The genus Eimeria (Apicomplexa, Coccidia) provides a wide range of different species with different hosts to study common and variable features within the genus and its species. A common characteristic of all known Eimeria species is the oocyst, the infectious stage where its life cycle starts and ends. In our study, we utilized Eimeria nieschulzi as a model organism. This rat-specific parasite has complex oocyst morphology and can be transfected and even cultivated in vitro up to the oocyst stage. We wanted to elucidate how the known oocyst wall-forming proteins are preserved in this rodent Eimeria species compared to other Eimeria. In newly obtained genomics data, we were able to identify different gametocyte genes that are orthologous to already known gam genes involved in the oocyst wall formation of avian Eimeria species. These genes appeared putatively as single exon genes, but cDNA analysis showed alternative splicing events in the transcripts. The analysis of the translated sequence revealed different conserved motifs but also dissimilar regions in GAM proteins, as well as polymorphic regions. The occurrence of an underrepresented gam56 gene version suggests the existence of a second distinct E. nieschulzi genotype within the E. nieschulzi Landers isolate that we maintain.

Die räumliche Organisation des Chromatins im Zellkern ist für die Regulierung der Genexpression von großer Bedeutung. Genomische Varianten können die räumliche Organisation jedoch stören und Fehlbildungen und Krankheiten verursachen. In diploiden Genomen sind die meisten genomischen Varianten heterozygot und beeinflussen hauptsächlich das homologe Chromosom, auf dem sie sich befinden. Daher ist eine allelspezifische Analyse wichtig, erweist sich aber mit aktuellen Methoden zur Erfassung der Chromatinkonformation als äußerst schwierig. Erstens ist der Haplotyp, der die Verteilung unterschiedlicher Allele über die homologen Chromosomen beschreibt, oft unbekannt. Zweitens ist, insbesondere in Genomen mit geringer Variantendichte, wie dem menschlichen Genom, eine eindeutige Zuordnung der sequenzierten Genomabschnitte (Reads) zu ihrem Ursprungschromosom häufig nicht möglich, was die Erstellung haplotypspezifischer Chromatinkontaktmatrizen von guter Qualität verhindert. Genome Architecture Mapping (GAM) ist eine vielversprechende neue Methode mit dem Potential zur haplotypspezifischen Analyse der Chromatinkonformation. In dieser Dissertation zeige ich zunächst, dass GAM-Daten wertvolle Haplotypinformationen enthalten. Dann stelle ich GAMIBHEAR vor, einen graphenbasierten Ansatz, der die von GAM-Daten abgeleiteten Phaseninformationen nutzt, um genaue und vollständige Haplotypen zu rekonstruieren. Schließlich stelle ich Co-Phasing vor, eine neue Read-Phasing-Strategie, die erstmalig die eindeutige Zuordnung von variantenfreien Reads zu ihrem homologen Ursprungschromosom ermöglicht und somit auch die Erstellung detaillierter haplotypspezifischer Chromatinkontaktmatrizen in Maus und Mensch. Im Gegensatz zu früheren Erkenntnissen belegen meine Ergebnisse große Unterschiede in der räumlichen Organisation homologer Chromosomenkopien und ermöglichen erstmals einen sehr detaillierten Einblick in die haplotypspezifische Chromatinkonformation des menschlichen Genoms. / The spatial organization of chromatin in the nucleus plays an essential role in precise gene expression. Genomic variants can disrupt this spatial organization, potentially causing malformations and diseases. In diploid genomes, most genomic variants are heterozygous and mainly influence the homologous chromosome they reside on. Studying the effects of these variants in an allele-specific manner is crucial but has proven challenging using current state-of-the-art techniques. First, the haplotype describing the distribution of variant alleles over the homologous chromosomes is often unknown. Second, especially in genomes with a low variant density, such as the human genome, most sequencing reads map to genomic regions that are identical between homologous chromosomes, making it difficult to determine their origin. Thus, the read-phasing efficiency is insufficient to generate haplotype-specific chromatin contact matrices of good quality. Genome Architecture Mapping (GAM) is a promising new method for haplotype-specific analysis of chromatin conformation. In this thesis, I first demonstrate the ability of GAM data to provide valuable haplotype information. Then, I introduce GAMIBHEAR, a graph-based approach that leverages the GAM-derived phase information to infer accurate and complete haplotypes. Finally, building on GAMIBHEAR, I present Co-Phasing, a novel read-phasing strategy that allows for the unique assignment of variant-free reads to their homologous chromosome of origin and thus enables the creation of detailed haplotype-specific chromatin contact matrices in mouse and human. In contrast to previous findings, my results show significant differences in the spatial organization of homologous chromosomes and provide the first detailed view of haplotype-specific chromatin conformation in the human genome.

In many scientific studies, the response variable bears a generalized nonlinear regression relationship with a certain covariate of interest, which may, however, be confounded by other covariates with unknown functional form. We propose a new class of models, the partly parametric generalized additive model (PPGAM) for doing generalized nonlinear regression with the confounding covariate effects adjusted nonparametrically. To avoid the curse of dimensionality, the PPGAM specifies that, conditional on the covariates, the response distribution belongs to the exponential family with the mean linked to an additive predictor comprising a nonlinear parametric function that is of main interest, plus additive, smooth functions of other covariates. The PPGAM extends both the generalized additive model (GAM) and the generalized nonlinear regression model. We propose to estimate a PPGAM by the method of penalized likelihood. We derive some asymptotic properties of the penalized likelihood estimator, including consistency and asymptotic normality of the parametric estimator of the nonlinear regression component. We propose a model selection criterion for the PPGAM, which resembles the BIC. We illustrate the new methodologies by simulations and real applications. We have developed an R package PPGAM that implements the methodologies expounded herein.

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Les institutions financières sont, dans l’exercice de leurs fonctions, confrontées à divers risques, entre autres le risque de crédit, le risque de marché et le risque opérationnel. L’instabilité de ces facteurs fragilise ces institutions et les rend vulnérables aux risques financiers qu’elles doivent, pour leur survie, être à même d’identifier, analyser, quantifier et gérer convenablement. Parmi ces risques, celui lié au crédit est le plus redouté par les banques compte tenu de sa capacité à générer une crise systémique. La probabilité de passage d’un individu d’un état non risqué à un état risqué est ainsi au cœur de nombreuses questions économiques. Dans les institutions de crédit, cette problématique se traduit par la probabilité qu’un emprunteur passe d’un état de "bon risque" à un état de "mauvais risque". Pour cette quantification, les institutions de crédit recourent de plus en plus à des modèles de credit-scoring. Cette thèse porte sur les techniques actuelles de credit-scoring adaptées aux besoins d’une institution de crédit, le CFCAL-banque, spécialisé dans les prêts garantis par hypothèques. Nous présentons en particulier deux modèles non paramétriques (SVM et GAM) dont nous comparons les performances en termes de classification avec celles du modèle logit traditionnellement utilisé dans les banques. Nos résultats montrent que les SVM sont plus performants si l’on s’intéresse uniquement à la capacité de prévision globale. Ils exhibent toutefois des sensibilités inférieures à celles des modèles logit et GAM. En d’autres termes, ils prévoient moins bien les emprunteurs défaillants. Dans l’état actuel de nos recherches, nous préconisons les modèles GAM qui ont certes une capacité de prévision globale moindre que les SVM, mais qui donnent des sensibilités, des spécificités et des performances de prévision plus équilibrées. En mettant en lumière des modèles ciblés de scoring de crédit, en les appliquant sur des données réelles de crédits hypothécaires, et en les confrontant au travers de leurs performances de classification, cette thèse apporte une contribution empirique à la recherche relative aux modèles de credit-scoring. / Financial institutions face in their functions a variety of risks such as credit, market and operational risk. These risks are not only related to the nature of the activities they perform, but also depend on predictable external factors. The instability of these factors makes them vulnerable to financial risks that they must appropriately identify, analyze, quantify and manage. Among these risks, credit risk is the most prominent due to its ability to generate a systemic crisis. The probability for an individual to switch from a risked to a riskless state is thus a central point to many economic issues. In credit institution, this problem is reflected in the probability for a borrower to switch from a state of “good risk” to a state of “bad risk”. For this quantification, banks increasingly rely on credit-scoring models. This thesis focuses on the current credit-scoring techniques tailored to the needs of a credit institution: the CFCAL-banque specialized in mortgage credits. We particularly present two nonparametric models (SVM and GAM) and compare their performance in terms of classification to those of logit model traditionally used in banks. Our results show that SVM are more effective if we only focus on the global prediction performance of the models. However, SVM models give lower sensitivities than logit and GAM models. In other words the predictions of SVM models on defaulted borrowers are not satisfactory as those of logit or GAM models. In the present state of our research, even GAM models have lower global prediction capabilities, we recommend these models that give more balanced sensitivities, specificities and performance prediction. This thesis is not completely exhaustive about the scoring techniques for credit risk management. By trying to highlight targeted credit scoring models, adapt and apply them on real mortgage data, and compare their performance through classification, this thesis provides an empirical and methodological contribution to research on scoring models for credit risk management.

Die 3D Struktur von Chromosomen im Zellkern reguliert verschiedene Funktionen in der Zelle und Fehler in der 3D Faltung des Genoms können pathogen sein. 3D Genomfaltung kann mit verschiedenen Methoden untersucht werden um Chromatinkontakte, sowie die Position von DNA in Relation zu sub-nuklearen Bereichen oder der Kernmembran zu detektieren. Hier verwende ich GAM und Hi-C um zwei Aspekte der 3D Genomtopologie zu untersuchen, die Allelspezifität von Chromatinkontakten und Kontakte zwischen Chromosomen. Ich untersuche allelspezifische Kontakte in murinen embryonalen Stammzellen und Interaktionen zwischen Chromosomen im Zusammenhang mit Autismus Spektrum Störung auf ihre Relevanz in der Regulation von Genen. Zur allelspezifischen Detektion von Chromatinkontakten generierte ich einen GAM Datensatz der tausende von nuklearen Cryodünnschnitten enthält. Die Generierung dieser Daten beinhaltete die Entwicklung einer verbesserten Version der GAM Methode zur Produktion von großen Datensätzen in Hochdurchsatz. Hier zeige ich, dass GAM effizient Haplotyp-spezifische Chromatinkontakte bestimmen kann. Erste Untersuchungen von allelspezifischer 3D Genomtopologie zeigten weitreichende Unterschiede zwischen den Allelen, welche „A/B compartments“ und spezifische Chromatinkontakte beinhalten, wie zum Beispiel am Imprinting Locus H19/Igf2. Zur Untersuchung von interchromosomalen Kontakten detektierte ich Chromatinkontakte mit Hi-C im Kontext einer genomischen Deletion am humanen 16p11.2 Locus, assoziiert mit Autismus Spektrum Störung. Ich zeige hier, dass die Deletion am 16p11.2 Locus zu der Reorganisation von spezifischen interchromosomalen Kontakten zwischen 16p11.2 und Chromosom 18 führt, und stelle eine Hypothese auf wie diese interchromosomalen Kontakte zur ektopischen Aktivierung von Pcdh Genen auf Chromosom 18 führen. Protocadherins haben wichtige Funktionen in neuronaler Konnektivität, ein Prozess dessen Störung zur Manifestierung von Autismus Spektrum Störung beitragen könnte. / The 3D folding of interphase chromosomes inside the nucleus regulates important nuclear functions and once disrupted can lead to the manifestation of disease. Different techniques can be used to map 3D genome folding and detect pairwise and multiway interactions of the genome, or map the positions of DNA with respect to subnuclear compartments or the nuclear lamina. Here, I use GAM and Hi-C to explore two aspects of 3D genome topology, the allele specificity of chromatin contacts and long-range contacts between chromosomes, respectively. I detect specific contacts of the parental alleles in mouse embryonic stem cells and interactions between chromosomes in the context of congenital disease and study them with regard to their functionality and importance in mammalian gene regulation. For detecting chromatin contacts with allele specificity, I produced a GAM dataset containing thousands of nuclear slices. The collection of this data was accompanied by the development of a high-throughput version of GAM that allows the generation of large datasets. I show that GAM can determine haplotype-specific chromatin contacts with high efficiencies. First explorations of allele-specific chromatin topologies reveal many differences between the parental alleles, including allele-specific compartments A and B, and specific chromatin contacts, for example at the imprinted H19/Igf2 locus. For the exploration of inter-chromosomal contacts in disease, I mapped chromatin interactions with Hi-C in the context of a CNV at the human 16p11.2 locus, associated with autism spectrum disorders. Here, I show that the deletion at the 16p11.2 locus results in the rearrangement of specific inter-chromosomal contacts between the 16p11.2 locus and chromosome 18 and propose a role for these inter-chromosomal contact changes in the upregulation of the nearby Pcdhb gene cluster, which comprises protocadherin genes with important functions in neuronal connectivity during development.

The main purpose of this study was to produce and test the reliability of predictive models of cetacean distributions off the west coast of Scotland. Passive acoustic and visual surveys were carried out from platforms of opportunity between 2003 and 2005. Acoustic identifications were made primarily of harbour porpoises (Phocoena phocoena), delphinids, and sperm whales (Physeter macrocephalus). Generalised Additive Models (GAMs) were used to relate species’ distributions to a range of environmental variables over a range of temporal and spatial scales. Predictive models of delphinid distributions showed both inter-annual and inter-month variations. Combining all data for all months and years resulted in a model that combined the environmental influences from each monthly and yearly model. Overall, delphinids were found to associate with the deep (> 400m) warm water (10.5°C-12.5°C), and in areas of deep thermocline. Relationships between sperm whales and environmental variables were consistent over changes in grain size (9 km or 18 km), but not between areas. Although sperm whales were distributed in deep water characterised by weak thermoclines and strong haloclines in the most northerly area (Faroe-Shetland Channel), they were found in deep productive areas with cold surface temperature in the more southerly waters (Rockall Trough). Within the southern Inner Hebrides, high use areas for harbour porpoises were consistently predicted over time (in years) and with differing survey techniques (acoustic versus visual), but not over space (southern Inner Hebrides versus whole of the Inner Hebrides). Harbour porpoises were mainly distributed in areas with low tidal currents and with higher detection rates during spring tides. The use of prey as a predictor variable within models of delphinid distribution shows some promise: there were correlations between delphinid and herring (Clupea harengus) in shelf-waters in 2005 but not in 2004. These models can be used in mitigating acoustic threats to cetaceans in predicted high use areas off the west coast of Scotland.

Predictions of species’ ranges from distribution modeling are often used to inform marine management and conservation efforts, but few studies justify the model selected or quantify the uncertainty of the model predictions in a spatial manner. This thesis employs a multi-model, multi-area SDM analysis to develop a higher certainty in the predictions where similarities exist across models and areas. Partial dependence plots and variable importance rankings were shown to be useful in producing further certainty in the results. The modeling indicated that glass sponges (Hexactinellida) are most likely to exist within the North Pacific Ocean where alkalinity is greater than 2.2 μmol l-1 and dissolved oxygen is lower than 2 ml l-1. Silicate was also found to be an important environmental predictor. All areas, except Hecate Strait, indicated that high glass sponge probability of presence coincided with silicate values of 150 μmol l-1 and over, although lower values in Hecate Strait confirmed that sponges can exist in areas with silicate values of as low as 40 μmol l-1. Three methods of showing spatial uncertainty of model predictions were presented: the standard error (SE) of a binomial GLM, the standard deviation of predictions made from 200 bootstrapped GLM models, and the standard deviation of eight commonly used SDM algorithms. Certain areas with few input data points or extreme ranges of predictor variables were highlighted by these methods as having high uncertainty. Such areas should be treated cautiously regardless of the overall accuracy of the model as indicated by accuracy metrics (AUC, TSS), and such areas could be targeted for future data collection. The uncertainty metrics produced by the multi-model SE varied from the GLM SE and the bootstrapped GLM. The uncertainty was lowest where models predicted low probability of presence and highest where the models predicted high probability of presence and these predictions differed slightly, indicating high confidence in where the models predicted the sponges would not exist.

Fuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data is of noisy and/or overlapping nature. To remedy this problem a number of researchers have designed modifications to the training phase of Fuzzy ARTMAP that had the beneficial effect of reducing this phenomenon. In this thesis we propose a new approach to handle the category proliferation problem in Fuzzy ARTMAP by evolving trained FAM architectures. We refer to the resulting FAM architectures as GFAM. We demonstrate through extensive experimentation that an evolved FAM (GFAM) exhibits good (sometimes optimal) generalization, small size (sometimes optimal size), and requires reasonable computational effort to produce an optimal or sub-optimal network. Furthermore, comparisons of the GFAM with other approaches, proposed in the literature, which address the FAM category proliferation problem, illustrate that the GFAM has a number of advantages (i.e. produces smaller or equal size architectures, of better or as good generalization, with reduced computational complexity). Furthermore, in this dissertation we have extended the approach used with Fuzzy ARTMAP to other ART architectures, such as Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM) that also suffer from the ART category proliferation problem. Thus, we have designed and experimented with genetically engineered EAM and GAM architectures, named GEAM and GGAM. Comparisons of GEAM and GGAM with other ART architectures that were introduced in the ART literature, addressing the category proliferation problem, illustrate similar advantages observed by GFAM (i.e, GEAM and GGAM produce smaller size ART architectures, of better or improved generalization, with reduced computational complexity). Moverover, to optimally cover the input space of a problem, we proposed a genetically engineered ART architecture that combines the category structures of two different ART networks, FAM and EAM. We named this architecture UART (Universal ART). We analyzed the order of search in UART, that is the order according to which a FAM category or an EAM category is accessed in UART. This analysis allowed us to better understand UART's functionality. Experiments were also conducted to compare UART with other ART architectures, in a similar fashion as GFAM and GEAM were compared. Similar conclusions were drawn from this comparison, as in the comparison of GFAM and GEAM with other ART architectures. Finally, we analyzed the computational complexity of the genetically engineered ART architectures and we compared it with the computational complexity of other ART architectures, introduced into the literature. This analytical comparison verified our claim that the genetically engineered ART architectures produce better generalization and smaller sizes ART structures, at reduced computational complexity, compared to other ART approaches. In review, a methodology was introduced of how to combine the answers (categories) of ART architectures, using genetic algorithms. This methodology was successfully applied to FAM, EAM and FAM and EAM ART architectures, with success, resulting in ART neural networks which outperformed other ART architectures, previously introduced into the literature, and quite often produced ART architectures that attained optimal classification results, at reduced computational complexity.