Appropriateness of Largemouth Bass as a Model Species for Detection of Endocrine Dissruption

Intersex in largemouth bass (Micropterus salmoides) has been correlated with regional anthropogenic activity, but has not been causally linked to environmental factors. Four groups of hatchery-reared largemouth bass (LMB) and fathead minnows (FHM) of varying ages and sex were exposed to aqueous poultry litter mixtures, 17β- estradiol (E2), and controls. Water samples were analyzed for estrogens through liquid chromatography tandem mass spectrometry and estrogenicity through the bioluminescent yeast estrogen screen assay. Fish plasma was analyzed for the egg yolk protein vitellogenin (Vtg) using enzyme–linked immunosorbent assay and gonad tissue was examined histologically for enumeration of testicular oocytes (TO). Water chemistry revealed typical E2 conversion to Estrone with subsequent decay over the exposure periods. A modest prevalence of TO (9.4%) was detected with no apparent treatment effect. While significant Vtg induction was found in E2 exposed FHM, minimal Vtg induction was found in male LMB. Despite field findings of intersex in male LMB, this species may be poorly suited for laboratory investigations into endocrine disruption.

FRAMEWORK SYNTHESIS FOR SYMBOLIC EXECUTION OF EVENT-DRIVEN FRAMEWORKS

Symbolic execution is a powerful program analysis technique, but it is very challenging to apply to programs built using event-driven frameworks, such as Android. The main reason is that the framework code itself is too complex to symbolically execute. The standard solution is to manually create a framework model that is simpler and more amenable to symbolic execution. However, developing and maintaining such a model by hand is difficult and error-prone. We claim that we can leverage program synthesis to introduce a high-degree of automation to the process of framework modeling. To support this thesis, we present three pieces of work. First, we introduced SymDroid, a symbolic executor for Android. While Android apps are written in Java, they are compiled to Dalvik bytecode format. Instead of analyzing an app’s Java source, which may not be available, or decompiling from Dalvik back to Java, which requires significant engineering effort and introduces yet another source of potential bugs in an analysis, SymDroid works directly on Dalvik bytecode. Second, we introduced Pasket, a new system that takes a first step toward automatically generating Java framework models to support symbolic execution. Pasket takes as input the framework API and tutorial programs that exercise the framework. From these artifacts and Pasket's internal knowledge of design patterns, Pasket synthesizes an executable framework model by instantiating design patterns, such that the behavior of a synthesized model on the tutorial programs matches that of the original framework. Lastly, in order to scale program synthesis to framework models, we devised adaptive concretization, a novel program synthesis algorithm that combines the best of the two major synthesis strategies: symbolic search, i.e., using SAT or SMT solvers, and explicit search, e.g., stochastic enumeration of possible solutions. Adaptive concretization parallelizes multiple sub-synthesis problems by partially concretizing highly influential unknowns in the original synthesis problem. Thanks to adaptive concretization, Pasket can generate a large-scale model, e.g., thousands lines of code. In addition, we have used an Android model synthesized by Pasket and found that the model is sufficient to allow SymDroid to execute a range of apps.

Genome Wide Association Studies of Phagocytosis and the Cellular Immune Response in Drosophila melanogaster.

Phagocytosis of bacteria by specialized blood cells, known as hemocytes, is a vital component of Drosophila cellular immunity. To identify novel genes that mediate the cellular response to bacteria, we conducted three separate genetic screens using the Drosophila Genetic Reference Panel (DGRP). Adult DGRP lines were tested for the ability of their hemocytes to phagocytose the Gram-positive bacteria Staphylococcus aureus or the Gram-negative bacteria Escherichia coli. The DGRP lines were also screened for the ability of their hemocytes to clear S. aureus infection through the process of phagosome maturation. Genome-wide association analyses were performed to identify potentially relevant single nucleotide polymorphisms (SNPs) associated with the cellular immune phenotypes. The S. aureus phagosome maturation screen identified SNPs near or in 528 candidate genes, many of which have no known role in immunity. Three genes, dpr10, fred, and CG42673, were identified whose loss-of-function in blood cells significantly impaired the innate immune response to S. aureus. The DGRP S. aureus screens identified variants in the gene, Ataxin 2 Binding Protein-1 (A2bp1) as important for the cellular immune response to S. aureus. A2bp1 belongs to the highly conserved Fox-1 family of RNA-binding proteins. Genetic studies revealed that A2bp1 transcript levels must be tightly controlled for hemocytes to successfully phagocytose S. aureus. The transcriptome of infected and uninfected hemocytes from wild type and A2bp1 mutant flies was analyzed and it was found that A2bp1 negatively regulates the expression of the Immunoglobulin-superfamily member Down syndrome adhesion molecule 4 (Dscam4). Silencing of A2bp1 and Dscam4 in hemocytes rescues the fly’s immune response to S. aureus indicating that Dscam4 negatively regulates S. aureus phagocytosis. Overall, we present an examination of the cellular immune response to bacteria with the aim of identifying and characterizing roles for novel mediators of innate immunity in Drosophila. By screening panel of lines in which all genetic variants are known, we successfully identified a large set of candidate genes that could provide a basis for future studies of Drosophila cellular immunity. Finally, we describe a novel, immune-specific role for the highly conserved Fox-1 family member, A2bp1.

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.