Influence of learning on range expansion and adaptation to novel habitats.

Learning has been postulated to 'drive' evolution, but its influence on adaptive evolution in heterogeneous environments has not been formally examined. We used a spatially explicit individual-based model to study the effect of learning on the expansion and adaptation of a species to a novel habitat. Fitness was mediated by a behavioural trait (resource preference), which in turn was determined by both the genotype and learning. Our findings indicate that learning substantially increases the range of parameters under which the species expands and adapts to the novel habitat, particularly if the two habitats are separated by a sharp ecotone (rather than a gradient). However, for a broad range of parameters, learning reduces the degree of genetically-based local adaptation following the expansion and facilitates maintenance of genetic variation within local populations. Thus, in heterogeneous environments learning may facilitate evolutionary range expansions and maintenance of the potential of local populations to respond to subsequent environmental changes.

Learning and adaptation in physical heterogeneous teams of robots

In this paper we present a novel approach to assigning roles to robots in a team of physical heterogeneous robots. Its members compete for these roles and get rewards for them. The rewards are used to determine each agent’s preferences and which agents are better adapted to the environment. These aspects are included in the decision making process. Agent interactions are modelled using the concept of an ecosystem in which each robot is a species, resulting in emergent behaviour of the whole set of agents. One of the most important features of this approach is its high adaptability. Unlike some other learning techniques, this approach does not need to start a whole exploitation process when the environment changes. All this is exemplified by means of experiments run on a simulator. In addition, the algorithm developed was applied as applied to several teams of robots in order to analyse the impact of heterogeneity in these systems

Transcript profiling suggests that differential metabolic adaptation of mice to a high fat diet is associated with changes in liver to muscle lipid fluxes.

Genetically homogenous C57Bl/6 mice display differential metabolic adaptation when fed a high fat diet for 9 months. Most become obese and diabetic, but a significant fraction remains lean and diabetic or lean and non-diabetic. Here, we performed microarray analysis of "metabolic" transcripts expressed in liver and hindlimb muscles to evaluate: (i) whether expressed transcript patterns could indicate changes in metabolic pathways associated with the different phenotypes, (ii) how these changes differed from the early metabolic adaptation to short term high fat feeding, and (iii) whether gene classifiers could be established that were characteristic of each metabolic phenotype. Our data indicate that obesity/diabetes was associated with preserved hepatic lipogenic gene expression and increased plasma levels of very low density lipoprotein and, in muscle, with an increase in lipoprotein lipase gene expression. This suggests increased muscle fatty acid uptake, which may favor insulin resistance. In contrast, the lean mice showed a strong reduction in the expression of hepatic lipogenic genes, in particular of Scd-1, a gene linked to sensitivity to diet-induced obesity; the lean and non-diabetic mice presented an additional increased expression of eNos in liver. After 1 week of high fat feeding the liver gene expression pattern was distinct from that seen at 9 months in any of the three mouse groups, thus indicating progressive establishment of the different phenotypes. Strikingly, development of the obese phenotype involved re-expression of Scd-1 and other lipogenic genes. Finally, gene classifiers could be established that were characteristic of each metabolic phenotype. Together, these data suggest that epigenetic mechanisms influence gene expression patterns and metabolic fates.

Role of the circadian clock gene Per2 in adaptation to cold temperature.

Adaptive thermogenesis allows mammals to resist to cold. For instance, in brown adipose tissue (BAT) the facultative uncoupling of the proton gradient from ATP synthesis in mitochondria is used to generate systemic heat. However, this system necessitates an increase of the Uncoupling protein 1 (Ucp1) and its activation by free fatty acids. Here we show that mice without functional Period2 (Per2) were cold sensitive because their adaptive thermogenesis system was less efficient. Upon cold-exposure, Heat shock factor 1 (HSF1) induced Per2 in the BAT. Subsequently, PER2 as a co-activator of PPARα increased expression of Ucp1. PER2 also increased Fatty acid binding protein 3 (Fabp3), a protein important to transport free fatty acids from the plasma to mitochondria to activate UCP1. Hence, in BAT PER2 is important for the coordination of the molecular response of mice exposed to cold by synchronizing UCP1 expression and its activation.

Adaptation to marginal habitats

The ability to adapt to marginal habitats, in which survival and reproduction are initially poor, plays a crucial role in the evolution of ecological niches and species ranges. Adaptation to marginal habitats may be limited by genetic, developmental, and functional constraints, but also by consequences of demographic characteristics of marginal populations. Marginal populations are often sparse, fragmented, prone to local extinctions, or are demographic sinks subject to high immigration from high-quality core habitats. This makes them demographically and genetically dependent on core habitats and prone to gene flow counteracting local selection. Theoretical and empirical research in the past decade has advanced our understanding of conditions that favor adaptation to marginal habitats despite those limitations. This review is an attempt at synthesis of those developments and of the emerging conceptual framework.

Birth and rapid subcellular adaptation of a hominoid-specific CDC14 protein

Gene duplication was prevalent during hominoid evolution, yet little is known about the functional fate of new ape gene copies. We characterized the CDC14B cell cycle gene and the functional evolution of its hominoid-specific daughter gene, CDC14Bretro. We found that CDC14B encodes four different splice isoforms that show different subcellular localizations (nucleus or microtubule-associated) and functional properties. A microtubular CDC14B variant spawned CDC14Bretro through retroposition in the hominoid ancestor 18-25 million years ago (Mya). CDC14Bretro evolved brain-/testis-specific expression after the duplication event and experienced a short period of intense positive selection in the African ape ancestor 7-12 Mya. Using resurrected ancestral protein variants, we demonstrate that by virtue of amino acid substitutions in distinct protein regions during this time, the subcellular localization of CDC14Bretro progressively shifted from the association with microtubules (stabilizing them) to an association with the endoplasmic reticulum. CDC14Bretro evolution represents a paradigm example of rapid, selectively driven subcellular relocalization, thus revealing a novel mode for the emergence of new gene function

Critères de sélection et d'adaptation d'un instrument de mesure pour la recherche clinique

The goal of this paper is to provide clinicians and researchers, who may not be experts in psychometrics, with a guide for the selection and adaptation of an instrument for clinical research. Issues related to the concept to be measured, the targeted clientele, the selection criteria for the instrument (algorithm), the strategies for translation and adaptation, as well as potential bias related to the administration of an instrument are reviewed and discussed.

Calcium homeostasis in the central nervous system: adaptation to neurodegeneration.

Here we review the results of our recent studies on neurodegeneration together with data on cerebral calcium precipitation in animal models and humans. A model that integrates the diversity of mechanisms involved in neurodegeneration is presented and discussed based on the functional relevance of calcium precipitation.

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.