• Journal of Life Science
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• v.18 no.6
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• pp.796-803
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• 2008

Mutations in the APP gene lead to enhanced cleavage by ${\beta}-$ and ${\gamma}-secretase$, and increased $A{\beta}$ formation, which are closely associated with Alzheimer's disease (AD)-like neuropathological changes. Recent studies have shown that exercise training can ameliorate pathogenic phenotypes ($A{\beta}-42$, BDNF, GLUT-1 and HSP70) in experimental models of Alzheimer's disease. Here, we have used NSE/APPsw transgenic mice to investigate directly whether exercise training ameliorates pathogenic phenotypes within Alzheimer's brains. Sixteen weeks of exercise training resulted in a reduction of $A{\beta}-42$ peptides and also facilitated improvement of cognitive function. Furthermore, GLUT -1 and BDNF proteins produced by exercise training may protect brain neurons by inducing the concomitant expression of genes that encode proteins (HSP-70) which suppress stress induced neuron cell damages from APPsw transgenic mice. Thus, the improved cognitive function by exercise training may be mechanistically linked to a reduction of $A{\beta}-42$ peptides, possibly via activation of BDNF, GLUT-1, and HSP-70 proteins. On the basis of the evidences presented in this study, exercise training may represent a practical therapeutic management strategy for human subjects suffering from Alzheimer's disease.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.87-94
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• 2022

Recently, with the development of information and communication infrastructure, the number of Internet access devices is rapidly increasing. Smartphones, laptops, computers, and even IoT devices are receiving information and communication services through Internet access. Since most of the device operating environment consists of web (WEB), it is vulnerable to web cyber attacks using web shells. When the web shell is uploaded to the web server, it is confirmed that the attack frequency is high because the control of the web server can be easily performed. As the damage caused by the web shell occurs a lot, each company is responding to attacks with various security devices such as intrusion prevention systems, firewalls, and web firewalls. In this case, it is difficult to detect, and in order to prevent and cope with web shell attacks due to these characteristics, it is difficult to respond only with the existing system and security software. Therefore, it is an automated defense system through the collection and analysis of web shells based on artificial intelligence machine learning that can cope with new cyber attacks such as detecting unknown web shells in advance by using artificial intelligence machine learning and deep learning techniques in existing security software. We would like to propose about. The machine learning-based web shell defense system model proposed in this paper quickly collects, analyzes, and detects malicious web shells, one of the cyberattacks on the web environment. I think it will be very helpful in designing and building a security system.