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Detecting adaptive loci in the genome is essential as it gives the possibility to understand what proportion of a genome or which genes are under the pressure of natural selection. In this paper, we used a Spatial Analysis Method (SAM) recently developed t ...
Understanding genetic basis of adaptation is of key importance, especially to insure food supply for present and future populations. Cultivated plants and livestock animals are subjected to high selection pressure due to climate change, increasing food nee ...
Even if many research projects in population genetics and conservation biology collect a quantity of spatially located biological samples, and despite its present predominance in Science and its direct application to concerns of public society (health, foo ...
The current thesis constitutes an interdisciplinary approach of detecting a selection pressure driven by the environment examining the contribution of Remote Sensing and Spatial Analysis in the field of Landscape Genetics. Even though several studies have ...
In habitat modelling, an initial step is to define geoenvironmental variables to be included in a predictive model. According to Guisan and Zimmermann (2000), the selection of predictors can be made either arbitrarily, automatically by stepwise procedures, ...
In its natural framework, genetic information is embedded within a geographic context. Plants and animals are directly influenced by the specific characteristics of their surrounding environment. Therefore, spatial information is a potentially important el ...
The local microcircuitry of the neocortex is structurally a tabula rasa, with the axon of each pyramidal neuron having numerous submicrometer appositions with the dendrites of all neighboring pyramidal neurons, but is functionally highly selective, with sy ...
We introduce a new method to detect signatures of natural selection in the genome based on the application of spatial analysis, with the contribution of Geographical Information Systems, environmental variables, molecular data, and multiple univariate logi ...
In biomedical signal analysis, artificial neural networks are often used for pattern classification because of their capability for nonlinear class separation and the possibility to efficiently implement them on a microcontroller. Typically, the network to ...
We applied a spatial approach to detect regions of the genome of the common frog (Rana temporaria) which are possibly selected along an altitude gradient. The identification of selected regions in the genome is important as it gives the possibility to unde ...
