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.

Discordances between phylogenetic and morphological patterns in alpine leaf beetles attest to an intricate biogeographic history of lineages in postglacial Europe.

Pleistocene glacial and interglacial periods have moulded the evolutionary history of European cold-adapted organisms. The role of the different mountain massifs has, however, not been accurately investigated in the case of high-altitude insect species. Here, we focus on three closely related species of non-flying leaf beetles of the genus Oreina (Coleoptera, Chrysomelidae), which are often found in sympatry within the mountain ranges of Europe. After showing that the species concept as currently applied does not match barcoding results, we show, based on more than 700 sequences from one nuclear and three mitochondrial genes, the role of biogeography in shaping the phylogenetic hypothesis. Dating the phylogeny using an insect molecular clock, we show that the earliest lineages diverged more than 1 Mya and that the main shift in diversification rate occurred between 0.36 and 0.18 Mya. By using a probabilistic approach on the parsimony-based dispersal/vicariance framework (MP-DIVA) as well as a direct likelihood method of state change optimization, we show that the Alps acted as a cross-roads with multiple events of dispersal to and reinvasion from neighbouring mountains. However, the relative importance of vicariance vs. dispersal events on the process of rapid diversification remains difficult to evaluate because of a bias towards overestimation of vicariance in the DIVA algorithm. Parallels are drawn with recent studies of cold-adapted species, although our study reveals novel patterns in diversity and genetic links between European mountains, and highlights the importance of neglected regions, such as the Jura and the Balkanic range.

The Arabidopsis PHYTOCHROME KINASE SUBSTRATE2 protein is a phototropin signaling element that regulates leaf flattening and leaf positioning.

In Arabidopsis (Arabidopsis thaliana), the blue light photoreceptor phototropins (phot1 and phot2) fine-tune the photosynthetic status of the plant by controlling several important adaptive processes in response to environmental light variations. These processes include stem and petiole phototropism (leaf positioning), leaf flattening, stomatal opening, and chloroplast movements. The PHYTOCHROME KINASE SUBSTRATE (PKS) protein family comprises four members in Arabidopsis (PKS1-PKS4). PKS1 is a novel phot1 signaling element during phototropism, as it interacts with phot1 and the important signaling element NONPHOTOTROPIC HYPOCOTYL3 (NPH3) and is required for normal phot1-mediated phototropism. In this study, we have analyzed more globally the role of three PKS members (PKS1, PKS2, and PKS4). Systematic analysis of mutants reveals that PKS2 (and to a lesser extent PKS1) act in the same subset of phototropin-controlled responses as NPH3, namely leaf flattening and positioning. PKS1, PKS2, and NPH3 coimmunoprecipitate with both phot1-green fluorescent protein and phot2-green fluorescent protein in leaf extracts. Genetic experiments position PKS2 within phot1 and phot2 pathways controlling leaf positioning and leaf flattening, respectively. NPH3 can act in both phot1 and phot2 pathways, and synergistic interactions observed between pks2 and nph3 mutants suggest complementary roles of PKS2 and NPH3 during phototropin signaling. Finally, several observations further suggest that PKS2 may regulate leaf flattening and positioning by controlling auxin homeostasis. Together with previous findings, our results indicate that the PKS proteins represent an important family of phototropin signaling proteins.