著者

Sang Hyun Sung So Young Kang Ki Yong Lee Mi Jung Park Jeong Hun Kim Jong Hee Park Young Chul Kim Jinwoong Kim Young Choong Kim

出版者

The Pharmaceutical Society of Japan

雑誌

Biological and Pharmaceutical Bulletin (ISSN:09186158)

巻号頁・発行日

vol.25, no.1, pp.125-127, 2002 (Released:2002-03-05)

参考文献数

14

被引用文献数

27 52

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In the course of screening natural products for anti-acetylcholinesterase (AChE) activity, we found that a total methanolic extract of the underground parts of Caragana chamlague (Leguminosae) had significant inhibition towards AChE. Bioactivity-guided fractionation of the total methanolic extract resulted in the isolation and identification of two active stilbene oligomers, (+)-α-viniferin (1) and kobophenol A (2). Both 1 and 2 inhibited AChE activity in a dose-dependent manner, and the IC50 values of 1 and 2 were 2.0 and 115.8 µM, respectively. The AChE inhibitory activity of 1 was specific, reversible and noncompetitive.

著者

Min Kyoung SUNG Ki Yong LEE Jun-Bum SHIN Yon Dohn CHUNG

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

IEICE TRANSACTIONS on Information and Systems (ISSN:09168532)

巻号頁・発行日

vol.E95-D, no.1, pp.152-160, 2012-01-01

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Recently, social network services are rapidly growing and this trend is expected to continue in the future. Social network data can be published for various purposes such as statistical analysis and population studies. When social network data are published, however, the privacy of some people may be disclosed. The most straightforward manner to preserve privacy in social network data is to remove the identifiers of persons from the social network data. However, an adversary can infer the identity of a person in the social network by using his/her background knowledge, which consists of content information such as the age, sex, or address of the person and structural information such as the number of persons having a relationship with the person. In this paper, we propose a privacy protection method for social network data. The proposed method anonymizes social network data to prevent privacy attacks that use both content and structural information, while minimizing the information loss or distortion of the anonymized social network data. Through extensive experiments, we verify the effectiveness and applicability of the proposed method.