Isolation of Acinetobacter calcoaceticus BP-2 Capable of Degradation of Bisphenol A (Bisphenol A 분해균주 Acinetobacter calcoaceticus BP-2의 분리 및 bisphenol A 분해 특성)
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- Journal of Life Science
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- v.16 no.7 s.80
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- pp.1158-1163
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- 2006
Bisphenol A (BPA), 2,2-bis(4-hydroxyphenyl) propane, has been widely used as a monomer for production of epoxy resins and polycarbonate plastics, and final products of BPA include adhesives, protective coatings, paints, optical lens, building materials, compact disks and other electrical parts. Since BPA is a toxic chemical to elicit acute cell cytotoxicity and chronic endocrine disrupting activity, the degradation of BPA has been focused during last decades. To overcome the problem of photo-, and chemical-degradation of BPA, in this study, a bacterium that is able to biodegrade BPA, was isolated. The bacterium, isolated froln the soil of plastic factory, was identified as Acinetobacter calcoaceticus (strain BP-2) based on physiological and 16S rDNA sequencing analysis. A. calcoaceticus BP-2 was able to grow in the presence of
We have constructed two flight models of airglow photometer system (AGP) to be onboard Korea Sounding Rocket-III (KSR-III) for detection of MUV dayglow above the Korean peninsular. The AGP system is designed to detect dayglow emissions of OI 2972
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used