Development and Application of Predictive Models for Survival, Growth, and Death of Enteric Pathogens in Leafy Greens Supply Chain

Leafy greens are essential part of a healthy diet. Because of their health benefits, production and consumption of leafy greens has increased considerably in the U.S. in the last few decades. However, leafy greens are also associated with a large number of foodborne disease outbreaks in the last few years. The overall goal of this dissertation was to use the current knowledge of predictive models and available data to understand the growth, survival, and death of enteric pathogens in leafy greens at pre- and post-harvest levels. Temperature plays a major role in the growth and death of bacteria in foods. A growth-death model was developed for Salmonella and Listeria monocytogenes in leafy greens for varying temperature conditions typically encountered during supply chain. The developed growth-death models were validated using experimental dynamic time-temperature profiles available in the literature. Furthermore, these growth-death models for Salmonella and Listeria monocytogenes and a similar model for E. coli O157:H7 were used to predict the growth of these pathogens in leafy greens during transportation without temperature control. Refrigeration of leafy greens meets the purposes of increasing their shelf-life and mitigating the bacterial growth, but at the same time, storage of foods at lower temperature increases the storage cost. Nonlinear programming was used to optimize the storage temperature of leafy greens during supply chain while minimizing the storage cost and maintaining the desired levels of sensory quality and microbial safety. Most of the outbreaks associated with consumption of leafy greens contaminated with E. coli O157:H7 have occurred during July-November in the U.S. A dynamic system model consisting of subsystems and inputs (soil, irrigation, cattle, wildlife, and rainfall) simulating a farm in a major leafy greens producing area in California was developed. The model was simulated incorporating the events of planting, irrigation, harvesting, ground preparation for the new crop, contamination of soil and plants, and survival of E. coli O157:H7. The predictions of this system model are in agreement with the seasonality of outbreaks. This dissertation utilized the growth, survival, and death models of enteric pathogens in leafy greens during production and supply chain.

Comparative transcriptome profiling of human foreskin fibroblasts infected with the Sylvio and Y strains of Trypanosoma cruzi

Trypanosoma cruzi, the causative agent of Chagas Disease, is phylogenetically distributed into nearly identical genetic strains which show divergent clinical presentations including differences in rates of cardiomyopathy in humans, different vector species and transmission cycles, and differential congenital transmission in a mouse model. The population structure of these strains divides into two groups, which are geographically and clinically distinct. The aim of this study was to compare the transcriptome of two strains of T. cruzi, Sylvio vs. Y to identify differences in expression that could account for clinical and biochemical differences. We collected and sequenced RNA from T. cruzi-infected and control Human Foreskin Fibroblasts at three timepoints. Differential expression analysis identified gene expression profiles at different timepoints in Sylvio infections, and between Sylvio and Y infections in both parasite and host. The Sylvio strain parasite and the host response to Sylvio infection largely mirrored the host-pathogen interaction seen in our previous Y strain work. IL-8 was more highly expressed in Sylvio-infected HFFs than in Y-infected HFFs.

ESSAYS ON DISPLACED WORKERS AND RESIDENTIAL MIGRATION

In this dissertation, I explore how workers’ human capital, local industry composition, and business cycles affect employment outcomes and residential migration for job losers and other workers. I first examine whether the poor employment outcomes of job losers are due to a lack of jobs that require their human capital within their local labor market. I answer this question by analyzing the extent to which the industry composition in the job loser’s local labor market affects employment outcomes when job loss occurs during expansions and during recessions. I find that if job losers reside in an area with a high employment concentration of their original industry of employment, they are 2.1-2.8 percent more likely to be re-employed at another job if job loss occurs during an expansion; I find an insignificant relationship in most specifications when job loss occurs during a recession, and in some specifications I even find a negative relationship between industry concentration and employment. I conclude that the industry composition within an area matters for job losers, since firms are more willing to hire workers from within their own industry, as these workers have more relevant accumulated human capital. However, firms are less likely to hire during a recession, making job losers’ human capital less important for job finding. Next, Erika McEntarfer, Henry Hyatt, and I examine whether the business cycle affects earnings changes for job losers, and the factors that explain these differences across time. We find that job losers who lost their job during the Great Recession have earnings changes that are 10 percent more negative relative to other job losers from other periods. This result is driven primarily by longer nonemployment lengths and worse subsequent job matches. Finally, Erika McEntarfer, Henry Hyatt, Alexandria Zhang, and I explore the extent to which residential migration is driven by job opportunities. We use four databases and find that changes in job moves explain some of the changes in residential migration, but the relationship is not as strong as previously documented. We find that migration patterns differ across databases, with some databases documenting steeper declines and more cyclicality.