Nous présentons dans cette thèse une méthode générique et automatique pour la localisation et la caractérisation de lésions cérébrales telles que les tumeurs primaires à partir de multiples contrastes IRM. Grâce à une récente généralisation des lois de probabilités de mélange par l'échelle de distributions gaussiennes, nous pouvons modéliser une large variété d'interactions entre les paramètres IRM mesurés, et cela afin de capter l'hétérogénéité présent dans les tissus cérébraux sains et endommagés. En nous basant sur ces lois de probabilités, nous proposons un protocole complet pour l'analyse de données IRM multi-contrastes : à partir de données quantitatives, ce protocole fournit, s'il y a lieu, la localisation et le type des lésions détectées au moyen de modèles probabilistes. Nous proposons également deux extensions de ce protocole. La première extension concerne la sélection automatique du nombre de composantes au sein du modèle probabiliste, sélection réalisée via une représentation bayésienne des modèles utilisés. La seconde extension traite de la prise en compte de la structure spatiale des données IRM par l'ajout d'un champ de Markov latent au sein du protocole développé. / We present in this thesis a generic and automatic method for the localization and the characterization of brain lesions such as primary tumor using multi-contrast MRI. From the recent generalization of scale mixtures of Gaussians, we reach to model a large variety of interactions between the MRI parameters, with the aim of capturing the heterogeneity inside the healthy and damaged brain tissues. Using these probability distributions we propose an all-in-one protocol to analyze multi-contrast MRI: starting from quantitative MRI data this protocol determines if there is a lesion and in this case the localization and the type of the lesion based on probability models. We also develop two extensions for this protocol. The first one concerns the selection of mixture components in a Bayesian framework. The second one is about taking into account the spatial structure of MRI data by the addition of a random Markov field to our protocol.

La première détection des ondes gravitationnelles en 2015 a ouvert un nouveau plan d'étude pour l'astrophysique des étoiles binaires compactes. En utilisant les données des détections faites par les détecteurs terrestres advanced LIGO et advanced Virgo, il est possible de contraindre les paramètres physiques de ces systèmes avec une analyse Bayésienne et ainsi approfondir notre connaissance physique des étoiles binaires compactes. Cependant, pour pouvoir être en mesure d'obtenir de tels résultats, il est essentiel d’avoir des algorithmes performants à la fois pour trouver les signaux de ces ondes gravitationnelles et pour l'estimation de paramètres. Le travail de cette thèse a ainsi été centré autour du développement d’algorithmes performants et adaptées au problème physique à la fois de la détection et de l'estimation des paramètres pour les ondes gravitationnelles. La plus grande partie de ce travail de thèse a ainsi été dédiée à l'implémentation d’un algorithme de type Hamiltonian Monte Carlo adapté à l'estimation de paramètres pour les signaux d’ondes gravitationnelles émises par des binaires compactes formées de deux étoiles à neutrons. L'algorithme développé a été testé sur une sélection de sources et a été capable de fournir de meilleures performances que d'autres algorithmes de type MCMC comme l'algorithme de Metropolis-Hasting et l'algorithme à évolution différentielle. L'implémentation d'un tel algorithme dans les pipelines d’analyse de données de la collaboration pourrait augmenter grandement l'efficacité de l'estimation de paramètres. De plus, il permettrait également de réduire drastiquement le temps de calcul nécessaire, ce qui est un facteur essentiel pour le futur où de nombreuses détections sont attendues. Un autre aspect de ce travail de thèse a été dédié à l'implémentation d'un algorithme de recherche de signaux gravitationnelles pour les binaires compactes monochromatiques qui seront observées par la future mission spatiale LISA. L'algorithme est une mixture de plusieurs algorithmes évolutionnistes, avec notamment l'inclusion d'un algorithme de Particle Swarm Optimisation. Cette algorithme a été testé dans plusieurs cas tests et a été capable de trouver toutes les sources gravitationnelles comprises dans un signal donné. De plus, l'algorithme a également été capable d'identifier des sources sur une bande de fréquence aussi grande que 1 mHz, ce qui n'avait pas été réalisé au moment de cette étude de thèse. / The first detection of gravitational waves in 2015 has opened a new window for the study of the astrophysics of compact binaries. Thanks to the data taken by the ground-based detectors advanced LIGO and advanced Virgo, it is now possible to constrain the physical parameters of compact binaries using a full Bayesian analysis in order to increase our physical knowledge on compact binaries. However, in order to be able to perform such analysis, it is essential to have efficient algorithms both to search for the signals and for parameter estimation. The main part of this thesis has been dedicated to the implementation of a Hamiltonian Monte Carlo algorithm suited for the parameter estimation of gravitational waves emitted by compact binaries composed of neutron stars. The algorithm has been tested on a selection of sources and has been able to produce better performances than other types of MCMC methods such as Metropolis-Hastings and Differential Evolution Monte Carlo. The implementation of the HMC algorithm in the data analysis pipelines of the Ligo/Virgo collaboration could greatly increase the efficiency of parameter estimation. In addition, it could also drastically reduce the computation time associated to the parameter estimation of such sources of gravitational waves, which will be of particular interest in the near future when there will many detections by the ground-based network of gravitational wave detectors. Another aspect of this work was dedicated to the implementation of a search algorithm for gravitational wave signals emitted by monochromatic compact binaries as observed by the space-based detector LISA. The developed algorithm is a mixture of several evolutionary algorithms, including Particle Swarm Optimisation. This algorithm has been tested on several test cases and has been able to find all the sources buried in a signal. Furthermore, the algorithm has been able to find the sources on a band of frequency as large as 1 mHz which wasn’t done at the time of this thesis study

<p>Sexual conflict can lead to antagonistic coevolution between the sexes, but empirical examples are few. In this thesis secondary sexual characters in diving beetles are interpreted in the light of sexual conflict theory. Whether the male tarsal suction cups and female dorsal modifications are involved in a coevolutionary arms race is tested in two ways. First eight populations of a species with dimorphic females that varied in frequency of the morphs were investigated and male tarsal characteristics quantified. The frequency of female morphs is shown to be significantly correlated to the average number and size of male tarsal suction cups in the population, a prediction of the arms race hypothesis. Second, the hypothesis is tested in a phylogenetic perspective by optimizing the secondary sexual characters on a phylogeny. A full taxonomic revision of the genus <i>Acilius</i> is presented, including new synonyms, lectotype designations, geographic distributions based on more than five thousand examined museum specimens and the description of a new species from northeastern USA. Specimens of all species (except one possibly extinct that failed to be found in Yunnan, China 2000), were field collected between 2000 and 2003 in Sardinia, Sweden, Russia, Honshu and Hokkaido in Japan, New York, Maryland, California and Alberta. Three genes (CO1, H3 and Wingless) were sequenced from the fresh material as well as scoring a morphological character matrix all of which was used to derive a robust and complete hypothesis of the phylogenetic relationship in the group. The phylogeny was derived using Bayesian phylogenetics with Markov Chain Monte Carlo techniques and received a posterior probability of 0.85. Changes in male and female characters turned out to be perfectly correlated across the phylogeny, providing one of the best empirical examples to date of an antagonistic arms race between the sexes in a group of organisms. Finally, a review of a pitfall to phylogenetic analysis known under the name long-branch attraction (LBA), is provided. The problem is well known theoretically but has been questioned to occur in real data, and LBA has been in the core center of the hard debate between parsimony and likelihood advocates since different inference methods vary in sensitivity to the phenomenon. Most important conclusions from the review are; LBA is very common in real data, and is most often introduced with the inclusion of outgroups that almost always provide long branches, pulling down long terminal ingroup branches towards the root. Therefore it is recommended to always run analyses with and without outgroups. Taxon sampling is very important to avoid the pitfall as well as including different kind of data, especially morphological data, i.e. many LBA-affected conclusions have recently been reached by analyses of few taxa with complete genomes. Long-branch extraction (incl. outgroup exclusion), methodological disconcordance (parsimony vs modelbased), separate partition analyses (morphology vs molecules, codon positions, genes, etc), parametric simulation (incl. random outgroups), and split graphs are available relevant methods for the detection of LBA that should be used in combinations, because none alone is enough to stipulate LBA.</p>

Sexual conflict can lead to antagonistic coevolution between the sexes, but empirical examples are few. In this thesis secondary sexual characters in diving beetles are interpreted in the light of sexual conflict theory. Whether the male tarsal suction cups and female dorsal modifications are involved in a coevolutionary arms race is tested in two ways. First eight populations of a species with dimorphic females that varied in frequency of the morphs were investigated and male tarsal characteristics quantified. The frequency of female morphs is shown to be significantly correlated to the average number and size of male tarsal suction cups in the population, a prediction of the arms race hypothesis. Second, the hypothesis is tested in a phylogenetic perspective by optimizing the secondary sexual characters on a phylogeny. A full taxonomic revision of the genus Acilius is presented, including new synonyms, lectotype designations, geographic distributions based on more than five thousand examined museum specimens and the description of a new species from northeastern USA. Specimens of all species (except one possibly extinct that failed to be found in Yunnan, China 2000), were field collected between 2000 and 2003 in Sardinia, Sweden, Russia, Honshu and Hokkaido in Japan, New York, Maryland, California and Alberta. Three genes (CO1, H3 and Wingless) were sequenced from the fresh material as well as scoring a morphological character matrix all of which was used to derive a robust and complete hypothesis of the phylogenetic relationship in the group. The phylogeny was derived using Bayesian phylogenetics with Markov Chain Monte Carlo techniques and received a posterior probability of 0.85. Changes in male and female characters turned out to be perfectly correlated across the phylogeny, providing one of the best empirical examples to date of an antagonistic arms race between the sexes in a group of organisms. Finally, a review of a pitfall to phylogenetic analysis known under the name long-branch attraction (LBA), is provided. The problem is well known theoretically but has been questioned to occur in real data, and LBA has been in the core center of the hard debate between parsimony and likelihood advocates since different inference methods vary in sensitivity to the phenomenon. Most important conclusions from the review are; LBA is very common in real data, and is most often introduced with the inclusion of outgroups that almost always provide long branches, pulling down long terminal ingroup branches towards the root. Therefore it is recommended to always run analyses with and without outgroups. Taxon sampling is very important to avoid the pitfall as well as including different kind of data, especially morphological data, i.e. many LBA-affected conclusions have recently been reached by analyses of few taxa with complete genomes. Long-branch extraction (incl. outgroup exclusion), methodological disconcordance (parsimony vs modelbased), separate partition analyses (morphology vs molecules, codon positions, genes, etc), parametric simulation (incl. random outgroups), and split graphs are available relevant methods for the detection of LBA that should be used in combinations, because none alone is enough to stipulate LBA.

Effective pollution mitigation is the key to successful air quality management. Although states invest millions of dollars to predict future air quality, the regulatory modeling and analysis process to inform pollution control strategy remains uncertain. Traditionally deterministic ‘bright-line’ tests are applied to evaluate the sufficiency of a control strategy to attain an air quality standard. A critical part of regulatory attainment demonstration is the prediction of future pollutant levels using photochemical air quality models. However, because models are uncertain, they yield a false sense of precision that pollutant response to emission controls is perfectly known and may eventually mislead the selection of control policies. These uncertainties in turn affect the health impact assessment of air pollution control strategies. This thesis explores beyond the conventional practice of deterministic attainment demonstration and presents novel approaches to yield probabilistic representations of pollutant response to emission controls by accounting for uncertainties in regional air quality planning. Computationally-efficient methods are developed and validated to characterize uncertainty in the prediction of secondary pollutant (ozone and particulate matter) sensitivities to precursor emissions in the presence of uncertainties in model assumptions and input parameters. We also introduce impact factors that enable identification of model inputs and scenarios that strongly influence pollutant concentrations and sensitivity to precursor emissions. We demonstrate how these probabilistic approaches could be applied to determine the likelihood that any control measure will yield regulatory attainment, or could be extended to evaluate probabilistic health benefits of emission controls, considering uncertainties in both air quality models and epidemiological concentration–response relationships. Finally, ground-level observations for pollutant (ozone) and precursor concentrations (oxides of nitrogen) have been used to adjust probabilistic estimates of pollutant sensitivities based on the performance of simulations in reliably reproducing ambient measurements. Various observational metrics have been explored for better scientific understanding of how sensitivity estimates vary with measurement constraints. Future work could extend these methods to incorporate additional modeling uncertainties and alternate observational metrics, and explore the responsiveness of future air quality to project trends in emissions and climate change.

Bayesian methods are often criticized on the grounds of subjectivity. Furthermore, misspecified priors can have a deleterious effect on Bayesian inference. Noting that model selection is effectively a test of many hypotheses, Dr. Valen E. Johnson sought to eliminate the need of prior specification by computing Bayes' factors from frequentist test statistics. In his pioneering work that was published in the year 2005, Dr. Johnson proposed using so-called local priors for computing Bayes? factors from test statistics. Dr. Johnson and Dr. Jianhua Hu used Bayes' factors for model selection in a linear model setting. In an independent work, Dr. Johnson and another colleage, David Rossell, investigated two families of non-local priors for testing the regression parameter in a linear model setting. These non-local priors enable greater separation between the theories of null and alternative hypotheses. In this dissertation, I extend model selection based on Bayes' factors and use nonlocal priors to define Bayes' factors based on test statistics. With these priors, I have been able to reduce the problem of prior specification to setting to just one scaling parameter. That scaling parameter can be easily set, for example, on the basis of frequentist operating characteristics of the corresponding Bayes' factors. Furthermore, the loss of information by basing a Bayes' factors on a test statistic is minimal. Along with Dr. Johnson and Dr. Hu, I used the Bayes' factors based on the likelihood ratio statistic to develop a method for clustering gene expression data. This method has performed well in both simulated examples and real datasets. An outline of that work is also included in this dissertation. Further, I extend the clustering model to a subclass of the decomposable graphical model class, which is more appropriate for genotype data sets, such as single-nucleotide polymorphism (SNP) data. Efficient FORTRAN programming has enabled me to apply the methodology to hundreds of nodes. For problems that produce computationally harder probability landscapes, I propose a modification of the Markov chain Monte Carlo algorithm to extract information regarding the important network structures in the data. This modified algorithm performs well in inferring complex network structures. I use this method to develop a prediction model for disease based on SNP data. My method performs well in cross-validation studies.

Η παρούσα διατριβή ασχολείται με τη μελέτη και την ανάπτυξη μοντέλων ταξινόμησης τα οποία βασίζονται στα Πιθανοτικά Νευρωνικά Δίκτυα (ΠΝΔ). Τα προτεινόμενα μοντέλα αναπτύχθηκαν ενσωματώνοντας στατιστικές μεθόδους αλλά και μεθόδους από διάφορα πεδία της Υπολογιστικής Νοημοσύνης (ΥΝ). Συγκεκριμένα, χρησιμοποιήθηκαν οι Διαφοροεξελικτικοί αλγόριθμοι βελτιστοποίησης και η Βελτιστοποίηση με Σμήνος Σωματιδίων (ΒΣΣ) για την αναζήτηση βέλτιστων τιμών των παραμέτρων των ΠΝΔ. Επιπλέον, ενσωματώθηκε η τεχνική bagging για την ανάπτυξη συστάδας μοντέλων ταξινόμησης. Μια άλλη προσέγγιση ήταν η ανάπτυξη ενός Μπεϋζιανού μοντέλου για την εκτίμηση των παραμέτρων του ΠΝΔ χρησιμοποιώντας τον δειγματολήπτη Gibbs. Επίσης, ενσωματώθηκε μια Ασαφή Συνάρτηση Συμμετοχής για την καλύτερη στάθμιση των τεχνητών νευρώνων του ΠΝΔ καθώς και ένα νέο σχήμα διάσπασης του συνόλου εκπαίδευσης σε προβλήματα ταξινόμησης πολλαπλών κλάσεων όταν ο ταξινομητής μπορεί να επιτύχει ταξινόμηση δύο κλάσεων.Τα προτεινόμενα μοντέλα ταξινόμησης εφαρμόστηκαν σε μια σειρά από πραγματικά προβλήματα από διάφορες επιστημονικές περιοχές με ενθαρρυντικά αποτελέσματα. / The present thesis is dealing with the study and the development of classification models that are based on Probabilistic Neural Networks (PNN). The proposed models were developed by the incorporation of statistical methods as well as methods from several fields of Computational Intelligence (CI) into PNNs. In particular, the Differential Evolutionary optimization algorithms and Particle Swarm Optimization algorithms are employed for the search of promising values of PNNs’ parameters. Moreover, the bagging technique was incorporated for the development of an ensemble of classification models. Another approach was the construction of a Bayesian model for the estimation of PNN’s parameters utilizing the Gibbs sampler. Furthermore, a Fuzzy Membership Function was incorporated to achieve an improved weighting of PNN’s neurons. A new decomposition scheme is proposed for multi-class classification problems when a two-class classifier is employed. The proposed classification models were applied to a series of real-world problems from several scientific areas with encouraging results.

No description available.

[Truncated abstract] Background: Genetic epidemiology draws on the mechanisms of heredity and the reproductive characteristics of populations to formulate methods to investigate the role of genetic factors and their interaction with the environment in disease aetiology. Asthma and atopy are complex genetic disorders and are among the most common diseases to affect the developed world. Twin studies provide an elegant means of disentangling genetic and environmental contributions to the aetiology of conditions that have a significant impact on the health of the general population in ways that cannot be achieved by any other study design, by comparing disease frequency in monozygotic (MZ) or identical twins, who share 100% of their genes with that in dizygotic (DZ) or non-identical twins who share, on average, 50% of their genes. Twin-family studies allow the complete partitioning of phenotypic variation into components representing additive genetic, dominance, shared environment and non-shared environment. ... For twin family data, the best fitting model was the one which included additive genetic effects and either genetic dominance or shared sibling environment, and that shared family environment was not important. With respect to asthma in WA twin families, there are no reasons to conclude that the EEA is not valid. Conclusions: The WA Twin Register is the first population-based register of childhood multiples to be established in Australia, and the WATCH study is one of only a few population-based twin-family studies in the world. Families who participated in the WATCH study were no different from non-participants with respect to social class and there was no difference in the prevalence of DDA in WATCH study twins and either their singleton siblings or the general population of WA children. Results from the GEE models replicate those found in numerous studies from many different countries. The BUGS models developed have been shown to produce consistent results with both simulated and real data sets and offer alternative methods of analyzing twin and twin-family data. By including an extra term in the partitioning of the variance to account for the environment effect of being a MZ twin, a numerical value is calculated for the difference in MZ and DZ correlation with respect to the phenotype examined, which allows the validity of the EEA to be directly assessed.

Habenaria é um dos maiores gêneros da família Orchidaceae, e estimativas atuais pressupoem a existência de aproximadamente 835 espécies. Habenaria seção Pentadactylae com 34 espécies é a maior entre as 14 seções do gênero existente no novo mundo e compreende um conjunto de espécies morfologicamente bastante heterogênea. A fim de investigar a monofilia da seção e sua relação com outras seções do gênero, foram executadas análise Bayesiana e de Máxima Parcimônia com o emprego de um marcador nuclear (ITS) e três marcadores plastidiais (matK, intron trnK, rps16-trnk). Os resultados demonstraram que a seção Pentadactylae é altamente polifilética. Baseado nas análises filogenéticas e reavaliação de caracteres morfológicos, a seção Pentadactylae foi recircunscrita neste trabalho e sete espécies são aceitas: H. dutraei, H. ekmaniana, H. exaltata, H. henscheniana, H. megapotamensis, H. montevidensis e H. pentadactyla, enquanto outras 32 espécies foram excluídas. Habenaria crassipes é reconhecida como um sinônimo de H. exaltata. Lectótipos são designados para H. crassipes e H. recta. Todas as espécies da seção habitam pântanos ou locais bastante úmidos; com área de distribuição passando pelo norte da Argentina, Uruguai, Paraguai, sul, sudeste e centro do Brasil. O estado do Rio Grande do Sul (sul do Brasil), possivelmente, constitui um centro de diversidade da seção onde todas as espécies podem ser encontradas. A biologia reprodutiva de duas espécies da seção Pentadactylae, H. megapotamensis e H. montevidensis; e duas espécies da seção Macroceratitae, H. johannensis e H. macronectar, foram estudas. Todas as espécies estudadas oferecem néctar como recompensa floral aos polinizadores, produzido no interior de um prolongamento do labelo denominado esporão. Habenaria montevidensis é polinizada por borboletas da família Hesperiidae, enquanto as demais espécies são polinizadas por mariposas da família Sphingidae. Todas as espécies estudadas são auto-compatíveis mas dependentes de agentes polinizadores para a produção de frutos. O sucesso reprodutivo é alto (69,48 - 93%). Na área de estudo, todas as quatro espécies estudadas são reprodutivamente isoladas devido a um conjunto de fatores tais como diferenças na morfologia floral e diferentes polinizadores. / Habenaria is one of the largest genus of Orchidaceae family and current stimates accounts to the existence of 835 species. Habenaria section Pentadactylae with 34 species is the largest among the 14 New World sections of the genus and comprises a morphologically heterogeneous group of species. To investigate the monophyly of the section and the relation with other sections of the genus, Bayesian and parsimony analyses using one nuclear marker (ITS) and three plastid markers (matK, trnK intron, rps16-trnK) were performed. The results demonstrated that sect. Pentadactylae is highly polyphyletic. Based on the phylogenetic analyses and re-evaluation of morphological characters, Habenaria sect. Pentadactylae is re-circumscribed and seven species are accepted for the section: H. dutraei, H. ekmaniana, H. exaltata, H. henscheniana, H. megapotamensis, H. montevidensis and H. pentadactyla, while other 32 species were excluded. Habenaria crassipes is included under the synonym of H. exaltata. Lectotypes are designated for H. crassipes and H. recta. All species in the section are from marshes or wet grasslands and range from Northern Argentina, Uruguai, Paraguai and south, southeast and center of Brazil. The Rio Grande do Sul state (south Brazil), possibly constitute a diversity center of the section where every species can be founded. Most are rare, known by few populations, and threatened due to loss of habitat and population decline. The reproductive biology of two species from the section Pentadactylae, H. megapotamensis and H. montevidensis; and two species from the section Macroceratitae, H. johannensis and H. macronectar, were studied. All studied species offer nectar as floral reward concealed in a labellar process termed spur. Habenaria montevidensis is pollinated by Hesperiidae butterflies, while the remaining species are pollinated by Sphingidae moths. All studied species are self-compatible, but pollinator-dependent. The reproductive success is high (69.48 - 93%). At the study site, every four studied species are reproductively isolated by a set of factors that includes differing floral morphologies and different pollinators.