ATTACHMENT, GROWTH AND PERSISTENCE OF CRONOBACTER ON GRANULAR ACTIVATED CARBON FILTERS

Several Cronobacter outbreaks have implicated contaminated drinking water. This study assessed the impact of granular activated carbon (GAC) on the microbial quality of the water produced. A simulated water filter system was installed by filling plastic columns with sterile GAC, followed by sterile water with a dilute nutrient flowing through the column at a steady rate. Carbon columns were inoculated with Cronobacter on the surface, and the effluent monitored for Cronobacter levels. During a second phase, commercial faucet filters were distributed to households for 4-month use. Used filters were backwashed with sterile peptone water, and analyzed for Cronobacter, total aerobic plate count, coliform bacteria and Enterobacteriaceae. Cronobacter colonized the simulated GAC and grew when provided minimal levels of nutrients. Backwashed used filters used in home settings yielded presumptive Escherichia coli, Pseudomonas and other waterborne bacteria. Presumptive Cronobacter strains were identified as negative through biochemical and genetic test.

Prevalence of MRSA and Antimicrobial Resistance of Staphylococcus aureus in Maryland Ground Meat Products

Gemstone Team Antibiotic Resistance

A Binary Classifier for Test Case Feasibility Applied to Automatically Generated Tests of Event-Driven Software

Modern software application testing, such as the testing of software driven by graphical user interfaces (GUIs) or leveraging event-driven architectures in general, requires paying careful attention to context. Model-based testing (MBT) approaches first acquire a model of an application, then use the model to construct test cases covering relevant contexts. A major shortcoming of state-of-the-art automated model-based testing is that many test cases proposed by the model are not actually executable. These \textit{infeasible} test cases threaten the integrity of the entire model-based suite, and any coverage of contexts the suite aims to provide. In this research, I develop and evaluate a novel approach for classifying the feasibility of test cases. I identify a set of pertinent features for the classifier, and develop novel methods for extracting these features from the outputs of MBT tools. I use a supervised logistic regression approach to obtain a model of test case feasibility from a randomly selected training suite of test cases. I evaluate this approach with a set of experiments. The outcomes of this investigation are as follows: I confirm that infeasibility is prevalent in MBT, even for test suites designed to cover a relatively small number of unique contexts. I confirm that the frequency of infeasibility varies widely across applications. I develop and train a binary classifier for feasibility with average overall error, false positive, and false negative rates under 5\%. I find that unique event IDs are key features of the feasibility classifier, while model-specific event types are not. I construct three types of features from the event IDs associated with test cases, and evaluate the relative effectiveness of each within the classifier. To support this study, I also develop a number of tools and infrastructure components for scalable execution of automated jobs, which use state-of-the-art container and continuous integration technologies to enable parallel test execution and the persistence of all experimental artifacts.