New insights into the long-term evolutionary history of the avian MHC

SummaryGene duplication and neofunctidnalization are important processes in the evolution of phenotypic complexity. They account for important evolutionary novelties that confer ecological adaptation, such as the major histocompatibility complex (MHC), a multigene family with a central role in vertebrates' adaptive immune system. Multigene families, which evolved in large part through duplication, represent promising systems to study the still strongly depbated relative roles of neutral and adaptive processes in the evolution of phenotypic complexity. Detailed knowledge on ecological function and a well-characterized evolutionary history place the mammals' MHC amongst ideal study systems. However mammalian MHCs usually encompass several million base pairs and hold a large number of functional and non-functional duplicate genes, which makes their study complex. Avian MHCs on the other hand are usually way more compact, but the reconstruction of. their evolutionary history has proven notoriously difficult. However, no focused attempt has been undertaken so far to study the avian MHC evolutionary history in a broad phylogenetic context and using adequate gene regions.In the present PhD, we were able to make important contributions to the understanding of the long-term evolution of the avian MHC class II Β (MHCI1B). First, we isolated and characterized MHCIIB genes in barn owl (Tyto alba?, Strigiformes, Tytonidae), a species from an avian lineage in which MHC has not been studied so far. Our results revealed that with only two functional MHCIIB genes the MHC organization of barn owl may be similar to the 'minimal essential' MHC of chicken (Gallus gallus), indicating that simple MHC organization may be ancestral to birds. Taking advantage of the sequence information from barn owl, we studied the evolution of MHCIIB genes in 13 additional species of 'typical' owls (Strigiformes, Strigidae). Phylogenetic analyses revealed that according to their function, in owls the peptide-binding region (PBR) encoding exon 2 and the non-PBR encoding exon 3 evolve by different patterns. Exon 2 exhibited an evolutionary history of positive selection and recombination, while exon 3 traced duplication history and revealed two paralogs evolving divergently from each other in owls, and in a shorebird, the great snipe {Gallinago media). The results from exon 3 were the first ever from birds to demonstrate gene orthology in species that diverged tens of millions of years ago, and strongly questioned whether the taxa studied before provided an adequate picture of avian MHC evolution. In a follow-up study, we aimed at explaining a striking pattern revealed by phylogenetic trees analyzing the owl sequences along with MHCIIB sequences from other birds: One owl paralog (termed DAB1) grouped with sequences of passerines and falcons, while the other (DAB2) grouped with wildfowl, penguins and birds of prey. This could be explained by either a duplication event preceding the evolution of these bird orders, or by convergent evolution of similar sequences in a number of orders. With extensive phylogenetic analyses we were able to show, that indeed a duplication event preceeded the major avian radiation -100 my ago, and that following this duplication, the paralogs evolved under positive selection. Furthermore, we showed that the divergently evolving amino acid residues in the MHCIIB-encoded β-chain potentially interact with the MHCI I α-chain, and that molecular coevolution of the interacting residues may have been involved in the divergent evolution of the MHCIIB paralogs.The findings of this PhD are of particular interest to the understanding of the evolutionary history of the avian MHC and, by providing essential information on long-term gene history in the avian MHC, open promising perspectives for advances in the understanding of the evolution of multigene families in general, and for avian MHC organization in particular. Amongst others I discuss the importance of including protein structure in the phylogenetic study of multigene families, and the roles of ecological versus molecular selection pressures. I conclude by providing a population genomic perspective on avian MHC, which may serve as a basis for future research to investigate the relative roles of neutral processes involving effective population size effects and of adaptation in the evolution of avian MHC diversity and organization.RésuméLa duplication de gènes et leur néo-fonctionnalisation sont des processus importants dans l'évolution de la complexité phénotypique. Ils sont impliqués dans l'apparition d'importantes nouveautés évolutives favorisant l'adaptation écologique, comme c'est le cas pour le complexe majeur d'histocompatibilité

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

UOC Algorithms

Aplicació per a iPad a mode de repositori de continguts relacionats amb l'ensenyament d'assignatures d'informàtica.

Evaluation of organ-specific peripheral doses after 2-dimensional, 3-dimensional and hybrid intensity modulated radiation therapy for breast cancer based on Monte Carlo and convolution/superposition algorithms: implications for secondary cancer risk assessment.

To make a comprehensive evaluation of organ-specific out-of-field doses using Monte Carlo (MC) simulations for different breast cancer irradiation techniques and to compare results with a commercial treatment planning system (TPS). Three breast radiotherapy techniques using 6MV tangential photon beams were compared: (a) 2DRT (open rectangular fields), (b) 3DCRT (conformal wedged fields), and (c) hybrid IMRT (open conformal+modulated fields). Over 35 organs were contoured in a whole-body CT scan and organ-specific dose distributions were determined with MC and the TPS. Large differences in out-of-field doses were observed between MC and TPS calculations, even for organs close to the target volume such as the heart, the lungs and the contralateral breast (up to 70% difference). MC simulations showed that a large fraction of the out-of-field dose comes from the out-of-field head scatter fluence (>40%) which is not adequately modeled by the TPS. Based on MC simulations, the 3DCRT technique using external wedges yielded significantly higher doses (up to a factor 4-5 in the pelvis) than the 2DRT and the hybrid IMRT techniques which yielded similar out-of-field doses. In sharp contrast to popular belief, the IMRT technique investigated here does not increase the out-of-field dose compared to conventional techniques and may offer the most optimal plan. The 3DCRT technique with external wedges yields the largest out-of-field doses. For accurate out-of-field dose assessment, a commercial TPS should not be used, even for organs near the target volume (contralateral breast, lungs, heart).

Evolutionary biology: genetics and bisexuality.

A population-genetic model indicates that if there is a gene responsible for homosexual behaviour it can readily spread in populations. The model also predicts widespread bisexuality in humans.

Evolutionary implications of a high selfing rate in the freshwater snail Lymnaea truncatula.

Self-compatible hermaphroditic organisms that mix self-fertilization and outcrossing are of great interest for investigating the evolution of mating systems. We investigate the evolution of selfing in Lymnaea truncatula, a self-compatible hermaphroditic freshwater snail. We first analyze the consequences of selfing in terms of genetic variability within and among populations and then investigate how these consequences along with the species ecology (harshness of the habitat and parasitism) might govern the evolution of selfing. Snails from 13 localities (classified as temporary or permanent depending on their water availability) were sampled in western Switzerland and genotyped for seven microsatellite loci. F(IS) (estimated on adults) and progeny array analyses (on hatchlings) provided similar selfing rate estimates of 80%. Populations presented a low polymorphism and were highly differentiated (F(ST) = 0.58). Although the reproductive assurance hypothesis would predict higher selfing rate in temporary populations, no difference in selfing level was observed between temporary and permanent populations. However, allelic richness and gene diversity declined in temporary habitats, presumably reflecting drift. Infection levels varied but were not simply related to either estimated population selfing rate or to differences in heterozygosity. These findings and the similar selfing rates estimated for hatchlings and adults suggest that within-population inbreeding depression is low in L. truncatula.

Enhancing fault management performance of two-step QoS routing algorithms in GMPLS networks

In this paper a novel methodology aimed at minimizing the probability of network failure and the failure impact (in terms of QoS degradation) while optimizing the resource consumption is introduced. A detailed study of MPLS recovery techniques and their GMPLS extensions are also presented. In this scenario, some features for reducing the failure impact and offering minimum failure probabilities at the same time are also analyzed. Novel two-step routing algorithms using this methodology are proposed. Results show that these methods offer high protection levels with optimal resource consumption

Enhancing MPLS QoS routing algorithms by using the network protection degree paradigm

IP based networks still do not have the required degree of reliability required by new multimedia services, achieving such reliability will be crucial in the success or failure of the new Internet generation. Most of existing schemes for QoS routing do not take into consideration parameters concerning the quality of the protection, such as packet loss or restoration time. In this paper, we define a new paradigm to develop new protection strategies for building reliable MPLS networks, based on what we have called the network protection degree (NPD). This NPD consists of an a priori evaluation, the failure sensibility degree (FSD), which provides the failure probability and an a posteriori evaluation, the failure impact degree (FID), to determine the impact on the network in case of failure. Having mathematical formulated these components, we point out the most relevant components. Experimental results demonstrate the benefits of the utilization of the NPD, when used to enhance some current QoS routing algorithms to offer a certain degree of protection

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach