions in human GnRH deficiency disorders.