Master of Science, The Ohio State University, 2017, Public Health

Colon cancers are second only to lung cancers in the number of cancer deaths in the United States per annum. Common treatment regimens are surgical resection, optionally followed by adjuvant chemotherapy. Successful outcomes are measured by lack of progression to advanced stages at the primary tumor site and the absence of recurrence elsewhere in the body. Stage 3 cancer patients relapse at 50%, while stage 2 cancer patients relapse 25-40%. The most common site of distant metastasis is the liver; about 11% of patients who relapse survive 5 years. Unfortunately, there are no clear means to predict which patients will relapse following treatment. Subtyping of colon cancers is detailed in literature, although no clear translation to predicting clinical outcome has occurred. A 2014 meta-analysis by the Agency for Healthcare Research and Quality showed existing commercial means of predicting relapse provided dubious benefits to patients. This thesis details a method to create a predictive model of relapse in colon cancer patients that is an improvement over existing standards of care using gene expression patterns in specific stages coordinated with histopathological subtypes to be examined in vitro. We conducted a retrospective analysis of mRNA expression in colon cancer patients at the time of treatment, integrating genomic data from microarray and RNA-seq platforms with matching clinical data. The main focus of this research was genes with bimodal gene expression due to the ability of bimodal genes to fall along tumor subtypes with unique biological, clinical, and prognostic characteristics. Our results successfully identified bimodal genes through a novel ensemble testing system that recognizes clusters of gene expression values that decompose a single Gaussian distribution into two component Gaussian distributions. The utility and efficacy of the method was demonstrated with known bimodal gene markers in breast cancer patients as a positive contro (open full item for complete abstract)

Committee: Kun Huang PhD (Advisor); Randall Harris MD,PhD (Committee Member); James Chen MD (Committee Member); Joanna Groden PhD (Committee Member) Subjects: Bioinformatics; Biostatistics; Medicine; Public Health

Master of Science (MS), Ohio University, 2008, Physics and Astronomy (Arts and Sciences)

Rotation periods or lower limits for 34 Near-Earth Asteroids (NEAs) were obtained through optical light curves. Two codes were developed in order to obtain the true fraction of Fast-Rotating Asteroids (FRAs), F, using Fortran 95 and IDL. The first code models the shape of an asteroid and simulates its light curve. The second code, uses the results obtained from the observational program and the simulated light curves to obtain the probability density of F, P(F). The observational and statistical analysis indicates that the population of asteroids with D<150m is almost equally divided between fast and slow rotators, and that the majority of the population of asteroids with D>150m consists of slow-rotators. These results also indicate that selection effects have significantly influenced the currently known distribution of rotation periods of NEAs and therefore that it is not representative of the real population of NEAs.

Committee: Thomas S. Statler PhD (Advisor); Alexander Nieman PhD (Committee Chair); Joseph C. Shields PhD (Committee Member) Subjects: Astrophysics

PhD, University of Cincinnati, 2013, Engineering and Applied Science: Computer Science and Engineering

Wireless sensor networks (WSNs) are one type of ad hoc networks with data-collecting function. Because of the low-power, low-cost features, WSN attracts much attention from both academia and industry. However, since WSN is driven by batteries and the multi-hop transmission pattern introduces energy hole problem, energy management of WSN became one of fundamental issues. In this dissertation, we study the energy management strategies for WSNs. Firstly, we propose a packets propagation scheme for both deterministic and random deployment of WSNs so to prolong their lifetime. The essence of packets propagation scheme is to control transmission power so as to balance the energy consumption for the entire WSN. Secondly, a characteristic correlation based data aggregation approach is presented. Redundant information during data collection can be effectively mitigated so as to reduce the packets transmission in the WSN. Lifetime of WSN is increased with limited overhead. Thirdly, we also provide a two-tier lifetime optimization strategy for wireless visual sensor network (VSN). By deploying redundant cheaper relay nodes into existing VSN, the lifetime of VSN is maximized with minimal cost. Fourthly, our two-tier visual sensor network deployment is further extended considering multiple base stations and image compression technique. Last but not the least, description of UC AirNet WSN project is presented. At the end, we also consider future research topics on energy management schemes for WSN.

Committee: Dharma Agrawal D.Sc. (Committee Chair); Kenneth Berman Ph.D. (Committee Member); Yizong Cheng Ph.D. (Committee Member); Chia Han Ph.D. (Committee Member); Wen Ben Jone Ph.D. (Committee Member) Subjects: Computer Engineering