• Journal of Life Science
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• v.21 no.8
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• pp.1149-1155
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• 2011

Myostatin (MSTN) belongs to the transforming growth factor-${\beta}$ superfamily or growth and differentiation factor 8 (GDF-8), and functions as a negative regulator of skeletal muscle development and growth. Previous studies in mammals have suggested that myostatin knock-out increased muscle mass and decreased fat content compared to those of the wide type. Recently, several studies on myostatin have beenconducted on the block myostatin signal pathway with myostatin antagonists and the MSTN regulation with RNAi to control myostatin function. This study was performed to analyze growth and muscle alteration of Oncorhychus masou by treatment with recombinant myostatin prodomains derived from fish. We designed myostatin prodomains derived from P. olivaceus (pMALc2x-poMSTNpro) and S. schlegeli (pMALc2x-sMSTNpro) in a pMALc2x expression vector, and then purified the recombinant proteins using affinity chromatography. The purified recombinant proteins were treated in O. masou through an immersion method. Recombinant protein treated groups did not show a significant difference in weight, protein, or lipid composition compared to the control. However, there was a difference in the average number and area for histological analyses in the muscle fiber. At twelve and twenty-two weeks from the initial treatment, there were differences in averagefiber number and area between the 0.05 mg/l treated-group and the control, but the numbers were similar to those of the control during the same time period. At twelve weeks, however, 0.2 mg/l treated-group had an increase in average fiber number and decrease in average fiber area compared to the control. At twenty-two weeks, the pMALc2x-sMSTNpro 0.2 mg/l treated-group was induced and showed a decrease in average fiber number and increase in average fiber area. The results between twelve and twenty-two weeks showed that the fiber numbers had decreased, whereas average fiberarea had increased due to sMSTNpro. It is understood that the sMSTNpro induced only hyperplasia at twelve weeks, after which it induced hypertrophy. Recombinant myostatin prodomains derived from fish may induce hyperplasia and hypertrophy in O. masou depending upon the time that has elapsed.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.

• Journal of Chest Surgery
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• v.38 no.5 s.250
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• pp.323-334
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• 2005

Background: Gene therapy is a new and promising option for the treatment of severe myocardial ischemia by therapeutic angiogenesis. The goal of this study was to elucidate the efficacy of therapeutic angiogenesis by using VEGF165 in large animals. Material and Method: Twenty-one pigs that underwent ligation of the distal left anterior descending coronary artery were randomly allocated to one of two treatments: intramyocardial injection of pCK-VEGF (VEGF) or intramyocardial injection of pCK-Null (Control). Injections were administered 30 days after ligation. Seven pigs died during the trial, but eight pigs from VEGF and six from Control survived. Echo-cardiography was performed on day 0 (preoperative) and on days 30 and 60 following coronary ligation. Gated myocardial single photon emission computed tomography imaging (SPECT) with $^{99m}Tc-labeled$ sestamibi was performed on days 30 and 60. Myocardial perfusion was assessed from the uptake of $^{99m}Tc-labeled$ sestamibi at rest. Global and regional myocardial function as well as post-infarction left ventricular remodeling were assessed from segmental wall thickening; left ventricular ejection fraction (EF); end systolic volume (ESV); and end diastolic volume (EDV) using gated SPECT and echocardiography. Myocardium of the ischemic border zone into which pCK plasmid vector had been injected was also sampled to assess micro-capillary density. Result: Micro-capillary density was significantly higher in the VEGF than in Control ($386\pm110/mm^{2}\;vs.\;291\pm127/mm^{2};\;p<0.001$). Segmental perfusion increased significantly from day 30 to day 60 after intramyocardial injection of plasmid vector in VEGF ($48.4\pm15.2\%\;vs.\;53.8\pm19.6\%;\;p<0.001$), while no significant change was observed in the Control ($45.1\pm17.0\%\;vs.\;43.4\pm17.7\%;\;p=0.186$). This resulted in a significant difference in the percentage changes between the two groups ($11.4\pm27.0\%\;increase\;vs.\;2.7\pm19.0\%\;decrease;\;p=0.003$). Segmental wall thickening increased significantly from day 30 to day 60 in both groups; the increments did not differ between groups. ESV measured using echocardiography increased significantly from day 0 to day 30 in VEGF ($22.9\pm9.9\;mL\;vs.\;32.3\pm9.1\;mL;\; p=0.006$) and in Control ($26.3\pm12.0\;mL\;vs.\;36.8\pm9.7\;mL;\;p=0.046$). EF decreased significantly in VEGF ($52.0\pm7.7\%\;vs.\;46.5\pm7.4\%;\;p=0.004$) and in Control ($48.2\pm9.2\%\;vs.\;41.6\pm10.0\%;\;p=0.028$). There was no significant change in EDV. The interval changes (days $30\~60$) of EF, ESV, and EDV did not differ significantly between groups both by gated SPECT and by echocardiography. Conclusion: Intramyocardial injection of pCK-VEGF165 induced therapeutic angiogenesis and improved myocardial perfusion. However, post-infarction remodeling and global myocardial function were not improved.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.36 no.3
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• pp.175-185
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• 2000

This paper described on a basic study to organize fishing vessel control system in order to control efficiently fishing vessel in Korean offshore. It was digitalized ARPA image on the fishing processing of a fleet of purse seiner in conducting fishing operation at Cheju offshore in Korea as a digital camera and then simulated by used VTMS. Futhermore, it was investigated on the application of FVTMS which can control efficiently fishing vessels in fishing ground. The results obtained were as follows ; (1) It was taken 16 minutes and 35 minutes to casting and hauling net in fishing processing respectively. The length of rope pulled by scout boat was 200m, tactical diameter in casting net was 340.8m, turning speed was 6kts as well. (2) The processing of casting and hauling net was moved to SW, NE as results of simulation when the current direction and speed set into NE, 2kts and SW, 2kts respectively. Such as these results suggest that can predict to control the fishing vessel previously with information of fishing ground, fishery and ship's maneuvering, etc. (3) The control range of VTMS radar used in simulation was about 16 miles. Although converting from a radar of the control vessel to another one, it was continuously acquired for the vector and the target data. The optimum control position could be determined by measuring and analyzing to distance and direction between the control vessel and the fleet of fishing vessel. (4) The FVTMS(fishing vessel traffic management services) model was suggested that fishing vessels received fishing conditions and safety navigation information can operate safely and efficiently.

• Development and Reproduction
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• v.14 no.2
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• pp.65-74
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• 2010

Placenta has been shown to be a site of expression of several of the monoamine membrane uptake transporters. However, the correlation between the expressions of norepinephrine transporter (NET) and placental development including gynecological diseases is still unknown. To investigate the expression and functions of NET in placenta, we conducted to compare NET expression in normal and preeclamptic placenta and analyzed the function of NET in HTR8-SV/neo trophoblast cells after NET gene transfection. The expression of NET was analyzed in placental tissues from the following groups of patients (none underwent labor): 1) term normal placenta (n=15); 2) term with preeclamptic placeneta (n=15); and 3) pre-term with preeclamptic placenta (n=11) using semi-quantitative RT-PCR, immunohistochemistry, and Western blot. In order to evaluate the function of NET, NET gene plasmid and NET gene-specific siRNA were trnasfected into HTR-8/SVneo trophoblast cells for 24 hours. NET had low expression in the pre-eclamptic placenta compare with normal placenta but no difference in western blot data. NET was expressed in the trophoblasts, and the up-regulation of NET gene stimulated the invasion of HTR-8/SVneo trophoblast cells by 2.5 fold (p<0.05), whereas the NET-siRNA treatment reduced invasion rates. Also, we observed that the expression of NET induces to expression and activity of MMP-9 in HTR-8/SVneo trophoblast cells in zymography. The results suggest that the expression of NET were reduced in pre-eclampsia and should be inhibited invasion activity of trophoblasts. Therefore, these findings provide useful guidelines for the mechanisms of trophoblast invasion as well as for the basic understanding of gynecological diseases including pre-eclampsia.

• Journal of the Korean Society of Hazard Mitigation
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• v.3 no.3 s.10
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• pp.151-163
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• 2003

In this study, the algorithm of groundwater flow process was established for koreanized groundwater program development dealing with the geographic and geologic conditions of the aquifer have dynamic behaviour in groundwater flow system. All the input data settings of the 3-DFM model which is developed in this study are organized in Korean, and the model contains help function for each input data. Thus, it is designed to get detailed information about each input parameter when the mouse pointer is placed on the corresponding input parameter. This model also is designed to easily specify the geologic boundary condition for each stratum or initial head data in the work sheet. In addition, this model is designed to display boxes for input parameter writing for each analysis condition so that the setting for each parameter is not so complicated as existing MODFLOW is when steady and unsteady flow analysis are performed as well as the analysis for the characteristics of each stratum. Descriptions for input data are displayed on the right side of the window while the analysis results are displayed on the left side as well as the TXT file for this results is available to see. The model developed in this study is a numerical model using finite differential method, and the applicability of the model was examined by comparing and analyzing observed and simulated groundwater heads computed by the application of real recharge amount and the estimation of parameters. The 3-DFM model is applied in this study to Sehwa-ri, and Songdang-ri area, Jeju, Korea for analysis of groundwater flow system according to pumping, and obtained the results that the observed and computed groundwater head were almost in accordance with each other showing the range of 0.03 - 0.07 error percent. It is analyzed that the groundwater flow distributed evenly from Nopen-orum and Munseogi-orum to Wolang-bong, Yongnuni-orum, and Songja-bong through the computation of equipotentials and velocity vector using the analysis result of simulation which was performed before the pumping started in the study area. These analysis results show the accordance with MODFLOW's.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Management & Information Systems Review
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• v.35 no.3
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• pp.1-22
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• 2016

We analyzed the international comovements and structural changes in the quarterly real GDP by the Markov-switching vector autoregressive model (MS-VAR) from 1971(1) to 2016(1). The main results of this study were as follows. First, the business cycle phenomenon that occurs in the models or individual time series in real GDP has been grasped through the MS-VAR models. Unlike previous studies, this study showed the significant comovements, asymmetry and structural changes in the MS-VAR model using a real GDP across countries. Second, even if there was a partial difference, there were remarkable structural changes in the economy contraction regime(recession), such as 1988(2) ending the global oil shock crisis and 2007(3) starting the global financial crisis by the MS-VAR model. Third, large-scale structural changes were generated in the economic expansion and/or contraction regime simultaneously among countries. We found that the second world oil shocks that occurred after the first global oil shocks of 1973 and 1974 were the main reasons that caused the large-scale comovements of the international real GDP among countries. In addition, the spillover between Korea and 5 countries has been weak during the Asian currency crisis from 1997 to 1999, but there was strong transmission between Korea and 5 countries at the end of 2007 including the period of the global financial crisis. Fourth, it showed characteristics that simultaneous correlation appeared to be high due to the country-specific shocks generated for each country with the regime switching using real GDP since 1973. Thus, we confirmed that conclusions were consistent with a number of theoretical and empirical evidence available, and the macro-economic changes were mainly caused by the global shocks for the past 30 years. This study found that the global business cycles were due to large-scale asymmetric shocks in addition to the general changes, and then showed the main international comovements and/or structural changes through country-specific shocks.