A ressonância magnética (RM) tem sido amplamente utilizada no diagnóstico por imagem de alterações de glândulas salivares. No entanto, a presença de aspectos similares nas imagens com técnicas convencionais de RM dificulta a distinção do diagnóstico entre patologias inflamatórias e neoplásicas. O objetivo deste estudo foi comparar valores dos coeficientes de difusão aparentes (ADC - Apparent Diffusion Coefficient) de imagem ponderada em difusão (DWI - Difusion Weighted Imaging) com ressonância magnética, entre glândulas salivares normais, casos com sialoadenite e com adenoma pleomórfico das glândulas salivares maiores. Vinte e dois pacientes (totalizando 44 glândulas salivares maiores) diagnosticados com sialoadenite unilateral (em glândula parótida ou submandibular) ou adenoma pleomórfico (apenas em parótida) foram selecionados. Todas as glândulas contralaterais não afetadas também foram analisadas. Imagens de RM ponderadas em T1, T2 e DWI foram obtidas utilizando sequências de pulso spin-eco (SE) com um aparelho de ressonância magnética de 1.5 Tesla. Primeiramente, a performance diagnóstica (sensitividade, especificidade e acurácia) foi calculada para três observadores após analisarem imagens de RM e DWI, separadamente. Em seguida, os valores médios de ADC foram comparados entre os três grupos analisados (glândulas normais contralateral, sialoadenite e adenoma pleomórfico). O uso da DWI rendeu uma melhor performance diagnóstica em geral para todos os observadores. Além disso, casos de adenoma pleomórfico apresentaram os maiores valores de ADC do estudo. Dentro das limitações deste estudo, os resultados sugerem que DWI permite a diferenciação entre sialoadenite e adenoma pleomórfico. / Alterations of the salivary glands are usually detected by conventional magnetic resonance imaging (MRI) techniques. However, their imaging presentation may present similar aspects. The aim of this study was to compare apparent diffusion coefficient (ADC) values from diffusion-weighted MRI (DWI) among normal salivary glands, cases with sialadenitis and with pleomorphic adenoma of major salivary glands. Twenty-two patients (totaling 44 major salivary glands) diagnosed with either unilateral sialadenitis (on either parotid or submandibular gland) or parotid gland pleomorphic adenoma were selected. Contralateral non-affected glands (normal) were also analyzed. DWI images were achieved using a spin-echo (SE) pulse sequence with a 1.5T MRI device. Mean ADC values were compared among the three groups analyzed (contralateral normal glands, sialadenitis and pleomorphic adenoma). Furthermore, diagnostic performance of MRI and DWI were calculated for three observers. DWI also presented better diagnostic performance results. In addition, cases of pleomorphic adenoma presented the highest ADC values of the study. Within the limitations of this study, the present results suggest that DWI allows for differentiation between parotid sialadenitis and pleomorphic adenoma.

Diffusion-weighted magnetic resonance imaging (dMRI) has been used to non-invasively investigate the integrity of white matter and the connectivity of the brain. In this work, high angular resolution diffusion imaging (HARDI), an advanced dMRI methodology was developed and employed in bonnet macaque monkeys to study the connectivity of the orbitofrontal cortex (OFC) and amygdala, two gray matter regions involved in making reward-guided decisions. With age, it is believed that there is a decline in the white matter connectivity between these two regions, also known as uncinate fasciculus (UF), and that this affects reward-value assignment and feedback learning in older adults. The analysis pipeline involved correction for distortions due to eddy currents and field inhomogeneity, noise reduction using a local principal component analysis based technique and subsequent registration to the high-resolution T1-weighted images. Gray matter regions corresponding to OFC and amygdala were identified on the T1-weighted images and probabilistic tractography was carried out to delineate the tracts belonging to UF. The output connectivity map from tractography was used to extract imaging parameters of interest such as fractional anisotropy, axial and radial diffusivity along the UF. A significant reduction in the fractional anisotropy index and the axial diffusivity index along the UF tract was observed with increased age of monkeys. Compared to the left hemisphere, stronger trends were observed in the right hemisphere of the monkeys, indicating possible laterality.

Phasing genotypes from sequence data is an important step betweendata gathering and downstream analysis in population genetics,disease studies, and multiple other fields. This determination ofthe sequences of markers corresponding to the individualchromosomes can be done on data where the markers are in lowdensity across the chromosome, such as from single nucleotidepolymorphism (SNP) microarrays, or on data with a higher localdensity of markers like in next generation sequencing (NGS). Thesorted markers may then be used for many different analyses anddata processing such as linkage analysis, or inference of missinggenotypes in the process of imputation cnF2freq is a haplotype phasing program that uses an uncommonapproach allowing it to divide big groups of related individualsinto smaller ones. It sets an initial haplotype phase and theniteratively changes it using estimations from Hidden MarkovModels. If a marker is judged to have been placed in the wronghaplotype, a switch needs to be made so that it belongs to thecorrect phase. The objective of this project was to go fromallowing only one individual within a group to be switched in aniteration to allowing multiple switches that are dependent on eachother. The result of this project is a theoretical solution for allowingmultiple dependent switches in cnF2freq, and an implementedsolution using the max-SAT solver toulbar2.

This thesis presents new methods to study the aerosol synthesis of nano-particles and a new model to simulate the formation of silicon nanoparticles. Population balance modelling is used to model nanoparticle synthesis and a stochastic numerical method is used to solve the governing equations. The population balance models are coupled to chemical kinetic models and offer insight into the fundamental physiochemical processes leading to particle formation. The first method developed in this work is a new mathematical expression for calculating the rate of Brownian coagulation with stochastic weighted algorithms (SWAs). The new expression permits the solution of the population balance equations with SWAs using a computationally-efficient technique of majorant rates and fictitious jumps. Convergence properties and efficiency of the expression are evaluated using a detailed silica particle model. A sequential-modular algorithm is subsequently presented which solves networks of perfectly stirred reactors with a population balance model using the stochastic method. The algorithm is tested in some simple network configurations, which are used to identify methods through which error in the stochastic solution may be reduced. It is observed that SWAs are useful in preventing accumulation of error in reactor networks. A new model for silicon nanoparticle synthesis is developed. The model includes gas-phase reactions describing silane decomposition, and a detailed multivariate particle model which tracks particle structure and composition. Systematic parameter estimation is used to fit the model to experimental cases. Results indicated that the key challenge in modelling silicon systems is obtaining a correct description of the particle nucleation process. Finally, the silicon model is used in conjunction with the reactor network algorithm to simulate the start-up of a plug-flow reactor. The power of stochastic methods in resolving characteristics of a particle ensemble is highlighted by investigating the number, size, degree of sintering and polydispersity along the length of the reactor.

Chronic musculoskeletal pain is a leading cause of disability worldwide yet the mechanisms of chronification and neural responses to effective treatment remain elusive. Non-invasive imaging techniques are useful for investigating brain alterations associated with health and disease. Thus the overall goal of this dissertation was to investigate the white (WM) and grey matter (GM) structural differences in patients with musculoskeletal pain before and after psychotherapeutic intervention: cognitive behavioral therapy (CBT). To aid in the interpretation of clinical findings, we used a novel porcine model of low back pain-like pathophysiology and developed a post-mortem, in situ, neuroimaging approach to facilitate translational investigation. The first objective of this dissertation (Chapter 2) was to identify structural brain alterations in chronic pain patients compared to healthy controls. To achieve this, we examined GM volume and diffusivity as well as WM metrics of complexity, density, and connectivity. Consistent with the literature, we observed robust differences in GM volume across a number of brain regions in chronic pain patients, however, findings of increased GM volume in several regions are in contrast to previous reports. We also identified WM changes, with pain patients exhibiting reduced WM density in tracts that project to descending pain modulatory regions as well as increased connectivity to default mode network structures, and bidirectional alterations in complexity. These findings may reflect network level dysfunction in patients with chronic pain. The second aim (Chapter 3) was to investigate reversibility or neuroplasticity of structural alterations in the chronic pain brain following CBT compared to an active control group. Longitudinal evaluation was carried out at baseline, following 11-week intervention, and a four-month follow-up. Similarly, we conducted structural brain assessments including GM morphometry and WM complexity and connectivity. We did not observe GM volumetric or WM connectivity changes, but we did discover differences in WM complexity after therapy and at follow-up visits. To facilitate mechanistic investigation of pain related brain changes, we used a novel porcine model of low back pain-like pathophysiology (Chapter 6). This model replicates hallmarks of chronic pain, such as soft tissue injury and movement alteration. We also developed a novel protocol to perform translational post-mortem, in situ, neuroimaging in our porcine model to reproduce WM and GM findings observed in humans, followed by a unique perfusion and immersion fixation protocol to enable histological assessment (Chapter 4). In conclusion, our clinical data suggest robust structural brain alterations in patients with chronic pain as compared to healthy individuals and in response to therapeutic intervention. However, the mechanism of these brain changes remains unknown. Therefore, we propose to use a porcine model of musculoskeletal pain with a novel neuroimaging protocol to promote mechanistic investigation and expand our interpretation of clinical findings.

En raison de l'expansion des données, les algorithmes de compression sont désormais cruciaux. Nous abordons ici le problème de trouver des algorithmes de compression optimaux par rapport à une source de Markov donnée. A cet effet, nous étendons l'algorithme de Huffman classique. Pour se faire premièrement on applique Huffman localement à chaque état de la source Markovienne, en donnant le résultat de l'efficacité obtenue pour cet algorithme. Mais pour bien approfondir et optimiser quasiment l'efficacité de l'algorithme, on donne un autre algorithme qui est toujours appliqué localement à chaque états de la source Markovienne, mais cette fois ci en codant les facteurs partant de ces états de la source Markovienne de sorte à ce que la probabilité du facteur soit une puissance de 1/2 (sachant que l'algorithme de Huffman est optimal si et seulement si tous les symboles à coder ont une probabilité puissance de 1/2). En perspective de ce chapitre on donne un autre algorithme (restreint à la compression de l'étoile) pour coder une expression à multiplicité, en attendant dans l'avenir à coder une expression complète / Due to the expansion of datas, compression algorithms are now crucial algorithms. We address here the problem of finding an optimal compression algorithm with respect to a given Markovian source. To this purpose, we extend the classical Huffman algorithm. The kernels are popular methods to measure the similarity between words for classication and learning. We generalize the definition of rational kernels in order to apply kernels to the comparison of languages. We study this generalization for factor and subsequence kerneland prove that these kernels are defined for parameters chosen in an appropriate interval. We give different methods to build weighted transducers which compute these kernels

China has undergone significant urban growth and industrialization over the last 30 years and its incredible development continues to move ahead at an increasingly rapid pace. In terms of urban expansion, China has just recently surpassed the world’s average urbanization rate of 50%, as it moves its massive population from rural to urban areas at an astonishing speed. It’s massive population and fast urbanizing speed aside, China is also unique in terms of its socio-political system and historical-cultural context: it is a hybrid of government planning and market forces. Since it encompasses a large part of the global population and has had a vastly different urbanization experience than that of Western countries, around which most theories are based, studying China’s urbanization is an opportunity to contribute to the field of urban studies in an unprecedented manner. However, these differences also make it difficult to develop a comprehensive study of China’s urban system since the predominant theories in the field are best suited to Western cities. This research rises to this challenge by systematically studying the relationship between the socioeconomic and biophysical processes in the Chinese urban system to understand the interaction between human and physical factors, and the landscape patterns that result from these interactions. This complex urban system is examined using a hierarchical, top-down approach. At the highest level is a Macro-scale analysis of the national urban system, followed by a study of the regional urban system: the JingJinJi Metropolitan Area at the Meso-scale, and finally a Micro-scale examination with a focus on the city of Beijing. Since urban systems develop over both time and space, the urban system is analyzed spatio-temporally on all three levels. Research at the national scale is composed of two parts. First, the challenges and opportunities of China’s urban development since the foundation of the People’s Republic of China in 1949 are investigated in a general context. The institutional barriers that impede the management and continuation of China’s urban development are also discussed. Rank-size Analysis and satellite images are used to present the structural transitions of city scaling and urban clusters. These changes come with a series of challenges that are also iterated and discussed. This is followed by an analysis of the spatial distribution and transition patterns of China’s urban system using Centrographic Analysis, particularly since the post-1979 reforms. Second, the Macro-scale research focuses on a study of the urban hierarchy that is based on inter-city interactions as determined by the Synthesized Gravity Model (SGM). Under this model socioeconomic variables are synthesized and represented by the Influential Factor, while the Function Distance is derived from a Network Analysis that is based on multiple transportation methods. As an improvement on the conventional Gravity Model (GM), the SGM is used to accurately establish and represent the nodal structure of China’s urban system, the evolution of its hierarchical structure, and the relationships that exist between the nodal structure and socioeconomic factors. The results based on the SGM indicate that China’s national urban system is characterized by the emergence of urban clusters with stronger inter-city interactions since the 1990s. However, development among cities within certain urban clusters is not even, although the general pattern indicates a lessening inequality among cities. Spatially, while most cities at the top of the hierarchy are located in the east of China, cities in the middle and west of the country are also gaining higher positions in the hierarchy over time. On the Meso-scale, the applicability of the Cellular Automata (CA)-based SLEUTH model for regional urban growth pattern is studied through a focus on the JingJinJi Metropolitan Area (Beijing-Tianjin-Hebei). By integrating socioeconomic factors into a modified SLEUTH model, the urban growth dynamics and future development scenarios of the area are simulated and predicted. The results based on the CA model show that this region is characterized by a dynamic development pattern with high spreading and breeding growth rules that relies greatly on the growing transportation systems. It also allows for the projection of three possible future urban growth scenarios, each occurring under different environmental and development conditions, showing the future urban growth with or without further intervention. This research confirms that four factors play essential roles in the formulation of the urban growth mechanism of the JingJinJi Metropolitan Area: Urban policies, Industry restructuring, Rural-urban migration, and Reclassification of urban boundaries. The Micro-scale study of Beijing is conducted from two perspectives: the social and natural. The social aspect adopts the factorial ecology approach to identify the social landscape patterns and the factors that have shaped Beijing’s social space in 1990 and 2000. The social mosaic has experienced a significant change due to suburbanization, resulting in a more dynamic and complex internal structure since the 2000s. From a natural perspective, Beijing’s physical landscape patterns are extracted by processing remotely sensed images that have the same temporal span. The physical change through landscape metrics demonstrates that Beijing’s expansion has generated a more complex and fragmented land use/cover pattern. Meanwhile, transportation systems play a significant role in urban expansion, although the expansion across the space (zonal rings and directional sectors) is not even. Finally, the relationship between the social and physical landscapes is quantitatively defined by the Geographically Weighted Regression (GWR) technique, using physical landscape metrics as dependent variables and social areas as independent variables. The GWR is able to demonstrate the relationship between the social and physical landscapes at this level: as a city’s social mosaic becomes more varied over time it results in the fragmentation of that city’s physical space.

The main focus of this thesis is the analysis of channels used by Czech banks to increase their capital ratios. We identify the increase in capital as the main channel used. Further, within these channels we find retained earnings to be the main tool used. In addition, growth in the loans volume was the dominant tool within the channel of asset volume. Furthermore, we observe an increase in the use of more advanced capital requirements quantification methods, especially in larger banks. We also identify several factors, other than capital regulation, that might have contributed to the observed developments.

A large number of quantitative studies have examined social inequities in the geographic distribution of air pollution. Although previous research has made strides towards understanding the nature and extent of inequities, they have been limited methodologically in three ways. First, the presence of pollutants have been rarely linked to their adverse health effects, with many studies using proximity to sources as a proxy for risk. Second, there has been a tendency to study a single pollution source instead of assessing multiple types of sources. Finally, conventional statistical methods such as multivariate regression have been limited by their inability to discern spatial variations in the relationships between dependent and explanatory variables. This thesis addresses these gaps in environmental justice analysis of air pollution by using data from U.S. Environmental Protection Agency's 1999 National-Scale Air Toxics Assessment in combination with 2000 U.S. Census data to evaluate inequities in the geography of cancer risks from hazardous air pollutants in Florida. The objective is to determine if there are racial/ethnic inequities in the distribution of estimated cancer risks from outdoor exposure to point and mobile sources of air pollutants, after controlling for well-documented contextual variables. The first phase of the study utilizes traditional correlation and regression techniques to reveal that cancer risk from most air pollution sources are distributed inequitably with respect to race, ethnicity, and socioeconomic state. In the second phase, geographically weighted regression is used along with choropleth mapping to explore the spatial nonstationarity of regression model parameters and geographic variations in the statistical association between cancer risks and various explanatory variables. Results indicate that while Black and Hispanic proportions remain consistent indicators of cancer risk from most pollution sources, these relationships vary across space within Florida. This thesis contributes to environmental justice analysis by demonstrating that conventional multivariate regression can hide important local variations in the relationships between environmental risk and explanatory variables such as race, ethnicity, and socioeconomic status. Since this spatial nonstationarity can be significant within an entire region or a single urban area, understanding its nature and extent is imperative to advancing environmental justice goals.

One technique being used to reduce employee turnover is the Weighted Application Blank. Data obtained from application blanks are analyzed and weights are assigned to each item. Utilizing these weights, predicted scores are derived and compared to each person's actual tenure to determine the effectiveness of the model. The present study analyzed application blank data from the files of 93 currently employed and 69 terminated female clerical workers. Twelve items were analyzed by means of a stepwise multiple linear regression procedure, with months of tenure being the dependent variable. The five most significant items yielded a multiple correlation of .54. The total sample also was divided randomly into two groups, and cross-group analyses resulted in simple correlations of .56 and .29.