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In the case of critically ill patients, recently published guidelines are available for their nutritional management. Further, several natural bioactive compounds interact with the angiotensin-converting enzyme 2 (ACE2) receptor, the gateway for severe acute respiratory syndrome (SARS) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Natural bioactive compounds can also reduce the inflammatory response induced by SARS-CoV-2. These compounds are potential beneficial tools in the nutritional management of COVID-19 patients.This review focuses on the wear mechanisms of natural and restorative dental materials, presenting a comprehensive description and analysis of the works published in the last two decades on the wear at the interface of occlusal surfaces. Different groups of tribological pairs were considered tooth-tooth, tooth-restorative material (tooth-ceramic, tooth-resin-based-materials, and tooth-metal), and restorative-restorative materials. The lack of standardization of the wear tests impairs the direct comparison of the obtained results. However, it was possible to infer about the main wear mechanisms observed on the different classes of dental materials. Concerning ceramics, their toughness and surface finishing determines the wear of antagonist tooth. Abrasion revealed to be the main wear mechanisms at occlusal interface. [https://www.selleckchem.com/products/brefeldin-a.html click here] In the case of resin-based composites, the cohesion of the organic matrix and the nature, shape, and amount of filler particles greatly influences the dental wear. The protruding and detachment of the filler particles are the main causes of abrasion of antagonist enamel. Metallic materials induce lower wear on antagonist enamel than the other classes of materials, because of their low hardness and high ductility. Most of the studies revealed plastic deformation and adhesive wear as the main wear mechanisms. Overall, more research in this area is needed for a better understanding of the mechanisms involved at the occlusal surfaces wear. This would be essential for the development of more suitable restoration materials.Bungowannah virus is a novel pestivirus identified from a disease outbreak in a piggery in Australia in June 2003. The aim of this study was to determine whether infection of pregnant pigs with Bungowannah virus induces the clinical signs and gross pathology observed during the initial outbreak and how this correlates with the time of infection. Twenty-four pregnant pigs were infected at one of four stages of gestation (approximately 35, 55, 75 or 90 days). The number of progeny born alive, stillborn or mummified, and signs of disease were recorded. Some surviving piglets were euthanased at weaning and others at ages up to 11 months. All piglets were subjected to a detailed necropsy. The greatest effects were observed following infection at 35 or 90 days of gestation. Infection at 35 days resulted in a significant reduction in the number of pigs born alive and an increased number of mummified foetuses (18%) and preweaning mortalities (70%). Preweaning losses were higher following infection at 90 days of gestation (29%) and were associated with sudden death and cardiorespiratory signs. Stunting occurred in chronically and persistently infected animals. This study reproduced the clinical signs and gross pathology of the porcine myocarditis syndrome and characterised the association between the time of infection and the clinical outcome.The brain-gut-microbiome axis is an emerging area of study, particularly in vertebrate systems. Existing evidence suggests that gut microbes can influence basic physiological functions and that perturbations to the gut microbiome can have deleterious effects on cognition and lead to neurodevelopmental disorders. While this relationship has been extensively studied in vertebrate systems, little is known about this relationship in insects. We hypothesized that because of its importance in bee health, the gut microbiota influences learning and memory in adult bumble bees. As an initial test of whether there is a brain-gut-microbiome axis in bumble bees, we reared microbe-inoculated and microbe-depleted bees from commercial Bombus impatiens colonies. We then conditioned experimental bees to associate a sucrose reward with a color and tested their ability to learn and remember the rewarding color. We found no difference between microbe-inoculated and microbe-depleted bumble bees in performance during the behavioral assay. While these results suggest that the brain-gut-microbiome axis is not evident in Bombus impatiens, future studies with different invertebrate systems are needed to further investigate this phenomenon.Leash tension forces exerted by dog and handler during walks affect their welfare. We developed a novel ambulatory measurement device using a load cell and a tri-axial accelerometer to record both the tension and direction of forces exerted on the leashes. Data were relayed telemetrically to a laptop for real time viewing and recording. Larger and heavier dogs exerted higher leash tension but had a lower pulling frequency than their smaller and lighter conspecifics. This pattern was observed in the reactional forces of handlers. Young dogs pulled more frequently during walks, which was also mirrored in handlers' pulling. Well-behaved dogs created lower leash tension, but handlers did not respond with lower forces. This novel method of recording leash tension will facilitate real-time monitoring of the behaviour of dogs and their handlers during walks.Golf swing segmentation with inertial measurement units (IMUs) is an essential process for swing analysis using wearables. However, no attempt has been made to apply machine learning models to estimate and divide golf swing phases. In this study, we proposed and verified two methods using machine learning models to segment the full golf swing into five major phases, including before and after the swing, from every single IMU attached to a body part. Proposed bidirectional long short-term memory-based and convolutional neural network-based methods rely on characteristics that automatically learn time-series features, including sequential body motion during a golf swing. Nine professional and eleven skilled male golfers participated in the experiment to collect swing data for training and verifying the methods. We verified the proposed methods using leave-one-out cross-validation. The results revealed average segmentation errors of 5-92 ms from each IMU attached to the head, wrist, and waist, accurate compared to the heuristic method in this study.