Underlying Mechanisms Of Arsenic-Induced Tumorigenesis: From Epigenetics To Malignancy

Arsenic is a well-recognized environmental health threat with the capability of inducing a number of human diseases, including cancer. The aim of this dissertation is to unveil the mechanisms underlying the carcinogenic activities of environmental arsenic. The biological functions of arsenic had been studied for decades. However, there are still many questions that remain to be fully answered, such as whether and how arsenic contributes to the epigenetic regulations and migration or metastasis control of the cancer cells. In this regard, we focused our attention on both histone modifications and miRNA regulations in the arsenic-induced malignant transformation of the cells, and tried to establish the signaling cascades that mediate arsenic-induced transformation. Furthermore, we investigated the downstream functional pathways related to malignancy through biochemical and proteomics analyses. Based on the results from the first specific aim, we had demonstrated that long term treatment of the cells with arsenic at concentrations that are comparable to environment arsenic exposure is able to induce EZH2 phosphorylation that facilitates its cytoplasmic localization, and the expression of c-myc. Meanwhile, we also noted that long-term treatment of the cells with arsenic induces expression of miR-214 and miR-199a along with a metabolic reprogramming of the cells from mitochondrial oxidative phosphorylation to cytoplasmic glycolysis (Warburg Effects). In the second specific aim, we further identified interaction of mdig and Filamin A phosphorylation that is involved in cell motility and migration induced by arsenic. Collectively, our studies on the novel pathways induced by arsenic provide new insights for the carcinogenetic mechanism of arsenic and shed light on the prevention and promising therapeutic strategies for human cancers that are associated with environmental arsenic exposure

Scalable Holistic Linear Regression

We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinearity as lazy constraints rather than checking the conditions iteratively. The resulting algorithm scales with the number of samples $n$ in the 10,000s, compared to the low 100s in the previous framework. Computational results on real and synthetic datasets show it greatly improves from previous algorithms in accuracy, false detection rate, computational time and scalability.Comment: Accepted by Operation Research Letter

Stochastic Cutting Planes for Data-Driven Optimization

We introduce a stochastic version of the cutting-plane method for a large class of data-driven Mixed-Integer Nonlinear Optimization (MINLO) problems. We show that under very weak assumptions the stochastic algorithm is able to converge to an $\epsilon$-optimal solution with high probability. Numerical experiments on several problems show that stochastic cutting planes is able to deliver a multiple order-of-magnitude speedup compared to the standard cutting-plane method. We further experimentally explore the lower limits of sampling for stochastic cutting planes and show that for many problems, a sampling size of $O(\sqrt[3]{n})$ appears to be sufficient for high quality solutions

Distributionally Robust Causal Inference with Observational Data

We consider the estimation of average treatment effects in observational studies without the standard assumption of unconfoundedness. We propose a new framework of robust causal inference under the general observational study setting with the possible existence of unobserved confounders. Our approach is based on the method of distributionally robust optimization and proceeds in two steps. We first specify the maximal degree to which the distribution of unobserved potential outcomes may deviate from that of obsered outcomes. We then derive sharp bounds on the average treatment effects under this assumption. Our framework encompasses the popular marginal sensitivity model as a special case and can be extended to the difference-in-difference and regression discontinuity designs as well as instrumental variables. Through simulation and empirical studies, we demonstrate the applicability of the proposed methodology to real-world settings