Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item POPULATION AND GENETIC DIVERSITY ANALYSIS OF LISTERIA MONOCYTOGENES IN SELECT FOODS AND FOOD PROCESSING ENVIRONMENTS(2023) Kwon, Hee Jin; Meng, Jianghong; Food Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Listeria monocytogenes, a Gram-positive bacterium, is a foodborne pathogen that causes listeriosis in humans. L. monocytogenes can persist in various environmental conditions, including food-relevant conditions such as high salinity, refrigerated temperatures, and low moisture contents. Contaminated food products, including dairy products, deli meats, fresh produce, and soft cheeses, are the primary transmission vehicles for L. monocytogenes. The complex and dynamic population structure of L. monocytogenes complicates control efforts, particularly due to certain strains that may possess increased resistance to stress conditions and/or enhanced virulence. The advent of whole genome sequencing has facilitated comprehensive genomic analyses of L. monocytogenes, enabling a comprehensive understanding of its adaptation and survival characteristics over time and across various geographic locations. Understanding the population and genetic diversity of L. monocytogenes is crucial for the development of effective control measures, as it helps infer the spread and transmission pathways of L. monocytogenes through the integration of spatial-temporal factors. Furthermore, these analyses provide insights into the evolutionary relationships among L. monocytogenes strains. This dissertation aimed to investigate the population diversity of L. monocytogenes in various food sources and food processing facilities, utilizing the whole genome sequencing technology. The findings contribute valuable insights into the genetic diversity and population structure of L. monocytogenes, thereby aiding the understanding of the risk associated with L. monocytogenes contamination and the development of effective control measures to ensure food safety.Item Development of machine learning and advanced data analytical techniques to incorporate genomic data in predictive modeling for Salmonella enterica(2021) Karanth, Shraddha; Pradhan, Abani K; Food Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The past few decades have seen a renaissance in the field of food safety, with the increasing usage of genomic data (e.g., whole genome sequencing (WGS)) in determining the cause of microbial foodborne illness, particularly for multi-serovar agents such as Salmonella enterica. However, utilizing such data in a preventative framework, specifically in the field of quantitative microbial risk assessment (QMRA) remains in its infancy, because incorporating such large-scale datasets in statistical models is hindered by the sheer number of variables/features introduced. Thus, the goal of this research is to introduce machine learning (ML)-based approaches to potentially incorporate WGS data in various stages of a risk assessment for Salmonella enterica. Specifically, we developed a machine learning-based workflow to obtain an association between gene presence/absence data from microbial whole genome sequences and severity of Salmonella-related health outcomes in host systems. A key contribution of this dissertation is assessing the applicability of Elastic Net model, a recursive feature selection technique, which resolves a well-known issue concerning WGS-based data analysis: variables/features outnumber the count of observations. Building on this finding, we developed a gene weighted Poisson regression method to incorporate genes into a dose-response framework for Salmonella enterica, thereby incorporating genetic variability directly into a risk assessment framework. Finally, we combined machine learning with count-based models to determine how significant genes interact with meteorological factors in impacting the severity of salmonellosis outbreaks. This dissertation uncovers some interesting findings. First, although commonly used classifiers (such as random forest) performed well in predicting disease severity, logistic regression, in conjunction with Elastic Net, performed significantly better. This finding is important, as the result of a logistic regression is generally more interpretable than that of other classifiers, easing its incorporation into predictive microbial modeling. Next, machine learning-supported count-based models, such as Poisson regression also proved to be a good fit for gene-informed dose-response modeling and determination of outbreak severity when combined with extrinsic factors such as atmospheric temperature and precipitation. Overall, this dissertation identified areas within a QMRA framework that could benefit from incorporating genetic information, and introduced ML models to incorporate such information.Item WHOLE GENOME SEQUENCING ANALYSIS ON SHIGA TOXIN-PRODUCING ESCHERICHIA COLI O157:H7 FROM CATTLE FED WITH DIFFERENT DIETARY PROTEIN CONCENTRATIONS(2017) YANG, XUN; MENG, JIANGHONG; Food Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Escherichia coli serotype O157:H7 was first recognized in 1982 as a human pathogen associated with outbreaks of bloody diarrhea in the United States and is now considered a major cause of foodborne infections because of its high hospitalization rate. Cattle is the major reservoir of E. coli O157:H7. Cattle harbor E. coli O157:H7 in the hindgut and shed the organisms in the feces, which serves as a source of contamination of food and water. It is hypothesized that dietary ingredients that reach the hindgut are likely to affect colonization and fecal shedding of STEC. Increased flow of dietary ingredients (starch, fiber, protein, and lipid) are likely to alter ecology of the hindgut, resulting in altered pH and fermentation products, which could have a positive or negative impact on E. coli O157:H7. The objectives of this study are to investigate E. coli O157:H7 populations in fecal shedding of cattle. The cattle in this study were fed with diets with different levels of ruminally-degradable and –undegradable protein. A total of 286 E. coli O157:H7 isolates were recovered from feces of 576 crossbred calves at the Clayton Livestock Research Center in Clayton, New Mexico. The organisms were sequenced using Illumina Miseq system. De novo assembly of raw reads was performed using SPAdes and SNPs analysis of the isolates was conducted using kSNP3. Virulence factor Database (VFDB), created by the MOH key laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, was used as a reference for BLAST. The results indicated that increased flow of undegradable protein may increase the shedding of E. coli O157. However, the effect of ractopamine was still unknown. Three clades were identified among the E. coli O157:H7 isolates, including clades 6, 7, and 8, most of which belong to clade 8 (205 of 286). 49588 SNPs were found according to kSNP3. 19043 SNPs were identified as core SNPs. The phylogenetic analysis showed that the E. coli O157 isolates which collected from neighboring Pens were more closely to each other.