• Journal of Life Science
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• v.29 no.4
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• pp.492-498
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• 2019

Previously, three kinds of lipases, lipAD1, lipAD2, and lipAD3, and lipase chaperone, lipBD, of Acinetobacter schindleri DYL129 isolated from soil sample were reported. In this report, three lipase and lipase chaperone were cloned into the pET32a(+) or pGEX-6P-1 vectors for protein expression in Escherichia coli, and named as pETLAD1, pETLAD2, pETLAD3 and pETLBD or pGEXLAD1, pGEXLAD 2, pGEXLAD3 and pGEXLBD, respectively. Protein expression rate was higher in pET system than in pGEX system. Although LipAD1 and LipAD2 were produced as inclusion bodies, their expression levels were high. So LipAD1 and LipAD2 could be solubilized in 8 M urea buffer and purified. LipAD3 and LipBD were overexpressed in soluble form and purified. Those proteins were purified by His-tag affinity chromatography connected in AKTA prime system. The activities of the purified lipases were demonstrated with 1% tributyrin agar plate. After purification, molecular mass was determined with sodium dodecyl sulfate-polyacrylamide gel electrophoresis. LipAD1 showed high activity toward ${\rho}$-nitrophenyl acetate and ${\rho}$-nitrophenyl butyrate, LipAD2 showed high activity toward ${\rho}$-nitrophenyl acetate and ${\rho}$-nitrophenyl myristate, and LipAD3 showed high activity toward ${\rho}$-nitrophenyl acetate, ${\rho}$-nitrophenyl butyrate, and ${\rho}$-nitrophenyl miristate, respectively. Three lipases, LipAD1, LipAD2, and LipAD3, showed optimal reaction at $50^{\circ}C$ using ${\rho}$-nitrophenyl butyrate, as substrate.

• Journal of Life Science
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• v.28 no.12
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• pp.1416-1423
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• 2018

With strong biotin binding affinity ($K_D=10^{-14}M$), the tetrameric feature of streptavidin could be used to increase the antigen binding activity of a camel heavy chain (VHH) antibody through their fusion, here stained with biotinylated horseradish peroxidase and subsequent immunoassays ELISA and Western blot analysis. For this application, we cloned the streptavidin gene amplified from the Streptomyces avidinii chromosome by PCR, and this was fused to the gene of the 8B9 VHH antibody which is specific to green fluorescent protein (GFP) antigens. To express a soluble fusion protein in Escherichia coli, we used the pUC119 plasmid-based expression system which uses the lacZ promoter for induction by IPTG, the pelB leader sequence at the N-terminus for secretion into the periplasmic space, and six polyhistidine tags at the C-terminus for purification of the expressed proteins using an $Ni^+$-NTA-agarose column. Although streptavidin is toxic to E. coli because of its strong biotin binding property, this soluble fusion protein was expressed successfully. In SDS-PAGE, the size of the purified fusion protein was 122.4 kDa in its native condition and 30.6 kDa once denatured by boiling, suggesting the tetramerization of the monomeric subunit by non-covalent association through the streptavidin moiety fusing to the 8B9 VHH antibody. In addition, this fusion protein showed biotin binding activity similar to streptavidin as well as GFP antigen binding activity through both ELISA and Western blot analysis. In conclusion, the protein resulting from the fusion of an 8B9 VHH antibody with streptavidin was successfully expressed and purified as a soluble tetramer in E. coli; it showed both biotin and GFP antigen binding activity suggesting the possible production of a tetrameric and bifunctional VHH antibody.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.63-88
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korea represented in South Korean mass media. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. In this study, R program was used to apply the text mining technique. R program is free software for statistical computing and graphics. Also, Text mining methods allow to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud. This study proposes a procedure to find meaningful tendencies based on a combination of word cloud, and co-occurrence networks. This study aims to more objectively explore the images of North Korea represented in South Korean newspapers by quantitatively reviewing the patterns of language use related to North Korea from 2016. 11. 1 to 2019. 5. 23 newspaper big data. In this study, we divided into three periods considering recent inter - Korean relations. Before January 1, 2018, it was set as a Before Phase of Peace Building. From January 1, 2018 to February 24, 2019, we have set up a Peace Building Phase. The New Year's message of Kim Jong-un and the Olympics of Pyeong Chang formed an atmosphere of peace on the Korean peninsula. After the Hanoi Pease summit, the third period was the silence of the relationship between North Korea and the United States. Therefore, it was called Depression Phase of Peace Building. This study analyzes news articles related to North Korea of the Korea Press Foundation database(www.bigkinds.or.kr) through text mining, to investigate characteristics of the Kim Jong-un regime's South Korea policy and unification discourse. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. In particular, it examines the changes in the international circumstances, domestic conflicts, the living conditions of North Korea, the South's Aid project for the North, the conflicts of the two Koreas, North Korean nuclear issue, and the North Korean refugee problem through the co-occurrence word analysis. It also offers an analysis of South Korean mentality toward North Korea in terms of the semantic prosody. In the Before Phase of Peace Building, the results of the analysis showed the order of 'Missiles', 'North Korea Nuclear', 'Diplomacy', 'Unification', and ' South-North Korean'. The results of Peace Building Phase are extracted the order of 'Panmunjom', 'Unification', 'North Korea Nuclear', 'Diplomacy', and 'Military'. The results of Depression Phase of Peace Building derived the order of 'North Korea Nuclear', 'North and South Korea', 'Missile', 'State Department', and 'International'. There are 16 words adopted in all three periods. The order is as follows: 'missile', 'North Korea Nuclear', 'Diplomacy', 'Unification', 'North and South Korea', 'Military', 'Kaesong Industrial Complex', 'Defense', 'Sanctions', 'Denuclearization', 'Peace', 'Exchange and Cooperation', and 'South Korea'. We expect that the results of this study will contribute to analyze the trends of news content of North Korea associated with North Korea's provocations. And future research on North Korean trends will be conducted based on the results of this study. We will continue to study the model development for North Korea risk measurement that can anticipate and respond to North Korea's behavior in advance. We expect that the text mining analysis method and the scientific data analysis technique will be applied to North Korea and unification research field. Through these academic studies, I hope to see a lot of studies that make important contributions to the nation.

• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.701-711
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• 2020

Purpose: In this study, the risk of flammability of a liquid mixture was experimentally confirmed because the purpose of this study was to confirm the increase or decrease of the flammability risk in a mixture of two substances (combustible+combustible) and to present the risk of the mixture. Method: Flash point test method and result processing were tested based on KS M 2010-2008, a tag sealing test method used as a flash point test method for crude oil and petroleum products. The manufacturer of the equipment used in this experiment was Japan's TANAKA. The flash point was measured with a test equipment that satisfies the test standards of KS M 2010 with equipment produced by the company, and LP gas was used as the ignition source and water as the cooling water. In addition, when measuring the flash point, the temperature of the cooling water was tested using cooling water of about 2℃. Results: First of all, in the case of flammable + combustible mixtures, there was little change in flash point if the flash point difference between the two substances was not large, and if the flash point difference between the two substances was low, the flash point tended to increase as the number of substances with high flash point increased. However, in the case of toluene and methanol, the flash point of the mixture was lower than that of the material with a lower flash point. Also, in the case of a paint thinner, it was not easy to predict the flash point of the material because it was composed of a mixture, but as a result of experimental measurement, it was measured between -24℃ and 7℃. Conclusion: The results of this study are to determine the risk of mixtures through experimental studies on flammable mixtures for the purpose of securing the effectiveness of the details of the criteria for determining dangerous goods in the existing dangerous goods safety management method and securing the reliability and reproducibility of the determination of dangerous goods Criteria have been presented, and reference data on experimental criteria for flammable liquids that are regulated in firefighting sites can be provided. In addition, if this study accumulates know-how on differences in test methods, it is expected that it can be used as a basis for research on risk assessment of dangerous goods and as a basis for research on dangerous goods determination.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..

• Journal of Life Science
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• v.32 no.2
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• pp.135-141
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• 2022

Thymosin β-4 (TB4) is a small peptide composed of 43 amino acids. To obtain sufficient biologically active mouse TB4 economically, we cloned and overexpressed this gene in an Escherichia coli system. With the isopropyl β-D-1-thiogalactopyranoside induction of the E. coli transformant, TB4 fusion protein with intein- and chitin-binding domain was successfully expressed in the soluble fraction within the E. coli cell. The TB4-intein - chitin-binding domain fusion protein was purified from the soluble fraction of E. coli cell lysate. The affinity chromatography with chitin beads and dithiothreitol-mediated intein self-cleavage reaction releases the TB4 peptide into the stripping solution. Sodium dodecyl sulphate - polyacrylamide gel electrophoresis and Western blot analyses were used to confirm that the recombinant TB4 peptide was produced with the expected size of 5 kDa. We found that the recombinant TB4 stimulated cell migration in the transwell plate chamber assay. After 18 hr of the treatment of the recombinant TB4 with 1 ng/ml concentration, the migration of the HT1080 cell was increased by 20% compared with that of the chemically synthesized TB4. The recombinant TB4 was also observed to promote the healing of a wound area in C57BL/6 mice by as high as 35% compared with that of the chemically synthesized TB4. These results suggest that the recombinant TB4 has better biological activity for cell migration and wound healing than that of the chemically synthesized TB4 peptide.

• Korean Journal of Social Welfare Studies
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• v.41 no.4
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• pp.161-187
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• 2010

This study begins with two research interests. Firstly, there seems to be a break of research in the field of family policy in Korea which exists especially in regard to family law. Family law was originally the core of state interventions in family life, but has been neglected because of the lack of literature with comparative research methods. This shortcoming needs to be addressed. Secondly, through inquiry into the definition of family or family policy with the lens of the law, the definition of family or family policy can be correctly extended. With these two interests combined, this research tries to derive an analytical tool - maintenance community - of the law and compare some important points of the family law of Korea with those of 16 EU-countries in terms of regime classification. The method used is, firstly, to describe the subjects of family law with a focus on partnering and parenting without subjective interpretation, and secondly, to classify the countries' family-law regimes with the criteria of privacy and autonomy using cluster analysis. The results show that the countries can be classified into three clusters: Nordic (Norway and Sweden), West-Northern (Denmark, France, England, Finland, and Belgium) and Middle South (Italy, Spain, Austria, Portugal, Netherlands, Greece, Ireland, Germany, and Korea). This result can be compared to a precedent research result which showed that 21 OECD countries can be classified in three clusters according to family policy. The number of the clusters is the same as this study, but some countries belong to other clusters; for example Denmark and Finland belong to the Nordic cluster according to family policy, while they belong to the West-Northern according to family law, and Austria, Germany, and Ireland belong to the Middle-South cluster according to family law, while they belong to the Continental according to family policy. From this result we can interpret Korean family law to be in the middle range according to both criteria of privacy and autonomy like other South-European countries including some Continental countries. We can make some theoretical suggestions. The fact that both family law and family policy regimes in countries can be classified into three clusters can be interpreted to mean that there exists parallelism between family law and family policy in a broad sense. But from the fact that some countries belong to different clusters according to family law and family policy, we can say that the family policy in a country is not always consistent with family law.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.346-358
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• 2023

Due to the acceleration of climate change or global warming, it is important to predict rice productivity in the future and investigate physiological changes in rice plants. The research aimed to explore how rice adapts to climate change by examining the response of nitrogen absorption and nitrogen use efficiency in rice under elevated levels of carbon dioxide and temperature, utilizing the SPAR system for analysis. The temperature increased by +4.7 ℃ in comparison to the period from 2001 to 2010, while the carbon dioxide concentration was held steady at 800 ppm, aligning with South Korea's late 21st-century RCP8.5 scenario. Nitrogen was applied as fertilizer at rates of 0, 9, and 18 kg 10a^-1, respectively. Under conditions of climate change, there was an 81% increase in the number of panicles compared to the present situation. However, grain weight decreased by 38% as a result of reduction in the grain filling rate. BNUE, indicative of the nitrogen use efficiency in plant biomass, exhibited a high value under climate change conditions. However, both NUEg and ANUE, associated with grain production, experienced a notable and significant decrease. In comparison to the current conditions, nitrogen uptake in leaves and stems increased by 100% and 151%, respectively. However, there was a 25% decrease in nitrogen uptake in the panicle. Likewise, the nitrogen content and NDFF (Nitrogen Derived from Fertilizer) in the sink organs, namely leaves and roots, were elevated in comparison to current levels. Therefore, it is imperative to ensure resources by mitigating the decrease in ripening rates under climate change conditions. Moreover, there seems to be a requirement for follow-up research to enhance the flow of photosynthetic products under climate change conditions.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.17 no.4
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• pp.292-302
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• 2012

This paper reviews comparison analysis of current and latest application for stock identification methods of Todarodes pacificus, and the pros and cons of each method and consideration of how to compensate for each other. Todarodes pacificus which migrates wide areas in western North Pacific is important fishery resource ecologically and commercially. Todarodes pacificus is also considered as 'biological indicator' of ocean environmental changes. And changes in its short and long term catch and distribution area occur along with environmental changes. For example, while the catch of pollack, a cold water fish, has dramatically decreased until today after the climate regime shift in 1987/1988, the catch of Todarodes pacificus has been dramatically increased. Regarding the decrease in pollack catch, overfishing and climate changes were considered as the main causes, but there has been no definite reason until today. One of the reasons why there is no definite answer is related with no proper analysis about ecological and environmental aspects based on stock identification. Subpopulation is a group sharing the same gene pool through sexual reproduction process within limited boundaries having similar ecological characteristics. Each individual with same stock might be affected by different environment in temporal and spatial during the process of spawning, recruitment and then reproduction. Thereby, accurate stock analysis about the species can play an efficient alternative to comply with effective resource management and rapid changes. Four main stock analysis were applied to Todarodes pacificus: Morphologic Method, Ecological Method, Tagging Method, Genetic Method. Ecological method is studies for analysis of differences in spawning grounds by analysing the individual ecological change, distribution, migration status, parasitic state of parasite, kinds of parasite and parasite infection rate etc. Currently the method has been studying lively can identify the group in the similar environment. However It is difficult to know to identify the same genetic group in each other. Tagging Method is direct method. It can analyse cohort's migration, distribution and location of spawning, but it is very difficult to recapture tagged squids and hard to tag juveniles. Genetic method, which is for useful fishery resource stock analysis has provided the basic information regarding resource management study. Genetic method for stock analysis is determined according to markers' sensitivity and need to select high multiform of genetic markers. For stock identification, isozyme multiform has been used for genetic markers. Recently there is increase in use of makers with high range variability among DNA sequencing like mitochondria, microsatellite. Even the current morphologic method, tagging method and ecological method played important rolls through finding Todarodes pacificus' life cycle, migration route and changes in spawning grounds, it is still difficult to analyze the stock of Todarodes pacificus as those are distributed in difference seas. Lately, by taking advantages of each stock analysis method, more complicated method is being applied. If based on such analysis and genetic method for improvement are played, there will be much advance in management system for the resource fluctuation of Todarodes pacificus.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.