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The goal of this study was to characterize the soil constitution and the presence of antibiotic resistance gene (ARG) erm(F) in soils across California to investigate the relationship between the levels of anthropogenic influence and ARG erm(F) levels locally and regionally. Soil samples were collected from 26 parks in four cities, San Diego, Los Angeles, Bakersfield, Fresno, where six publicly accessible parks sites were selected in each, and two pristine sites, Yosemite National Park and Mount Baldy. DNA was extracted, normalized, and tested for the presence and quantity of genes 16S rRNA and erm(F) using quantitative polymerase chain reaction (qPCR). Erm(F) quantities were determined via two analysis approaches—on a per gram of soil basis, and relative to 16S gene abundance. Locally among 26 parks, the five quantifiably detected erm(F) sites all occurred in high-level anthropogenic influenced sites. Regionally among four cities, urban and agricultural cities shared the similar soil constitution respectively and urban cities had higher erm(F) gene levels than agricultural ones. Population and population density in each city is positively correlated with erm(F) gene levels whichever analysis approach was applied.

The first part of this dissertation is on A-optimal designs for stated choice experiments. Stated choice experiments are widely used in areas such as marketing, planning, transportation, medical care, etc. In such studies, a set of $n$ choice sets is presented to the subjects. Each choice set consists of two or more profiles. Subjects are asked to choose their favorite profile from each choice set. Therefore the outcomes of such studies are discrete and nonlinear models are usually used. The multinomial logit model (MNL) is one of the most frequently used models for stated choice experiments. There are discussions in literature about how to generate optimal designs with the MNL model but primarily with the assumption that all profiles are equally attractive.In this dissertation, a new approach is proposed to generate A-optimal designs by the local linearization of the MNL model. Under the assumption that all options are equally attractive, this approach gives the same A-optimal designs as in the literature under the same setting but in a wider class of designs. This approach is also extendable to more general settings when profiles are unequally attractive.The second part of this dissertation deals with sensitivity analysis for computer experiments. Sensitivity analysis is widely used for identifying influential input variables. Two approaches to evaluating sensitivity statistically are (1) estimating global sensitivityindices based on Sobol' variance decomposition, and (2) evaluating local sensitivity indices based on a gradient measureusing a one-at-a-time sampling design. Although both approaches have been studied for (hyper-) rectangular input regions,they have not been considered carefully for the non-rectangular input region setting.In this dissertation, a more flexible gradient-based method is proposed to evaluate sensitivity indices for non-rectangular regions. In addition, the use of variable-length gradients is introduced and the importance of the starting design is emphasized.It is shown by examples that the proposed method works well in both the standard and non-rectangular settings.

Hydrogen production from abundant renewable biomass would decrease reliance on crude oils, achieve nearly zero net greenhouse gas emissions, create more jobs, and enhance national energy security. Cell-free synthetic pathway biotransformation (SyPaB) is the implementation of complicated chemical reaction by the in vitro assembly of numerous enzymes and coenzymes that microbes cannot do. One of the largest challenges is the high cost and instability of enzymes and cofactors. To overcome this obstacle, strong motivations have driven intensive efforts in discovering, engineering, and producing thermostable enzymes. In this project, ribose-5-phosphate isomerase (RpiB), one of the most important enzymes in the pentose phosphate pathway, was cloned from a thermophile Thermotoga maritima, and heterologously expressed in Escherichia coli, purified and characterized. High-purity RpiB was obtained by heat pretreatment through its optimization in buffer choice, buffer pH, as well as temperature and duration of pretreatment. This enzyme had the maximum activity at 80°C and pH 6.5-8.0. It had a half lifetime of 71 h at 60°C, resulting in its turn-over number of more than 2 x108 mol of product per mol of enzyme. Another two thermostable enzymes glucose-6-phosphate dehydrogenase (G6PDH) and diaphorase (DI) and their fusion proteins G6PDH-DI and DI-G6PDH were cloned from Geobacillus stearothermophilus, heterologouely expressed in E. coli and purified through its His-tag. The individual proteins G6PDH and DI have good thermostability and reactivity. However, the presence of DI in fusion proteins drastically decreased G6DPH activity. However, a mixture of G6PDH and a fusion protein G6PDH-DI not only restored G6PDH activity through the formation of heteromultimeric network but also facilitated substrate channeling between DI and G6PDH, especially at low enzyme concentrations. My researches would provide important building blocks for the on-going projects: high-yield hydrogen production through cell-free enzymatic pathways and electrical energy production through enzymatic fuel cells.

Diagnostic tests are central in the field of modern medicine. One of the main factors for interpreting a diagnostic test is the discriminatory accuracy. For a continuous-scale diagnostic test, the area under the receiver operating characteristic (ROC) curve, AUC, is a useful one-number summary index for the diagnostic accuracy of the test. When only a particular region of the ROC curve would be of interest, the partial AUC (pAUC) is a more appropriate index for the diagnostic accuracy. In this thesis, we develop seven confidence intervals for the pAUC under the semi-parametric models for the diseased and non-diseased populations by using the normal approximation, bootstrap and empirical likelihood methods. In addition, we conduct simulation studies to compare the finite sample performance of the proposed confidence intervals for the pAUC. A real example is also used to illustrate the application of the recommended intervals.