The Effects of High Pressure Processing on Peanut Sauce Inoculated with Salmonella

Recent Salmonella outbreaks have prompted the need for new processing options for peanut products. Traditional heating kill-steps have shown to be ineffective in lipid-rich matrices such as peanut products. High pressure processing is one such option for peanut sauce because it has a high water activity, which has proved to be a large contributing factor in microbial lethality due to high pressure processing. Four different formulations of peanut sauce were inoculated with a five strain Salmonella cocktail and high pressure processed. Results indicate that increasing pressure or increasing hold time increases log10 reductions. The Weibull model was fitted to each kill curve, with b and n values significantly optimized for each curve (p-value < 0.05). Most curves had an n parameter value less than 1, indicating that the population had a dramatic initial reduction, but tailed off as time increased, leaving a small resistant population. ANOVA analysis of the b and n parameters show that there are more significant differences between b parameters than n parameters, meaning that most treatments showed similar tailing effect, but differed on the shape of the curve. Comparisons between peanut sauce formulations at the same pressure treatments indicate that increasing amount of organic peanut butter within the sauce formulation decreases log10 reductions. This could be due to a protective effect from the lipids in the peanut butter, or it may be due to other factors such as nutrient availability or water activity. Sauces pressurized at lower temperatures had decreased log10 reductions, indicating that cooler temperatures offered some protective effect. Log10 reductions exceeded 5 logs, indicating that high pressure processing may be a suitable option as a kill-step for Salmonella in industrial processing of peanut sauces. Future research should include high pressure processing on other peanut products with high water activities such as sauces and syrups as well as research to determine the effects of water activity and lipid composition with a food matrix such as peanut sauces.

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.

INFLUENCE OF TYPE 2 BOVINE VIRAL DIARRHEA VIRUS NPRO ON ENHANCEMENT OF BOVINE RESPIRATORY SYNCYTIAL VIRUS REPLICATION MEDIATED BY ANTAGONISM OF HOST CELL INTERFERON TYPE I RESPONSES

Bovine viral diarrhea virus (BVDV) is a member of the genus Pestivirus, Family Flaviviridae. The virus can infect many species of animals of the order Artiodactyla. The BVDV genome encodes an auto protease, Npro, that degrades interferon regulatory factor-3 (IRF-3) reducing type I interferon (IFN-I) production from host cells. Bovine respiratory syncytial virus (BRSV) is a member of the genus Pneumovirus, Family Paramyxoviridae. Concurrent infection with BVDV and BRSV causes more severe respiratory and enteric disease than infection with either virus alone. Our hypothesis was that Npro modulates the innate immune responses to BVDV infection and enhances replication of BVDV or BRSV co-infection. The noncytopathic BVDV2 viruses NY93/c N- Npro 18 EGFP (a mutant with modified Npro fused with enhanced green fluorescent protein), NY93 infectious clone (NY93/c), wild-type NY93-BVDV2 (NY93-wt), and BRSV were evaluated in this study. The objectives of this study were: (1) to characterize the replication kinetics and IFN-I induction in Madin-Darby bovine kidney (MDBK) cells following infection with each of the BVDV isolates, and (2) to characterize the influence of BVDV-mediated IFN-I antagonism on enhancement of BRSV replication in bovine turbinate (BT) cells. NY93/c N- Npro 18 EGFP replicated 0.4 – 1.6 TCID50 logs lower than NY93-wt in MDBK cells. NY93/c N- Npro 18 EGFP-infected MDBK cells synthesized IFN-I significantly higher than NY93/c- and NY93-wt-infected MDBK cells. BT cells co-infected with NY93/c N- Npro 18 EGFP/BRSV or NY93-wt/BRSV were evaluated to determine the effects of co-infection on BRSV replication and IFN-I induction in BT cells. BRSV RNA levels in NY93-wt/BRSV co-infected BT cells were 2.49, 2.79, and 2.89 copy number logs significantly greater than in NY93/c N- Npro 18 EGFP/BRSV co-infected BT cells on days 5, 7, and 9 post-infection, respectively. BVDV RNA levels in NY93/c N- Npro 18 EGFP-infected BT cells were 1.64 – 4.38 copy number logs lower than in NY93-wt-infected BT cells. NY93/c N- Npro 18 EGFP single and co-infected BT cells synthesized IFN-I significantly higher than NY93-wt single and co-infected BT cells. In summary, these findings suggest: (1) NY93/c N- Npro 18 EGFP BVDV2 induced higher levels of IFN-I than BVDV2-wt and may be useful as a safer, replicating BVDV vaccine, and (2) Enhancement of BRSV infection by BVDV co-infection is mediated by antagonism of IFN-I.

CHARACTERISTICS OF THE PRIVATE NUISANCE WILDLIFE CONTROL INDUSTRY IN NEW YORK

The nuisance wildlife control industry is rapidly expanding in New York State. To gain additional insight about this industry and the number of animals handled, we reviewed the 1989-90 annual logs submitted by Nuisance Wildlife Control Orators (NWC0s) to the New York State Department of Environmental Conservation (DEC). The specific objectives of this study were to determine: (1) the number and species of different wildlife responsible for damage incidents, (2) the cause of damage complaints, (3) the disposition of animals handled, (4) the location of damage events (i.e., urban, suburban, rural), and (5) an estimate of the economic impact of the nuisance wildlife industry in Upstate New York. The Nuisance Wildlife Logs (NWLs) were examined for 7 urban and 7 rural counties (25.5% of Upstate counties), and these data were used to estimate total NWCO activity in DEC Regions 3 through 9 (excludes Long Island). Approximately 75% of NWCOs licensed by DEC were active during 1989-90, and nearly 2,800 complaints were handled in the 14 counties sampled. More than 90% of complaints came from urban counties, and we estimated that NWC0s responded to more than 11,000 calls in Upstate New York. At a conservative estimate of $35/call, revenue generated by this industry exceeded $385,000 annually. Six wildlife species accounted for 85% of the nuisance complaints in urban and rural counties. During 1986 to 1993, the number of NWCOs licensed by DEC nearly quadrupled, and there is no indication that this trend will change in the near future.