EXPRESSION OF MOUSE FULL-LENGTH ARYL HYDROCARBON RECEPTOR AND HUMAN ARYL HYDROCARBON RECEPTOR LIGAND BINDING DOMAIN IN PICHIA PASTORIS

Aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor that regulates biological responses to planar aromatic hydrocarbon. AHR activates gene transcription by binding to its corresponding enhancer with its partner-aryl hydrocarbon receptor nuclear translocator (ARNT). In addition, this receptor has been shown to regulate xenobiotic-metabolizing enzymes such as cytochrome P450. AHR exists widely in body tissues and affects bioactivation of carcinogenic compounds, T cell differentiation, fatty acid synthesis, cell proliferation, hematopoietic stem cell differentiation, respiratory reactivity, and insulin sensitivity. Although the precise mechanism illustrating the endogenous AHR function remains unclear, there has been intense interest in exploring AHR as a potential target for the treatment of diseases such as cancer and autoimmune diseases. It is known that mouse ahr d-allele possesses low ligand-binding affinity, whereas mouse ahr b-allele has a higher ligand-binding affinity. The d-allele functions more similarly to human AHR than the b-allele, which is most commonly studied. Human AHR can be rather difficult to study since it is relatively unstable and less sensitive to some ligands in vitro. Thus we generated a deletion construct which has the ligand-binding domain of human AHR and hoped that the expression yield could be increased. Here, I present the process and the results of expressing the mouse full-length b-allele of AHR and the human AHR ligand binding domain (LBD, amino acids 108 to 400) in Pichia pastoris. A higher enrichment of the b-allele and LBD was observed in wild-type yeast (yJC100) strain when compared to the protease-deficient yeast (ySMD1163) strain. This observation was consistent with the increased copy number in the wild-type strain. Although the LBD transcript was detected in both the wild-type and protease-deficient strains, the LBD protein was only detected in the wild-type strain

Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection

Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation. However, to achieve adequately high resolution in real-world signal analysis, the dictionary atoms have to be close to each other in frequency, thereby resulting in a coherent design. The popular convex compressed sensing methods break down in presence of high coherence and large noise. We propose a new regularization approach to handle model collinearity and obtain parsimonious frequency selection simultaneously. It takes advantage of the pairing structure of sine and cosine atoms in the frequency dictionary. A probabilistic spectrum screening is also developed for fast computation in high dimensions. A data-resampling version of high-dimensional Bayesian Information Criterion is used to determine the regularization parameters. Experiments show the efficacy and efficiency of the proposed algorithms in challenging situations with small sample size, high frequency resolution, and low signal-to-noise ratio

Improving Local Search for Minimum Weighted Connected Dominating Set Problem by Inner-Layer Local Search

The minimum weighted connected dominating set (MWCDS) problem is an important variant of connected dominating set problems with wide applications, especially in heterogenous networks and gene regulatory networks. In the paper, we develop a nested local search algorithm called NestedLS for solving MWCDS on classic benchmarks and massive graphs. In this local search framework, we propose two novel ideas to make it effective by utilizing previous search information. First, we design the restart based smoothing mechanism as a diversification method to escape from local optimal. Second, we propose a novel inner-layer local search method to enlarge the candidate removal set, which can be modelled as an optimized version of spanning tree problem. Moreover, inner-layer local search method is a general method for maintaining the connectivity constraint when dealing with massive graphs. Experimental results show that NestedLS outperforms state-of-the-art meta-heuristic algorithms on most instances

Gaining Outlier Resistance with Progressive Quantiles: Fast Algorithms and Theoretical Studies

Outliers widely occur in big-data applications and may severely affect statistical estimation and inference. In this paper, a framework of outlier-resistant estimation is introduced to robustify an arbitrarily given loss function. It has a close connection to the method of trimming and includes explicit outlyingness parameters for all samples, which in turn facilitates computation, theory, and parameter tuning. To tackle the issues of nonconvexity and nonsmoothness, we develop scalable algorithms with implementation ease and guaranteed fast convergence. In particular, a new technique is proposed to alleviate the requirement on the starting point such that on regular datasets, the number of data resamplings can be substantially reduced. Based on combined statistical and computational treatments, we are able to perform nonasymptotic analysis beyond M-estimation. The obtained resistant estimators, though not necessarily globally or even locally optimal, enjoy minimax rate optimality in both low dimensions and high dimensions. Experiments in regression, classification, and neural networks show excellent performance of the proposed methodology at the occurrence of gross outliers

Transmission Dynamics of Mosquito-Borne Diseases: Modeling, Analysis, Prediction and Control

Mosquito-borne diseases (MBD), such as West Nile virus (WNV), dengue, and Zika virus, have become a significant global health burden for human society. Complex factors, including weather conditions, anthropogenic land use and vector-virus-host interactions, greatly affect the mosquito abundance and distribution, and the disease transmission process. In this dissertation, I will investigate the mosquito population dynamics and transmission dynamics of MBDs, and explore how these factors play roles in the MBDs. Particularly, we use WNV and Culex mosquitoes (WNV vectors) in the Region of Peel, Ontario, Canada, as an example for this study. We first study single species population models for the mosquito and the bird respectively. For mosquitoes, we take into account the contribution of the mosquito feeding preference to the oviposition and the intraspecific competition among preadult mosquitoes. For birds, we summarize the impacts of bird species, migration and age states on the transmission of WNV and explore the influence of WNV on bird populations. Then we establish a model to track the number of mosquitoes collected in a trap, predict mosquito trap counts and real adult mosquito population in an effective trapping zone. We consider the trapping mechanism of a CDC light trap and collecting procedure, and show how weather, mosquito and host selecting behaviors affect the trap counts. To explore the transmission dynamics of WNV, we develop a single-season mosquito-bird model considering stormwater management ponds, temperature and precipitation. We reveal that moderate temperature and precipitation, weaker intraspecific competition will increase the mosquito population and consequently the potential for an outbreak. This work can be used to guide WNV programs in local health units where monitoring standing water and larviciding is often used to control mosquito populations and the spread of WNV. To investigate backward bifurcation, threshold dynamics and outbreak recurrence mechanisms, we propose improved mosquito-bird compartment models. We define a new risk index to characterize the potential risk of WNV infections. We also develop the risk assessment criteria, which can be helpful to determine the risk level if there is an outbreak. Our evaluation results are generally consistent with results based on the minimum infection rate

Are Millenials Urbanists? Residental Location Choices of Millennials-Evidence from Chicago Urbanized Area

A thesis looking at three questions: 1) Are millenials leading the re-urbanization by moving back to and staying in the cities? 2) Are their location choices driven by their economic constraints or unique attitudes? 3) What factors of neighborhoods do they value in their location choices?Ope