La maladie de l’œdème est une maladie connue depuis de nombreuses années chez le porc. Les premiers cas recensés dans une population de suidés sauvages sont apparus en 2013 en Ardèche. Un nouveau foyer de cette maladie est ensuite apparu en 2016 dans les Pyrénées-Orientales à la frontière entre la France et l’Espagne. Comprendre les facteurs permettant son apparition ainsi que sa transmission est nécessaire afin d’anticiper de futures mortalités dues à cette maladie. Dans cette thèse, une analyse épidémiologique de cette maladie chez le sanglier a été réalisée. Des clusters de mortalités sont alors apparus et ont permis de mettre en évidence une possible source de contamination unique et récurrente dans le temps. La mise en place d’une nouvelle méthode pour étudier la détectabilité des cadavres de sanglier a souligné la difficulté de retrouver des cadavres de sanglier en forêt. La dernière analyse épidémiologique à partir d’un modèle de type « Spatial point pattern » a mis en avant de possibles facteurs de risque d’apparition et de transmission qui ont ensuite été analysés plus précisément. L’analyse des données issus des tableaux de chasse en Ardèche a été réalisée afin de détecter des variations de la densité et du ratio J/A des populations de sanglier suggérant un stress alimentaire chez le sanglier, un prodrome ou une conséquence de la maladie. Aucun stress alimentaire ne fut détecté lors de cette analyse. Des hypothèses ont pu être émises pour expliquer certaines variations observées : i) la conséquence directe de la maladie, ii) un phénomène environnemental particulier et iii) un évènement pathogénique. La piste de l’événement pathogénique a été approfondie avec la découverte du SDRP (syndrome dysgénésique et respiratoire du porc). Les interactions porcs-sangliers, nombreuses en Ardèche, ont été déterminées comme potentiellement responsables du passage de la bactérie entre le compartiment domestique et sauvage. Une étude génétique a également été effectuée pour investiguer le gène alpha-1-fucosyltransferase associé à la sensibilité du porc à la maladie. Tous les sangliers analysés étaient sensibles à la maladie. D’autres analyses complémentaires sont nécessaires afin de comprendre au mieux cette maladie ainsi que les différents facteurs de risque pour l’apparition mais également la transmission. / Edema disease has been a known disease in pigs for many years. The first cases recorded in a population of wild suids appeared in 2013 in Ardèche. A new outbreak of this disease then emerged in 2016 in the Pyrénées-Orientales on the border between France and Spain. Understanding the factors that enable its onset and transmission is necessary to anticipate future mortality from this disease. In this thesis, an epidemiological analysis of this disease in wild boar was carried out. Clusters of mortalities then emerged, highlighting a possible single and recurrent source of contamination over time. The introduction of a new method to study the detectability of wild boar corpses highlighted the difficulty of finding wild boar corpses in the forest. The latest epidemiological analysis using a Spatial point pattern model highlighted possible risk factors for onset and transmission, which were then analysed more precisely. Analysis of data from hunting tables in the Ardèche was carried out in order to detect variations in the density and J/A ratio of wild boar populations suggesting food stress in the wild boar, a prodrome or consequence of the disease. No dietary stress was detected during this analysis. Assumptions could be made to explain some observed variations: i) the direct consequence of the disease, ii) a particular environmental phenomenon and iii) a pathogenic event. The trail of the pathogenic event was deepened with the discovery of the PRRS (Pork Respiratory and Dygesic Syndrome). The pig-boar interactions, numerous in the Ardeche, were determined as potentially responsible for the passage of the bacteria between the domestic and wild compartment. A genetic study was also conducted to investigate the alpha-1-fucosyltransferase gene associated with the susceptibility of pigs to the disease. All the wild boars tested were susceptible to the disease.Further further analysis is needed in order to better understand this disease as well as the different risk factors for both onset and transmission.

Functional Magnetic Resonance Imaging (fMRI) is a noninvasive imaging method that reflects local changes in brain activity. FMRI group studies involves the analysis of the functional images acquired for each of a group of subjects under the same experimental conditions. We propose a spatial marked point-process model for the activation patterns of the subjects in a group study. Each pattern is described as the sum of individual centres of activation. The marked point-process that we propose allows the researcher to enforce repulsion between all pairs of centres of an individual subject that are within a specified minimum distance of each other. It also allows the researcher to enforce attraction between similarly-located centres from different subjects. This attraction helps to compensate for the misalignment of corresponding functional areas across subjects and is a novel method of addressing the problem of imperfect inter-subject registration of functional images. We use a Bayesian framework and choose prior distributions according to current understanding of brain activity. Simulation studies and exploratory studies of our reference dataset are used to fine-tune the prior distributions. We perform inference via Markov chain Monte Carlo. The fitted model gives a summary of the activation in terms of its location, height and size. We use this summary both to identify brain regions that were activated in response to the stimuli under study and to quantify the discrepancies between the activation maps of subjects. Applied to our reference dataset, our measure is successful in separating out those subjects with activation patterns that do not agree with the overall group pattern. In addition, our measure is sensitive to subjects with a large number of activation centres relative to the other subjects in the group. The activation summary given by our model makes it possible to pursue a range of inferential questions that cannot be addressed with ease by current model-based approaches.

Multiphoton laser scanning microscopy (MPLSM) is an advanced fluorescence imaging technology which can produce a less noisy microscope image and minimize the damage in living tissue. The MPLSM image in this research is the dehydroergosterol (DHE, a fluorescent sterol which closely mimics those of cholesterol in lipoproteins and membranes) on living cell's plasma membrane area. The objective is to use a statistical image analysis method to describe how cholesterol is distributed on a living cell's membrane. Statistical image analysis methods applied in this research include image segmentation/classification and spatial analysis. In image segmentation analysis, we design a supervised learning method by using smoothing technique with rank statistics. This approach is especially useful in a situation where we have only very limited information of classes we want to segment. We also apply unsupervised leaning methods on the image data. In image data spatial analysis, we explore the spatial correlation of segmented data by a Monte Carlo test. Our research shows that the distributions of DHE exhibit a spatially aggregated pattern. We fit two aggregated point pattern models, an area-interaction process model and a Poisson cluster process model, to the data. For the area interaction process model, we design algorithms for maximum pseudo-likelihood estimator and Monte Carlo maximum likelihood estimator under lattice data setting. For the Poisson Cluster process parameter estimation, the method for implicit statistical model parameter estimate is used. A group of simulation studies shows that the Monte Carlo maximum estimation method produces consistent parameter estimates. The goodness-of-fit tests show that we cannot reject both models. We propose to use the area interaction process model in further research.

The aim of the thesis is to create a pack of functions in Wolfram Mathematica software to simulate spatial point patterns of chosen hard-core and Gibbs processes. Then it tries to apply these models to real data, estimate the parameters of models for real data and test the validity of models. The thesis can serve as introduction into the theory of spatial point processes.

Stochastic point processes and associated methodology provide a means for the statistical analysis and modeling of the spatial point pattern formed from forest tree stem locations. Stochastic Gibbs point processes were explored as models that could simulate short-range clustering arising from reproduction of trees by stump sprouting, and intermediate-range inhibition of trees that may result from competition for light and growing space. This study developed and compared three pairwise interaction processes with parametric models for 2nd-order potentials and three triplets processes with models for 2nd- and 3rd-order potentials applied to a mixed-species hardwood forest in the Southern Appalachian Mountains of western North Carolina. Although the 2nd-order potentials of both the pairwise interaction and triplets processes were allowed to be purely or partially attractive, the proposed Gibbs point process models were demonstrated to be locally stable. The proposed Gibbs point processes were simulated using Markov Chain Monte Carlo (MCMC) methods; in particular, a reversible-jump Metropolis-Hastings algorithm with birth, death, and shift proposals was utilized. Parameters for the models were estimated by a Bayesian inferential procedure that utilizes MCMC methods to draw samples from the Gibbs posterior density. Two Metropolis-Hastings algorithms that do this sampling were compared; one that estimated ratios of intractable normalizing constants of the Gibbs likelihood by importance sampling and another that introduced an auxiliary variable to cancel the normalizing constants with those in the auxiliary variable's proposal distribution. Results from this research indicated that attractive pairwise interaction models easily degenerate into excessively clustered patterns, whereas triplets processes with attractive 2nd-order and repulsive 3rd-order interactions are more robust against excessive clustering. Bayesian inference for the proposed triplets models was found to be very computationally expensive. Slow mixing of both algorithms used for the inference combined with the long iteration times limited the practicality of the Bayesian approach. However the results obtained here indicate that triplets processes can be used to draw inference for and simulate patterns of mixed-species Appalachian hardwood forests. / Ph. D.

Invasive exotic plants can persist and successfully spread within ecosystems and negatively affect the recruitment of native species. The exotic invasive Ailanthus altissima and the native Robinia pseudoacacia are frequently found in disturbed sites and exhibit similar growth and reproductive characteristics, yet each has distinct functional roles such as allelopathy and nitrogen fixation, respectively. 1) A four-month full additive series in the greenhouse and 2) spatial point pattern analysis of trees in a silvicultural experiment were used to analyze the intraspecific and interspecific interference between these two species. In the greenhouse experiment, total biomass responses per plant for both species were significantly affected by interspecific but not by intraspecific interference (p <0.05). Competition indices such as Relative Yield Total and Relative Crowding Coefficient suggested that A. altissima was the better competitor in mixed plantings. Ailanthus altissima consistently produced a larger above ground and below ground relative yield while R. pseudoacacia generated a larger aboveground relative yield in high density mixed species pots. However, R. pseudoacacia exhibited more variation for multiple biomass traits, occasionally giving it an above ground advantage in some mixed species pots. Analysis of spatial point patterns in the field with Ripley's K indicated that the two species were positively associated with each other along highly disturbed skid trails in the majority of the field sites. Locally, increased disturbances could lead to more opportunities for A. altissima to invade, negatively interact with R. pseudoacacia (as was evident in the greenhouse study), and become established in place of native species. / Master of Science

With increasing levels of motor vehicle ownership, automobile crashes have become a serious public issue in the U.S. and around the world. Knowing when, where, and how traffic accidents happen is critical in order to ensure road safety and to plan for adequate road infrastructure. There is a rich body of literature pertaining to time-related fatal crashes, most of which focuses on non-spatial factors such as a driver’s visibility at night, drinking and drug use, and road conditions. These studies provide a theoretical basis for understanding the causes of crashes from a non-spatial perspective, and a number of traffic laws and policies consequently have been enacted to minimize the impacts of non-spatial factors. Over the past few years, advances in Geographic Information Systems (GIS) have greatly enhanced our ability to analyze traffic accidents from a spatial perspective. This study aims to fill a void in traffic safety studies by comparing and analyzing the differences in the spatial distribution of fatal crashes based on temporal factors, specifically in three periods: 1) day and night; 2) A.M. rush hours and P.M. rush hours; and 3) weekdays and weekends. With the Nashville Metropolitan Area as the study area, the research utilized a number of spatial point-pattern analysis (SPPA) methods, including planar KDE, planar global auto K function, network global cross K functions, and network local cross K functions. All fatal crashes in the Nashville area were found to be clustered and generally follow the patterns of average daily traffic flow. All time-based subtypes of fatal crashes also were found to be concentrated within the central urban area of Nashville, mostly along major roads, and especially near major road intersections and highway interchanges. No notable spatial differences were detected among the subtypes of fatal crashes when applying network global cross K function. However, with the help of the network local cross K function, some localized spatial differences were identified. Some specific locations of hotspots of nighttime and P.M. rush hour fatal crashes were found not to be at the same locations as those at of daytime and A.M. rush hour fatal crashes, respectively. The approach adopted in this study not only provides a new way to analyze spatial distribution of spatial point events such as fatal crashes, but it also can be applied readily to real-world applications. A good understanding of where these spatial differences are should help various agencies practice effective measures and policies in order to improve road conditions, reduce traffic accidents, and ensure road safety.

We study the minimum radius required for connectivity (CTR Critical Transmission Range) within homogeneous stationary Wireless Sensor Networks (WSN) topology control, considering different levels of attractivity within the sensors. Due to the complexity of dealing with this problem from a theoretical viewpoint, a Monte Carlo experience is devised for estimating the CTR distribution. With this information, we propose optimization procedures that, using as additional input a few known parameters (overall available budget, sensor cost, maximum available transmission radius, minimum probability of connectivity, environmental path loss and deployment cost) leads to the decision of the number and type of sensors to be acquired, their optimal communication radius and the ideal deployment strategy that maximize the WSN lifetime. As a previous result, the accuracy of several computational platforms for statistical computing was assessed, being the main conclusion that R (http://www.r-project.org) is the best choice / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Apresentamos a caracterização do raio de transmissão mínimo necessário para garantir conectividade, CTR (Critical Transmission Range), num cenário de controle de topologia em RSSF homogêneas e estacionárias. Dada a complexidade de se trabalhar com modelos analíticos, por meio de experiências Monte Carlo obtemos um estimador da distribuição do CTR sobre processos pontuais espaciais que descrevem o posicionamento dos nós sensores no ambiente para diferentes níveis de atratividade. Propomos modelos de otimização práticos que consideram diversos fatores conhecidos a priori pelo projetista como, por exemplo, os preços de diversos sensores, o raio de transmissão máximo disponível pelo sensor, os custos de posicionamento no ambiente (função da atratividade), o orçamento total do projeto, a probabilidade de conectividade mínima admissível e o exponente de path loss do ambiente. O modelo determina quais e quantos sensores devem ser comprados, com que raio de transmissão devem ser configurados e qual o preço conveniente a pagar pelo posicionamento (função da atratividade), para maximizar o tempo de vida de uma RSSF. Para guiar a escolha da plataforma de simulação e análise de dados, vários resultados a respeito de precisão numérica são apresentados, obtidos aplicando protocolos de avaliação já consolidados. Desta análise, determinamos que R (http://www.r-project.org) é a melhor escolha

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L'un des problèmes les plus complexes dans l'analyse des images histologiques est l'évaluation de l¿organisation spatiale des structures histologiques dans le tissu. En fait, les sections histologiques peuvent contenir un très grand nombre de cellules de différents types et irrégulièrement réparties dans le tissu, ce qui rend leur contenu spatial indescriptible d'une manière simple. Les méthodes fondées sur la théorie des graphes ont été largement explorées dans cette direction, car elles sont des outils de représentation efficaces ayant la capacité expressive de décrire les caractéristiques spatiales et les relations de voisinage qui sont interprétées visuellement par le pathologiste. On peut distinguer trois familles principales de méthodes des graphes utilisées à cette fin: analyse de structure syntaxique, analyse de réseau et analyse spectrale. Cependant, un autre ensemble distinctif de méthodes basées sur la morphologie mathématique sur les graphes peut être développé et adapté pour ce problème. L'objectif principal de cette thèse est le développement d'un outil capable de fournir une évaluation quantitative des arrangements spatiaux des structures histologiques en utilisant la morphologie mathématique basée sur les graphes. / One of the most challenging problems in histological image analysis is the evaluation of the spatial organizations of histological structures in the tissue. In fact, histological sections may contain a very large number of cells of different types and irregularly distributed, which makes their spatial content indescribable in a simple manner. Graph-based methods have been widely explored in this direction, as they are effective representation tools having the expressive ability to describe spatial characteristics and neighborhood relationships that are visually interpreted by the pathologist. We can distinguish three main families of graph-based methods used for this purpose: syntactic structure analysis, network analysis and spectral analysis. However, another distinctive set of methods based on mathematical morphology on graphs can be additionally developed for this issue. The main goal of this dissertation is the development of a framework able to provide quantitative evaluation of the spatial arrangements of histological structures using graph-based mathematical morphology.