Societal Statement on the Role of Occupational Therapy with Survivors of Human Sex Trafficking in the United States

As part of a specialized course, OTD 8340 Wellness and Health Promotion in Occupational Therapy, students from the Nova Southeastern University Entry Level Doctor of Occupational Therapy program, drafted a Societal Statement on the role of occupational therapy with survivors of human sex trafficking in the United States. The students explored the issue of domestic human sex trafficking from an occupational perspective, under the guidance of their professor, Mirtha Montejo Whaley, PhD, OTR/L. As of the publication of this journal, the document is under review by the American Occupational Therapy Association (AOTA

Protein-Pacing and Multi-Component Exercise Training Improves Physical Performance Outcomes in Exercise-Trained Women: The PRISE 3 Study

The beneficial cardiometabolic and body composition effects of combined protein-pacing (P; 5-6 meals/day at 2.0 g/kg BW/day) and multi-mode exercise (resistance, interval, stretching, endurance; RISE) training (PRISE) in obese adults has previously been established. The current study examines PRISE on physical performance (endurance, strength and power) outcomes in healthy, physically active women. Thirty exercise-trained women (\u3e4 days exercise/week) were randomized to either PRISE (n = 15) or a control (CON, 5-6 meals/day at 1.0 g/kg BW/day; n = 15) for 12 weeks. Muscular strength (1-RM bench press, 1-RM BP) endurance (sit-ups, SUs; push-ups, PUs), power (bench throws, BTs), blood pressure (BP), augmentation index, (AIx), and abdominal fat mass were assessed at Weeks 0 (pre) and 13 (post). At baseline, no differences existed between groups. Following the 12-week intervention, PRISE had greater gains (p \u3c 0.05) in SUs, PUs (6 ± 7 vs. 10 ± 7, 40%; 8 ± 13 vs. 14 ± 12, 43% ∆reps, respectively), BTs (11 ± 35 vs. 44 ± 34, 75% ∆watts), AIx (1 ± 9 vs. -5 ± 11, 120%), and DBP (-5 ± 9 vs. -11 ± 11, 55% ∆mmHg). These findings suggest that combined protein-pacing (P; 5-6 meals/day at 2.0 g/kg BW/day) diet and multi-component exercise (RISE) training (PRISE) enhances muscular endurance, strength, power, and cardiovascular health in exercise-trained, active women

Smart glass film reduced ascorbic acid in red and orange capsicum fruit cultivars without impacting shelf life

Smart Glass Film (SGF) is a glasshouse covering material designed to permit 80% trans-mission of photosynthetically active light and block heat-generating solar energy. SGF can reduce crop water and nutrient consumption and improve glasshouse energy use efficiency yet can reduce crop yield. The effect of SGF on the postharvest shelf life of fruits remains unknown. Two capsicum varieties, Red (Gina) and Orange (O06614), were cultivated within a glasshouse covered in SGF to assess fruit quality and shelf life during the winter season. SGF reduced cuticle thickness in the Red cultivar (5%) and decreased ascorbic acid in both cultivars (9–14%) without altering the overall morphology of the mature fruits. The ratio of total soluble solids (TSSs) to titratable acidity (TA) was significantly higher in Red (29%) and Orange (89%) cultivars grown under SGF. The Red fruits had a thicker cuticle that reduced water loss and extended shelf life when compared to the Orange fruits, yet neither water loss nor firmness were impacted by SGF. Reducing the storage temperature to 2◦C and increasing relative humidity to 90% extended the shelf life in both cultivars without evidence of chilling injury. In summary, SGF had minimal impact on fruit development and postharvest traits and did not compromise the shelf life of mature fruits. SGF provides a promising technology to block heat-generating solar radiation energy without affecting fruit ripening and marketable quality of capsicum fruits grown during the winter season

BridgeData V2: A Dataset for Robot Learning at Scale

We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors designed to facilitate research on scalable robot learning. BridgeData V2 contains 60,096 trajectories collected across 24 environments on a publicly available low-cost robot. BridgeData V2 provides extensive task and environment variability, leading to skills that can generalize across environments, domains, and institutions, making the dataset a useful resource for a broad range of researchers. Additionally, the dataset is compatible with a wide variety of open-vocabulary, multi-task learning methods conditioned on goal images or natural language instructions. In our experiments, we train 6 state-of-the-art imitation learning and offline reinforcement learning methods on our dataset, and find that they succeed on a suite of tasks requiring varying amounts of generalization. We also demonstrate that the performance of these methods improves with more data and higher capacity models, and that training on a greater variety of skills leads to improved generalization. By publicly sharing BridgeData V2 and our pre-trained models, we aim to accelerate research in scalable robot learning methods. Project page at https://rail-berkeley.github.io/bridgedataComment: 9 page

Multi-Modal Exercise Training and Protein-Pacing Enhances Physical Performance Adaptations Independent of Growth Hormone and BDNF but May Be Dependent on IGF-1 in Exercise-Trained Men

OBJECTIVE: Protein-pacing (P; 5-6meals/day @ 2.0g/kgBW/day) and multi-mode exercise (RISE; resistance, interval, stretching, endurance) training (PRISE) improves muscular endurance, strength, power and arterial health in exercise-trained women. The current study extends these findings by examining PRISE on fitness, growth hormone (GH), insulin-like growth factor-1 (IGF-1), and brain-derived neurotrophic factor (BDNF) response, cardiometabolic health, and body composition in exercise-trained men. DESIGN: Twenty active males (\u3e4daysexercise/week) completed either: PRISE (n=11) or RISE (5-6meals/day @ 1.0g/kgBW/day; n=9) for 12weeks. Muscular strength (1-repetition maximum bench and leg press, 1-RM BP, and 1-RM LP), endurance (sit-ups, SU; push-ups, PU), power (squat jump, SJ, and bench throw, BT), flexibility (sit-and-reach, SR), aerobic performance (5km cycling time-trial, TT), GH, IGF-1, BDNF, augmentation index, (AIx), and body composition, were assessed at weeks 0 (pre) and 13 (post). RESULTS:At baseline, no differences existed between groups except for GH (RISE, 230±13 vs. PRISE, 382±59pg/ml, p CONCLUSIONS: Exercise-trained men consuming a P diet combined with multi-component exercise training (PRISE) enhance muscular power, strength, aerobic performance, and flexibility which are not likely related to GH or BDNF but possibly to IGF-1 response

Representation theory of three-dimensional Sklyanin algebras

We determine the dimensions of the irreducible representations of the Sklyanin algebras with global dimension 3. This contributes to the study of marginal deformations of the N=4 super Yang-Mills theory in four dimensions in supersymmetric string theory. Namely, the classification of such representations is equivalent to determining the vacua of the aforementioned deformed theories. We also provide the polynomial identity degree for the Sklyanin algebras that are module finite over their center. The Calabi-Yau geometry of these algebras is also discussed.Comment: 22 page

Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network

A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta-analyses across many independent experiments. However, results from single-site studies tend to have limited generality. Although meta-analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long-term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high- and low-elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above- and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities

Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network

A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta-analyses across many independent experiments. However, results from single-site studies tend to have limited generality. Although meta-analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long-term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high- and low-elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above- and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities