The Maggia cross-fold: An enigmatic structure of the lower Penninic nappes of the Lepontine Alps

The geometry and kinematics of the map scale Maggia cross-fold structure has been studied by several generations of geologists over seventy years and different models have been proposed for its formation. New observations indicate that the Maggia structure is a SW-verging cross-fold created after earlier NW-directed overthrusting of the Maggia nappe onto the deeper Simano and Antigorio recumbent fold nappes. The nappe emplacement and later cross-folding occurred under amphibolite facies conditions by detachment of the upper European crust during its SE-directed underthrusting below the Adriatic plate.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

The influence of social structure on brood survival and development in a socially polymorphic ant: insights from a cross-fostering experiment.

Animal societies vary in the number of breeders per group, which affects many socially and ecologically relevant traits. In several social insect species, including our study species Formica selysi, the presence of either one or multiple reproducing females per colony is generally associated with differences in a suite of traits such as the body size of individuals. However, the proximate mechanisms and ontogenetic processes generating such differences between social structures are poorly known. Here, we cross-fostered eggs originating from single-queen (= monogynous) or multiple-queen (= polygynous) colonies into experimental groups of workers from each social structure to investigate whether differences in offspring survival, development time and body size are shaped by the genotype and/or prefoster maternal effects present in the eggs, or by the social origin of the rearing workers. Eggs produced by polygynous queens were more likely to survive to adulthood than eggs from monogynous queens, regardless of the social origin of the rearing workers. However, brood from monogynous queens grew faster than brood from polygynous queens. The social origin of the rearing workers influenced the probability of brood survival, with workers from monogynous colonies rearing more brood to adulthood than workers from polygynous colonies. The social origin of eggs or rearing workers had no significant effect on the head size of the resulting workers in our standardized laboratory conditions. Overall, the social backgrounds of the parents and of the rearing workers appear to shape distinct survival and developmental traits of ant brood.

Prediction of cross-recognition of peptide-HLA A2 by Melan-A-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships.

The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521-46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347-59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q (2)) is used as the fitness criterion and all obtained models are evaluated based on their q (2) values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the cross-recognition.

The Identity Style Inventory (ISI-3) and the Utrecht-Management of Identity Commitments Scale (U-MICS): Factor structure, reliability, and convergent validity in French-speaking college students

The purpose of this study was to evaluate the factor structure and the reliability of the French versions of the Identity Style Inventory (ISI-3) and the Utrecht-Management of Identity Commitments Scale (U-MICS) in a sample of college students (N = 457, 18 to 25 years old). Confirmatory factor analyses confirmed the hypothesized three-factor solution of the ISI-3 identity styles (i.e. informational, normative, and diffuse-avoidant styles), the one-factor solution of the ISI-3 identity commitment, and the three-factor structure of the U-MICS (i.e. commitment, in-depth exploration, and reconsideration of commitment). Additionally, theoretically consistent and meaningful associations among the ISI-3, U-MICS, and Ego Identity Process Questionnaire (EIPQ) confirmed convergent validity. Overall, the results of the present study indicate that the French versions of the ISI-3 and UMICS are useful instruments for assessing identity styles and processes, and provide additional support to the cross-cultural validity of these tools.

Instrumental leadership: Measurement and extension of transformational-transactional leadership theory.

Leaders must scan the internal and external environment, chart strategic and task objectives, and provide performance feedback. These instrumental leadership (IL) functions go beyond the motivational and quid-pro quo leader behaviors that comprise the full-range-transformational, transactional, and laissez faire-leadership model. In four studies we examined the construct validity of IL. We found evidence for a four-factor IL model that was highly prototypical of good leadership. IL predicted top-level leader emergence controlling for the full-range factors, initiating structure, and consideration. It also explained unique variance in outcomes beyond the full-range factors; the effects of transformational leadership were vastly overstated when IL was omitted from the model. We discuss the importance of a "fuller full-range" leadership theory for theory and practice. We also showcase our methodological contributions regarding corrections for common method variance (i.e., endogeneity) bias using two-stage least squares (2SLS) regression and Monte Carlo split-sample designs.

Novel microsatellite loci for Sebaea aurea (Gentianaceae) and cross-amplification in related species.

Premise of the study: Microsatellite loci were developed in Sebaea aurea (Gentianaceae) to investigate the functional role of diplostigmaty (i.e., the presence of additional stigmas along the style). Methods and Results: One hundred seventy-four and 180 microsatellite loci were isolated through 454 shotgun sequencing of genomic and microsatellite-enriched DNA libraries, respectively. Sixteen polymorphic microsatellite loci were characterized, and 12 of them were selected to genotype individuals from two populations. Microsatellite amplification was conducted in two multiplex groups, each containing six microsatellite loci. Cross-species amplification was tested in seven other species of Sebaea. The 12 novel microsatellite loci amplified only in the two most closely related species to S. aurea (i.e., S. ambigua and S. minutiflora) and were also polymorphic in these two species. Conclusions: These results demonstrate the usefulness of this set of newly developed microsatellite loci to investigate the mating system and population genetic structure in S. aurea and related species.

Extended family structure in the ant Formica paralugubris: the role of the breeding system

In populations of various ant species, many queens reproduce in the same nest (polygyny), and colony boundaries appear to be absent with individuals able to move fi eely between nests (unicoloniality). Such societies depart strongly from a simple family structure and pose a potential challenge to kin selection theory, because high queen number coupled with unrestricted gene flow among nests should result in levels of relatedness among nestmates close to zero. This study investigated the breeding system and genetic structure of a highly polygynous and largely unicolonial population of the wood ant Formica paralugubris. A microsatellite analysis revealed that nestmate workers, reproductive queens and reproductive males (the queens' mates) are all equally related to each other, with relatedness estimates centring around 0.14. This suggests that most of the queens and males reproducing in the study population had mated within or close to their natal nest, and that the queens did not disperse far after mating. We developed a theoretical model to investigate how the breeding system affects the relatedness structure of polygynous colonies. By combining the model and our empirical data, it was estimated that about 99.8% of the reproducing queens and males originated from within the nest, or from a nearby nest. This high rate of local mating and the rarity of long-distance dispersal maintain significant relatedness among nestmates, and contrast with the common view that unicoloniality is coupled with unrestricted gene flow among nests.

False identity documents forensic profiling: study of known-sources and cross-border seizures

A forensic intelligence process was conducted over cross-border seizures of false identity documents whose sources were partly known to be the same. Visual features of 300 counterfeit Portuguese and French identity cards seized in France and Switzerland were observed and integrated in a structured database developed to detect and analyze forensic links. Based on a few batches of documents known to come from common sources, the forensic profiling method could be validated and its performance evaluated. The method also proved efficient and complementary to conventional means of detecting connections between cases. Cross-border links were detected, highlighting the need for more collaboration. Forensic intelligence could be produced, uncovering the structure of counterfeits' illegal trade, the concentration of their sources and the evolution of their quality over time. In addition, two case examples illustrated how forensic profiling may support specific investigations. The forensic intelligence process and its results will underline the need to develop such approaches to support the fight against fraudulent documents and organized crime.

Structure et processus dans une « Agence de développement de réseaux locaux de services de santé et de services sociaux » [Document électronique] : des problèmes et un diagnostic?

Ce cas décrit la structure organisationnelle en place dans une régie régionale de la santé et des services sociaux, ainsi que le processus de prise de décision pour un dossier particulier : la mise en oeuvre d'un programme d'interruption volontaire de grossesses dans la région. L'objectif est d'illustrer comment la structure organisationnelle peut influencer (positivement ou négativement) la façon de traiter les dossiers et les résultats qui en découlent. [Auteurs]

The genetical theory of social behaviour.

We survey the population genetic basis of social evolution, using a logically consistent set of arguments to cover a wide range of biological scenarios. We start by reconsidering Hamilton's (Hamilton 1964 J. Theoret. Biol. 7, 1-16 (doi:10.1016/0022-5193(64)90038-4)) results for selection on a social trait under the assumptions of additive gene action, weak selection and constant environment and demography. This yields a prediction for the direction of allele frequency change in terms of phenotypic costs and benefits and genealogical concepts of relatedness, which holds for any frequency of the trait in the population, and provides the foundation for further developments and extensions. We then allow for any type of gene interaction within and between individuals, strong selection and fluctuating environments and demography, which may depend on the evolving trait itself. We reach three conclusions pertaining to selection on social behaviours under broad conditions. (i) Selection can be understood by focusing on a one-generation change in mean allele frequency, a computation which underpins the utility of reproductive value weights; (ii) in large populations under the assumptions of additive gene action and weak selection, this change is of constant sign for any allele frequency and is predicted by a phenotypic selection gradient; (iii) under the assumptions of trait substitution sequences, such phenotypic selection gradients suffice to characterize long-term multi-dimensional stochastic evolution, with almost no knowledge about the genetic details underlying the coevolving traits. Having such simple results about the effect of selection regardless of population structure and type of social interactions can help to delineate the common features of distinct biological processes. Finally, we clarify some persistent divergences within social evolution theory, with respect to exactness, synergies, maximization, dynamic sufficiency and the role of genetic arguments.

Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling

The interpretation of the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) is based on a 4-factor model, which is only partially compatible with the mainstream Cattell-Horn-Carroll (CHC) model of intelligence measurement. The structure of cognitive batteries is frequently analyzed via exploratory factor analysis and/or confirmatory factor analysis. With classical confirmatory factor analysis, almost all crossloadings between latent variables and measures are fixed to zero in order to allow the model to be identified. However, inappropriate zero cross-loadings can contribute to poor model fit, distorted factors, and biased factor correlations; most important, they do not necessarily faithfully reflect theory. To deal with these methodological and theoretical limitations, we used a new statistical approach, Bayesian structural equation modeling (BSEM), among a sample of 249 French-speaking Swiss children (8-12 years). With BSEM, zero-fixed cross-loadings between latent variables and measures are replaced by approximate zeros, based on informative, small-variance priors. Results indicated that a direct hierarchical CHC-based model with 5 factors plus a general intelligence factor better represented the structure of the WISC-IV than did the 4-factor structure and the higher order models. Because a direct hierarchical CHC model was more adequate, it was concluded that the general factor should be considered as a breadth rather than a superordinate factor. Because it was possible for us to estimate the influence of each of the latent variables on the 15 subtest scores, BSEM allowed improvement of the understanding of the structure of intelligence tests and the clinical interpretation of the subtest scores.

Cross-amplification of polymorphic microsatellites reveals extra-pair paternity and brood parasitism in Sturnus vulgaris

We tested the cross-amplification of 37 microsatellites in a population of starlings (Stumus vulgaris). Twenty-three of them amplified and five exhibited a large number of alleles per locus and high heterozygosity (on average: 14.6 alleles/locus and H. = 0.704). We assessed the occurrence of extra-pair paternity (EPP) and intraspecific brood parasitism GBP) in this population. The EPP rate was 16% to 18% offspring from 43% to 45% of nests. IBP was very variable between two successive years (14% to 27% chicks from 25% to 64% of clutches). These five polymorphic markers will be of potential use in studies of genetic diversity, population structure and reproductive strategy of this species.