Communication-efficient decentralized sequential detection

The problem of decentralized sequential detection is studied in this thesis, where local sensors are memoryless, receive independent observations, and no feedback from the fusion center. In addition to traditional criteria of detection delay and error probability, we introduce a new constraint: the number of communications between local sensors and the fusion center. This metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. A new formulation for communication-efficient decentralized sequential detection is proposed where the overall detection delay is minimized with constraints on both error probabilities and the communication cost. Two types of problems are investigated based on the communication-efficient formulation: decentralized hypothesis testing and decentralized change detection. In the former case, an asymptotically person-by-person optimum detection framework is developed, where the fusion center performs a sequential probability ratio test based on dependent observations. The proposed algorithm utilizes not only reported statistics from local sensors, but also the reporting times. The asymptotically relative efficiency of proposed algorithm with respect to the centralized strategy is expressed in closed form. When the probabilities of false alarm and missed detection are close to one another, a reduced-complexity algorithm is proposed based on a Poisson arrival approximation. In addition, decentralized change detection with a communication cost constraint is also investigated. A person-by-person optimum change detection algorithm is proposed, where transmissions of sensing reports are modeled as a Poisson process. The optimum threshold value is obtained through dynamic programming. An alternative method with a simpler fusion rule is also proposed, where the threshold values in the algorithm are determined by a combination of sequential detection analysis and constrained optimization. In both decentralized hypothesis testing and change detection problems, tradeoffs in parameter choices are investigated through Monte Carlo simulations.

CRISPR/Cas9-Mediated Gene Editing of ARG1 in Cell Lines and Primary Cells

Arginase 1 deficiency, a urea cycle disorder resulting from an inability of the body to convert arginine into urea, results in hyperargininemia and sporadic episodes of hyperammonemia. Arginase 1 deficiency can lead to a range of developmental disorders and progressive spastic diplegia in children, and current therapeutic options are limited. Clustered regularly interspaced short palindromic repeat (CRISPR) /CRISPR associated protein (Cas) 9 gene editing systems serve as a novel means of treating genetic disorders such as Arginase 1 (ARG1) deficiency, and must be thoroughly examined to determine their curative capabilities. In these experiments numerous guide RNAs and CRISPR/Cas9 systems targeting the ARG1 gene were designed and observed by heteroduplex assay for their targeting capabilities and cleavage efficiencies in multiple cell lines. The CRISPR/Cas9 system utilized in these experiments, along with a panel of guide RNAs targeting various locations in the arginase 1 gene, successfully produced targeted cleavage in HEK293, MCF7, A549, K562, HeLa, and HepG2 cells; however, targeted cleavage in human dermal fibroblasts, blood outgrowth endothelial cells, and induced pluripotent stem cells was not observed. Additionally, a CRISPR/Cas system involving partially inactivated Cas9 was capable of producing targeted DNA cleavage in intron 1 of ARG1, while a Cas protein termed Cpf1 was incapable of producing targeted cleavage. These results indicate a complex set of variables determining the CRISPR/Cas9 systems’ capabilities in the cell lines and primary cells tested. By examining epigenetic factors and alternative CRISPR/Cas9 gene targeting systems, the CRISPR/Cas9 system can be more thoroughly considered in its ability to act as a means towards editing the genome of arginase 1-deficient individuals.

An R-package for the Estimation and Testing of Multiple Covariates and Biomarker Interactions for Survival Data Based on Local Partial Likelihood

When we study the variables that a ffect survival time, we usually estimate their eff ects by the Cox regression model. In biomedical research, e ffects of the covariates are often modi ed by a biomarker variable. This leads to covariates-biomarker interactions. Here biomarker is an objective measurement of the patient characteristics at baseline. Liu et al. (2015) has built up a local partial likelihood bootstrap model to estimate and test this interaction e ffect of covariates and biomarker, but the R code developed by Liu et al. (2015) can only handle one variable and one interaction term and can not t the model with adjustment to nuisance variables. In this project, we expand the model to allow adjustment to nuisance variables, expand the R code to take any chosen interaction terms, and we set up many parameters for users to customize their research. We also build up an R package called "lplb" to integrate the complex computations into a simple interface. We conduct numerical simulation to show that the new method has excellent fi nite sample properties under both the null and alternative hypothesis. We also applied the method to analyze data from a prostate cancer clinical trial with acid phosphatase (AP) biomarker.