LUNG CANCER IN NEPAL: THE ROLE OF TRADITIONAL TOBACCO PRODUCTS AND COMBUSTION RELATED HOUSEHOLD AIR POLLUTION

The burden of chronic diseases such as cancer is increasing in low and middle income countries around the globe. Nepal, one of the world’s poorest countries, is no exception to this trend, with lung cancer as the leading causes of cancer deaths. Despite this, limited data is available on the environmental and behavioral risk factors that contribute to the lung cancer etiology in Nepal. The objectives of this dissertation are to: 1) investigate the ethnic differences in consumption of local tobacco products and their role in lung cancer risk in Nepal; 2) evaluate urinary metabolite of 1,3-butadiene as a biomarker of exposure to combustion related household air pollution (CRHAP); 3) investigate the association between CRHAP exposure and lung cancer risk using urinary metabolite of 1,3-butadiene as a biomarker of exposure; 4) investigate the association between CRHAP exposure and lung cancer risk using questionnaire based measure of exposure. Lung cancer cases (n=606) and frequency matched controls (N=606) were recruited from B.P. Koirala Memorial Cancer Hospital. We obtained biological samples and information on lifestyles including cooking habits and type of fuels used. We used liquid chromatograph tandem mass spectrometer (LC-MS/MS) to quantify urinary metabolites of 1,3-butadiene in urine samples. We employed a combination of logistic and linear regression models to detect any exposure-disease associations while controlling for known confounding variables. Overall, we found that ethnic groups in Nepal use different tobacco products that have different differing cancer potency -we observed the highest odds ratios for the traditional tobacco products. The biomarker analysis showed strong evidence that monohydroxybutyl mercapturic acid is associated with biomass fuel use among participants. However, we did not find significant association between urinary MHMBA and lung cancer risk. When we used questionnaire based measure of exposure to household air pollution, we observed significant, dose-response associations between CRHAP exposure and lung cancer risk, particularly among never-smokers. Our results show that important role of local tobacco products in lung cancer risk in Nepal. Furthermore, we demonstrate that CRHAP exposure is a risk factor for lung cancer risk, independent of tobacco smoking.

Spectral Frame Analysis and Learning through Graph Structure

This dissertation investigates the connection between spectral analysis and frame theory. When considering the spectral properties of a frame, we present a few novel results relating to the spectral decomposition. We first show that scalable frames have the property that the inner product of the scaling coefficients and the eigenvectors must equal the inverse eigenvalues. From this, we prove a similar result when an approximate scaling is obtained. We then focus on the optimization problems inherent to the scalable frames by first showing that there is an equivalence between scaling a frame and optimization problems with a non-restrictive objective function. Various objective functions are considered, and an analysis of the solution type is presented. For linear objectives, we can encourage sparse scalings, and with barrier objective functions, we force dense solutions. We further consider frames in high dimensions, and derive various solution techniques. From here, we restrict ourselves to various frame classes, to add more specificity to the results. Using frames generated from distributions allows for the placement of probabilistic bounds on scalability. For discrete distributions (Bernoulli and Rademacher), we bound the probability of encountering an ONB, and for continuous symmetric distributions (Uniform and Gaussian), we show that symmetry is retained in the transformed domain. We also prove several hyperplane-separation results. With the theory developed, we discuss graph applications of the scalability framework. We make a connection with graph conditioning, and show the in-feasibility of the problem in the general case. After a modification, we show that any complete graph can be conditioned. We then present a modification of standard PCA (robust PCA) developed by Cand\`es, and give some background into Electron Energy-Loss Spectroscopy (EELS). We design a novel scheme for the processing of EELS through robust PCA and least-squares regression, and test this scheme on biological samples. Finally, we take the idea of robust PCA and apply the technique of kernel PCA to perform robust manifold learning. We derive the problem and present an algorithm for its solution. There is also discussion of the differences with RPCA that make theoretical guarantees difficult.