• Clinical and Experimental Pediatrics
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• v.52 no.7
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• pp.811-817
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• 2009

Purpose : Inhaled corticosteroids (ICS) are used as first-line agents for the treatment of persistent asthma; however, their use is accompanied by apprehension of potential systemic adverse effects. This study aimed to assess the effects of ICS on bone mineral density (BMD) and bone metabolism in children with asthma. Methods : From February 2008 to September 2008, 26 asthmatic children treated with ICS (ICS group), 15 asthmatic children treated with leukotriene receptor antagonist (LTRA) (LTRA group), and 30 healthy children (Control group) were selected from the Korea University Anam Hospital. BMD and serum bone-specific alkaline phosphatase (BALP) levels were measured. The asthmatic children underwent spirometry and methacholine bronchial challenge test. Results : There were no significant differences in BMD in the lumbar spine (P=0.254) and proximal femur (P=0.297) among the 3 groups. The serum BALP levels were significantly higher in both the ICS (P=0.017) and LTRA (P=0.025) groups than in the Control group. None of the parameters pertaining to ICS use, such as the mean daily dose during the last 6 months, the total cumulative dose, duration of use, and age of commencement of use, showed significant correlations with BMD (P>0.05 for all parameters). Conclusions : We demonstrated that a low dose of ICS does not exert any significant adverse effect on bone metabolism in asthmatic children. These findings support the current recommendations with regard to the use of ICS for asthmatic children.

• Clinical and Experimental Pediatrics
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• v.52 no.2
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• pp.181-186
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• 2009

Purpose : Inflammation plays a potential role in the pathogenesis of bronchopulmonary dysplasia (BPD). Strategies for preventing BPD include respiratory management, antioxidants, nutritional treatment, and others such as anti-inflammatory agents. We aimed to assess the safety, tolerability, and efficacy of montelukast (MK), a cysteinyl leukotriene 1 receptor antagonist, as an add-on therapy in BPD. Methods : In addition to currently available standard measures such as oxygen supplementation, bronchodilators, nutritional support, and/or diuretics, montelukast was administered to 15 preterm infants with BPD. MK was given orally (1 mg/kg/d) for a mean period of 12 weeks. We compared safety and efficacy parameters with historical controls. Results : All 15 patients survived, and no differences were found in the incidence of adverse reactions between the 2 groups. The ventilation index was significantly improved after 2 weeks in MK group compared with historical controls. There were no significant differences in other respiratory parameters (MAP, oxygen dependency, and ventilator dependency) between the groups, but the MK group showed trends of greater improvement. Conclusion : Administration of MK 1 mg/kg/d was well tolerated in preterm BPD patients as an add-on therapy. We demonstrated that after 2 weeks of MK administration of 1 mg/kg/d, MK had beneficial therapeutic effects on BPD patients as an add-on to the standard therapy. Further multicenter randomized controlled clinical trials are needed to confirm the efficacy and safety of MK as a useful supplement to standard therapy for BPD patients.

• Journal of Chest Surgery
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• v.36 no.8
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• pp.576-582
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• 2003

During the Off-Pump Coronary Arterial Bypass surgery (OPCAB), the manipulation of the heart can depress cardiac contractility and cause hemodynamic instability. In this study, hemodynamic parameters were measured during operation and the laboratory and clinical data were investigated to evaluate their effects on postoperative outcome. Material and Method: From March 2001 to August 2002, 50 consecutive patients who underwent OPCAB were included in this study. During the same period, total number of CABG was 71 The blood pressure, pulmonary artery pressure, mixed venous oxygen saturation, and cardiac index were measured before manipulation, after application of stabilizer, and at the end of anastomosis. Postoperatively, we measured the cardiac enzymes such as CK-MB, troponin 1 and checked the amount of inotropes required, chest tube drainage, the amount of transfusion, duration of ventilator support, and duration of ICU stay. Result: The number of mean distal anastomoses was 2.8$\pm$0.9 per patient. On elevation and stabilization of the heart, systolic blood pressure was depressed and pulmonary artery pressure was elevated significantly, but during each anastomosis no significant changes were detected. The peak level of cardiac markers was 29.2$\pm$46.7 for CK-MB, 0.69$\pm$0.86 for troponin 1 on postoperative day f. Among the intraoperative hemodynamic parameters, the ischemic change of EKG and bolus injection of inotropes significantly affected the posteroperative cardiac enzymes. But, no difference other than the level of cardiac enzymes between the two groups with or without the ischemic change of EKG and bolus injection of inotropes was noticed. Conclusion: The significant hemodynamic changes occurred when the heart was elevated and stabilized, however during anastomoses there were no significant changes. Serum cardiac enzymes rose significantly in the group that showed the ischemic charge of EKG or needed the bolus injection of inotropes for maintaining hemodynamic stability intraoperatively, but it did not affect the postoperative outcome. In conclusion, the ischemic change of EKG and the need for bolus injection of intropes during operation may be very indicative for probable ischemia.

• Korean Journal of Environment and Ecology
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• v.29 no.3
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• pp.391-400
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• 2015

In order to investigate the distribution and species composition of fish in shallow wetlands that might be affected by environmental factors, we investigated the physicochemical parameters, macrophytes biomass, and fish assemblage in 24 shallow wetlands in South Korea from May to June, 2012. In this study, a total of 20 fish species were identified, and Cypinidae were found to be the most dominant species. Physicochemical parameters and macrophyte biomass were different in the survey sites, and macrophytes biomass, in particular, showed a positive relationship with fish abundance in stepwise multiple regression (df=1, F=32.00, P=0.001). According to the result of the cluster analysis between survey sites, the survey sites were divided into three groups in accordance with species composition of fish in relation to macrophytes biomass. In the wetlands of the first group, Lepomis macrochirus which belongs to Centrarchidae was found to be dominant and other fish assemblages were hardly seen. In the second group, unlike the first group, Carassius auratus that belongs to Cypinidae was found to be dominant. In the third group, Lepomis macrochirus was found to be as dominant as the first group but various other fish species appeared. Where there was abundance of the main food sources (i. e. zooplankton) of fish in the survey sites, there were more diverse macrophyte biomass. Consequently, it is proven that macrophytes strongly affect the species composition and abundance of fish, and high biomass of macrophytes support high assemblage of fish. Based on these results, we recommend establishing diverse aquatic macrophytes communities when restoring or creating wetlands to assure high diversity of fish species that use macrophytes as their habitat.

• Journal of Chest Surgery
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• v.38 no.2 s.247
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• pp.101-109
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• 2005

Background: In sudden cardiac arrest, the effective maintenance of coronary artery blood flow is of paramount importance for myocardial preservation as well as cardiac recovery and patient survival. The purpose of this study was to directly compare the effects of pulsatile and non-pulsatile circulation to coronary artery flow and myocardial preservation in cardiac arrest condition. Material and Method: A cardiopulmonary bypass circuit was constructed in a ventricular fibrillation model using fourteen Yorkshire swine weighing $25\~35$ kg each. The animals were randomly assigned to group I (n=7, non-pulsatile centrifugal pump) or group II (n=7, pulsatile T-PLS pump). Extra-corporeal circulation was maintained for two hours at a pump flow of 2 L/min. The left anterior descending coronary artery flow was measured with an ultrasonic coronary artery flow measurement system at baseline (before bypass) and at every 20 minutes after bypass. Serologic parameters were collected simultaneously at baseline, 1 hour, and 2 hours after bypass in the coronary sinus venous blood. The Mann-Whitney U test of STATISTICA 6.0 was used to determine intergroup significances using a p value of < 0.05. Result: The resistance index of the coronary artery was lower in group II and the difference was significant at 40 min, 80 min, 100 min and 120 min (p < 0.05). The mean velocity of the coronary artery was higher in group II throughout the study, and the difference was significant from 20 min after starting the pump (p < 0.05). The coronary artery blood flow was higher in group II throughout the study, and the difference was significant from 40 min to 120 min (p < 0.05) except at 80 min. Serologic parameters showed no differences between the groups at 1 hour and 2 hours after bypass in the coronary sinus blood. Conclusion: In cardiac arrest condition, pulsatile extracorporeal circulation provides more blood flow, higher flow velocity and less resistance to coronary artery than non-pulsatile circulation.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.235-243
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• 2007

Hepatocellular carcinoma is significant worldwide public health problem with an estimated annually mortality of 1,000,000 people. Radiofrequency (RF) ablation is an interventional technique that in recent years has come to be used for treatment of the hepatocellualr carcinoma, by destructing tumor tissues in high temperatures. Numerous studies have been attempted to prove excellence of RF ablation and to improve its efficiency by various methods. However, the attempts are sometimes paradox to advantages of a minimum invasive characteristic and an operative simplicity in RF ablation. The aim of the current study is, therefore, to suggest an improved RF ablation technique by identifying an optimum RF pattern, which is one of important factors capable of controlling the extent of high temperature region in lossless of the advantages of RF ablation. Three-dimensional finite element (FE) model was developed and validated comparing with the results reported by literature. Four representative Rf patterns (sine, square, exponential, and simulated RF waves), which were corresponding to currents fed during simulated RF ablation, were investigated. Following parameters for each RF pattern were analyzed to identify which is the most optimum in eliminating effectively tumor tissues. 1) maximum temperature, 2) a degree of alteration of maximum temperature in a constant time range (30-40 second), 3) a domain of temperature over $47^{\circ}C$ isothermal temperature (IT), and 4) a domain inducing over 63% cell damage. Here, heat transfer characteristics within the tissues were determined by Bioheat Governing Equation. Developed FE model showed 90-95% accuracy approximately in prediction of maximum temperature and domain of interests achieved during RF ablation. Maximum temperatures for sine, square, exponential, and simulated RF waves were $69.0^{\circ}C,\;66.9^{\circ}C,\;65.4^{\circ}C,\;and\;51.8^{\circ}C$, respectively. While the maximum temperatures were decreased in the constant time range, average time intervals for sine, square, exponential, and simulated RE waves were $0.49{\pm}0.14,\;1.00{\pm}0.00,\;1.65{\pm}0.02,\;and\;1.66{\pm}0.02$ seconds, respectively. Average magnitudes of the decreased maximum temperatures in the time range were $0.45{\pm}0.15^{\circ}C$ for sine wave, $1.93{\pm}0.02^{\circ}C$ for square wave, $2.94{\pm}0.05^{\circ}C$ for exponential wave, and $1.53{\pm}0.06^{\circ}C$ for simulated RF wave. Volumes of temperature domain over $47^{\circ}C$ IT for sine, square, exponential, and simulated RF waves were 1480mm3, 1440mm3, 1380mm3, and 395mm3, respectively. Volumes inducing over 63% cell damage for sine, square, exponential, and simulated RF waves were 114mm3, 62mm3, 17mm3, and 0mm3, respectively. These results support that applying sine wave during RF ablation may be generally the most optimum in destructing effectively tumor tissues, compared with other RF patterns.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.10
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• pp.1406-1413
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• 2016

This study was carried out to evaluate the toxicity of ethanolic extracts of Morus alba L. branch (ME). In the reverse mutation test, Salmonella Typhimurium TA98, TA100, TA1535, TA1357, and Escherichia coli WP2uvrA were used to estimate the mutagenic potential of ME. Sprague-Dawley rats were orally administered ME at levels of 1,250, 2,500, and 5,000 mg/kg for the single-dose toxicity test and 500, 1,000, and 2,000 mg/kg/d for the repeated-dose toxicity test for 28 consecutive days. As expected, reverse mutation was not detected at any concentration of ME, regardless of application of the metabolic activation system with or without S9 mix. In the single-dose toxicity test, ME caused neither significant visible signs of toxicity nor mortality in rats, and $LD_{50}$ was estimated to be over 5,000 mg/kg. In the repeated-dose toxicity test, ME administration at 500, 1,000, and 2,000 mg/kg for 28 days to male or female rats did not result in mortality. Similarly, no toxicologically significant treatment-related changes in body weight, food intake, or organ weights were noted. Several hematological and biochemical parameters in both genders showed significant differences, but these were within normal ranges. These results support the safe use of ME.