• Journal of Veterinary Clinics
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• v.27 no.5
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• pp.501-507
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• 2010

The aim of the current study was to analyze the diversity of the major surface protein (Msp) gene in Theileria buffeli, which is known as the major antigenic protein recognized by the immune system of the host. In addition, we characterized the diversification of the Msp gene and its relationship to with the pathogenicity of Theileria. Complete blood counts (CBC) and Theileria 18S rRNA PCR sequence analysis were performed for 177 Korean indigenous cattle (KIC) in Jeju Island. A total of 28 KIC (16 anemic and 12 non-anemic KIC) were then randomly selected based on 18s rRNA PCR positive samples for sequence analysis of the Theileria Msp gene, which was performed twice for each specimen. The resulting 56 Msp gene sequences were classified into five antigenicity types (type I to V), according to the variable region (517-571 bp), which exhibited high similarity (${\geq}$ 98.9%) to several available GenBank sequences (Theileria spp. from China-EU584237; T. sergenti from China-DQ078264; Theileria spp. from Thailand-AB081329; Theileria spp. from Japan-AB218442; T. sergenti from Japan-AB016280). The 56 Msp sequences consisted of 22, 15, 9, 8, and 2 cases of type I to type V Msp genes, respectively. The most prevalent type in both anemic and non-anemic KIC was type I (37.5% in anemic and 41.7% in non-anemic). Among the remaining types, type II was the most prevalent (37.5%) in anemic KIC, while type IV was the most prevalent (25%) in non-anemic KIC. The results of our study help confirm the diversity of Msp gene types and demonstrate that the gene type distribution of Msp genes varies among Theileria-infected KIC in Jeju Island.

• Journal of Life Science
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• v.25 no.3
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• pp.349-356
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• 2015

Thanks to recent advances in next-generation sequencing (NGS) technology, diverse livestock species have been dissected at the genome-wide sequence level. As for cattle, there are currently four Korean indigenous breeds registered with the Domestic Animal Diversity Information System of the Food and Agricultural Organization of the United Nations: Hanwoo, Chikso, Heugu, and Jeju Heugu. These native genetic resources were recently whole-genome resequenced using various NGS technologies, providing enormous single nucleotide polymorphism information across the genomes. The NGS application further provided biological such that Korean native cattle are genetically distant from some cattle breeds of European origins. In addition, the NGS technology was successfully applied to detect structural variations, particularly copy number variations that were usually difficult to identify at the genome-wide level with reasonable accuracy. Despite the success, those recent studies also showed an inherent limitation in sequencing only a representative individual of each breed. To elucidate the biological implications of the sequenced data, further confirmatory studies should be followed by sequencing or validating the population of each breed. Because NGS sequencing prices have consistently dropped, various population genomic theories can now be applied to the sequencing data obtained from the population of each breed of interest. There are still few such population studies available for the Korean native cattle breeds, but this situation will soon be improved with the recent initiative for NGS sequencing of diverse native livestock resources, including the Korean native cattle breeds.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Tuberculosis and Respiratory Diseases
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• v.58 no.4
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• pp.352-358
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• 2005

Microsatellites are short tandem repeated nucleotide sequences that are present throughout the human genome. Variations in the repeat number or a loss of heterozygosity around the microsatellites have been termed a microsatellite alteration (MA). A MA reflects the genetic instability caused by an impairment in the DNA mismatch repair system and is suggested to be a novel tumorigenic mechanism. A number of studies have reported that MA in the DNA extracted from the plasma occurs at varying frequencies among patients with a non-small cell lung carcinoma (NSCLC). The genomic DNA from 9 subjects with a non-small cell lung cancer (squamous cell cancer 6, adenocarcinoma 2, non-small cell lung cancer1) and 9 age matched non-cancer control subjects (AMC: tuberculosis 3, other inflammatory lung disease 6) and 12 normal control subjects (NC) were extracted from the peripheral blood leukocytes and plasma. Three microsatellite loci were amplified with the primers targeting the Gene Bank sequence D21S1245, D3S1300, and D3S1234. MA in the form of an allelic loss or a band shift was examined with 6% polyacrylamide gel electrophoresis and silver staining. None (0/12) of the NC subjects less than 40 years of age showed a MA in any of the three markers, while 88.9%(8/9) of the AMC above 40 showed a MA in at least one of the three markers (p<0.05). Sixty percent(6/10) of the control subjects with a smoking history showed a MA in one of the three markers, while 9.1%(1/11) of the control subjects without smoking history showed a MA (p<0.05). However, not only did 66.7%(6/9) of lung cancer patients show a MA in at least one of the three markers but so did 88.9%(8/21) of the AMC patients (p>0.05). In conclusion, a MA in the D21S1245, D3S1300, and D3S1234 loci using DNA extracted from the plasma was detected in 66.7% of lung cancer while no MA was found in the young non-smoking control subjects. However, many of the non-cancer control subjects (aged smokers) also showed a MA, which compromised the specificity of the MA analysis as a screening test. Therefore, a further study with a larger sample size will be needed.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of Embryo Transfer
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• v.30 no.3
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• pp.149-159
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• 2015

This study was designed to know the possibility in repeat uses of elite donor cows for getting mass production of OPU-derived embryo production (OPU-IVP). Ultrasound transvaginal ovum pick-up (OPU) performed in 6 Korean native cows was aged 4 to 10 years old. The aspiration of immature oocytes for OPU derived embryo was carried out 2 times per week, and OPU-IVP of $1^{st}$ period was carried out 22~48 sessions from each donors. And the break time for OPU-IVP of $2^{nd}$ period after $1^{st}$ OPU from each donors were 2~25 months. The OPU-IVP of $2^{nd}$ period each donors conducted total 15~65 times for 2~8 months by an ultrasonographic, was guided follicular aspiration system. The average numbers of collected oocytes, grade 1 + grade 2(G1+G2) oocytes and cleavage embryo from $1^{st}$ period OPU-IVP were significantly differences between donors (p<0.05). Total collected oocytes of donor D were significantly higher compared with donors of A, B, C, E and F (average 17.0 per session vs. 11.2, 10.1, 8.5, 10.2 and 9.6; p<0.05) and also oocytes of G1+G2 were significantly higher compared with r A and D and subsequently to donors of B, C, E and F (average 7.9 and 8.5 per session vs. 5.0, 2.7, 6.0 and 1.6; p<0.05). Cleavage rate of donor D was significantly higher compared with donors of A, B, C, E and F (average 13.1 per session vs. 10.1, 9.1, 6.9, 8.9 and 6.7; p<0.05). The average numbers of OPU-IVP for $1^{st}$ period was significantly higher from donors of B, D and E than those from donors of A, C and F (average 6.5, 7.1 and 6.5 per session vs. 3.5, 4.2 and 2.8; p<0.05). The possibility investigation of $2^{nd}$ OPU-IVP was carried out after 2~25 months rest periods from $1^{st}$ period OPU session. Total average numbers of collected oocytes, cleavages and blastocyst development rates were significantly higher from $1^{st}$ period OPU compared with $2^{nd}$ period one (p<0.05). The OPU-IVP efficiency by break for more embryo production from elite cow was analysis comparing without rest of donor A, under 6 months rest period as B and over 6 months rest period as C and then the average numbers of collected oocytes, cleavages and blastocysts were significantly higher from A group (11.8, 9.5 and 5.2 per session) than those from B and C groups (7.9, 6.2 and 2.6 vs. 9.2, 7.5 and 3.9, p<0.05), and also C group was significantly higher than B group. In conclusion, $1^{st}$ period OPU-IVP was more efficient compared with $2^{nd}$ period repeated uses of donor, and the break times for additional production of embryo on donor were needed more than over 6 months after $1^{st}$ period OPU-IVP. This repeating uses of elite donor cows given more emphasis for getting the opportunity on mass production of elite cow OPU-IVP embryo should be increased G1+G2 possibility of genetic improvement of livestock within short period.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Economic and Environmental Geology
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• v.54 no.1
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• pp.49-60
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• 2021

The Geumhwa Au-Ag deposit is located within the Cretaceous Gyeongsang basin. Mineral paragenesis can be divided into two stages (stage I and II) by major tectonic fracturing. Stage II is economically barren. Stage I, at which the precipitation of major ore minerals occurred, is further divided into three substages(early, middle and late) with paragenetic time based on minor fractures and discernible mineral assemblages: early substage, marked by deposition of pyrite with minor wolframite; middle substage, characterized by introduction of electrum and base-metal sulfides with Cu-As and/or Cu-Sb sulfosalts; late substage, marked by hematite and Bi-sulfosalts with secondary minerals. Changes in vein mineralogy reflect decreases in temperature and sulfur fugacity with a concomitant increase in oxygen fugacity. Fluid inclusion data indicate progressive decreases in temperature and salinity within each substage with increasing paragenetic time. During the early portion of stage I, high-temperature (≥410℃), high-salinity fluids (up to ≈44 equiv. wt. % NaCl) formed by condensation during decompression of a magmatic vapor phase. During waning of early substage, high-temperature, high-salinity fluids gave way to progressively cooler, more dilute fluids associated with main Au-Ag mineralization (middle) and finally to ≈180℃ and ≥0.7 equiv. wt. % NaCl fluids associated with hematite and sulfosalts (± secondary) mineralization (late substage). These trends are interpreted to indicate progressive mixing of high- and medium to low-salinity hydrothermal fluids with cooler, more dilute, oxidizing meteoric waters. The Geumhwa Au-Ag deposit may represent a vein-type system transitional between porphyry-type and epithermal-type.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.26 no.1
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• pp.32-39
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• 1981

In order to study the genetic system of the blast resistant varieties, the conidial suspension of mutant races of T-2$^{+t}$, N-2$^{+t}$, C-8$^{+t}$ was inoculated at 4-5 leaf stage by injector for F_2 seedlings from the crosses between seven resistant varieties and four maker lines easily detectable at seedling stage. The results are summarized as follows; 1. The fertility of cross between Semi-dwaf testers and Indica resistant varieties except Carreon was about 74 percents. 2. The segregation modes of resistance varied with varieties and blast races. However, the resistance was expressed as dominance in all cases. Tetep, Tadukan and Carreon showed more complicated segregation for resistance than that of the bred lines. 3. For blast races used, four segregation ratios such as 3:1, 9:7, 13:3 and 37:27 were found in the Tatukan, Tetep, and IR747, and three segregation ratios such as 3:1, 13:3 and 15:1 in the Carreon, and two segregation ratios of 3:1 and 13:3 with Suweon 287, Suweon 288, and Iri342. 4. In the segregation of the resistance to the each races, the ratios of 3:1, 13:3, 15:1 were fitted to T-2$^{+t}$, and the ratios of 3:1, 13:3, 9:7 and 37:27 to N-2$^{+t}$ and C-8$^{+t}$. 5. Suweon 287, Suweon 288 and Iri342 carried one simple dominant gene and inhibitor gene was considered in some cross combinations. Meanwhile Tadukan, Tetep and IR747 seemed to carry one to three resistant genes, and in some cross combinations, the expression of these genes were simple dominant, inhibiting, duplicating and complimentary action. 6. Resistance genes to blast races, T-2$^{+t}$, N-2$^{+t}$ and C-8$^{+t}$ in the Tadukan, Tetep, Carreon, Suweon 287, Suweon 288 and Iri342 were found to be independent with the linkage group of II(lg), VIII(la), XI(bc), and XII(gl).bc), and XII(gl).

• Journal of Plant Biotechnology
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• v.50
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• pp.248-254
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• 2023

This study was conducted to establish an efficient plant regeneration system in 'Dayu', a Korean variety of Perilla frutescens developed for seed oil production, in conjunction with the previously studied variety 'Namcheon'. The healthiest callus was formed on the hypocotyl explants cultured on a medium containing 0.1 mg/L NAA and 0.5 mg/L BA, outperforming the leaf and cotyledon samples. In both dark and long-day conditions, Dayu consistently exhibited significantly higher shoot regeneration rates compared with Namcheon. The highest shoot regeneration rates in Dayu were observed from the hypocotyl explants cultured on 0.1 mg/L NAA and 0.5 mg/L BA media, with shoot regeneration rates of 84.4% and 86.7% under dark and long-day conditions, respectively. Various combinations of plant growth regulators were tested to establish the optimal shoot regeneration conditions for Dayu hypocotyl explants. The results demonstrated that the highest shoot regeneration rate (90%) was achieved when 0.5 mg/L of BA was added to the medium without NAA. Among the regenerated shoots, 70.5% were normal plants, while 19.3% were abnormal. The addition of NAA or an increase in its concentration led to a higher occurrence of abnormal plants. After the regenerated shoots were transferred to 1/2 MS medium, roots were observed within 10-15 days. By day 30, they had developed into complete plants. The results obtained from the regeneration experiments with the perilla variety Dayu can valuably inform molecular breeding reliant on transformation techniques such as genome-editing and genetic modification technology.