Abstract: In our previous study, low-frequency, short-duration high-intensity interval training (HIIT) was performed for 8-12 weeks in untrained healthy subjects (Once a week, 3 sets, less than 10 minutes total training time), and the results showed that regardless of training intensity, maximal oxygen uptake (VO2max) and exhaustion time during maximal exercise increased by 6-13% and 33-89%. However, it is not clear whether similar results can be obtained in athletes who perform strenuous exercise on a daily basis. The purpose of this study was to investigate the effects of HIIT at different intensities on cardiorespiratory function and exercise performance during maximal exercise in college athletes. The subjects were 13 male college student athletes. The training was determined for each individual using a ramp exercise test to determine the maximum load, divided into 80% (TG80%) or 90% (TG90%) intensity training groups. and conducted once a week for 8 weeks. The training consisted of three bouts of exercises to volitional fatigue at TG80% and TG90% maximum work rate. Regardless different of training intensity, increased VO2max (TG80%: +11%, TG90%: +12%) and maximal work rate during the ramp exercise test (TG80%: +13%, TG90%: +16%) in both groups were observed after HIIT (P < 0.01). Maximal exhaustion time during the HIIT (TG80%: +110%, TG90%: +95%) was also significantly prolonged in both groups after training (P < 0.05). In conclusion, low-frequency for short-duration once a week HIIT improves cardiorespiratory function and exercise performance during maximal exercise for athletes, regardless of its training intensity. These findings will contribute to the development of new and optimal exercise programs for various age groups and people, even competitive athletes.

This study estimated the stiffness of the medial gastrocnemius muscle and ankle joint with decreased somatosensory input from the soles due to cooling. Two healthy young male volunteers participated in the experiment. The participants’ soles and the surface of the force plate were iced. Each participant stood quietly on the force plate. Electrical stimulation was applied to the medial gastrocnemius muscle. The center of pressure (COP) was measured with the force plate. The fluctuation of the COP due to electrical stimulation was extracted using a Kalman filter. The transfer function from the stimulation to the fluctuation was calculated using a system identification method. The poles of the transfer function were used to estimate the muscle and joint stiffness. The muscle and joint stiffness was decreased by cooling the soles.

Mathematical models have been used to describe the complex physiological processes of light response in photoreceptors for decades. However, the detailed model has not yet been invented. In this study, we aimed to construct a new photoreceptor model for estimating the chemical and electrical responses to a light stimulus. The proposed model was developed from our previous works (Takeda et at. 2021; Ito et al. 2017; Sato et al. 2017) based on the ionic current model of vertebrate photoreceptor incorporating the classical Goldman-Hodgkin-Katz constant field equation. The novel model successfully describes the biophysical processes of ionic current dynamics during the light responses. Furthermore, the changes in intracellular ion concentrations are in good agreement with theoretical works and experimental data. This study provides an effective framework for future experimental studies and serves as a powerful guideline for better understanding of the pathophysiology of the retina and improving the new treatment studies.

Sit-to-Stand (STS) transition is an important movement in our daily life and is identified as the most difficult and mechanically demanding activity. However, due to various physical disabilities, people who need assistance for the STS are increasing. This study aims to reveal the muscle synergy-based network when STS transition was performed with and without robot assistance. Eight muscular activities were measured through surface electromyogram. Muscle synergy was extracted using non-negative matrix factorization. The spatial pattern of muscle coordination was used for network modeling and analysis. Extended Bayesian Information Criterion graphical lasso network was constructed and analyzed. Network centrality, edge and centrality stability, network similarity comparison were then tested. Network analysis results indicated different muscle correlations in each synergy. Investigating the spatial muscle synergy network provides new knowledge and viewpoint for human-centered assistance robot design and evaluation.