An integrated model for pre- and post-harvest aflatoxin contamination in maize.

Aflatoxin contamination caused by colonization of maize by Aspergillus flavus continues to pose a major human and livestock health hazard in the food chain. Increasing attention has been focused on the development of models to predict risk and to identify effective intervention strategies. Most risk prediction models have focused on elucidating weather and site variables on the pre-harvest dynamics of A.

Monitoring the microbiome for food safety and quality using deep shotgun sequencing.

In this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced the total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.

Extended Enrichment Procedures Can Be Used To Define False-Negative Probabilities for Cultural Gold Standard Methods for Salmonella Detection, Facilitating Comparisons between Gold Standard and Alternative Methods.

Abstract: Evaluation of alternative detection methods for foodborne pathogens typically involves comparisons against a "gold standard" culture method, which may produce false-negative (FN) results, particularly under worst-case scenarios such as low contamination levels, difficult-to-detect strains, and challenging food matrices (e.g., matrices with a water activity of <0.

Food authentication from shotgun sequencing reads with an application on high protein powders.

Here we propose that using shotgun sequencing to examine food leads to accurate authentication of ingredients and detection of contaminants. To demonstrate this, we developed a bioinformatic pipeline, FASER (Food Authentication from SEquencing Reads), designed to resolve the relative composition of mixtures of eukaryotic species using RNA or DNA sequencing. Our comprehensive database includes >6000 plants and animals that may be present in food.

Syndromic surveillance in companion animals utilizing electronic medical records data: development and proof of concept.

In an effort to recognize and address communicable and point-source epidemics in dog and cat populations, this project created a near real-time syndromic surveillance system devoted to companion animal health in the United States. With over 150 million owned pets in the US, the development of such a system is timely in light of previous epidemics due to various causes that were only recognized in retrospect. The goal of this study was to develop epidemiologic and statistical methods for veterinary hospital-based surveillance, and to demonstrate its efficacy by detection of simulated foodborne outbreaks using a database of over 700 hospitals.