• Parasites, Hosts and Diseases
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• v.62 no.1
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• pp.151-156
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• 2024

Bovine borreliosis, caused by Borrelia theileri which is transmitted via hard tick bites, is associated with mild clinical symptoms, such as fever, lethargy, hemoglobinuria, anorexia, and anemia. Borrelia theileri infects various animals, such as cattle, deer, horses, goats, sheep, and wild ruminants, in Africa, Australia, and South America. Notably, no case of B. theileri infection has been reported in Korean cattle to date. In this study, 101 blood samples were collected from a Korean indigenous cattle breed, among which 1.98% tested positive for B. theileri via nested PCR. The obtained sequences exhibited high homology with B. theileri strains identified in other regions. Phylogenetic analysis of 16S rRNA confirmed the B. theileri group affiliation; however, flagellin B sequences exhibited divergence, potentially due to regional evolutionary differences. This study provides the first molecular confirmation of B. theileri infection in Korean livestock. Further isolation and nucleotide sequence analyses are necessary to better understand the presence of B. theileri strains in cows in Korea.

• Molecular Medicine Reports
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• v.20 no.4
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• pp.3301-3307
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• 2019

c-Myc is a characteristic oncogene with dual functions in cell proliferation and apoptosis. Since the overexpression of the c-Myc proto-oncogene is a common event in the development and growth of various human types of cancer, the present study investigated whether oncogenic c-Myc can alter natural killer (NK) cell-mediated immunity through the expression of associated genes, using PCR, western blotting and flow cytometry assays. Furthermore, whether c-Myc could influence the expression levels of natural killer group 2 member D (NKG2D) ligands, which are well known NK activation molecules, as well as NK cell-mediated immunity, was investigated. c-Myc was inhibited by 10058-F4 treatment and small interfering RNA transfection. Upregulation of c-Myc was achieved by transfection with a pCMV6-myc vector. The inhibition of c-Myc increased MHC class I polyeptide-related sequence B and UL16 binding protein 1 expressions among NKG2D ligands, and the overexpression of c-Myc suppressed the expression of all NKG2D ligands, except MHC class I polyeptide-related sequence A. Furthermore, the alteration of c-Myc activity altered the susceptibility of K562 cells to NK cells. These results suggested that the overexpression of c-Myc may contribute to the immune escape of cancer cells and cell proliferation. Combined treatment with NK-based cancer immunotherapy and inhibition of c-Myc may achieve improved therapeutic results.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.3933-3940
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• 2013

The purpose of present study was to determine the frequency of RA with age and to investigate the age-related trends and changing-factors in RA, CA and IAs. The refractive power of the eye and the power of corneal anterior surface were measured with auto-refractor among 1,017 inhabitants aged 5 to 59 years in Cheongju in July 2010. The overall frequency of RA was 22.7%, and frequency of RA increased from 8.9% in 5~9 years age group to 36.8% in 20~29 years age group. It then dipped to 19.2% in 40~49 years age group but increased again 28.6% in 50~59 years age group. $J_{45}$ components for RA, CA, and IAs were fairly stable in different age groups, the changes in $J_0$ components for both RA and CA appeared to be decreased after age of 30 years. In addition, the refractive power on the vertical direction was changed slightly with age, but the refractive power on the horizontal direction was changed significantly with age. It was expected that the change in the frequency of astigmatism with age was due to the change in the refractive power of horizontal meridian.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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• pp.2045-2049
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• 2012

Objective: To investigate the effect of down-regulation of Sentrin/SUMO-specific protease 1 (SENP1) expression on the apoptosis of human Burkitt lymphoma cells (Daudi cells) and potential mechanisms. Methods: Short hairpin RNA (shRNA) targeting SENP1 was designed and synthesized and then cloned into a lentiviral vector. A lentiviral packaging plasmid was used to transfect Daudi cells (sh-SENP1-Daudi group). Daudi cells without transfection (Daudi group) and Daudi cells transfected with blank plasmid (sh-NC-Daudi group) served as control groups. Flow cytometry was performed to screen GFP positive cells and semiquantitative PCR and Western blot assays were employed to detect the inference efficiency. The morphology of cells was observed under a microscope before and after transfection. Fluorescence quantitative PCR and Western blot assays were conducted to measure the mRNA and protein expression of apoptosis related molecules (caspase-3, 8 and 9). After treatment with $COCl_2$ for 24 h, the mRNA and protein expression of hypoxia inducible factor -$1{\alpha}$ (HIF-$1{\alpha}$) was determined. Results: Sequencing showed the expression vectors of shRNA targeting SENP1 to be successfully constructed. Following screening of GFP positive cells by FCM, semiqualitative PCR showed the interference efficiency was $79.2{\pm}0.026%$. At 48 h after transfection, the Daudi cells became shrunken, had irregular edges and presented apoptotic bodies. Western blot assay revealed increase in expression of caspase-3, 8 and 9 with prolongation of transfection (P<0.05). Following hypoxia treatment, mRNA expression of HIF-$1{\alpha}$ remained unchanged in three groups (P>0.05) but the protein expression of HIF-$1{\alpha}$ markedly increased (P<0.05). However, in the sh-SENP1-Daudi group, the protein expression of HIF-$1{\alpha}$ remained unchanged Conclusion: SENP1-shRNA can efficiently inhibit SENP1 expression in Daudi cells. SENP1 inhibition may promote cell apoptosis. These findings suggest that SENP1 may serve as an important target in the gene therapy of Burkitts lymphoma.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.101-113
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• 2007

The period of military personnel service will be phased down by 2014 according to 'The law of National Defense Reformation' issued by the Ministry of National Defense. For this reason, the ROK army provides discrimination education to 'newly recruited privates' for more effective individual performance in the on-the-job training. For the training to be more effective, it would be essential to predict the degree of achievements by new privates in the training. Thus, we used data mining techniques to develop a classification model which classifies the new privates into one of two achievements groups, so that different skills of education are applied to each group. The target variable for this model is a binary variable, whose value can be either 'a group of general control' or 'a group of special control'. We developed four pure classification models using Neural Network, Decision Tree, Support Vector Machine and Naive Bayesian. We also built four hybrid models, each of which combines k-means clustering algorithm with one of these four mining technique. Experimental results demonstrated that the highest performance model was the hybrid model of k-means and Neural Network. We expect that various military education programs could be supported by these classification models for better educational performance.

• Animal Bioscience
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• v.36 no.2
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• pp.200-208
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• 2023

Objective: Muscle acetylcholine receptors have five alpha subunits (α, β, δ, ε, or γ), and cholinergic receptor nicotinic gamma subunit (CHRNG) is the γ subunit. It may also play an essential role in biological processes, including cell differentiation, growth, and survival, while the role of CHRNG has not been studied in the literature. Therefore, the purpose of this study is to clarify the effect of CHRNG on the proliferation and differentiation of bovine preadipocytes. Methods: We constructed a CHRNG overexpression adenovirus vector and successfully overexpressed it on bovine preadipocytes. The effects of CHRNG on bovine preadipocyte proliferation were detected by Edu assay, cell counting Kit-8 (CCK-8), real-time fluorescence quantitative polymerase chain reaction (RT-qPCR), Western blot and other techniques. We also performed oil red O, RT-qPCR, Western blot to explore its effect on the differentiation of preadipocytes. Results: The results of Edu proliferation experiments showed that the number of EDU-positive cells in the overexpression group was significantly less. CCK-8 experiments found that the optical density values of the cells in the overexpression group were lower than those of the control group, the mRNA levels of proliferating cell nuclear antigen (PCNA), cyclin A2 (CCNA2), cyclin B1 (CCNB1), cyclin D2 (CCND2) decreased significantly after CHRNG gene overexpression, the mRNA levels of cyclin dependent kinase inhibitor 1A (CDKN1A) increased significantly, and the protein levels of PCNA, CCNB1, CCND2 decreased significantly. Overexpression of CHRNG inhibited the differentiation of bovine preadipocytes. The results of oil red O and triglyceride determination showed that the size and speed of lipid droplets accumulation in the overexpression group were significantly lower. The mRNA and protein levels of peroxisome proliferator activated receptor gamma (PPAR class="checkNonKBPoint">γ), CCAAT enhancer binding protein alpha (CEBPα), fatty acid binding protein 4 (FABP4), fatty acid synthase (FASN) decreased significantly. Conclusion: Overexpression of CHRNG in bovine preadipocytes inhibits the proliferation and differentiation of bovine preadipocytes.

• The Korean Journal of Pesticide Science
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• v.18 no.3
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• pp.202-209
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• 2014

RNA interference (RNAi) is the method which controls phenotypes of gene in live cells. Chitinase is the enzyme helping digestion and absorption of old cuticles during the ecdysis of insects. In order to investigate molting-inhibition effect with the chitinase related gene in Spodoptera litura, RNA was extracted from the $5^{th}$ instars. cDNA was synthesized and then we obtained about 700 bp size chitinase. After PCR products were cloned into a pGEM T-easy vector, colonies were picked. DNA was extracted from the colony cultures. EcoR I enzyme was used to check whether PCR products were inserted or not. And then we confirmed vector band of about 3 kb and insert band of about 700 bp. To synthesize the dsRNA, each DNA was cut with Spe I and Nco I enzymes (Circular DNA became lineared DNA). After synthesis of dsRNA, approximately 5 ul dsRNA was injected into the $3^{rd}$ abdominal segment of S. litura $4^{th}$ larvae. The concentration of dsRNA was about $10{\mu}g/{\mu}l$. We confirmed larval-larval molting : there were phenotypically abnormal individuals - for instance malformation, molting inhibition and change of integument color. Pupaadult molting : there were phenotypically abnormal individuals - for instance molting inhibition, change of wings and malformation. Also we could investigate the pupation, emergence and variation about noninjection, treated with DW and dsRNA. Each pupation was non-injection 83.3%, DW 78.3% and dsRNA 66.7%. Each emergence was non-injection 90.0%, DW 72.3% and dsRNA 65.0%. So we considered that chitinase dsRNA induced molting inhibition effect. But each variation was non-injection 8.9%, DW 2.9% and dsRNA 19.2%. Therefore dsRNA group showed the highest variation value. When 18 hours after injecting dsRNA, we could obtain abnormal individual.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• The Korean Journal of Nuclear Medicine
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• v.38 no.3
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• pp.253-258
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• 2004

Purpose: Chitosan has been studied as a non-viral gene delivery vector, drug delivery carrier, metal chelator, food additive, and radiopharmaceutical, among other things. Recently, galactose-graft chitosan was studied as a non-viral gene and drug delivery vector to target hepatocytes. The aim of this study was to investigate the usefulness of nuclear imaging for in vivo evaluation of targeting the hepatocyte by galactose grafting. Methods and Materials: Galactosyl methylated chitosan (GMC) was produced by methylation to lactobionic acid coupled chitosan. Cytotoxicity of $^{99m}Tc$-GMC was determined by MTT assay. Rabbits were injected via their auricular vein with $^{99m}Tc$-GMC and $^{99m}Tc$-methylated chitosan (MC), the latter of which does not contain a galactose group, and images were acquired with a gamma camera equipped with a parallel hole collimator. The composition of the galactose group in galactosylated chitosan (GC), as well as the tri-, di-, or mono-methylation of GMC, was confirmed by NMR spectroscopy. Results: The results of MTT assay indicated that $^{99m}Tc$-GMC was non-toxic. $^{99m}Tc$-GMC specifically accumulated in the liver within 10 minutes of injection and maintained high hepatic uptake. In contrast, $^{99m}Tc$-MC showed faint liver uptake. $^{99m}Tc$-GMC scintigraphy of rabbits showed that the galactose ligand principally targeted the liver while the chitosan functionalities led to excretion through the urinary system. Conclusion: Bioconjugation with a specific ligand endows some degree of targetability to an administered molecule or drug, as in the case of galactose for hepatocyte in vivo, and evaluating said targetabililty is a clear example of the great benefit proffered by nuclear imaging.