No abstract

Buckwheat sprouts have been widely consumed all around world due to their great abundance of bioactive compounds. In this study, the anti-inflammatory effects of flavonoid-rich common buckwheat sprout (CBS) and tartary buckwheat sprout (TBS) extracts were evaluated in lipopolysaccharide- (LPS-) stimulated RAW 264.7 murine macrophages and primary peritoneal macrophages from male BALB/c mice. Based on the reversed-phase HPLC analysis, the major flavonoids in CBS were determined to be C-glycosylflavones (orientin, isoorientin, vitexin, and isovitexin), quercetin-3-O-robinobioside, and rutin, whereas TBS contained only high amounts of rutin. The TBS extract exhibited higher inhibitory activity as assessed by the production of proinflammatory mediators such as nitric oxide and cytokines including tumor necrosis factor-α, interleukin- (IL-) 6, and IL-12 in LPS-stimulated RAW 264.7 macrophages than CBS extract. In addition, TBS extract suppressed nuclear factor-kappa B activation by preventing inhibitor kappa B-alpha degradation and mitogen-activated protein kinase phosphorylation in LPS-stimulated RAW 264.7 macrophages. Moreover, the TBS extract markedly reduced LPS-induced cytokine production in peritoneal macrophages. Taken together, these findings suggest that TBS extract can be a potential source of anti-inflammatory agents that may influence macrophage-mediated inflammatory disorders.

No abstract

Load carriage induces systematic alterations in gait patterns and pelvic-thoracic coordination. Leveraging this information, the objective of this study was to develop and assess a statistical prediction algorithm that uses body-worn inertial sensor data for classifying load carrying modes and load levels. Nine men participated in an experiment carrying a hand load in four modes: onehanded right and left carry, and two-handed side and anterior carry, each at 50% and 75% of the participant's maximum acceptable weight of carry, and a no-load reference condition. Twelve gait parameters calculated from inertial sensor data for each gait cycle, including gait phase durations, torso and pelvis postural sway, and thoracic-pelvic coordination were used as predictors in a twostage hierarchical random forest classification model with Bayesian inference. The model correctly classified 96.9% of the carrying modes and 93.1% of the load levels. Coronal thoracicpelvic coordination and pelvis postural sway were the most relevant predictors although their relative importance differed between carrying mode and load level prediction models. This study presents an algorithmic framework for combining inertial sensing with statistical prediction with potential use for quantifying physical exposures from load carriage.

People with visual impairments may experience difficulties in learning new physical exercises due to a lack of visual feedback. Learning and practicing yoga is especially challenging for this population as yoga requires imitation-oriented learning. A typical yoga class requires students to observe and copy poses and movements as the instructor presents them, while maintaining postural balance during the practice. Without additional, nonvisual feedback, it can be difficult for students with visual impairments to understand whether they have accurately copied a pose – and if they have not, how to fix an inaccurate pose. Therefore, there is a need for an intelligent learning system that can capture a person’s physical posture and provide additional, nonvisual feedback to guide them into a correct pose. This study is a preliminary step toward the development of a wearable inertial sensor-based virtual learning system for people who are blind or have low vision. Using hierarchical task analysis, we developed a step-by-step conceptual model of yoga poses, which can be used in constructing an effective nonvisual feedback system. We also ranked sensor locations according to their importance by analyzing postural deviations in each pose compared to the reference starting pose.