Terrestrial Animal Source Foods and Health Outcomes for Those with Special Nutrient Needs in the Life Course.

Background. Animal source foods are under scrutiny for their role in human health, yet some nutritionally vulnerable populations are largely absent from consideration. Methods. Applying a Population Intervention/Exposure Comparator Outcome (PICO/PECO) framework and prioritizing systematic review and meta-analyses, we reviewed the literature on terrestrial animal source foods (TASFs) and human health, by life course phase. Results. There were consistent findings for milk and dairy products on positive health outcomes during pregnancy and lactation, childhood, and among older adults. Eggs were found to promote early childhood growth, depending on context. Unprocessed meat consumption was associated with a reduced risk for anemia during pregnancy, improved cognition among school-age children, and muscle health in older adults. Milk and eggs represent a risk for food sensitivities/allergies, though prevalence is low, and individuals tend to outgrow the allergies. TASFs affect the human microbiome and associated metabolites with both positive and negative health repercussions, varying by type and quantity. Conclusions. There were substantial gaps in the evidence base for studies limiting our review, specifically for studies in populations outside high-income countries and for several TASF types (pig, poultry, less common livestock species, wild animals, and insects). Nonetheless, sufficient evidence supports an important role for TASFs in health during certain periods of the life course.

Testing a new active learning approach to advance motor learning knowledge and self-efficacy in physical therapy undergraduate education.

BACKGROUND: Motor learning (ML) science is foundational for physical therapy. However, multiple sources of evidence have indicated a science-practice gap. Clinicians report low self-efficacy with ML concepts and indicate that the lack of access to systematic training is a barrier for practical implementation. The general goal of this preliminary study was to describe the effects of a new educational intervention on physical therapy student's ML self-efficacy and knowledge. METHODS: Self-efficacy was assessed with the Physical Therapists' Perceptions of Motor Learning questionnaire. Data was acquired from third-semester students before their participation in the ML educational intervention. Reference self-efficacy data was also acquired from physical therapy professionals and first and last-semester students. The educational intervention for third-semester students was designed around an established framework to apply ML principles to rehabilitation. A direct experience, the "Learning by Doing" approach, in which students had to choose a motor skill to acquire over 10 weeks, provided the opportunity to apply ML theory to practice in a personally meaningful way. After the intervention self-efficacy was re-tested. ML knowledge was tested with an objective final exam. Content analysis of coursework material was used to determine how students comprehended ML theory and related it to their practical experience. The Kruskal-Wallis and Mann-Whitney U tests were used to compare self-efficacy scores between the four groups. Changes in self-efficacy after the educational intervention were analyzed with the Wilcoxon test. Spearman rank correlation analysis was used to test the association between self-efficacy and final exam grades. RESULTS: By the end of the intervention, students' self-efficacy had significantly increased (p < 0.03), was higher than that of senior students (p < 0.00) and experienced professionals (p < 0.00) and correlated with performance on an objective knowledge test (p < 0.03). Content analysis revealed that students learned to apply the elements of ML-based interventions present in the scientific literature to a real-life, structured ML program tailored to personal objectives. CONCLUSIONS: Positive improvements were observed after the intervention. These results need confirmation with a controlled study. Because self-efficacy mediates the clinical application of knowledge and skills, systematic, active training in ML may help reduce the science-practice gap.

System Analyzer for a Bioinspired Mars Flight Vehicle System for Varying Mission Contexts.

The Marsbee is a novel bioinspired flapping flight vehicle concept for aerial Mars exploration. The Marsbee design addresses the challenges of flying on Mars by mimicking the unsteady lift generation mechanisms seen in terrestrial insects To enable the comparison of the Marsbee system to other flying Mars exploration concepts, a study was performed that employs a Multidisciplinary Design Optimization architecture to analyze and optimize the Marsbee system to suit a wide variety of missions. This study developed an analyzer for a Multidisciplinary Design Feasible (MDF) architecture, as well as explored the design space and attributes necessary in an objective function for Mars flying system missions. The analyzer is based on physical models developed in previous studies. Its functionality was demonstrated by analyzing 100,000 randomly generated designs, with design variables close to a prototype Marsbee tested in Martian density conditions. These results show that by using flexible wings rather than rigid wings the maximum flight times increased from 53 minutes to 114 minutes, and the maximum payload masses increased from 28 grams to 61 grams. These are competing effects and cannot be maximized simultaneously. The results of this study will be used to determine the optimal Marsbee system.