Bayesian modeling of two- and three-species bacterial competition in milk

Strain-specific Growth Parameters are Important to Accurately Model Bacterial Growth on Baby Spinach in Simulation Models

Digital tools to predict produce shelf life have the potential to reduce food waste and improve consumer satisfaction. To address this need, we (i) performed an observational study on the microbial quality of baby spinach, (ii) completed growth experiments of bacteria that are representative of the baby spinach microbiota, and (iii) developed an initial simulation model of bacterial growth on baby spinach. Our observational data showed that the predominant genera found on baby spinach were Pseudomonas, Pantoea and Exiguobacterium. Rifampicin-resistant mutants (rifR mutants) of representative bacterial subtypes were subsequently generated to obtain strain-specific growth parameters on baby spinach. These experiments showed that: (i) it is difficult to select rifR mutants that do not have fitness costs affecting growth (9 of 15 rifR mutants showed substantial differences in growth, compared to their corresponding wild-type strain) and (ii) based on estimates from primary growth models, the mean (geometric) maximum population of rifR mutants on baby spinach (7.6 log10 CFU/g, at 6°C) appears lower than that of the spinach microbiota (9.6 log10 CFU/g, at 6°C), even if rifR mutants did not have substantial growth-related fitness costs. Thus, a simulation model, parameterized with the data obtained here as well as literature data on home refrigeration temperatures, underestimated bacterial growth on baby spinach. The root mean square error of the simulation's output, compared against data from the observational study, was 1.11 log10 CFU/g. Sensitivity analysis was used to identify key parameters (e.g., strain maximum population) that impact the simulation model's output, allowing for prioritization of future data collection to improve the simulation model. Overall, this study provides a roadmap for the development of models to predict bacterial growth on leafy vegetables with strain-specific parameters and suggests that additional data are required to improve these models.

Multi-order analytical solving computation of rainstorm causal decomposition during typhoons using a designed key-lock quasi-Newton optimizing derivation

To precisely identify multi-dimensional spatiotemporal rain-making parameters, generate an approximate Hessian matrix, and solve the nonlinear ill-posed problem, this study uses composite logical tangent hyperbolic functions to construct the rain-generating simulation model as nonlinear algebraic equations with designed key-lock quasi-Newton optimization for deriving multi-order objective functional derivatives for rainstorm causal decomposition into advanced functional, analytical solution (lock) and Newton's conditional constraints. Specifically, the rank-two approximate structure of the Levenberg-Marquardt and Broyden-Fletcher-Goldfarb-Shanno quasi-Newton algorithms are modified as the symmetric rank-four structure to efficiently calculate a positive definite stable Hessian and solve the constrained nonlinear rain-making threshold. The model projects various rain-making factors into multi-rank loading scores, characterizing rain-generating mechanisms and causal components as associated DNAs. To accelerate/modify directional convergence, avoid local minimum, and detect global optimum, the devised vectorized limited switchable step sizes are optimized using advanced double-bracketing approaches combined with candidate parameters' correction vectors (key) and referenced step-size distributions solved by Newton's constrained analytical solution to reduce heterogeneous differences and eliminate the conventional overestimated Hessian. The identified rain-making DNAs reveal that typhoons with similar DNAs move in similar directions. Specifically, rain-making DNAs in Taipei Category 1 were correlated with wind force/direction and cloud height along PCs 1, 3, 4, and 7, and those in Category 2 were correlated with cloud-cover distribution along PCs 1, 2, and 5. The identified rain-making thresholds of typhoons with constant direction/structure showed a weaker steady state, whereas the unsteady rest produced multi-peak rainfall hydrographs. Rain evolution analysis reveals that cloudy rainbands, carried by the wind field, move along the Tamsui River valley when traveling between northeast and south-southeast of Taipei; converge with gradient and geostrophic winds when traveling between east-northeast and southwest; merge with southwest monsoon when traveling between west-southwest and northeast of Kaohsiung.

Modeling the Fate of Listeria monocytogenes and Salmonella enterica on Fresh Whole and Chopped Wood Ear and Enoki Mushrooms

Two recent foodborne illness outbreaks linked to specialty mushrooms have occurred in the United States, both representing novel pathogen-commodity pairings. Listeria monocytogenes and Salmonella enterica were linked to enoki and wood ear mushrooms, respectively. The aim of this study was therefore to examine the survival of both L. monocytogenes and S. enterica on raw whole and chopped enoki and wood ear mushrooms during storage at different temperatures. Fresh mushrooms were either left whole or chopped and subsequently inoculated with a cocktail of either S. enterica or rifampicin-resistant L. monocytogenes, resulting in an initial inoculation level of 3 log CFU/g. Mushroom samples were stored at 5, 10, or 25°C for up to 7 d. During storage, the population levels of S. enterica or L. monocytogenes on the mushrooms were enumerated. The primary Baranyi model was used to estimate the growth rates of both pathogens and the secondary Ratkowsky square root model was used to model the relationship between growth rates and temperature. Both L. monocytogenes and S. enterica survived on both mushroom types and preparations at all temperatures. No proliferation of either pathogen was observed on mushrooms stored at 5°C. At 10°C, moderate growth was observed for both pathogens on enoki mushrooms and for L. monocytogenes on wood ear mushrooms; no growth was observed for S. enterica on wood ear mushrooms. At 25°C, both pathogens proliferated on both mushroom types with growth rates ranging from 0.43 to 3.27 log CFU/g/d, resulting in 1 log CFU/g increases in only 0.31 d (7.44 h) to 2.32 d. Secondary models were generated for L. monocytogenes on whole wood ear mushrooms and S. enterica on whole enoki mushrooms with goodness-of-fit parameters of r² = 0.9855/RMSE = 0.0479 and r² = 0.9882/RMSE = 0.1417, respectively. Results from this study can aid in understanding the dynamics of L. monocytogenes and S. enterica on two types of specialty mushrooms.

Competitive growth kinetics of Campylobacter jejuni, Escherichia coli O157:H7 and Listeria monocytogenes with enteric microflora in a small-intestine model

Aims

The biological events occurring during human digestion help to understand the mechanisms underlying the dose–response relationships of enteric bacterial pathogens. To better understand these events, we investigated the growth and reduction behaviour of bacterial pathogens in an in vitro model simulating the environment of the small intestine.

Methods and Results

The foodborne pathogens Campylobacter jejuni, Listeria monocytogenes and Escherichia coli O157:H7 were cultured with multiple competing enteric bacteria. Differences in the pathogen's growth kinetics due to the relative amount of competing enteric bacteria were investigated. These growth differences were described using a mathematical model based on Bayesian inference. When pathogenic and enteric bacteria were inoculated at 1 log CFU per ml and 9 log CFU per ml, respectively, L. monocytogenes was inactivated over time, while C. jejuni and E. coli O157:H7 survived without multiplying. However, as pathogen inocula were increased, its inhibition by enteric bacteria also decreased.

Conclusions

Although the growth of pathogenic species was inhibited by enteric bacteria, the pathogens still survived.

Significance and Impact of the Study

Competition experiments in a small‐intestine model have enhanced understanding of the infection risk in the intestine and provide insights for evaluating dose–response relationships.

High-throughput sequencing provides new insights into the roles and implications of core microbiota present in pasteurized milk

Residual microorganisms in dairy products are closely related to their quality deterioration and safety. Based on the minimum sterilization conditions required by Grade A Pasteurized Milk Ordinance, this study explored the microbiota present in milk products that were high temperature short time pasteurized at 72, 75, 80, 83, or 85 °C for 15 s, 20 s, and 30 s separately. Based on high-throughput sequencing results, 6 phyla and 18 genera were identified as dominant microbiota. Proteobacteria and Firmicutes were the maior bacteria in phyla, and each comprising more than 50%. Pseudomonas was account for more than 42% of all the genera detected in all samples. Moreover, the changes in flavor substances in pasteurized milk, including 16 free amino acids, 9 fatty acids, and 17 volatile compounds, were detected using principal component and multi factor analyses. The Pearson correlation coefficient analysis identified six bacteria genera as the core functional microbiota that significantly affected the flavor compounds and the safety and quality of pasteurized milk. Interestingly, Pseudomonas, Omithimimicrobium, Cyanobacteria and Corynebacterium had positive correlations with the flavor substances, whereas Streptococcus and Paeniclostridium had significant negative correlations with these substances. The results may help enhance the quality control of dairy products and can be used as indicators of microbial contamination of pasteurized dairy products.

Modeling the Growth and Interaction Between Brochothrix thermosphacta, Pseudomonas spp., and Leuconostoc gelidum in Minced Pork Samples

The aim of this study was to obtain the growth parameters of specific spoilage micro-organisms previously isolated in minced pork (MP) samples and to develop a three-spoilage species interaction model under different storage conditions. Naturally contaminated samples were used to validate this approach by considering the effect of the food microbiota. Three groups of bacteria were inoculated on irradiated samples, in mono- and in co-culture experiments (n = 1152): Brochothrix thermosphacta, Leuconostoc gelidum, and Pseudomonas spp. (Pseudomonas fluorescens and Pseudomonas fragi). Samples were stored in two food packaging [food wrap and modified atmosphere packaging (CO2 30%/O2 70%)] at three isothermal conditions (4, 8, and 12°C). Analysis was carried out by using both 16S rRNA gene amplicon sequencing and classical microbiology in order to estimate bacterial counts during the storage period. Growth parameters were obtained by fitting primary (Baranyi) and secondary (square root) models. The food packaging shows the highest impact on bacterial growth rates, which in turn have the strongest influence on the shelf life of food products. Based on these results, a three-spoilage species interaction model was developed by using the modified Jameson-effect model and the Lotka Volterra (prey-predator) model. The modified Jameson-effect model showed slightly better performances, with 40-86% out of the observed counts falling into the Acceptable Simulation Zone (ASZ). It only concerns 14-48% for the prey-predator approach. These results can be explained by the fact that the dynamics of experimental and validation datasets seems to follow a Jameson behavior. On the other hand, the Lotka Volterra model is based on complex interaction factors, which are included in highly variable intervals. More datasets are probably needed to obtained reliable factors, and so better model fittings, especially for three- or more-spoilage species interaction models. Further studies are also needed to better understand the interaction of spoilage bacteria between them and in the presence of natural microbiota.

Modelling Growth and Decline in a Two-Species Model System: Pathogenic Escherichia coli O157:H7 and Psychrotrophic Spoilage Bacteria in Milk

Shiga toxin-producing Escherichia coli O157:H7 is a food-borne pathogen and the major cause of hemorrhagic colitis. Pseudomonas is the genus most frequent psychrotrophic spoilage microorganisms present in milk. Two-species bacterial systems with E. coli O157:H7, non-pathogenic E. coli, and P. fluorescens in skimmed milk at 7, 13, 19, or 25 °C were studied. Bacterial interactions were modelled after applying a Bayesian approach. No direct correlation between P. fluorescens's growth rate and its effect on the maximum population densities of E. coli species was found. The results show the complexity of the interactions between two species in a food model. The use of natural microbiota members to control foodborne pathogens could be useful to improve food safety during the processing and storage of refrigerated foods.