• Journal of Life Science
• /
• v.24 no.11
• /
• pp.1180-1186
• /
• 2014

Autophagy, the lysosomal degradation pathway, is an intracellular recycling system that is necessary for the metabolic benefits of exercise and for producing lasting beneficial effects of exercise in various diseases. However, the most recent studies have only examined the effect of a single bout of exercise or resistance exercise on autophagic responses. To determine the differential effects of acute and chronic exercise on the expression of autophagy-related genes in D. melanogaster, white-eyed mutant D. melanogaster were assigned randomly to four groups: control, acute exercise, 2 hr chronic exercise, and 3 hr chronic exercise. The flies were exercised using a mechanized platform known as the Power Tower. Our results revealed that a single bout of exercise resulted in increased mRNA levels of the Atg8a gene (~20%, p<0.05). However, Atg1 and Atg6 mRNA expression were not induced by acute exercise. Transcript levels of Atg6 (~29%, p<0.05) related to the nucleation of autophagosomes were significantly induced by 2 hr of chronic exercise. However, this chronic exercise was not enough to increase Atg1 and Atg8a mRNA expression. On the other hand, 3 hr of exercise for 7 days significantly increased Atg1, Atg6, and Atg8a gene expression-about 57%, 37%, and 71%, respectively (p<0.05). These results suggest that a single bout of exercise is not enough to induce full activation of selected autophagy-related genes in D. melanogaster. Our results demonstrated that chronic regular exercise induced autophagy-related gene expression, suggesting that chronic regular exercise training might be required to activate autophagic responses important for producing beneficial effects of exercise in various diseases.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.949-965
• /
• 2023

In this study, we propose a method to monitor the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar images and the deep learning model, Swin Transformer. Utilizing the Google Earth Engine platform, datasets from 2017 to 2021 were constructed for seven agricultural reservoirs, categorized into 700 K-ton, 900 K-ton, and 1.5 M-ton capacities. For four of the reservoirs, a total of 1,283 images were used for model training through shuffling and 5-fold cross-validation techniques. Upon evaluation, the Swin Transformer Large model, configured with a window size of 12, demonstrated superior semantic segmentation performance, showing an average accuracy of 99.54% and a mean intersection over union (mIoU) of 95.15% for all folds. When the best-performing model was applied to the datasets of the remaining three reservoirsfor validation, it achieved an accuracy of over 99% and mIoU of over 94% for all reservoirs. These results indicate that the Swin Transformer model can effectively monitor the surface area of agricultural reservoirs in South Korea.