Background: Aquatic exercise utilizing hot springs helps individuals with nonspecific knee pain by reducing joint stress and providing a safe environment for movement. It can improve muscle strength and balance, enhancing overall functional mobility. Objects: This study aims to examine the muscle strength of knee flexion, knee extension, dorsiflexion, and plantar flexion, as well as to investigate static and dynamic balance in middleaged females after performing hot spring aquatic exercise for 4 weeks. Methods: Twenty-two middle-aged females participated in the study. The participants performed hot spring aquatic exercise for 4 weeks. The hot spring aquatic exercise consisted of aquatic walking, aquatic stretching, aquatic side step, aquatic forward reach, aquatic squat, leg lift, and aquatic arm and leg rotation. Muscle strength was measured using microFET2, while static balance was assessed through the one-leg stance test, and dynamic balance was evaluated using the Y-balance test. This study utilized the paired t-test for statistical analysis, with a significance level set at 0.05. Results: The muscle strength of bilateral knee flexion showed a significant increase in the pre- and post-comparison (p < 0.05), and the muscle strength of bilateral knee extension also improved significantly (p < 0.05). In addition, a significant increase was observed in the muscle strength of bilateral dorsiflexion (p < 0.05). The one-leg stance test performed while supporting on the right leg showed a significant increase in the pre- and post-comparison (p < 0.05). Furthermore, the dynamic balance measurements performed while supporting on both the right and left legs demonstrated significant improvements in both legs when compared before and after the intervention (p < 0.05). Conclusion: In individuals with nonspecific knee pain, a 4-week hot spring aquatic exercise program can contribute to the improvement of lower extremity strength, as well as static and dynamic balance ability.

A machine learning-based algorithms have used for constructing species distribution models (SDMs), but their performances depend on the selection of backgrounds. This study attempted to develop a noble method for selecting backgrounds in machine-learning SDMs. Two machine-learning based SDMs (MaxEnt, and Random Forest) were employed with an example species (Spodoptera litura), and different background selection methods (random sampling, biased sampling, and ensemble sampling by using CLIMEX) were tested with multiple performance metrics (TSS, Kappa, F1-score). As a result, the model with ensemble sampling predicted the widest occurrence areas with the highest performance, suggesting the potential application of the developed method for enhancing a machine-learning SDM.

Rice is widely dominated by two strain, Indica (Oryza sativa indica) and Japonica (Oryza sativa japonica), and there is a great amount of the genetic diversity between two strain. Understanding the genetic difference and variants between two strain requires high-quality reference genome information. While Nipponbare, a japonica reference sequence, has been mainly used as the rice reference genome, the Indica reference sequences, R93-11 and R498, were recently published. Based on the two reference sequences, in this study, we investigated the effect of the reference genome in the rice genetic study. To this end, we utilized relevant bioinformatic tools and statistical methods to analyze all possible combinations of the reference genome and the variety. In this way, we suggested possible combinations of the reference sequence and the variety suitable for the genetic study of rice.