• Journal of Life Science
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• v.26 no.10
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• pp.1121-1129
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• 2016

High-molecular-weight glutenin subunits (HMW-GSs) are extremely important determinants of the functional properties of wheat dough. Transgenic rice plants containing a wheat TaGlu-Ax1 gene encoding a HMG-GS were produced from the Korean wheat cultivar ‘Jokyeong’ and used to enhance the bread-making quality of rice dough using the Agrobacterium-mediated co-transformation method. Two expression cassettes with separate DNA fragments containing only TaGlu-Ax1 and hygromycin phosphotransferase II (HPTII) resistance genes were introduced separately into the Agrobacterium tumefaciens EHA105 strain for co-infection. Rice calli were infected with each EHA105 strain harboring TaGlu-Ax1 or HPTII at a 3:1 ratio of TaGlu-Ax1 and HPTII. Among 210 hygromycin-resistant T₀ plants, 20 transgenic lines harboring both the TaGlu-Ax1 and HPTII genes in the rice genome were obtained. The integration of the TaGlu-Ax1 gene into the rice genome was reconfirmed by Southern blot analysis. The transcripts and proteins of the wheat TaGlu-Ax1 were stably expressed in rice T₁ seeds. Finally, the marker-free plants harboring only the TaGlu-Ax1 gene were successfully screened in the T₁ generation. There were no morphological differences between the wild-type and marker-free transgenic plants. The quality of only one HMW-GS (TaGlu-Ax1) was unsuitable for bread making using transgenic rice dough. Greater numbers and combinations of HMW and LMW-GSs and gliadins of wheat are required to further improve the processing qualities of rice dough. TaGlu-Ax1 marker-free transgenic plants could provide good materials to make transgenic rice with improved bread-making qualities.

• Journal of Environmental Policy
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• v.6 no.3
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• pp.115-145
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• 2007

This study was performed for developing upscaling Environmental Conservation Value Assessment Map(ECVAM) of National Land in South Korea and presenting the application method of ECVAM. This ECVAM adopted the least indicator method and uses a Geographic Information System(GIS). This map is made through evaluation of 67 items. As a result, the construction of ECVAM was defined as a process of identifying land use to scientifically assess the physical and environmental value of land and classify conservation value into several grades for the sustainable management of environmental resources. After applying ECVAM criteria of five degrees to the whole of study area, Grade I, showing the highest conservation value, accounted for 29.3% by land area of ECVAM. Grades II, III, IV and V likewise accounted for, respectively, 21.7%, 17.2%, 7.1% and the lowest conservation value of 24.7%. other result, ECVAM and land suitability assessment agreement rate is Grade I 33.05%, Grades II, III, IV and V likewise accounted for 12.92%, 15.05%, 36.93% and last value of 53.28% This study set up "the realization of the improvement ECVAM" as the vision of the advancing strategy. In order to accomplish the vision, this study established the purpose as follow; constructing strategic assessment value relation to ECVAM based on knowledge, arranging the foundation to upscaling assessment value And this study devised preparatory plans to achieve the vision and the purpose as next; construction on base theme map by 1:5,000 scalie, base on land register theme map and precision land cover map. Therefore, for applying the result of this study to the upscaling Environmental Conservation Value Assessment Map(ECVAM), it considers regularly the systematic categorization of preceding item, consideration issue of national environmental geographic information using the ECVAM.

• Korean Journal of Social Welfare
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• v.57 no.4
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• pp.147-175
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• 2005

Efforts to identify the public's perception of social welfare as an academic discipline have never been conducted in Korea since the establishment of social welfare department in 1947 at Ewha Womans University. Such efforts are very meaningful in identifying directions and tasks to strengthen Korean social welfare as well as in clarifying and promoting our understanding concerning status of the academic discipline. This study attempts to explore and describe the degree of the public's perception in Korea with analyzing data surveyed in 2004 by our interdisciplinary research team. This study develops and uses a questionnaire having a Likert scale format that is composed of 8 points and measures the public's perception in the following dimensions: (1) personal interests on academic discipline; (2) contribution of academic discipline; (3) prospect of academic discipline; (4) importance of academic discipline; (5) expertise of academic discipline; and (6) personal knowledge on academic discipline. To avoid social desirability and promote objectivity with comparative measurement, this study selects ten representative academic disciplines as follows: medicine; physics; biology; social welfare; economics; psychology; sociology; political science; library science; and communication & journalism. This study attempts to identify (1) the degree of the public's perception on ten academic disciplines; (2) the position of social welfare by comparing it with each academic discipline and by comparing mean of social welfare with overall mean of six social science disciplines in the six dimensions; (3) the differences in the public's perceptions of social welfare on six dimensions by the respondents' status factor(high school students, college and graduate students, and citizens) and gender factor by using MANCOVA, and (4) the differences in the public's perceptions of social welfare on six dimensions by major factor(social welfare, social science majors, and natural science majors) and gender factor of college and graduate school students by using MANCOVA. The results of data analysis are as follows: (1) while the 3,319 respondents gave relatively high rating on natural sciences in the dimensions of contribution and expertise, they did the same on social sciences in the dimensions of personal interests and personal knowledge; (2) in overall comparisons, while the 3,319 respondents gave relatively high rating on social welfare in the dimensions of contribution, prospect and importance, they gave the lowest rating on the expertise of social welfare; (3) in the comparisons with social science disciplines, while the 3,319 respondents gave relatively high rating on social welfare in the dimensions of contribution, prospect and importance, they gave the lowest rating on the expertise of social welfare; (4) when analyzing all the respondents, there were differences in the vector of personal interests, contribution, prospect, importance, expertise, and personal knowledge by status factor, gender factor, and interaction effect factor; and (5) when analyzing only the respondents in college and graduate schools, there were differences in the vector of personal interests, contribution, prospect, importance, expertise, and personal knowledge by only major factor and gender factor. The results provide empirical backgrounds for discussing current image, status and major characteristics of social welfare as a discipline in Korea. Indeed, this study provides new meaningful and thoughtful guide for further investigation on the topic. In addition, contributing to clarifying and broadening our understandings about the public's perception on social welfare in Korea, this study discusses the tasks for dealing with expertise issue that is the most vulnerable issue of Korean social welfare discipline and research directions to strengthen and promote social welfare discipline in Korea.

• Tuberculosis and Respiratory Diseases
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• v.50 no.1
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• pp.67-75
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• 2001

Background : Two tumor suppressor genes, p53 and p16, which have different roles in controlling the cell cycle and inducing apoptosis, are frequently inactivated during carcinogenesis including lung cancer. Single tumor suppressor gene therapies using either with p53 or p16 have been studied extensively. However, there is a paucity of reports regarding a combined gene therapy using these two genes. Methods : The combined effect of p53 and p16 gene transfer by the adenoviral vector on the growth of lung cancer cell lines and its interactive mechanism was investigated. Results : An isobologram showed that the co-transduction of p53 and p16 exhibited a synergistic growth in hibitory effect on NCI H358 and an additive effect on NCI H23. Cell cycle analysis demonstrated the induction of a synergistic G1/S arrest by a combined p53 and p16 transfer. This synergistic interaction was again confirmed in a soft agar confirmed in a soft agar clonogenic assay. Conclusion : These observations suggest the potential of a p53 and p16 combination gene therapy as another potent strategy in cancer gene therapy.

• KSBB Journal
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• v.21 no.3
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• pp.204-211
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• 2006

For the production of the recombinant human interferon-gamma(rhIFN-${\gamma}$) in Escherichia coli, human glucagon and ferritin heavy chain were used as fusion partners. Even though rhIFN-${\gamma}$ is expressed as an inclusion body form in E. coli because of strong hydrophobicity of itself, over 50% of fused rhIFN-${\gamma}$ was expressed as soluble form in E. coli $Origami^{TM}$(DE3) harboring pT7FH(HE)-IFN-${\gamma}$ which encodes ferritin heavy chain-fused rhIFN-${\gamma}$. In the case of using glucagon-ferritin heavy chain hybrid mutant as a fusion partner, 6X His-tag was additionally introduced to N-terminus of GFHM(HE)-IFN-${\gamma}$ for enhancing purification yields of rhIFN-${\gamma}$. Fusion protein HGFHM(HE)-IFN-${\gamma}$ with two 6X His-tag was more effectively bound to Ni-NTA agarose bead than GFHM(HE)-IFN-${\gamma}$ with a 6X His-tag. rhIFN-${\gamma}$ was completely purified from enterokinase-treated HGFHM(HE)-IFN-${\gamma}$ by Ni-NTA affinity column. For high-level production of rhIFN-${\gamma}$, glucose was used as the sole carbon source with simple exponential feeding rate($2.4{\sim}7.2g/h$) in fed-batch process. The effective lactose concentration for the expression of the rhIFN-${\gamma}$ was $10{\sim}20mM$. Under the fed-batch culture conditions, rhIFN-${\gamma}$ production yield reached 11 g DCW/L for 6 hours after lactose induction.

• KSBB Journal
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• v.23 no.6
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• pp.519-524
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• 2008

Monacolin-K is a strong anti-hypercholesterolemic agent produced by Monascus sp. via polyketide pathway. High-yielding mutants of monacolin-K were developed through rational screening strategies adopted based on understanding of monacolin-K biosynthetic pathway. Through the experiments for investigating various amino acids as putative precursors for the monacolin-K biosynthesis, it was found that production level of monacolin-K was remarkably increased when optimum amount of cysteine was supplemented into the production medium. We suggested that these phenomena might be related to the special roles of SAM (S-adenosyl methionine), a putative methyl group donor in the biosynthetic pathway of monacolin-K, demonstrating close interrelationship between SAM-synthesizing primary metabolism and monacolin-K synthesizing secondary metabolism. Namely, increase in the intracellular amount of SAM derived from the putative precursor, cysteine which was extracellularly supplemented into the production medium might contribute to the significant enhancement in the monacolin-K biosynthetic capability of the highly mutated producers. On the basis of these assumptions derived from the above fermentation results, we decided to construct efficient expression vectors harboring SAM synthetase gene (metK) cloned from A. nidulans, with the hope that increased intracellular level of SAM could lead to further enhancement in the monacolin-K production through overcoming a rate-limiting step associated with monacolin-K biosynthesis. Hence, in order to overcome the plausible rate-limiting step associated with monacolin-K biosynthesis by increasing intracellular level of SAM, we transformed the producer mutants with an efficient expression vector harboring gpdA promoter of the producer microorganism, and metK gene. Notably, from the resulting various transformants, we were able to screen a very high-yielding transformant which showed approximately 3.3 fold higher monacolin-K productivity than the parallel nontransformed mutants in shake flask cultures performed under the identical fermentation conditions.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.19-36
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• 2015

Social media is an emerging issue in content services and in current business environment. YouTube is the most representative social media service in the world. YouTube is different from other conventional content services in its open user participation and contents creation methods. To promote a content in YouTube, it is important to understand the diffusion phenomena of contents and the network structural characteristics. Most previous studies analyzed impact factors of contents diffusion from the view point of general behavioral factors. Currently some researchers use network structure factors. However, these two approaches have been used separately. However this study tries to analyze the general impact factors on the view count and content based network structures all together. In addition, when building a content based network, this study forms the network structure by analyzing user comments on 22,370 contents of YouTube not based on the individual user based network. From this study, we re-proved statistically the causal relations between view count and not only general factors but also network factors. Moreover by analyzing this integrated research model, we found that these factors affect the view count of YouTube according to the following order; Uploader Followers, Video Age, Betweenness Centrality, Comments, Closeness Centrality, Clustering Coefficient and Rating. However Degree Centrality and Eigenvector Centrality affect the view count negatively. From this research some strategic points for the utilizing of contents diffusion are as followings. First, it is needed to manage general factors such as the number of uploader followers or subscribers, the video age, the number of comments, average rating points, and etc. The impact of average rating points is not so much important as we thought before. However, it is needed to increase the number of uploader followers strategically and sustain the contents in the service as long as possible. Second, we need to pay attention to the impacts of betweenness centrality and closeness centrality among other network factors. Users seems to search the related subject or similar contents after watching a content. It is needed to shorten the distance between other popular contents in the service. Namely, this study showed that it is beneficial for increasing view counts by decreasing the number of search attempts and increasing similarity with many other contents. This is consistent with the result of the clustering coefficient impact analysis. Third, it is important to notice the negative impact of degree centrality and eigenvector centrality on the view count. If the number of connections with other contents is too much increased it means there are many similar contents and eventually it might distribute the view counts. Moreover, too high eigenvector centrality means that there are connections with popular contents around the content, and it might lose the view count because of the impact of the popular contents. It would be better to avoid connections with too powerful popular contents. From this study we analyzed the phenomenon and verified diffusion factors of Youtube contents by using an integrated model consisting of general factors and network structure factors. From the viewpoints of social contribution, this study might provide useful information to music or movie industry or other contents vendors for their effective contents services. This research provides basic schemes that can be applied strategically in online contents marketing. One of the limitations of this study is that this study formed a contents based network for the network structure analysis. It might be an indirect method to see the content network structure. We can use more various methods to establish direct content network. Further researches include more detailed researches like an analysis according to the types of contents or domains or characteristics of the contents or users, and etc.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.105-122
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• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.