Based on empirical qualitative data collected by interviewing eight elementary school teachers from across four different school boards in Ontario and analyzing new Ontario Ministry of Education policy and guidelines for supporting and programming for English Literacy Development (ELD) students, this research seeks to better understand how teachers’ discourses influence their perception of ELD students’ experiences in elementary schools. In particular, I look at how they view their roles as teachers, the purpose of education and schooling, their personal views on diversity, and how they program literacy for ELD students. The participants’ discourses reveal that although they prioritize having positive relationships with their students, they often struggled to relate positively with their ELD students. Reassessing how the formal school is structured and providing directed professional development on teaching ELD students could work towards creating more positive learning experiences for ELD students in Ontario elementary schools.

In the paper at hand we apply it to Bayesian statistics to obtain "Fuzzy Bayesian Inference". In the subsequent sections we will discuss a fuzzy valued likelihood function, Bayes' theorem for both fuzzy data and fuzzy priors, a fuzzy Bayes' estimator, fuzzy predictive densities and distributions, and fuzzy H.P.D .-Regions. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

We are interested in variable sélection in linear régression models. This research is motivated by recent development in microarrays, proteomics, brain images, among others. We study this problem in both frequentist and bayesian viewpoints.In a frequentist framework, we propose methods to deal with the problem of variable sélection, when the number of variables is much larger than the sample size with a possibly présence of additional structure in the predictor variables, such as high corrélations or order between successive variables. The performance of the proposed methods is theoretically investigated ; we prove that, under regularity conditions, the proposed estimators possess statistical good properties, such as Sparsity Oracle Inequalities, variable sélection consistency and asymptotic normality.In a Bayesian Framework, we propose a global noninformative approach for Bayesian variable sélection. In this thesis, we pay spécial attention to two calibration-free hierarchical Zellner's g-priors. The first one is the Jeffreys prior which is not location invariant. A second one avoids this problem by only considering models with at least one variable in the model. The practical performance of the proposed methods is illustrated through numerical experiments on simulated and real world datasets, with a comparison betwenn Bayesian and frequentist approaches under a low informative constraint when the number of variables is almost equal to the number of observations.

We develop statistical methods for tackling two important problems in genetic association studies. First, we propose a Bayesian approach to overcome the winner's curse in genetic studies. Second, we consider a Bayesian latent variable model for analyzing longitudinal family data with pleiotropic phenotypes. Winner's curse in genetic association studies refers to the estimation bias of the reported odds ratios (OR) for an associated genetic variant from the initial discovery samples. It is a consequence of the sequential procedure in which the estimated effect of an associated genetic marker must first pass a stringent significance threshold. We propose a hierarchical Bayes method in which a spike-and-slab prior is used to account for the possibility that the significant test result may be due to chance. We examine the robustness of the method using different priors corresponding to different degrees of confidence in the testing results and propose a Bayesian model averaging procedure to combine estimates produced by different models. The Bayesian estimators yield smaller variance compared to the conditional likelihood estimator and outperform the latter in the low power studies. We investigate the performance of the method with simulations and applications to four real data examples. Pleiotropy occurs when a single genetic factor influences multiple quantitative or qualitative phenotypes, and it is present in many genetic studies of complex human traits. The longitudinal family studies combine the features of longitudinal studies in individuals and cross-sectional studies in families. Therefore, they provide more information about the genetic and environmental factors associated with the trait of interest. We propose a Bayesian latent variable modeling approach to model multiple phenotypes simultaneously in order to detect the pleiotropic effect and allow for longitudinal and/or family data. An efficient MCMC algorithm is developed to obtain the posterior samples by using hierarchical centering and parameter expansion techniques. We apply spike and slab prior methods to test whether the phenotypes are significantly associated with the latent disease status. We compute Bayes factors using path sampling and discuss their application in testing the significance of factor loadings and the indirect fixed effects. We examine the performance of our methods via extensive simulations and apply them to the blood pressure data from a genetic study of type 1 diabetes (T1D) complications.

Influenza virus infects the human population worldwide and causes acute respiratory disease. Currently, the primary strategy for preventing influenza is seasonal vaccination which is capable of providing protection in most populations. However, seasonal vaccines are less efficacious to immunize the elderly and poorly induce cross-protective immunity against the reassorted pandemic virus in the recipients. Neuraminidase (NA) inhibitors have also been widely utilized to limit disease outcome. The currently used NA inhibitors, nonetheless, generate the drug-resistant progeny viruses; moreover, they are unable to directly target the host immune responses which cause immunopathology in severe cases. Therefore, new strategies that provide more effective immunogenicity, cross-protection and therapies against influenza infection must be developed. In this thesis, the adjuvanticity of CpG oligodeoxynucleotide (ODN), type I interferon (IFN) and Complete Freund’s adjuvant (CFA) when coadministered with seasonal influenza vaccines in ferrets is presented. It has been found that the adjuvanted vaccines are efficacious to induce neutralizing antibody responses. Several common and distinguished signaling pathways leading to dendritic cell (DC) maturation and B cell activation have been discovered from their adjuvanticity. Furthermore, it was determined that seasonal H1N1 prior infection more effectively induces cross-protection against the newly emerged 2009 pandemic H1N1 (H1N1pdm) virus in ferrets and mice than the seasonal vaccines. The prior infection-induced cross-reactive but non-neutralizing antibodies are capable of providing substantial protection in the H1N1pdm infected mice when CD8 T cells are absent. Lastly, function of different vaccine adjuvants for controlling H1N1pdm infection in mice has been investigated. Unlike other adjuvants, CFA is capable of protecting the mice from infection through enhancement of Treg cell suppressive molecules galectin-1 and CTLA-4 which downregulated DC costimulation and effector T cell responses. Overall, this thesis has provided novel mechanistic insights for developing protective strategies against influenza infection.

We develop statistical methods for tackling two important problems in genetic association studies. First, we propose a Bayesian approach to overcome the winner's curse in genetic studies. Second, we consider a Bayesian latent variable model for analyzing longitudinal family data with pleiotropic phenotypes. Winner's curse in genetic association studies refers to the estimation bias of the reported odds ratios (OR) for an associated genetic variant from the initial discovery samples. It is a consequence of the sequential procedure in which the estimated effect of an associated genetic marker must first pass a stringent significance threshold. We propose a hierarchical Bayes method in which a spike-and-slab prior is used to account for the possibility that the significant test result may be due to chance. We examine the robustness of the method using different priors corresponding to different degrees of confidence in the testing results and propose a Bayesian model averaging procedure to combine estimates produced by different models. The Bayesian estimators yield smaller variance compared to the conditional likelihood estimator and outperform the latter in the low power studies. We investigate the performance of the method with simulations and applications to four real data examples. Pleiotropy occurs when a single genetic factor influences multiple quantitative or qualitative phenotypes, and it is present in many genetic studies of complex human traits. The longitudinal family studies combine the features of longitudinal studies in individuals and cross-sectional studies in families. Therefore, they provide more information about the genetic and environmental factors associated with the trait of interest. We propose a Bayesian latent variable modeling approach to model multiple phenotypes simultaneously in order to detect the pleiotropic effect and allow for longitudinal and/or family data. An efficient MCMC algorithm is developed to obtain the posterior samples by using hierarchical centering and parameter expansion techniques. We apply spike and slab prior methods to test whether the phenotypes are significantly associated with the latent disease status. We compute Bayes factors using path sampling and discuss their application in testing the significance of factor loadings and the indirect fixed effects. We examine the performance of our methods via extensive simulations and apply them to the blood pressure data from a genetic study of type 1 diabetes (T1D) complications.

Influenza virus infects the human population worldwide and causes acute respiratory disease. Currently, the primary strategy for preventing influenza is seasonal vaccination which is capable of providing protection in most populations. However, seasonal vaccines are less efficacious to immunize the elderly and poorly induce cross-protective immunity against the reassorted pandemic virus in the recipients. Neuraminidase (NA) inhibitors have also been widely utilized to limit disease outcome. The currently used NA inhibitors, nonetheless, generate the drug-resistant progeny viruses; moreover, they are unable to directly target the host immune responses which cause immunopathology in severe cases. Therefore, new strategies that provide more effective immunogenicity, cross-protection and therapies against influenza infection must be developed. In this thesis, the adjuvanticity of CpG oligodeoxynucleotide (ODN), type I interferon (IFN) and Complete Freund’s adjuvant (CFA) when coadministered with seasonal influenza vaccines in ferrets is presented. It has been found that the adjuvanted vaccines are efficacious to induce neutralizing antibody responses. Several common and distinguished signaling pathways leading to dendritic cell (DC) maturation and B cell activation have been discovered from their adjuvanticity. Furthermore, it was determined that seasonal H1N1 prior infection more effectively induces cross-protection against the newly emerged 2009 pandemic H1N1 (H1N1pdm) virus in ferrets and mice than the seasonal vaccines. The prior infection-induced cross-reactive but non-neutralizing antibodies are capable of providing substantial protection in the H1N1pdm infected mice when CD8 T cells are absent. Lastly, function of different vaccine adjuvants for controlling H1N1pdm infection in mice has been investigated. Unlike other adjuvants, CFA is capable of protecting the mice from infection through enhancement of Treg cell suppressive molecules galectin-1 and CTLA-4 which downregulated DC costimulation and effector T cell responses. Overall, this thesis has provided novel mechanistic insights for developing protective strategies against influenza infection.

There is a great amount of building stock built in earthquake regions where earthquakes frequently occur. It is very probable that such buildings experience earthquakes more than once throughout their economic life. The motivation of this thesis arose from the lack of procedures to determine the change in building capacity as a result of prior earthquake damage. This study focuses on establishing a method that can be employed to determine the loss in the building capacity after experiencing an earthquake. In order to achieve this goal a number of frames were analyzed under several randomly selected earthquakes. Nonlinear time-history analyses and nonlinear static analyses were conducted to assess the prior and subsequent capacities of the frames under consideration. The structural analysis programs DRAIN-2DX and SAP2000 were employed for this purpose. The capacity curves obtained by these methods were investigated to propose a procedure by which the capacity of previously damaged structures can be determined. For time-history analyses the prior earthquake damage can be taken into account by applying the ground motion histories successively to the structure under consideration. In the case of nonlinear static analyses this was achieved by modifying the elements of the damaged structure in relation to the plastic deformation they experience. Finally a simple approximate procedure was developed using the regression analysis of the results. This procedure relies on the modification of the structure stiffness in proportion to the ductility demand the former earthquake imposes. The proposed procedures were applied to an existing 3D building to validate their applicability.

En imagerie nucléaire (scintigraphie, TEMP, TEP), les diagnostics sont fréquemment faits à l'aide des courbes d'activité temporelles des différents organes et tissus étudiés. Ces courbes représentent l'évolution de la distribution d'un traceur radioactif injecté dans le patient. Leur obtention est compliquée par la superposition des organes et des tissus dans les séquences d'images 2D, et il convient donc de séparer les différentes contributions présentes dans les pixels. Le problème de séparation de sources sous-jacent étant sous-déterminé, nous proposons d'y faire face dans cette thèse en exploitant différentes connaissances a priori d'ordre spatial et temporel sur les sources. Les principales connaissances intégrées ici sont les régions d'intérêt (ROI) des sources qui apportent des informations spatiales riches. Contrairement aux travaux antérieurs qui ont une approche binaire, nous intégrons cette connaissance de manière robuste à la méthode de séparation, afin que cette dernière ne soit pas sensible aux variations inter et intra-utilisateurs dans la sélection des ROI. La méthode de séparation générique proposée prend la forme d'une fonctionnelle à minimiser, constituée d'un terme d'attache aux données ainsi que de pénalisations et de relâchements de contraintes exprimant les connaissances a priori. L'étude sur des images de synthèse montrent les bons résultats de notre approche par rapport à l'état de l'art. Deux applications, l'une sur les reins, l'autre sur le cœur illustrent les résultats sur des données cliniques réelles. / In nuclear imaging (scintigraphy, SPECT, PET), diagnostics are often made with time activity curves (TAC) of organs and tissues. These TACs represent the dynamic evolution of tracer distribution inside patient's body. Extraction of TACs can be complicated by overlapping in the 2D image sequences, hence source separation methods must be used in order to extract TAC properly. However, the underlying separation problem is underdetermined. We propose to overcome this difficulty by adding some spatial and temporal prior knowledge about sources on the separation process. The main knowledge used in this work is region of interest (ROI) of organs and tissues. Unlike state of the art methods, ROI are integrated in a robust way in our method, in order to face user-dependancy in their selection. The proposed method is generic and minimize an objective function composed with a data fidelity criterion, penalizations and relaxations expressing prior knowledge. Results on synthetic datasets show the efficiency of the proposed method compare to state of the art methods. Two clinical applications on the kidney and on the heart are also adressed.

This study was conducted at a school designated as a higher primary school comprised of grade 0-9 learners (GET band) in Grahamstown in the Eastern Cape, South Africa. With the advent of the new curriculum in South Africa, we are also grappling with the implementation of the new curriculum at this school. This motivated me to investigate the benefits of eliciting and integrating learners’ prior everyday knowledge and experiences during teaching and learning of acids and bases. Essentially, the study sought to gain insight into whether engaging learners during practical activities using easily accessible materials from their homes facilitated meaning-making of acids and bases. This study is located within an interpretive paradigm. Within this paradigm, a qualitative case study approach was conducted with the researcher’s Grade 7 class. To gather data, document analysis, semi-structured interviews, questionnaires, lesson observations, stimulated recall discussions while watching the videotaped lessons as well as focus group interviews with learners were used. An inductive analysis to discover patterns and themes was applied during the data analysis process. The validation process was done through watching the videotaped lessons with the teachers who observed the lessons. Also, transcripts of the interviews and a summary of discussions were given back to the respondents to verify their responses and check for any misinterpretations. Rich data sets were analysed in relation to the research questions which were: How do Natural Sciences teachers elicit and integrate learners’ prioreveryday knowledge and experiences to facilitate learning of scientific concepts of acids and bases in their classrooms? Does engaging learners in practical activities using everyday substances enhance their conceptual development and understanding of acids and bases? The findings from the study revealed that the use of learners’ prior everyday knowledge and experiences during teaching and learning of acids and bases facilitated meaningful learning. Furthermore, linking learning to learners’ everyday experiences enabled them to learn scientific concepts in a relaxed and non-threatening environment. It is thus recommended that teachers should be supported in their endeavours to incorporate learners’ real life experiences during their teaching and learning repertoires. Notwithstanding, as much as there were benefits in this study there were, however, also some challenges that were encountered, such as language, which warrants further research.