Fabrication of 1-dimensional nanowires from genetically modified M13 phage through surfactant-mediated hybridization and the applications in medical diagnosis, energy devices, and catalysis

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2010.Vita.Includes bibliographical references.Biological building blocks served as excellent templates for the preparation of various nano-materials due to their beneficial interactions at the molecular level. The bio-mineralization of genetically engineered M13 bacteriophage resulted in one-dimensional nanowires having outstanding properties in diverse applications. As a bridge between the chemical synthesis of nanostructures and the bio-mineralization of M13 phage, surfactant molecules were introduced to the biological systems. The specific affinity of M13 phage with Au-binding peptides was strong enough to attract Au ions despite the existence of surfactant molecules. Consequently, the surfactant-mediated bio-mineralization of M13 phage enabled us to precisely control the morphologies and structures in nanometer scale. The Au-binding M13 phage could also integrate other noble metals (Ag/Pt/Pd) to prepare homogeneous Au-based noble metal alloy nanowires in structures and compositions, and their electrochemical properties upon the systematic changes in compositions were investigated. Especially for the Au-Pt system, the catalytic activity study on the two distinct structures, the alloy and the core/shell, provided us important factors to design new catalysts with optimized activities.by Youjin Lee.Ph.D

Policy effect evaluation under counterfactual neighborhood interventions in the presence of spillover

Policy interventions can spill over to units of a population that are not directly exposed to the policy but are geographically close to the units receiving the intervention. In recent work, investigations of spillover effects on neighboring regions have focused on estimating the average treatment effect of a particular policy in an observed setting. Our research question broadens this scope by asking what policy consequences would the treated units have experienced under hypothetical exposure settings. When we only observe treated unit(s) surrounded by controls -- as is common when a policy intervention is implemented in a single city or state -- this effect inquires about the policy effects under a counterfactual neighborhood policy status that we do not, in actuality, observe. In this work, we extend difference-in-differences (DiD) approaches to spillover settings and develop identification conditions required to evaluate policy effects in counterfactual treatment scenarios. These causal quantities are policy-relevant for designing effective policies for populations subject to various neighborhood statuses. We develop doubly robust estimators and use extensive numerical experiments to examine their performance under heterogeneous spillover effects. We apply our proposed method to investigate the effect of the Philadelphia beverage tax on unit sales

Statistical Reasoning in Network Data

Networks are collections of nodes, which can represent entities like people, genes, or brain regions, and ties between pairs of nodes, which represent various forms of connection, e.g. social relationships, between them. The study of networks is booming in biology, economics, statistics, psychology, physics, computer science, social science, public health, and beyond. Despite the increased interest in network data and its application, methods do not yet exist to answer many types of statistical and causal questions about observations collected from networks. In this dissertation, we illustrate an unacknowledged problem for statistical methods using network data, namely network dependence, and propose a test for the existence of such dependence. We demonstrate how this kind of dependence affects the validity of statistical inference. In particular, one of the most important sources of data on cardiovascular disease epidemiology, the Framingham Heart Study, is shown to exhibit dependence that could lead to false statistical conclusions. We also propose a network dependence test that overcomes the high-dimensional structure of network data. Many researchers interested in social networks in public health and social science are ultimately interested in causal inference on certain collective behaviors or health outcomes observed over the whole network -- such as the causal effect of a certain vaccination plan on the overall rate of infections, or the causal effect of an online viral marketing program on the sales of products. In the last part of the dissertation, we focus on one of those questions that aims to identify the most influential subjects in networks

RobustIV and controlfunctionIV: Causal Inference for Linear and Nonlinear Models with Invalid Instrumental Variables

We present R software packages RobustIV and controlfunctionIV for causal inference with possibly invalid instrumental variables. RobustIV focuses on the linear outcome model. It implements the two-stage hard thresholding method to select valid instrumental variables from a set of candidate instrumental variables and make inferences for the causal effect in both low- and high-dimensional settings. Furthermore, RobustIV implements the high-dimensional endogeneity test and the searching and sampling method, a uniformly valid inference method robust to errors in instrumental variable selection. controlfunctionIV considers the nonlinear outcome model and makes inferences about the causal effect based on the control function method. Our packages are demonstrated using two publicly available economic data sets together with applications to the Framingham Heart Study