• The Plant Pathology Journal
• /
• v.35 no.4
• /
• pp.381-388
• /
• 2019

For several years, temperatures in the Korean peninsula have gradually increased due to climate change, resulting in a changing environment for growth of crops and vegetables. An associated consequence is that emerging species of insect vector have caused increased viral transmission. In Jeju Island, Korea, occurrences of viral disease have increased. Here, we report characterization of five newly collected turnip mosaic virus (TuMV) isolates named KBJ1, KBJ2, KBJ3, KBJ4 and KBJ5 from a survey on Jeju Island in 2017. Full-length cDNAs of each isolate were cloned into the pJY vector downstream of cauliflower mosaic virus 35S and bacteriophage T7 RNA polymerase promoters. Their fulllength sequences share 98.9-99.9% nucleotide sequence identity and were most closely related to previously reported Korean TuMV isolates. All isolates belonged to the BR group and infected both Chinese cabbage and radish. Four isolates induced very mild symptoms in Nicotiana benthamiana but KBJ5 induced a hypersensitive response. Symptom differences may result from three amino acid differences uniquely present in KBJ5; Gly(382)Asp, Ile(891)Val, and Lys(2522)Glu in P1, P3, and NIb, respectively.

• Development and Reproduction
• /
• v.12 no.3
• /
• pp.297-303
• /
• 2008

BCL-2 family members are essential protein for the regulation of cell death and survival consisting both antiapoptotic and pro-apoptotic proteins. In the present study, we designed and cloned a new apoptotic molecule MCL-1ES BH3M coding a modified protein of MCL-1L. Compared to MCL-1L protein, MCL-1ES BH3M lacks the PEST motifs known to be involved in MCL-1L protein degradation and has seven mutated residues in BH3 domain critical for dimerization with BCL-2 family members. Overexpression of MCL-1ES BH3M induced death of different cells, and its cell killing effect was not blocked by forced expression of the pro-survival protein MCL-1L. Expression of MCL-1ES BH3M protein led to the activation of caspase 9 and caspase 3, suggesting apoptotic cell death, and confocal fluorescent microscopic analyses showed that MCL-1ES BH3M was partially localized in mitochondria. In conclusion, we reported a new apoptotic molecule and determined its cell death activity in cells.

• Asian-Australasian Journal of Animal Sciences
• /
• v.22 no.8
• /
• pp.1167-1173
• /
• 2009

This study was conducted to evaluate the effects of providing fermented soy protein to weaned pigs on nitrogen balance and apparent fecal and apparent ileal digestibility (AID) of AA. Four weaned ((Yorkshire${\times}$Landrace)${\times}$Duroc) barrows (BW = 6.58${\pm}$0.98 kg), surgically fitted with a simple T-cannula approximately 15 cm prior to the ileo-cecal junction, were fed four diets according to 4${\times}$4 Latin square design. Diets were a basal diet supplemented with one of the following: 3% SDPP (spray dried plasma protein), 5% RBP (soy protein fermented by Lactobacillus spp.), 5% PSP (soy protein fermented by Aspergillus oryzae and Bacillus subtilis), and 2.5% RPP (2.5% RBP+2.5% PSP). No differences were observed in DM and N intakes among treatments. However, the level of urine excretion was greater in the RPP group than in the PSP group. Additionally, fecal DM excretion, fecal N concentration and fecal N excretion were increased in the RBP, PSP and RPP groups when compared with the SDPP group (p<0.05). Furthermore, total excretion was increased in the RPP group when compared with the PSP group (p<0.05). In addition, N absorption and the N absorption ratio were higher in the SDPP group than in the RPP group (p<0.05). Moreover, the DM and N digestibilities were lower in the RBP, PSP and RPP groups than in the SDPP group (p<0.05), and the ash and energy digestibilities were higher in the SDPP and RBP groups than in the PSP and RPP groups (p<0.05). However, no significant differences were observed in the DM, N, Ash, Ca, P or ileal digestibilities among treatments, although the energy digestibility was higher in the SDPP group than the RBP group (p<0.05). In addition, the apparent ileal digestibilities of essential amino acids (Arg, His, Iso, Leu, Lys, Phe, Thr, and Val) were significantly higher in the SDPP group than in the other groups (p<0.05), and the levels of Ala, Cys, Glu and Try were greater in the SDPP treatment group than the RBP, PSP and RPP groups (p<0.05). Additionally, the levels of Asp, Gly and Ser were higher in the SDPP group than the PSP and RPP groups, and the level of Pro was higher in the SDPP group than the RPP group (p<0.05). Finally, total non-essential amino acid and total amino acid digestibility were higher in the SDPP group than in the other treatments (p<0.05). Taken together, the results of this study indicate that animal protein is more bioavailable than plant protein. However, the N absorption ratio and ileal digestibility were found to be similar in the SDPP and RBP groups.

• Journal of Korea Spatial Information System Society
• /
• v.10 no.4
• /
• pp.77-88
• /
• 2008

There has been growing demands to manage the water quality of west coastal region due to the large scale urbanization along the coastal zone, the possibility of application of TMDL(Total Maximum Daily Loadings) to Han river, and the natural disaster such as oil spill incident in Taean, Chungnam. However, no system has been developed for such purposes. In this background, the demand of GIS based effective water quality management has been increased to monitor water quality environment and propose best management alternatives for Han river and Kyeonggi bay. This study mainly focused on the development of integrated water quality management system for Han river bas in and its estuary are a connected to Kyeonggi bay to support integrated water quality management and its plan. Integration was made based on GIS by spatial linking between water quality attributes and location information. A GIS DB was built to estimate the amount of generated and discharged water pollutants according to TMDL technical guide and it included input data to use two different water quality models--W ASP7 for Han river and EFDC for coastal area--to forecast water quality and to suggest BMP(Best management Practices). The results of BOD, TN, and TP from WASP7 were used as the input to run EFDC. Based on the study results, some critical areas which have relatively higher pollutant loadings were identified, and it was also identified that the locations discharging water pollutant loadings to river and seasonal factor affected water quality. And the relationship of water quality between river and its estuary area was quantitatively verified. The results showed that GIS based integrated system could be used as a tool for estimating status-quo of water quality and proposing economically effective BMPs to mitigate water pollution. Further studies need to be made for improving system's capabilities such as adding decision making function as well as cost-benefit analysis, etc. Also, the concrete methodology for water quality management using the system need to be developed.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.39-54
• /
• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.