• Journal of Life Science
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• v.21 no.8
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• pp.1134-1141
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• 2011

Transmembrane protein 21 (TMP21) is a member of the p24 cargo protein family and has been shown to modulate ${\alpha}$-secretase-mediated A${\beta}$ production which was specifically observed in the brains of subjects with Alzheimer's disease (AD). In order to investigate whether TMP21 could affect nerve growth factor (NGF) receptor signaling pathway, the alteration of NGF receptors and their downstream proteins were detected in TMP21 over-expressed cells. CMV/hTMP21 vector used in this study was successfully expressed into TMP21 proteins in B35 cells after lipofectamin transfection. Expressed TMP21 proteins induced the down-regulation of ${\gamma}$-secretase complex components including Presenlin-1 (PS-1), PS-2, Nicastrin (NST), Pen-2 and APH-1. Also, the expression level of NGF receptor $p75^{NTR}$ and RhoA were significantly higher in CMV/hTMP21 transfectants than vehicle transfectants, while their levels returned to vehicle levels after NGF treatment. However, the phosphorylation of NGF receptor TrkA was dramtically decreased in NGF No-treated CMV/hTMP21 transfectants compared with vehicle transfectants, and increased in NGF treated CMV/hTMP21 transfectants. In TrkA downstream signaling pathway, the phosphorylation level of ERK was also decreased in CMV/hTMP21 transfectants, while the phosphorylation of Akt was increased in the same transfectants. Furthermore, NGF treatment induced the increase of phosphorylation level of Akt and ERK in CMV/hTMP21 transfectants. Therefore, these results suggested that over-expression of TMP21may simultaneously induce the up-regulation of $p75^{NTR}$/RhoA expression and the down-regulation of TrkA/ERK phosphorylation through the inhibition of ${\gamma}$-secretase activity.

• Journal of Life Science
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• v.31 no.11
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• pp.1028-1036
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• 2021

Firefly luciferase (FLuc) can function as an efficient marker in the gene and viral therapies. Nonetheless, its clinical translation has been unaccomplished with the concerns on its exogenous nature and the similarity with human fatty acyl-CoA synthetase. In this study, we aimed to show safety of FLuc by conducting a set of preclinical experiments and a human use. Initially, FLuc permeability across the plasma membrane was investigated by delivering the FLuc-carrying viral vector, OTS-412, or the FLuc recombinant protein. After in vitro infection of OTS-412 into different cancer cell lines, FLuc activity was detected only in the cell lysates, but not in culture media. In addition, recombinant FLuc protein further showed the impermeability against the plasma membrane. Similar result was also observed in the in vivo experiment. After being injected into the VX2 tumor-bearing rabbit, the FLuc exclusively resided within the tumor tissue without being detected in the blood plasma or other organs. Human cancer cell lines originated from various organs were lysed and treated to the FLuc, and none of the human substrates was reactive against the FLuc. As a final step, FLuc recombinant protein was intravenously injected into a human. The luciferase was degraded with the half-life of 20 to 30 minutes in blood, and was untraceable from 1.5 hr after the injection. In addition, the blood plasma was nonresponsive against the fatty acids. Hematological analysis was also comparable between the pre- and post-injection. Altogether, our study collectively demonstrates the safety of the firefly luciferase.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Tuberculosis and Respiratory Diseases
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• v.45 no.3
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• pp.587-595
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• 1998

Background: Recombinant adenovirus hold promise as vectors to carry therapeutic genes for several reasons: 1) they can infect both dividing and non-dividing cells; 2) they have the ability to directly transduce tissues in vivo; 3) they can easily be produced in high titer; and 4) they have an established record of safety as vaccination material. However, one of the major limitation in the use of adenoviruses is that transgene expression is quite short because adenovirusees insert their DNA genome episomally rather than by chromosomal integration, and an immune response against the virus destroys cells expressing the therapeutic gene. Since sodium butyrate has been reported to induce adenovirus-mediated gene expression, we hypothesized that treatment of tumor cells, transduced with herpes simples virus thymidine kinase(HSVtk) gene using adenoviral vector, with butyrate could augment the effect of gene therapy. Methods: We transduced HSVtk gene, driven by the cytomegalovirus promoter, into REN cell line(human mesothelioma cell line). Before proceeding with the comparison of HSVtk/ganciclovir mediated bystander killing, we evaluated the effect of butyrate on the growth of tumor cells in order to rule out a potential antitumor effect of butyrate alone, and also on expression of HSVtk gene by Western blot analysis. Then we determined the effects of butyrate on bystander-mediated cell killing in vitro. Results: There was no inhibition of growth of cells exposed to butyrate for 24 hours at a concentration of 1.5mM/L. Toxic effects were seen when the concentration of butyrate was greater than 2.0mM/L. Gene expression was more stable and bystander effect was augmented by butyrate treatment of a concentration of 1.5mM/L. Conclusion: These results provide evidence that butyrate can augment the efficiency of cell killing with HSVtk/GCV system by inducing transgene expression and may thus by a promising new approach to improve responses in gene therapy using adenoviral vectors.

• Korean journal of applied entomology
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• v.59 no.3
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• pp.233-241
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• 2020

Several hard tick species in Ixodidae are medically important pests that transmit infectious disease including severe fever with thrombocytopenia syndrome (SFTS). In Korea, the number of SFTS patients continues to steadily increase since its first report in 2013, and Gyeongsangbuk-do (province) is the second most frequent area of SFTS patient occurrence. In order to investigate the distribution of ticks in Sangju city, we collected ticks using the flagging method from 26 sites comprising urban green area, vulnerable area of management, and rural area, and examined SFTS virus from the collected ticks in 2019. Based on Collection Index (CI = tick number / 1 h / 2 people), CI 143 ticks, including Haemaphysalis longicornis, Haemaphysalis flava, and Ixodes nipponensis, were collected; the most abundant species among the three tick species was H. longicornis (CI 138, 96.5%). Ninety two percent (CI 131) of ticks were collected in vulnerable area of management, where people cannot easily enter and are not managed by city hall, whereas only 8.4% (CI 12) were collected in the urban green area and rural area. Regarding SFTS virus detection, virus was not investigated from 26 pools containing CI 143 ticks. The results of this study are expected to be used as a recommendation for ensuring the safety of local residents from tick-borne diseases.

• Tunnel and Underground Space
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• v.13 no.3
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• pp.187-195
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• 2003

This case study which is to make 2-Dimension earth's crust structures clearly is about the great boring-hole blasting to provide ground vibration source of the reflected wave research on the Korean Peninsula earth's crust structures research. For this study we've done blasting twice-500 ㎏/charge per delay, 1,000 ㎏/charge per delay, and the specifications of blasting are the following - dia.: 300 ㎜, boring-depth : 100m, besides, we used the explosives and electric detonators which have sufficient detonating velocity and very excellent safety, capacity of detonating, accurate delay time. We charged explosives into steel pipe with bulk type to avoid dead pressure by ground water. And then we tested about pipe airtight and blasting to certificate which has no problem by using on this study. In the results, we succeeded each blasting in Seosan, Youngdong. For the Peak Sum Vector(PSV) around the blasting at the main points, its real measured PSV is higher 180 % than estimated PSV with USBM. In this study we can't to be analysis of vibration velocity, but to be key providing vibration source.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1099-1104
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• 2007

The visual implementation of 3-dimensional national environment is focused by the requirement and importance in the fields such as, national development plan, telecommunication facility deployment plan, railway construction, construction engineering, spatial city development, safety and disaster prevention engineering. The currently used DEM system using contour lines, which embodies national geographic information based on the 2-D digital maps and facility information has limitation in implementation in reproducing the 3-D spatial city. Moreover, this method often neglects the altitude of the rail way infrastructure which has narrow width and long length. There it is needed to apply laser measurement technique in the spatial target object to obtain accuracy. Currently, the LiDAR data which combines the laser measurement skill and GPS has been introduced to obtain high resolution accuracy in the altitude measurement. In this paper, we first investigate the LiDAR based researches in advanced foreign countries, then we propose data a generation scheme and an algorithm for the optimal manage and synthesis of railway facility system in our 3-D spatial terrain information. For this object, LiDAR based height data transformed to DEM, and the realtime unification of the vector via digital image mapping and raster via exactness evaluation is transformed to make it possible to trace the model of generated 3-dimensional railway model with long distance for 3D tract model generation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.675-680
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• 2017

Considering that most sewage treatment facilities have a water head of less than 2.0 m and a constant flow rate, the development of a small hydro power generation device capable of maintaining stable power generation and efficiency is urgently needed. In this study, a numerical analysis using the CFD code was carried out to develop a drag force type vertical axis hydro turbine for the improvement of the production efficiency of small-scale hydro energy underlow flow velocity conditions. The blade pressure changes and internal flows were analyzed in the presence or absence of hydro turbine blade holes at a flow velocity of less than 2.0 m/s. The pressure distribution of the hydro turbine blades with holes was found to be about 5.1 % lower than that of the hydro turbine blades without holes. The analysis of the internal flow around the water tank and hydro turbine blade revealed that the flow velocity varied with the vector distribution and that the flow velocity of the hydro turbine blades with holes was 5.6 % less than that of the hydro turbine blades without holes. It is believed that forming a hole in the blade may be helpful for its structural safety.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.30-36
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• 2008

The Spatial Image contents of Geomorphology 3-D environment is focused by the requirement and importance in the fields such as, national land development plan, telecommunication facility management, railway construction, general construction engineering, Ubiquitous city development, safety and disaster prevention engineering. The currently used DEM system using contour lines, which embodies geographic information based on the 2-D digital maps and facility information has limitation in implementation in reproducing the 3-D spatial city. Moreover, this method often neglects the altitude of the rail way infrastructure which has narrow width and long length. There it is needed to apply laser measurement technique in the spatial target object to obtain accuracy. Currently, the LiDAR data which combines the laser measurement skill and GPS has been introduced to obtain high resolution accuracy in the altitude measurement. In this paper, we tested of the railway facilities using laser surveying system, then we propose data a generation of spatial images for the optimal manage and synthesis of railway facility system in our 3-D spatial terrain information. For this object, LiDAR based height data transformed to DEM, and the realtime unification of the vector via digital image mapping and raster via exactness evaluation is transformed to make it possible to trace the model of generated 3-dimensional railway model with long distance for 3D tract model generation. As the results, We confirmed the solutions of varieties application for railway facilities management using 3-D spatial image contents.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.2
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• pp.123-131
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• 2021

The buffer is a key component of an engineered barrier system that safeguards the disposal of high-level radioactive waste. Buffers are located between disposal canisters and host rock, and they can restrain the release of radionuclides and protect canisters from the inflow of ground water. Since considerable heat is released from a disposal canister to the surrounding buffer, the thermal conductivity of the buffer is a very important parameter in the entire disposal safety. For this reason, a lot of research has been conducted on thermal conductivity prediction models that consider various factors. In this study, the thermal conductivity of a buffer is estimated using the machine learning methods of: linear regression, decision tree, support vector machine (SVM), ensemble, Gaussian process regression (GPR), neural network, deep belief network, and genetic programming. In the results, the machine learning methods such as ensemble, genetic programming, SVM with cubic parameter, and GPR showed better performance compared with the regression model, with the ensemble with XGBoost and Gaussian process regression models showing best performance.