Performance of Manufacturer Cleaning Recommendations Applied to 3D Food Ink Capsules for the Control of a Human Norovirus Surrogate

With the widespread availability of 3D food printing systems for purchase, users can customize their food in new ways. Manufacturer recommendations for cleaning these machines remain untested with regard to the prevention of foodborne pathogen transmission. This study aimed to determine if manufacturer cleaning recommendations for food ink capsules utilized in 3D food printers are adequate to control human norovirus (HuNoV). A HuNoV surrogate, Tulane virus (TuV; ~?6 log₁₀ PFU/mL), was inoculated onto the interior surface of stainless steel food ink capsules. Capsules were either unsoiled or soiled with one of the following: butter, protein powder solution, powdered sugar solution, or a mixture containing all three food components. The capsules were allowed to dry and then one of three hygienic protocols was applied: manual washing (MW), a dishwasher speed cycle (DSC), or a dishwasher heavy cycle (DHC). The interaction effect between DSC and pure butter was a significant predictor of log reduction (P?=?0.0067), with the pure butter and DSC combination achieving an estimated mean log reduction of 4.83 (95% CI 4.13, 5.59). The DSC was the least effective method of cleaning when compared with MW and the DHC. The 3-way interaction effects between wash type, soil, and capsule position were a significant predictor of log reduction (P?=?0.00341). Capsules with butter in the DSC achieved an estimated mean log reduction of 2.81 (95% CI 2.80, 2.83) for the front-most position versus 6.35 (95% CI 6.33, 6.37) for the back-most position. Soil matrix, cleaning protocol, and capsule position all significantly impact capsule cleanability and potential food safety risk. The DHC is recommended for all capsules, and the corners should be avoided when placing capsules into the dishwasher. The current study seeks to provide recommendations for users of additive manufacturing and 3D food printing including consumers, restaurants, industry, and regulatory industries.

     

Reliability and input-data induced uncertainty of the EPIC model to estimate climate change impact on sorghum yields in the U.S. Great Plains

Crop simulation models are frequently used to estimate the impact of climate change on crop production. However, few studies have evaluated the model performance in ways that most researchers practiced in climate impact studies. In this article, we examined the reliability of the EPIC model in simulating grain sorghum (Sorghum bicolor (L.) Moench) yields in the U.S. Great Plains under different climate scenarios, namely in years with normal or extreme temperature and precipitation. We also investigated model uncertainties introduced by input data that are not site-specific but commonly used or available for climate change studies. Historical field trial data of sorghum at the Mead Experimental Center, NE, were used for model evaluations. The results showed that overall model reliability was about 56%. The mean absolute relative error (absRE) was about 29%. The degree of accuracy and reliability varied with climate-classes and nitrogen (N)-treatments. The largest bias occurred in drought years (RE = ?25%) and the most unreliable results were found in N-0 treatment (reliability = 32%). There was more than 69% probability that input-data-induced uncertainties were limited to less than 20% of absRE. Our results support the application of the EPIC model to climate change impact studies in the U.S. Great Plains. However, efforts are needed to improve the accuracy in simulating crop responses to extreme water- and nitrogen-stressed conditions.     

Mercury exposure in terrestrial birds far downstream of an historical point source

Mercury (Hg) is a persistent environmental contaminant found in many freshwater and marine ecosystems. Historical Hg contamination in rivers can impact the surrounding terrestrial ecosystem, but there is little known about how far downstream this contamination persists. In 2009, we sampled terrestrial forest songbirds at five floodplain sites up to 137 km downstream of an historical source of Hg along the South and South Fork Shenandoah Rivers (Virginia, USA). We found that blood total Hg concentrations remained elevated over the entire sampling area and there was little evidence of decline with distance. While it is well known that Hg is a pervasive and long-lasting aquatic contaminant, it has only been recently recognized that it also biomagnifies effectively in floodplain forest food webs. This study extends the area of concern for terrestrial habitats near contaminated rivers for more than 100 km downstream from a waterborne Hg point source.     

Collaborative Modeling for Decision Support in Water Resources: Principles and Best Practices

Collaborative Modeling for Decision Support integrates collaborative modeling with participatory processes to inform natural resources decisions. Practitioners and advocates claim that the approach will lead to better water management, balancing interests more effectively and reducing the likelihood of costly legal delays. These claims are easy to make, but the benefits will only be realized if the process is conducted effectively. To provide guidance for how to conduct an effective collaborative modeling process, a task committee cosponsored by the Environmental Water Resources Institute (EWRI) of the American Society of Civil Engineers and by the U.S. Army Corps of Engineers' Institute for Water Resources developed a set of Principles and Best Practices for anyone who might convene or conduct collaborative modeling processes. The guidance is intended for both conflict resolution professionals and modelers, and our goal is to integrate these two fields in a way that will improve water resources planning and decision making. Here, the set of eight principles is presented along with a selection of associated best practices, illustrated by two different case examples. The complete document is available at: http://www.computeraideddisputeresolution.us/bestpractices/ .     

Independent gradients of producer, consumer, and microbial diversity in lake plankton

Interactions between trophic levels during food web assembly can drive positive correlations in diversity between producers, consumers, and decomposers. However, the contribution of trophic interactions relative to local environmental factors in promoting species diversity is poorly understood, with many studies only considering two trophic levels. Here we examine correlations in diversity among zooplankton, phytoplankton, and bacteria in the pelagic zone of 31 lakes in British Columbia, Canada. We sampled species diversity of zooplankton and phytoplankton through morphological identification, and bacterial genetic diversity was estimated by denaturing gradient gel electrophoresis (DGGE) of 16S rDNA polymorphisms. We looked for correlations in diversity that were independent of the abiotic environment by statistically controlling for 18 limnological variables. No significant correlations were found between the diversity of zooplankton, phytoplankton, and bacteria. In addition, the physical factors that were associated with species composition in one trophic level were independent of those that were important for another. Our results provide no support for the importance of direct feedbacks between producers, consumers, and decomposers in maintaining diversity. Zooplankton, phytoplankton, and bacterial diversity and composition are regulated independently from one another and respond to different environmental variables. These results suggest that species of lake plankton show loose trophic associations with one another due to broad diets in consumers and decomposers.     

Spectral deconvolution and operational use of stripping ratios in airborne radiometrics

Spectral deconvolution using stripping ratios for a set of pre-defined energy windows is the simplest means of reducing the most important part of gamma-ray spectral information. In this way, the effective interferences between the measured peaks are removed, leading, through a calibration, to clear estimates of radionuclide inventory. While laboratory measurements of stripping ratios are relatively easy to acquire, with detectors placed above small-scale calibration pads of known radionuclide concentrations, the extrapolation to measurements at altitudes where airborne survey detectors are used bring difficulties such as air-path attenuation and greater uncertainties in knowing ground level inventories. Stripping ratios are altitude dependent, and laboratory measurements using various absorbers to simulate the air-path have been used with some success. Full-scale measurements from an aircraft require a suitable location where radionuclide concentrations vary little over the field of view of the detector (which may be hundreds of metres). Monte Carlo simulations offer the potential of full-scale reproduction of gamma-ray transport and detection mechanisms. Investigations have been made to evaluate stripping ratios using experimental and Monte Carlo methods.