Antigen analysis of Toxoplasma gondii Iysate and excretory-secretory materials by enzyme-linked immunoelectrotransfer blot (EITB) (효소면역 전기영동이적법에 의한 톡소포자충 용해물 및 분비 항원의 분석)
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- Parasites, Hosts and Diseases
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- v.32 no.4
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- pp.249-258
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- 1994
Recently, the importance of toxoplasmosis is raised as a complication in immunosuppressed or AIDS patients. Our study focused on the identification of a variety of Toxoplasma antigens by immunoblotting. Rabbits and BALB/c mice were immunized with Toxoplosmo Iysate (RH strain) , frozen tachyzoites (RH strain) or cysts (Beverly and Fukaya strain) . Blood were collected from ear vein, heart or orbital plexus for detecting the serum antibody levels. For excretory-secretory (E.S) antigens, T gondii (RH) tachyzoite were cultured in CHL (Chinese hamster lung) cells with MEM containing of 5% FCS. After 72hrs, culture supernatant was collected. BALB/c mice were inoculated with RH tachyzoite intraperitoneally and peritoneal fluids were extracted three days later. E.S antigens were detected in culture supernatant and infected mouse peritoneal fluid by EITB. Serum IgG levels in rabbit were 1 :512 of 10 days after primary immunization, 1 : 2,048 of 10 days after secondary immunization, 1: 1,024 of 20 days after secondary immunization by IFAT, respectively. Serum IgG levels of immunized mice were 1:128 after 7 weeks. Tachyzoite antigens of the RH strain were detected 25 protein bands ranging 10 kDa-220 kDa of molecular weights with Coomassie blue stain. Toxoplcsma major antigens corresponding to n of 24 kDa, 27 kDa,30 kDa, 35 kDa, 38 kDa were recognized by IgG and IgM antibodies. Excretory-secretory antigens present in culture supernatant with M. W. of 20, 30 kDa and in infected mouse peritoneal fluid with M.W. of 33 (P30), 45 kDa. When RH tachyzoite antigen was probed with different mice sera immunized with 2 strains of T gondii, the IgG antibody bud of Fukaya and Beverly strain (8 week-serum) is identical to those of RH strain. It is considered that the 30 kDa polypeptide detected in excretory- secretory materials and Iysate was important major antigen of T gondii (RH).
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