Immune mechanisms in chronic rheumatic muscle inflammation, myositis

The idiopathic inflammatory myopathies (IIM), shortly named myositis, is a group of heterogeneous and rare autoimmune diseases with the target tissue of skeletal muscle. It is not curable and affects the daily life of patients. The treatment of myositis is mainly glucocorticoids in combination with immunosuppressive agents. The incomplete response and common side effects of conventional treatment requires new therapies. The pathological mechanisms of myositis are still not known, however, it is widely accepted that both innate immunity and adaptive immunity contribute to the pathogenesis of myositis. T cells play an important role in the pathogenesis of myositis, which is also a promising target to develop novel treatment. The main aim of Project 1 and 2 was to investigate the effects of abatacept (CTLA4-Ig), a costimulatory modulator of T cells, on disease activity, expression of different molecules in muscle biopsies, and phenotypes of T and B cells in blood samples of adult patients with refractory dermatomyositis (DM) or polymyositis (PM). In this pilot study, we found that almost half of the DM and PM patients obtained improvement of disease activity after abatacept treatment and few side effects. The elevated number of FOXP3+ Tregs in repeated muscle biopsies indicates a positive effect of treatment in muscle tissue. CyTOF technology requires a larger patient cohort for discovery research and for immune-monitoring if the patients are clinically heterogeneous. Furthermore, CD4/CD8 ratio in circulation at time of active disease may be a predictor of response to abatacept treatment in patients with DM and PM. Type І IFN is also thought to be an important pathway involved in the pathogenesis of patients with DM and PM. In Project 3, we found evidence to support our hypothesis that CD66b+ neutrophils express LL-37, which may stimulate BDCA2+ pDCs to produce type I IFN (MxA) in muscle and skin samples of patients with DM and PM, regardless of short or long disease duration. The higher number of CD66b+ neutrophils and the association between neutrophils and MxA in patients negative for autoantibodies targeting RNA-binding proteins may suggest that our hypothesis is a potential alternative pathway to induce the elevated type І IFN in myositis patients without these autoantibodies. In project 4, we aimed to identify biomarkers in repeated muscle biopsies or blood samples, taken before and after conventional immunosuppressive therapy, to predict therapeutic response in patients with myositis. In this pilot study, we conclude that baseline biopsy, or monocyte profile did not predict long-term treatment response, but in the repeated biopsy taken within 1 year of immunosuppressive treatment, the lower number of macrophages (CD68+) seemed to predict a more favorable long-term response with regard to improved muscle strength. Furthermore, T cells in muscle biopsies were not affected by the conventional immunosuppressive therapy. This may indicate that repeated muscle biopsies provide additional information to guide immunosuppressive treatment. In conclusion, our findings provide more information about effects of treatment and pathologic immune mechanisms of myositis and strengthen the critical role of innate and adaptive immune response in the pathogenesis of myositis by investigation from different perspectives, which may provide basis to develop novel and effective therapies for patients with myositis

GIS-based urban land use characterization and population modeling with subpixel information measured from remote sensing data

This dissertation provides deeper understanding on the application of Vegetation-Impervious Surface-Soil (V-I-S) model in the urban land use characterization and population modeling, focusing on New Orleans area. Previous research on the V-I-S model used in urban land use classification emphasized on the accuracy improvement while ignoring the discussion of the stability of classifiers. I developed an evaluation framework by using randomization techniques and decision tree method to assess and compare the performance of classifiers and input features. The proposed evaluation framework is applied to demonstrate the superiority of V-I-S fractions and LST for urban land use classification. It could also be applied to the assessment of input features and classifiers for other remote sensing image classification context. An innovative urban land use classification based on the V-I-S model is implemented and tested in this dissertation. Due to the shape of the V-I-S bivariate histogram that resembles topological surfaces, a pattern that honors the Lu-Weng’s urban model, the V-I-S feature space is rasterized into grey-scale image and subsequently partitioned by marker-controlled watershed segmentation, leading to an urban land use classification. This new approach is proven to be insensitive to the selection of initial markers as long as they are positioned around the underlying watershed centers. This dissertation links the population distribution of New Orleans with its physiogeographic conditions indicated by the V-I-S sub-pixel composition and the land use information. It shows that the V-I-S fractions cannot be directly used to model the population distribution. Both the OLS and GWR models produced poor model fit. In contrast, the land use information extracted from the V-I-S information and LST significantly improved regression models. A three-class land use model is fitted adequately. The GWR model reveals the spatial nonstationarity as the relationship between the population distribution and the land use is relatively poor in the city center and becomes stronger towards the city fringe, depicting a classic urban concentric pattern. It highlighted that New Orleans is a complex metropolitan area, and its population distribution cannot be fully modeled with the physiogeographic measurements

Extending MPT Models to Rasch MPT: A General Framework, Demonstration, and Applications

Multinomial processing tree (MPT) models, as a family of hierarchical multinomial models tailored by cognitive theories, have been proven to be successful and applied to cognitive psychometrics Traditional MPT models measure the probability of success for each cognitive stage given their hierarchical relationships. However, this measure neither addresses individual and item difference, nor characterizes the subject\u27s ability and the difficulty of the cognitive stage. In this study, I extend the cognitive stage parameter in MPT models to a Rasch model, and recruit MPT models for a source monitoring paradigm as an example to demonstrate the extension. To evaluate the properties of Rasch MPT models, I conduct systematic simulation studies to test parameter recovery under different conditions including various sample sizes, boundary values of parameters, and missing data. In addition, I use a simple lexical decision experiment and a set of force concept inventory (FCI) multiple-choice questions which are a popular measurement tool in physics teaching research to demonstrate and validate the practical uses of Rasch MPT modeling

A Systematic Comparison of MLE and Bayesian Estimation for MPT Models

As a family of statistical models for categorical data, multinomial processingtree (MPT) models have become popular in cognitive psychology over the courseof the past two decades. Classic estimation methods, such as maximumlikelihood estimation (MLE) and model fit test (G2 test), have been applied to MPTmodels widely. Recent development of Bayesian inference suggests a theoreticalalternative for model estimation, though its practical implementation was limiteddue to the difficulties of computation and sampling capacity of the computers. Inthis thesis, I apply Bayesian inference to MPT models, develop the programs thatimplement Bayesian inference for MPT models, and conduct systematiccomparisons between the two approaches in terms of their parameter estimationand model evaluation