Résumé : Imager la vascularisation cérébrale de la manière la plus exacte, précise et rapide possible représente un enjeu important pour plusieurs domaines de recherche. En plus d’aider à mieux comprendre le fonctionnement normal du cerveau, cela peut servir à caractériser diverses pathologies ou à développer de nouveaux traitements. Dans un premier temps, ce mémoire présente l’optimisation d’une technique d’angiographie cérébrale in vivo chez un modèle animal fréquemment utilisé, la souris. La technique emploie une séquence d’imagerie par résonance magnétique (IRM) 3D pondérée en susceptibilité ainsi qu’un agent de contraste, le Resovist. Les paramètres d’acquisition à l’IRM ont été optimisés à l’aide d’images acquises avant l’injection du Resovist. Ces paramètres permettent d’imager le cerveau entier en 41 minutes avec une résolution de 78 × 78 × 104 μm3. L’emploi d’une pondération en susceptibilité offre une excellente sensibilité aux petits vaisseaux (diamètre ≃ 40μm). L’analyse des images permet d’extraire des informations sur la morphologie vasculaire. Dans un second temps, la méthode de l’acquisition compressée (AcqC) a été implémentée dans le but d’accélérer l’acquisition des images angiographiques. La méthode de l’AcqC utilise des hypothèses de compressibilité des images pour diminuer la quantité de données acquise. L’AcqC a jusqu’à présent principalement été développée pour des images réelles (au sens des nombres complexes). Or, les images angiographiques obtenues présentent d’importantes variations de phase en raison de la pondération en susceptibilité. La présence de ces variations diminue d’une part la force des hypothèses de compressibilité habituelles et rend d’autre part l’espace-k moins propice au sous-échantillonnage requis par l’AcqC. En raison de ces deux facteurs, l’AcqC standard s’avère inefficace pour accélérer l’acquisition des images angiographiques acquises. Leur mise en lumière suggère cependant différentes pistes pour améliorer l’AcqC appliquée aux images comportant d’importantes variations de phase. / Abstract : Imaging neurovasculature with highest exactitude, precision and speed is of critical importance for several research fields. Beside providing an insight on normal brain activity, it can help characterize numerous pathologies or develop novel treatments. This thesis presents in its first part the optimization of a cerebral angiographic in vivo technique in a frequently used animal model, the mouse. The technique uses both a 3D magnetic resonance imaging (MRI) susceptibility weighted sequence and a strongly paramagnetic contrast agent, Resovist. MRI acquisition parameters were optimized using images acquired before contrast agent injection. Those parameters allow whole brain vascular imaging of the mouse brain in 41 minutes with a 78 × 78 × 104 μm3 resolution. Susceptibility weighting offers an excellent detection sensitivity for small vessels (diameter ≃ 40μm). Image treatment and analysis allow the extraction of vascular morphological information such as vessel size and vessel density. In the second part of this thesis, an attempt to accelerate angiographic images acquisition using the compressed sensing (CS) method is presented. CS method aims at reducing the acquired data by using compressibility hypothesis on images. Presently, CS is mainly developped for real images (within the meaning of complex numbers). However, the previously obtained angiographic images contain important phase variations due to the susceptibility weighting. First, those variations reduce the strength of the compressibility hypothesis normally used in CS. Second, those same variations make information distribution in k-space less appropriate for the undersampling required by CS. For those reasons, standard CS does not allow significant acceleration of the acquisition process for the presented angiographic technique. Studying those reasons however suggests new ways to increase CS efficiency when applied to images with important phase variations.

Objective: Chronic recurrent multifocal osteomyelitis/ chronic non-bacterial osteomyelitis (CRMO/CNO) is a rare auto-inflammatory disease and typically manifests in terms of musculoskeletal pain. Because of a high frequency of musculoskeletal disorders in children/ adolescents, it can be quite challenging to distinguish CRMO/ CNO from nonspecific musculosketetal pain or from malignancies. The purpose of this study was to evaluate the visibility of CRMO lesions in a whole-body diffusion-weighted imaging (WB-DWI) technique and its potential clinical value to better characterize MR-visible lesions. Materials and methods: Whole-body imaging at 3T was performed in 16 patients (average: 13 years) with confirmed CRMO. The protocol included 2D Short Tau Inversion Recovery (STIR) imaging in coronal and axial orientation as well as diffusion-weighted imaging in axial orientation. Visibility of lesions in DWI and STIR was evaluated by two readers in consensus. The apparent diffusion coefficient (ADC) was measured for every lesion and corresponding reference locations. Results: A total of 33 lesions (on average 2 per patient) visible in STIR and DWI images (b = 800 s/mm2 and ADC maps) were included, predominantly located in the long bones. With a mean value of 1283 mm2/s in lesions, the ADC was significantly higher than in corresponding reference regions (782 mm2/s). By calculating the ratio (lesion to reference), 82% of all lesions showed a relative signal increase of 10% or higher and 76% (25 lesions) showed a signal increase of more than 15%. The median relative signal increase was 69%. Conclusion: This study shows that WB-DWI can be reliably performed in children at 3T and predominantly, the ADC values were substantially elevated in CRMO lesions. WB-DWI in conjunction with clinical data is seen as a promising technique to distinguish benign inflammatory processes (in terms of increased ADC values) from particular malignancies.

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

Analytical modelling and characterization of Weighted Fair Queueing (WFQ) have recently received considerable attention by several researches since WFQ offers the minimum delay and optimal fairness guarantee. However, all previous work on WFQ has focused on developing approximations of the scheduler with an infinite buffer because of supposed scalability problems in the WFQ computation. The main aims of this thesis are to study WFQ system, by providing an analytical WFQ model which is a theoretical construct based on a form of processor sharing for finite capacity. Furthermore, the solutions for classes with Poisson arrivals and exponential service are derived and verified against global balance solution. This thesis shows that the analytical models proposed can give very good results under particular conditions which are very close to WFQ algorithms, where accuracy of the models is verified by simulations of WFQ model. Simulations were performed with QNAP-2 simulator. In addition, the thesis presents several performance studies signifying the power of the proposed analytical model in providing an accurate delay bounds to a large number of classes. These results are not able to cover all unsolved issues in the WFQ system. They represent a starting point for the research activities that the Author will conduct in the future. The author believes that the most promising research activities exist in the scheduler method to provide statistical guarantees to multi-class services. The author is convinced that alternative software, for example, on the three class model buffer case, is able to satisfy the large number of buffer because of the software limitation in this thesis. While they can be a good topic for long-term research, the short-medium term will show an increasing interest in the modification of the WFQ models to provide differentiated services.

The aim with this report is to formulate a strategic method to optimize selection processes of DNA-samples from a faeces inventory to identify as many individuals in as few analyzes as possible, and by that keep down the costs of brown bear management. Brown bear management in Sweden founds today on results from faeces inventory and is substantially led by the county administration boards. Data from the years of 2004 and 2009´s inventories in Västerbotten was used to test and evaluate different methods in selection processes of which faeces that should be sampled. Comparison were made between making selection by chance, by spatial distribution and by calculating variations in logistic regressions coefficient b, in other words bear density and probability in finding same individual in several faeces. We can show making selection by chance is the most uncertain method. Making selection by spatial distribution, without take in account variations in b, provides the highest number of identified individuals at a low labour and thus a low cost. Therefor we strongly recommend future brown bear management to, if not possible to sample all faces in a dataset, make selection by spatial distribution to minimize the risk of sampling the same bear several times.

Receiver operating characteristic (ROC) curves are widely used in medical decision making. It was recognized in the last decade that only a specific region of the ROC curve is of clinical interest, which can be summarized by the partial area under the ROC curve (partial AUC). Early statistical methods for evaluating partial AUC assume that the data are from a specified underlying distribution. Nonparametric estimators of the partial AUC emerged recently, but there are theoretical issues to be addressed. In this dissertation, we propose two new nonparametric statistics, partially integrated ROC and partially integrated weighted ROC, for estimating partial AUC. We show that our partially integrated ROC statistic is a consistent estimator of the partial AUC, and derive its asymptotic distribution which is distribution free under the null hypothesis. In the partially integrated ROC statistic, when the ROC curve crosses the Uniform distribution function (CDF) and if the partial area evaluated contains the crossing point, or when there are multiple crossing, the partially integrated ROC statistic might not perform well. To address this issue, we propose the partially integrated weighted ROC statistic. This statistic evaluates the partially weighted AUC, where larger weight is given when the ROC curve is above the Uniform CDF and smaller weight is given when the ROC curve is below the Uniform CDF. We show that our partially integrated weighted ROC statistic is a consistent estimator of the partially weighted AUC. We derive its asymptotic distribution which is distribution free under the null hypothesis. We propose to apply our two nonparametric statistics to functional category analysis in microarray experiments. We define the functional category analysis to be the statistical identification of over-represented functional gene categories in a microarray experiment based on differential gene expression. We compare our statistics with existing methods for the functional category analysis both via simulation study and application to a real microarray data, and demonstrate that our two statistics are effective for identifying over-represented functional gene categories. We also emphasize the essential role of the empirical distribution function plots and the ROC curves in the functional category analysis.

Cancer is one of the major causes of worldwide mortality. Imaging plays a vital role in the staging, follow-up, and evaluation of therapeutic response in cancer patients. Whole-body (WB) magnetic resonance imaging (MRI), as a non-ionizing imaging technique, is a promising procedure to assess tumor spreading in a single examination. New MRI technological developments now enable the application of diffusion-weighted imaging (DWI) of the entire body. DWI reflects the random motion of water molecules and provides functional information of body tissues. DWI can be quantified with the use of the apparent diffusion coefficient (ADC). The aim of this dissertation was to demonstrate the value of WB MRI including DWI in cancer patients. WB MRI including DWI, 18F-NaF PET/CT, and bone scintigraphy was performed on 49 patients with newly diagnosed, high-risk prostate cancer, for the purpose of detecting bone metastases. WB DWI showed higher specificity, but lower sensitivity compared to 18F-NaF PET/CT. In addition, WB MRI including DWI, and CT of the chest and abdomen was performed in 23 patients with malignant melanoma. We concluded that WB MRI could not completely supplant CT for the staging of malignant melanoma, especially with respect to the detection of lesions in the chest region. In this study, WB MRI and DWI were able to detect more bone lesions compared to CT, and showed several lesions outside the CT field of view, reinforcing the advantage of whole-body examination. WB MRI, including DWI, was performed in 71 patients with testicular cancer. This modality demonstrated its feasibility for use in the follow-up of such patients. WB MRI, including DWI, and 18F-FDG PET-CT, were carried out in 50 patients with malignant lymphoma. Both these imaging modalities proved to be promising approaches for predicting clinical outcomes and discriminating between different subtypes of lymphomas. In conclusion, WB MRI, including DWI, is an evolving technique that is continuing to undergo technical refinement. Standardization of image acquisition and analysis will be invaluable, allowing for more accurate comparison between studies, and widespread application of this technique in clinical practice. Both WB MRI, including DWI and PET/CT, have their particular strengths and weaknesses in the evaluation of metastatic disease. DWI and PET/CT are different functional techniques, so that combinations of these techniques may provide complementary and more comprehensive information of tumor tissue.

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.

Die Magnetresonanztomographie stellt für die Detektion von zerebralen und spinalen Läsionen bei der Multiplen Sklerose die sensitivste bildgebende Methode dar und ist ein Instrument, die räumliche und zeitliche Dissemination der Erkrankung abbilden zu können. Die Spezifität des Verfahrens ist aber gering und die Applikation von MR-Kontrastmittel bei der Diagnostik zwingend notwendig. Bei der suszeptibilitätsgewichteten Magnetresonanzbildgebung (SWI) handelt sich um ein MR-Verfahren, das Schwankungen der magnetischen Suszeptibilität in der Gradientenechosequenz nutzt, um einen Bildkontrast zu erzeugen. Dadurch ist es möglich, hochaufgelöst und sensitiv Magnetfeldinhomogenitäten zu detektieren. In der vorliegenden prospektiven Studie wurden im Zeitraum von 2010 bis 2013 MRT-Untersuchungen unter Einschluss der suszeptibilitätsgewichteten Bildgebung in einem Kollektiv von 41 Patienten (33 weiblich, 8 männlich;; Durchschnittsalter 40 Jahre) mit gesicherter Multipler Sklerose und einem Vergleichskollektiv von 43 Patienten (28 weiblich, 15 männlich;; Durchschnittsalter 45 Jahre), bei denen weder bildgebend noch klinisch Hinweise auf eine Multiple Sklerose vorlagen, durchgeführt. Die Untersuchung wurde mit einem 1,5-Tesla-Magnetresonanz- tomographen realisiert. Das besondere wissenschaftliche Interesse galt dabei der „normal erscheinenden weißen Substanz“ (NAWM) und den zerebralen Läsionen. In der FLAIR-Sequenz wurden die MS-Läsionen und ROIs detektiert und markiert. Anschließend erfolgte die Übertragung in gleicher Schichthöhe auf die SWI-, T1w- und ADC-Sequenz. Zur Differenzierung von akuten und chronischen Läsionen erfolgte im Untersuchungsablauf die intravenöse Gabe von Gadolinium-DTPA- Kontrastmittel. Schon längere Zeit werden im wissenschaftlichen Diskurs krankheitsspezifische Veränderungen in der NAWM vor Auftreten der MS-Läsionen vermutet. Die Sensitivität der FLAIR-Sequenz ist aber scheinbar unzureichend. Mit der SWI- Bildgebung konnten statistisch signifikante SI-Unterschiede zwischen Referenz- und MS-Gruppe in der NAWM herausgearbeitet werden. Nach Kontrastmittelgabe wurden dabei keine Veränderungen der Signalintensität der NAWM in den beiden Gruppen festgestellt, was gegen die Hypothese einer primären Schrankenstörung in der Pathogenese der Erkrankung spricht. Insgesamt wurden 669 Läsionen identifiziert. Es folgte eine Differenzierung in 11 KM-aufnehmende (ACM-) Läsionen, 546 nicht KM-aufnehmende (NACM-) Läsionen und 112 „black holes“ (BLH). Eine gezielte Auswertung der Phasen- und Magnitudenbilder wurde nicht durchgeführt. Besonders in den KM-anreichernden Läsionen (ACM) sind bereits vor der KM-Gabe statistisch erhöhte Signalintensitäten in der SWI-Sequenz nachweisbar. Dies könnte theoretisch für den Nachweis akuter Läsionen, ohne dass eine KM-Gabe notwendig ist, genutzt werden. Doch ist die Anzahl dieser Läsionen in der Untersuchung zu gering, um verlässliche Aussagen diesbezüglich machen zu können. Dafür sind weitere Studien notwendig. Zusammenfassend betrachtet handelt es sich bei der SWI um ein hoch sensitives bildgebendes Verfahren, welches eine ausgezeichnete Differenzierung von ZNS- Läsionen ermöglicht und Veränderungen der NAWM bei der Enzephalomyelitis disseminata nachweisen kann. Es stellt somit eine sinnvolle Ergänzung zur konventionellen MS-Diagnostik dar und ist ein innovatives bildgebendes In-vivo- Verfahren zur weiteren Erforschung der Multiplen Sklerose.

Abstract Weighted Quantile Sum Regression for Analyzing Correlated Predictors Acting Through a Mediation Pathway on a Biological Outcome By Bhanu M. Evani, Ph.D. A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2017. Major Director: Robert A. Perera, Asst. Professor, Department of Biostatistics This work examines mediated effects of a set of correlated predictors using the recently developed Weighted Quantile Sum (WQS) regression method. Traditionally, mediation analysis has been conducted using the multiple regression method, first proposed by Baron and Kenny (1986), which has since been advanced by several authors like MacKinnon (2008). Mediation analysis of a highly correlated predictor set is challenging due to the condition of multicollinearity. Weighted Quantile Sum (WQS) regression can be used as an alternative method to analyze the mediated effects, when predictor correlations are high. As part of the WQS method, a weighted quartile sum index (WQSindex) is computed to represent the predictor set as an entity. The predictor variables in classic mediation are then replaced with the WQSindex, allowing for the estimation of the total indirect effect between all the predictors and the outcome. Predictors having a high relative importance in their association with the outcome can be identified by examining the empirical weights for the individual predictors estimated by the WQS regression method. Other constrained optimization methods (e.g. LASSO) focus on reducing dimensionality of the correlated predictors to reduce multicollinearity. WQS regression in the context of mediation is studied using Monte Carlo simulation for mediation models with two and three correlated predictors. WQS regression’s performance is compared to the classic OLS multiple regression and the regularized LASSO regression methods. An application of these three methods to the National Health and Nutrition Examination Survey (NHANES) dataset examines the effect of serum concentrations of Polychlorinated Biphenyls (independent variables) on the liver enzyme, alanine aminotransferase ALT (outcome), with chromosomal telomere length as a potential mediator. Keywords: Multicollinearity, Weighted Quantile Sum Regression, Mediation Analysis