Nonparametric variable selection and dimension reduction methods and their applications in pharmacogenomics

Nowadays it is common to collect large volumes of data in many fields with an extensive amount of variables, but often a small or moderate number of samples. For example, in the analysis of genomic data, the number of genes can be very large, varying from tens of thousands to several millions, whereas the number of samples is several hundreds to thousands. Pharmacogenomics is an example of genomics data analysis that we are considering here. Pharmacogenomics research uses whole-genome genetic information to predict individuals\u27 drug response. Because whole-genome data are high dimensional and their relationships to drug response are complicated, we are developing a variety of nonparametric methods, including variable selection using local regression and extended dimension reduction techniques, to detect nonlinear patterns in the relationship between genetic variants and clinical response.^ High dimensional data analysis has become a popular research topic in the Statistics society in recent years. However, the nature of high dimensional data makes many traditional statistical methods fail, because most methods rely on the assumption that the sample size n is larger than the variable dimension p. Consequently, variable selection or dimension reduction is often the first step in high dimensional data analysis. Meanwhile, another important issue arises as the choice of an appropriate statistical modeling strategy for conducting variable selection or dimension reduction. It has been found from our studies that the traditional parametric linear model might not work well for detecting nonlinear patterns of relationships between predictors and response. The limitations of the linear model and other parametric statistical approaches motivate us to consider nonparametric/nonlinear models for conducting variable selection or dimension reduction.^ The thesis is composed of two major parts. In the first part, we develop a nonparametric predictive model of the response based on a small number of predictors, which are selected from a nonparametric forward variable selection procedure. We also propose strategies to identify subpopulations with enhanced treatment effects. In the second part, we develop an alternating least squares method to extend the classical Sliced Inverse Regression (SIR) [Li, 1991] to the context of high dimensional data. Both methods are demonstrated by simulation studies and a pharmacogenomics study of bortezomib in multiple myeloma [Mulligan et al., 2007]. The proposed methods have favorable performances compared to other existing methods in the literature

Defects in Graphene: Electrochemical, Magnetic, and Optical Properties

Graphene has attracted tremendous attention due to its unique proper- ties, such as its two-dimensional structure, zero-band-gap, and linear dispersion relation of its electronic band structure, which are all very interesting from a fundamental standpoint. In addition, its ultra-light weight, high surface area, exceptional electrical and thermal conductivities, as well as robust mechanical strength portends huge potential in diverse applications. Defects in the otherwise perfectly hexagonal lattice of graphene lead to lattice symmetry breaking, and the emergence of new fundamental properties of graphene. Therefore, to understand the role of defects in graphene and further to control the fundamental characteristics of graphene through quantity and configuration of defects (or defect-engineering), it is essential to develop effective synthesis methods. This thesis describes such synthesis methods and the role of controlled defects on the electrochemical, magnetic, as well as the optical properties of graphene. Following the first two introductory Chapters, in Chapter 3 I describe the effects of vacancies and dopants on the electrochemical properties of graphene. Carbon is an excellent electrode material in high-energy and high-power density supercapacitors (SCs) due to its economic viability, high-surface area, and high stability. Although graphene has high theoretical surface area, and hence high double layer capacitance, the net amount of energy stored in graphene-SCs is much below the theoretical limits due to two inherent bottlenecks: i) their low quantum capacitance, and ii) limited ion-accessible surface area. We demonstrate that properly designed defects in graphene effectively mitigates these bottlenecks by drastically increasing the quantum capacitance and opening new channels to facilitate ion diffusion in the otherwise inaccessible interlayer gallery space in few layer graphene. Our results support the emergence of a new energy paradigm in SCs with 150% enhancement in double layer capacitance beyond the theoretical limit. Furthermore, we demonstrate defect engineering in graphene foams as an example of prototype bulk SCs with energy densities of 500% higher than the state-of-the-art commercial SCs without compromising the power density. Chapter 4 focuses on the magnetic properties of graphene when a dopant, such as a sulfur atom, is incorporated into the hexagonal framework of graphene. Bulk graphite is diamagnetic in nature, however, graphene is known to exhibit either a paramagnetic response or weak ferromagnetic ordering. Although many groups have attributed this magnetism in graphene to defects or presence of unintentional magnetic impurities, compelling evidence to pinpoint origin of magnetism in graphene was lacking. To address this issue, we systematically studied the influence of entropically necessary intrinsic defects (e.g., vacancies, edges) and extrinsic dopants (e.g., S-dopants) on the magnetic properties of graphene. We found that the saturation magnetization of graphene decreased upon sulfur doping suggesting that S-dopants demagnetized vacancies and edges. Our density functional theory calculations provided evidence for: i) intrinsic defect demagnetization by the formation of covalent bonds between S-dopant and edges/vacancies concurring with the experimental results, and ii) a net magnetization from only zig-zag edges, suggesting that the contradictory conclusions on graphene magnetism reported in the literature may stem from the magnetic properties due to different defect-types. Interestingly, we observed peculiar local maxima in the temperature dependent magnetizations that suggest the coexistence of different magnetic phases within the same graphene samples. Finally, in Chapter 5, we demonstrated the relation between defects in graphene and a Raman feature - the so-called G* band which is present at 2450 cm-1. Although most of the prominent Raman features in graphene are well understood within the double resonance (DR) picture, the origin of the G* band still remains unclear. We performed detailed Raman studies of mechanically exfoliated and chemical vapor deposited single- and few-layer graphene using multiple laser excitations to unravel the origin of G* band. Our study concludes that the G* band arises from a combination of transverse optical (iTO) and longitudinal acoustic (LA) phonons, and its asymmetric lineshape is due to the presence of two different time-order phonon processes. As detailed in Chapter 5, we attribute the lower (/higher) frequency sub-peak to an LA-first (iTO-first) process. Such time-ordered processes are necessary to rationalize the dispersion of the G* band sub-peak frequencies with respect to the excitation energy. Our study also shows that defects in graphene induce new scattering channels and thereby weaken both the time-ordered combination modes. Finally, we also discuss that the effect of layer stacking on the structure of the G* band and attribute its increasing asymmetry to multiple processes between electronic sub-bands, similar to the physics that is responsible for the G\u27 band in multi-layer graphene

Neural representation of social, monetary and chocolate reinforcer processing

Little attention has been paid to social reinforcer processing compared with food and monetary reinforcers, in the reward-related functional magnetic resonance imaging (fMRI) literature. This is surprising as social reinforcers pervade our daily lives and are often experienced more frequently than food or monetary reinforcers. The question of whether social reinforcers are processed in the same or different brain regions as other reinforcer types remains poorly understood. In this thesis, three fMRI studies were employed to investigate this question, in healthy individuals. The experimental paradigms focused on two main aspects of reward processing: neural patterns of activation associated with different reward types and valance, and also correlations between neural activation to rewards and participants’ hedonic level. The studies reported in this thesis revealed that amygdala and a subregion of the OFC responded more sensitively to social reinforcers than monetary, or food reinforcers, indicating social reinforcers modulate the affective response more strongly in the brain reward network. The results also provide evidence for a medial-lateral functional dissociation in the OFC to rewards and punishment, so that medial OFC responded more strongly to rewards and lateral OFC to punishments. Moreover, fMRI study-1 revealed a crossover interaction between reinforcement valence and reward type in the lateral OFC, indicating this region may be involved in the functional integration of both reward type and valence. This is consistent with the theory of a common neural currency, for valuing different rewards in the OFC. As activation in the reward network may also be attributed to the hedonic experience of gaining rewards, fMRI study-2 and study-3 also explored the relationship between BOLD activity in response to rewards and participants’ hedonic scores. These two studies demonstrated highly significant correlations between BOLD activity in the OFC (positive correlation) and insula (negative correlation) and self-reported levels of hedonic response. The findings of the correlations between reward and hedonic level could have important implications for understanding how human hedonic levels affect responses to various reinforcements

Heterotopia and Equilibrium of Contested Urban Space: An Investigation of an Accommodation-Assimilation Mechanism

The cities in our generation are marked by the presence of discontinuous, highly contested urban spaces and extremely mixed population. Growing urban heterogeneity brings new urban materials for future development, as well as the question of how we can understand the space produced in this changing scenario and how the space itself adapts to multiple urban changes. In this article, we propose that the concept of heterotopia can be applied to understand such unsettling space produced within shifting urban paradigms, and through a mechanism of accommodation-assimilation, heterotopia has the ability to adapt to turmoils and changes. We use Milan Chinatown as an example to show how heterotopia can be used as an analytical tool to understand the transformation of urban space and the possibility this point of view offers to future planning practice

Performance of boilers equipped with vapor-pump (BEVP) system equipped with a novel air-flue gas total heat exchanger

Because of high humidity and nonlinearity of flue gas, waste heat from flue gas is hard to recovery. Boilers equipped with vapor-pump system is developed to solve the problem caused by high humidity. In this system, double spray towers subsystem is equipped to realize total heat waste heat recovery. However, caused by nonlinearity, limited waste heat recovery efficiency is just 83 % (1 segment) and 93 % (2 segment). Further, based on boilers equipped with vapor-pump (BEVP) system, enthalpy wheel system is developed to solve the problem caused by nonlinearity. However, enthalpy wheel system cannot solve the problem completely. In this article, a novel air-flue gas total heat exchanger is put forward to achieve full waste heat recovery. In this system, waste heat recovery efficiency limit is up to 100 %. Then, the limit condition of total heat transfer process is discussed. Performance of the total heat exchanger is discussed and compared to double spray towers system and enthalpy wheel system. As the result, considering heat transfer temperature difference, the total heat exchanger total heat transfer efficiency of the total heat exchanger is 7 % higher than 2-segment BEVP system and 10 % higher than enthalpy wheel system.</p

Error Bounds and Applications for Stochastic Approximation with Non-Decaying Gain

This work analyzes the stochastic approximation algorithm with non-decaying gains as applied in time-varying problems. The setting is to minimize a sequence of scalar-valued loss functions $f_k(\cdot)$ at sampling times $\tau_k$ or to locate the root of a sequence of vector-valued functions $g_k(\cdot)$ at $\tau_k$ with respect to a parameter $\theta\in R^p$. The available information is the noise-corrupted observation(s) of either $f_k(\cdot)$ or $g_k(\cdot)$ evaluated at one or two design points only. Given the time-varying stochastic approximation setup, we apply stochastic approximation algorithms with non-decaying gains, so that the recursive estimate denoted as $\hat{\theta}_k$ can maintain its momentum in tracking the time-varying optimum denoted as $\theta_k^*$. Chapter 3 provides a bound for the root-mean-squared error $\sqrt{E(\|\hat{\theta}_k-\theta_k^*\|^2})$. Overall, the bounds are applicable under a mild assumption on the time-varying drift and a modest restriction on the observation noise and the bias term. After establishing the tracking capability in Chapter 3, we also discuss the concentration behavior of $\hat{\theta}_k$ in Chapter 4. The weak convergence limit of the continuous interpolation of $\hat{\theta}_k$ is shown to follow the trajectory of a non-autonomous ordinary differential equation. Both Chapter 3 and Chapter 4 are probabilistic arguments and may not provide much guidance on the gain-tuning strategies useful for one single experiment run. Therefore, Chapter 5 discusses a data-dependent gain-tuning strategy based on estimating the Hessian information and the noise level. Overall, this work answers the questions "what is the estimate for the dynamical system $\theta_k^*$" and "how much we can trust $\hat{\theta}_k$ as an estimate for $\theta_k^*$."Comment: arXiv admin note: text overlap with arXiv:1906.0953

Solvatochromic Parameters of the Binary Mixtures of Imidazolium Chloride Ionic Liquid Plus Molecular Solvent

Imidazolium-based chloride ionic liquids (ILs) have exhibited remarkable performance in several important applications such as biomass dissolution and extraction, but their large viscosity is a non-negligible problem. Adding molecular co-solvents into chloride ILs is effective in reducing viscosity; nevertheless, understanding of the accompanied change of thermodynamic polarity is quite few. Therefore, in this work we reported three Kamlet-Taft solvatochromic parameters, including dipolarity/polarizability π*), hydrogen-bond acidity (α) and hydrogen-bond basicity (β), for the binary mixtures of several imidazolium-based chloride ILs plus either dipolar protic solvents (water and methanol) or dipolar aprotic solvents (dimethyl sulfoxide, N,N-dimethylformamide and acetonitrile). The results demonstrated that those parameters could be altered by the structure of IL and type of co-solvent owing to the solute-solvent and solvent-solvent interactions. The structure of alkyl chain of cation had considerable impact on the π* variation of IL aqueous solution against IL concentration but hardly affected other mixtures. Moreover, remarkable preferential solvation of probes was observed for β and α in the mixtures of IL and dipolar aprotic co-solvents, whereas the hydrogen-bond interactions between IL and dipolar protic co-solvent enabled the preferential solvation to be alleviated and resulted in more linear variation of β and α against the molar fraction of IL. The results not only contribute to a better understanding of the effect of co-solvent on imidazolium-based chloride ILs, but also are instructive for improving the thermodynamic performance of IL-based applications via providing IL+co-solvent mixtures with desirable physicochemical properties