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Contamination of fresh produce with Listeria monocytogenes can occur throughout the supply chain, including at retail, where Listeria spp., including L. monocytogenes, may be introduced and spread via various routes. However, limited tools are available for retailers to assess practices that can enhance control of Listeria transmission to fresh produce. Therefore, we developed an agent-based model that can simulate Listeria transmission in retail produce sections to optimize environmental sampling programs and evaluate control strategies. A single retail store was used as a model environment, in which various routes of Listeria introduction into and transmission between environmental surfaces were modeled. Model prediction (i.e., Listeria prevalence) was validated using a published longitudinal study for all surfaces that were included in both the model and the validation data. Sensitivity analysis using the Partial Rank Correlation Coefficient showed that (i) initial Listeria concentration from incoming produce, (ii) transfer coefficient from produce to employee's hands, and (iii) transfer coefficient from consumer to produce were the top three parameters that were significantly (p < 0.0018) associated with the mean Listeria prevalence across all agents, suggesting that the accuracy of these parameters are important for prediction of overall Listeria prevalence at retail. Cluster analysis grouped agents with similar contamination patterns into six unique clusters; this information can be used to optimize the sampling plans for retail environments. Scenario analysis suggested that (i) more stringent supplier control as well as (ii) practices reducing Listeria transmission via consumer hands may have the largest impact on reducing finished product contamination. Overall, we show that an agent-based model can serve as a foundational tool to help with decision making on Listeria control strategies at retail.
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This study addresses the limited tools available for assessing food safety risks from cytotoxic Bacillus cereus group strains in contaminated food. We quantified the growth, in skim milk broth, of 17 cytotoxic B. cereus strains across 6 phylogenetic groups with various virulence gene profiles. The strains did not grow in HTST milk at 4 or 6°C. At 10°C, 15 strains exhibited growth; at 8°C, one strain grew; and all strains grew at temperatures ≥ 14°C. Using growth data from 16 strains, we developed linear secondary growth models and an exposure assessment model. This model, simulating a 5-stage HTST milk supply chain and up to 35 d of consumer storage with an initial contamination of 100 cfu/mL, estimated that 2.81 ± 0.66% and 4.13 ± 2.53% of milk containers would surpass 105 cfu/mL of B. cereus by d 21 and 35, respectively. A sensitivity analysis identified the initial physiological state of cells (Q0) as the most influential variable affecting predictions for specific isolates. What-if scenarios indicated that increases in mean and variability of consumer storage temperatures significantly affected the predicted B. cereus concentrations in milk. This model serves as an initial tool for risk-based food safety decision making regarding low-level B. cereus contamination.
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Collation of the current scope of literature related to population dynamics (i.e., growth, die-off, survival) of foodborne pathogens on fresh produce can aid in informing future research directions and help stakeholders identify relevant research literature. A scoping review was conducted to gather and synthesize literature that investigates population dynamics of pathogenic and non-pathogenic Listeria spp., Salmonella spp., and Escherichia coli on whole unprocessed fresh produce (defined as produce not having undergone chopping, cutting, homogenization, irradiation, or pasteurization). Literature sources were identified using an exhaustive search of research and industry reports published prior to September 23, 2021, followed by screening for relevance based on strict, a priori eligibility criteria. A total of 277 studies that met all eligibility criteria were subjected to an in-depth qualitative review of various factors (e.g., produce commodities, study settings, inoculation methodologies) that affect population dynamics. Included studies represent investigations of population dynamics on produce before (i.e., pre-harvest; n = 143) and after (i.e., post-harvest; n = 144) harvest. Several knowledge gaps were identified, including the limited representation of (i) pre-harvest studies that investigated population dynamics of Listeria spp. on produce (n = 13, 9% of pre-harvest studies), (ii) pre-harvest studies that were carried out on non-sprouts produce types grown using hydroponic cultivation practices (n = 7, 5% of pre-harvest studies), and (iii) post-harvest studies that reported the relative humidity conditions under which experiments were carried out (n = 56, 39% of post-harvest studies). These and other knowledge gaps summarized in this scoping review represent areas of research that can be investigated in future studies.
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Listeria , Escherichia coli , Microbiologia de Alimentos , SalmonellaRESUMO
Over the past few years, graphene grown by chemical vapor deposition (CVD) has gained prominence as a template to grow transition metal dichalcogenide (TMD) overlayers. The resulting two-dimensional (2D) TMD/graphene vertical heterostructures are attractive for optoelectronic and energy applications. However, the effects of the microstructural heterogeneities of graphene grown by CVD on the growth of the TMD overlayers are relatively unknown. Here, we present a detailed investigation of how the stacking order and twist angle of CVD graphene influence the nucleation of WSe2 triangular crystals. Through the combination of experiments and theory, we correlate the presence of interlayer dislocations in bilayer graphene with how WSe2 nucleates, in agreement with the observation of a higher nucleation density of WSe2 on top of Bernal-stacked bilayer graphene versus twisted bilayer graphene. Scanning/transmission electron microscopy (S/TEM) data show that interlayer dislocations are present only in Bernal-stacked bilayer graphene but not in twisted bilayer graphene. Atomistic ReaxFF reactive force field molecular dynamics simulations reveal that strain relaxation promotes the formation of these interlayer dislocations with localized buckling in Bernal-stacked bilayer graphene, whereas the strain becomes distributed in twisted bilayer graphene. Furthermore, these localized buckles in graphene are predicted to serve as thermodynamically favorable sites for binding WSex molecules, leading to the higher nucleation density of WSe2 on Bernal-stacked graphene. Overall, this study explores synthesis-structure correlations in the WSe2/graphene vertical heterostructure system toward the site-selective synthesis of TMDs by controlling the structural attributes of the graphene substrate.
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In this age of data, digital tools are widely promoted as having tremendous potential for enhancing food safety. However, the potential of these digital tools depends on the availability and quality of data, and a number of obstacles need to be overcome to achieve the goal of digitally enabled "smarter food safety" approaches. One key obstacle is that participants in the food system and in food safety often lack the willingness to share data, due to fears of data abuse, bad publicity, liability, and the need to keep certain data (e.g., human illness data) confidential. As these multifaceted concerns lead to tension between data utility and privacy, the solutions to these challenges need to be multifaceted. This review outlines the data needs in digital food safety systems, exemplified in different data categories and model types, and key concerns associated with sharing of food safety data, including confidentiality and privacy of shared data. To address the data privacy issue a combination of innovative strategies to protect privacy as well as legal protection against data abuse need to be pursued. Existing solutions for maximizing data utility, while not compromising data privacy, are discussed, most notably differential privacy and federated learning.
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Engineering atomic-scale defects is crucial for realizing wafer-scale, single-crystalline transition metal dichalcogenide monolayers for electronic devices. However, connecting atomic-scale defects to larger morphologies poses a significant challenge. Using electron microscopy and ReaxFF reactive force field-based molecular dynamics simulations, we provide insights into WS2 crystal growth mechanisms, providing a direct link between synthetic conditions and microstructure. Dark-field TEM imaging of coalesced monolayer WS2 films illuminates defect arrays that atomic-resolution STEM imaging identifies as translational grain boundaries. Electron diffraction and high-resolution imaging reveal that the films have nearly a single orientation with imperfectly stitched domains that tilt out-of-plane when released from the substrate. Imaging and ReaxFF simulations uncover two types of translational mismatch, and we discuss their origin related to relatively fast growth rates. Statistical analysis of >1300 facets demonstrates that microstructural features are constructed from nanometer-scale building blocks, describing the system across sub-Ångstrom to multimicrometer length scales.
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In this paper, a (Fe50Mn30Co10Cr10)100-xCx high-entropy alloy (HEA) was successfully prepared by using the vacuum arc melting method. The peak shape analysis of the X-ray diffraction patterns, the EBSD observations, and the EDS spectra of the alloys with different compositions show that the characteristics of the dendrites and the hard phase, Cr23C6, into the initial single-phase face-centered cubic (FCC) matrix becomes gradually visible as the carbon content increases from 0 to 4%. The crystal phase variations lead to a non-linear orientation of the microstructure, to a refinement of the grains, and to a higher elastic modulus. This study presents the solid saturation limit of the interstitial carbon atoms in such alloys and establishes an empirical relation between an alloy's elastic modulus and its carbon content.
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Ligas/química , Carbono/química , Cromo/química , Cobalto/química , Módulo de Elasticidade , Entropia , Ferro/química , Manganês/química , Teste de Materiais , Difração de Raios XRESUMO
This work experimentally investigates the effect of layered structure on the static and impact response of a new layered syntactic foam developed for impact energy absorption. The layered syntactic foam had the same density of 1.6 g/cm3 and the same components of 50% large spheres (L) and 50% small spheres (S) with different structures from two layers to five layers. The impact response and energy absorption were investigated by drop-weight impact tests. Under static loading, more layers led to higher yield stress and lower energy absorption. There were three types of progressive failures of layered syntactic form under impact loading. The failure propagation was examined and found to be dependent on the layer number and impact energy. Interestingly, layered syntactic foam absorbed more energy than both of its components in terms of ductility. The ductility of layered syntactic foam decreased with the increase in layer number. The peak stress of layered syntactic foam increased with the increase in layer number. Two-layered syntactic foam LS had the highest ductility under 60 J/g impact, as well as an energy absorption of 35 J/g, compared to other layered syntactic foams. Specifically, its component L had a ductility under 70 J/g and an energy absorption of 25 J/g, while component S had a ductility under 10 J/g and an energy absorption of 10 J/g.