Cushing's syndrome is caused by overproduction of the adrenocorticotropic hormone (ACTH), which stimulates the adrenal grand to make cortisol. Skeletal muscle wasting occurs in pathophysiological response to Cushing's syndrome. The forkhead box (FOX) protein family has been implicated as a key regulator of muscle loss under conditions such as diabetes and sepsis. However, the mechanistic role of the FOXO family in ACTH-induced muscle atrophy is not understood. We hypothesized that FOXO3a plays a role in muscle atrophy through expression of the E3 ubiquitin ligases, muscle RING finger protein-1 (MuRF-1), and atrogin-1 in Cushing's syndrome. For establishment of a Cushing's syndrome animal model, Sprague-Dawley rats were implanted with osmotic minipumps containing ACTH (40 ng·kg·day). ACTH infusion significantly reduced muscle weight. In ACTH-infused rats, MuRF-1, atrogin-1, and FOXO3a were upregulated and the FOXO3a promoter was targeted by the glucocorticoid receptor (GR). Transcriptional activity and expression of FOXO3a were significantly decreased by the GR antagonist RU486. Treatment with RU486 reduced MuRF-1 and atrogin-1 expression in accordance with reduced enrichment of FOXO3a and Pol II on the promoters. Knockdown of FOXO3a prevented dexamethasone-induced MuRF-1 and atrogin-1 expression. These results indicate that FOXO3a plays a role in muscle atrophy through expression of MuRF-1 and atrogin-1 in Cushing's syndrome.

Game bots are a critical threat to Massively Multiplayer Online Role-Playing Games (MMORPGs) because they can seriously damage the reputation and in-game economy equilibrium of MMORPGs. Existing game bot detection techniques are not only generally sensitive to changes in game contents but also limited in detecting emerging bot patterns that were hitherto unknown. To overcome the limitation of learning bot patterns over time, we propose a framework that detects game bots through machine learning technique. The proposed framework utilizes self-similarity to effectively measure the frequency of repeated activities per player over time, which is an important clue to identifying bots. Consequently, we use realworld MMORPG ("Lineage", "Aion" and "Blade & Soul") datasets to evaluate the feasibility of the proposed framework. Our experimental results demonstrate that 1) self-similarity can be used as a general feature in various MMORPGs, 2) a detection model maintenance process with newly updated bot behaviors can be implemented, and 3) our bot detection framework is practicable. Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.

No abstract

No abstract

No abstract