Bayesian networks and the evaluation of scientific evidence in forensic science

Résumé Ce travail de thèse étudie des moyens de formalisation permettant d'assister l'expert forensique dans la gestion des facteurs influençant l'évaluation des indices scientifiques, tout en respectant des procédures d'inférence établies et acceptables. Selon une vue préconisée par une partie majoritaire de la littérature forensique et juridique - adoptée ici sans réserve comme point de départ - la conceptualisation d'une procédure évaluative est dite 'cohérente' lors qu'elle repose sur une implémentation systématique de la théorie des probabilités. Souvent, par contre, la mise en oeuvre du raisonnement probabiliste ne découle pas de manière automatique et peut se heurter à des problèmes de complexité, dus, par exemple, à des connaissances limitées du domaine en question ou encore au nombre important de facteurs pouvant entrer en ligne de compte. En vue de gérer ce genre de complications, le présent travail propose d'investiguer une formalisation de la théorie des probabilités au moyen d'un environment graphique, connu sous le nom de Réseaux bayesiens (Bayesian networks). L'hypothèse principale que cette recherche envisage d'examiner considère que les Réseaux bayesiens, en concert avec certains concepts accessoires (tels que des analyses qualitatives et de sensitivité), constituent une ressource clé dont dispose l'expert forensique pour approcher des problèmes d'inférence de manière cohérente, tant sur un plan conceptuel que pratique. De cette hypothèse de travail, des problèmes individuels ont été extraits, articulés et abordés dans une série de recherches distinctes, mais interconnectées, et dont les résultats - publiés dans des revues à comité de lecture - sont présentés sous forme d'annexes. D'un point de vue général, ce travail apporte trois catégories de résultats. Un premier groupe de résultats met en évidence, sur la base de nombreux exemples touchant à des domaines forensiques divers, l'adéquation en termes de compatibilité et complémentarité entre des modèles de Réseaux bayesiens et des procédures d'évaluation probabilistes existantes. Sur la base de ces indications, les deux autres catégories de résultats montrent, respectivement, que les Réseaux bayesiens permettent également d'aborder des domaines auparavant largement inexplorés d'un point de vue probabiliste et que la disponibilité de données numériques dites 'dures' n'est pas une condition indispensable pour permettre l'implémentation des approches proposées dans ce travail. Le présent ouvrage discute ces résultats par rapport à la littérature actuelle et conclut en proposant les Réseaux bayesiens comme moyen d'explorer des nouvelles voies de recherche, telles que l'étude de diverses formes de combinaison d'indices ainsi que l'analyse de la prise de décision. Pour ce dernier aspect, l'évaluation des probabilités constitue, dans la façon dont elle est préconisée dans ce travail, une étape préliminaire fondamentale de même qu'un moyen opérationnel.

Aggregation invariance in general clustering approaches

General clustering deals with weighted objects and fuzzy memberships. We investigate the group- or object-aggregation-invariance properties possessed by the relevant functionals (effective number of groups or objects, centroids, dispersion, mutual object-group information, etc.). The classical squared Euclidean case can be generalized to non-Euclidean distances, as well as to non-linear transformations of the memberships, yielding the c-means clustering algorithm as well as two presumably new procedures, the convex and pairwise convex clustering. Cluster stability and aggregation-invariance of the optimal memberships associated to the various clustering schemes are examined as well.

Bayesian Networks and the Value of the Evidence for the Forensic Two-Trace Transfer Problem

Forensic scientists face increasingly complex inference problems for evaluating likelihood ratios (LRs) for an appropriate pair of propositions. Up to now, scientists and statisticians have derived LR formulae using an algebraic approach. However, this approach reaches its limits when addressing cases with an increasing number of variables and dependence relationships between these variables. In this study, we suggest using a graphical approach, based on the construction of Bayesian networks (BNs). We first construct a BN that captures the problem, and then deduce the expression for calculating the LR from this model to compare it with existing LR formulae. We illustrate this idea by applying it to the evaluation of an activity level LR in the context of the two-trace transfer problem. Our approach allows us to relax assumptions made in previous LR developments, produce a new LR formula for the two-trace transfer problem and generalize this scenario to n traces.

Bayesian Markov-chain-Monte-Carlo inversion of time-lapse cross hole ground-penetrating radar data to characterize the vadose zone at the Arrenaes field site, Denmark

The ground-penetrating radar (GPR) geophysical method has the potential to provide valuable information on the hydraulic properties of the vadose zone because of its strong sensitivity to soil water content. In particular, recent evidence has suggested that the stochastic inversion of crosshole GPR traveltime data can allow for a significant reduction in uncertainty regarding subsurface van Genuchten-Mualem (VGM) parameters. Much of the previous work on the stochastic estimation of VGM parameters from crosshole GPR data has considered the case of steady-state infiltration conditions, which represent only a small fraction of practically relevant scenarios. We explored in detail the dynamic infiltration case, specifically examining to what extent time-lapse crosshole GPR traveltimes, measured during a forced infiltration experiment at the Arreneas field site in Denmark, could help to quantify VGM parameters and their uncertainties in a layered medium, as well as the corresponding soil hydraulic properties. We used a Bayesian Markov-chain-Monte-Carlo inversion approach. We first explored the advantages and limitations of this approach with regard to a realistic synthetic example before applying it to field measurements. In our analysis, we also considered different degrees of prior information. Our findings indicate that the stochastic inversion of the time-lapse GPR data does indeed allow for a substantial refinement in the inferred posterior VGM parameter distributions compared with the corresponding priors, which in turn significantly improves knowledge of soil hydraulic properties. Overall, the results obtained clearly demonstrate the value of the information contained in time-lapse GPR data for characterizing vadose zone dynamics.

Inferring the mode of origin of polyploid species from next-generation sequence data.

Many eukaryote organisms are polyploid. However, despite their importance, evolutionary inference of polyploid origins and modes of inheritance has been limited by a need for analyses of allele segregation at multiple loci using crosses. The increasing availability of sequence data for nonmodel species now allows the application of established approaches for the analysis of genomic data in polyploids. Here, we ask whether approximate Bayesian computation (ABC), applied to realistic traditional and next-generation sequence data, allows correct inference of the evolutionary and demographic history of polyploids. Using simulations, we evaluate the robustness of evolutionary inference by ABC for tetraploid species as a function of the number of individuals and loci sampled, and the presence or absence of an outgroup. We find that ABC adequately retrieves the recent evolutionary history of polyploid species on the basis of both old and new sequencing technologies. The application of ABC to sequence data from diploid and polyploid species of the plant genus Capsella confirms its utility. Our analysis strongly supports an allopolyploid origin of C. bursa-pastoris about 80 000 years ago. This conclusion runs contrary to previous findings based on the same data set but using an alternative approach and is in agreement with recent findings based on whole-genome sequencing. Our results indicate that ABC is a promising and powerful method for revealing the evolution of polyploid species, without the need to attribute alleles to a homeologous chromosome pair. The approach can readily be extended to more complex scenarios involving higher ploidy levels.

Wheat alleles introgress into selfing wild relatives: empirical estimates from approximate Bayesian computation in Aegilops triuncialis.

Extensive gene flow between wheat (Triticum sp.) and several wild relatives of the genus Aegilops has recently been detected despite notoriously high levels of selfing in these species. Here, we assess and model the spread of wheat alleles into natural populations of the barbed goatgrass (Aegilops triuncialis), a wild wheat relative prevailing in the Mediterranean flora. Our sampling, based on an extensive survey of 31 Ae. triuncialis populations collected along a 60 km × 20 km area in southern Spain (Grazalema Mountain chain, Andalousia, totalling 458 specimens), is completed with 33 wheat cultivars representative of the European domesticated pool. All specimens were genotyped with amplified fragment length polymorphism with the aim of estimating wheat admixture levels in Ae. triuncialis populations. This survey first confirmed extensive hybridization and backcrossing of wheat into the wild species. We then used explicit modelling of populations and approximate Bayesian computation to estimate the selfing rate of Ae. triuncialis along with the magnitude, the tempo and the geographical distance over which wheat alleles introgress into Ae. triuncialis populations. These simulations confirmed that extensive introgression of wheat alleles (2.7 × 10(-4) wheat immigrants for each Ae. triuncialis resident, at each generation) into Ae. triuncialis occurs despite a high selfing rate (Fis ≈ 1 and selfing rate = 97%). These results are discussed in the light of risks associated with the release of genetically modified wheat cultivars in Mediterranean agrosystems.

Insights into the biogeographical history of the Lower Guinea Forest Domain: evidence for the role of refugia in the intraspecific differentiation of Aucoumea klaineana.

Determining the biogeographical histories of rainforests is central to our understanding of the present distribution of tropical biodiversity. Ice age fragmentation of central African rainforests strongly influenced species distributions. Elevated areas characterized by higher species richness and endemism have been postulated to be Pleistocene forest refugia. However, it is often difficult to separate the effects of history and of present-day ecological conditions on diversity patterns at the interspecific level. Intraspecific genetic variation could yield new insights into history, because refugia hypotheses predict patterns not expected on the basis of contemporary environmental dynamics. Here, we test geographically explicit hypotheses of vicariance associated with the presence of putative refugia and provide clues about their location. We intensively sampled populations of Aucoumea klaineana, a forest tree sensitive to forest fragmentation, throughout its geographical range. Characterizing variation at 10 nuclear microsatellite loci, we were able to obtain phylogeographic data of unprecedented detail for this region. Using Bayesian clustering approaches, we demonstrated the presence of four differentiated genetic units. Their distribution matched that of forest refugia postulated from patterns of species richness and endemism. Our data also show differences in diversity dynamics at leading and trailing edges of the species' shifting distribution. Our results confirm predictions based on refugia hypotheses and cannot be explained on the basis of present-day ecological conditions.

Nouvelles approches therapeutiques de l'hypertension arterielle. [New therapeutic approaches to arterial hypertension]

Today two largely new approaches are available for the treatment of clinical hypertension. First, captopril, an orally active angiotensin converting enzyme inhibitor, makes possible chronic blockade of the renin-angiotensin system. This compound, given alone or in combination with a diuretic, normalizes the blood pressure of most hypertensive patients. Unfortunately, because captopril may induce serious adverse effects the use of this inhibitor must be restricted to patients with high blood pressure refractory to conventional antihypertensive drugs. Second, compounds such as verapamil and nifedipine are capable of producing a marked vasodilating effect by inhibiting the entry of calcium into the vascular smooth muscle cells. However, the role of calcium channel blockers in the treatment of hypertensive disease awaits more precise definition.

Potential approaches for more successful dendritic cell-based immunotherapy.

INTRODUCTION: Dendritic cells (DCs) are the most important antigen-presenting cell population for activating antitumor T-cell responses; therefore, they offer a unique opportunity for specific targeting of tumors. AREAS COVERED: We will discuss the critical factors for the enhancement of DC vaccine efficacy: different DC subsets, types of in vitro DC manufacturing protocol, types of tumor antigen to be loaded and finally different adjuvants for activating them. We will cover potential combinatorial strategies with immunomodulatory therapies: depleting T-regulatory (Treg) cells, blocking VEGF and blocking inhibitory signals. Furthermore, recommendations to incorporate these criteria into DC-based tumor immunotherapy will be suggested. EXPERT OPINION: Monocyte-derived DCs are the most widely used DC subset in the clinic, whereas Langerhans cells and plasmacytoid DCs are two emerging DC subsets that are highly effective in eliciting cytotoxic T lymphocyte responses. Depending on the type of tumor antigens selected for loading DCs, it is important to optimize a protocol that will generate highly potent DCs. The future aim of DC-based immunotherapy is to combine it with one or more immunomodulatory therapies, for example, Treg cell depletion, VEGF blockage and T-cell checkpoint blockage, to elicit the most optimal antitumor immunity to induce long-term remission or even cure cancer patients.

Approches de gene silencing pour le traitement de la maladie de Huntington [Gene silencing approaches for the treatment of Huntington's disease].

Huntington's disease is a rare neurodegenerative disease caused by a pathologic CAG expansion in the exon 1 of the huntingtin (HTT) gene. Aggregation and abnormal function of the mutant HTT (mHTT) cause motor, cognitive and psychiatric symptoms in patients, which lead to death in 15-20 years. Currently, there is no treatment for HD. Experimental approaches based on drug, cell or gene therapy are developed and reach progressively to the clinic. Among them, mHTT silencing using small non-coding nucleic acids display important physiopathological benefit in HD experimental models.

Ultra-low dose - new approaches in menopausal hormone therapy.

Despite increasing life expectancy, the age of onset of natural menopause has not significantly changed in recent decades. Thus, women spend about one-third of their lives in an estrogen-deficient state if untreated. There is a need for appropriate treatment of acute symptoms and prevention of the sequelae of chronic estrogen deficiency. International guidelines call for the use of the lowest effective hormone dosage for vasomotor symptom relief, the major indication for menopausal hormone therapy (MHT). In 2011, an oral continuous combined ultra-low-dose MHT was approved in Switzerland. This publication was elaborated by eight national menopause specialists and intends to review the advantages and disadvantages of ultra-low-dose MHT after the first years of its general use in Switzerland. It concludes that, for many women, ultra-low-dose MHT may be sufficient to decrease vasomotor symptoms, but not necessarily to guarantee fracture prevention.

Assessing chronic disease management in European health systems : concepts and approaches

This book comprises two volumes and builds on the findings of the DISMEVAL project (Developing and validating DISease Management EVALuation methods for European health care systems), funded under the European Union's (EU) Seventh Framework Programme (FP7) (Agreement no. 223277). DISMEVAL was a three-year European collaborative project conducted between 2009 and 2011. It contributed to developing new research methods and generating the evidence base to inform decision-making in the field of chronic disease management evaluation (www.dismeval.eu). In this book, we report on the findings of the project's first phase, capturing the diverse range of contexts in which new approaches to chronic care are being implemented and evaluating the outcomes of these initiatives using an explicit comparative approach and a unified assessment framework. In this first volume, we describe the range of approaches to chronic care adopted in 12 European countries. By reflecting on the facilitators and barriers to implementation, we aim to provide policy-makers and practitioners with a portfolio of options to advance chronic care approaches in a given policy context.