• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.5
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• pp.619-624
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• 2011

Tea extract (TE) has been shown to have anti-tumor properties in a wide variety of experimental systems. We evaluated green tea extract (GTE) as a biochemical modulator for the antitumor activity of cisplatin and doxorubicin in the treatment of human lung cancer A549 cells. Cells were grown in RPMI-1640 medium supplemented with 10% (v/v) heat-inactivated fetal bovine serum and two antibiotics (100 units/mL penicillin and $100\;{\mu}g$/mL streptomycin). Two types of TE, epigallocatechin galate (EGCG) and GTE, were used in this experiment. The cells were seeded at $1{\times}10^4$ cells/well in the RPMI-1640 media with or without TE ($100\;{\mu}g$/mL) and then treated with different concentrations of doxorubicin ($0{\sim}14\;{\mu}g$/mL) or cisplatin ($0{\sim}35\;{\mu}g$/mL). After incubation in 5% $CO_2$ at $37^{\circ}C$ for 24 hr, cell viability was determined with a MTT assay. We used a Western blot to detect the influence of EGCG and GTE on the expression of p53 and caspase-3 genes in the A549 cells. A549 cell viability decreased to 15% with a $10\;{\mu}g$/mL concentration of cisplatin, and to 21% with a $8\;{\mu}g$/mL concentration of doxorubicin, as measured with the MTT assay. However, pre-treatment of the cells with EGCG ($100\;{\mu}g$/mL) or GTE ($100\;{\mu}g$/mL) resulted in decreased cell viability with $6\;{\mu}g$/mL of cisplatin and $4\;{\mu}g$/mL of doxorubicin. There was no apparent change in cell viability between EGCG or GTE administration in cisplatin- or doxorubicin-induced cytotoxicity in A549 cells. The levels of p53 and caspase-3 in the A549 cells increased with both EGCG and GTE treatment. We found that GTE could potentially affect cisplatin- or doxorubicin-induced cytotoxicity of A549 cells, which may be useful in the chemotreatment of cancer.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.8
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• pp.1166-1173
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• 2014

This study investigated the detoxification effects of enzymatic hydrolysate from oyster on acetaminophen-induced toxicity using HepG-2 cells. Oyster hydrolysate was made with 1% Protamex and 1% Neutrase after treatment with transglutaminase (TGPN) or without (PN). Two types of oyster hydrolysate were added to human-derived HepG-2 hepatocytes damaged by acetaminophen, after which the survival rate of HepG-2 cell was measured. In addition, glutamic oxaloacetic transaminase (GOT) and glutamic pyruvic transaminase (GPT) activities in the culture media were evaluated. The survival rates of HepG-2 cells were $136.2{\pm}1.4%$ at $100{\mu}g/mL$ of TGPN and $179.6{\pm}3.8%$ at $200{\mu}g/mL$ of TGPN. These cell survival rates were higher compared to that of the negative control group ($60.7{\pm}3.2%$) treated only with acetaminophen. GOT activity was $38.3{\pm}0.2$ Karmen/mL in the negative control group, whereas it was $19.9{\pm}0.5$ for TGPN ($200{\mu}g/mL$) and $22.0{\pm}2.4$ Karmen/mL for PN ($200{\mu}g/mL$). GOT and GTP activities were shown to be dependent on TGPN concentration, and significant reduction in activities could be conformed. The detoxification efficacy of TGPN was higher compared to that of PN. These results suggest that oyster hydrolysate has potential as a healthy food or pro-drug for liver protection.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.81-96
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• 2013

Video information retrieval has become a very important issue because of the explosive increase in video data from Web content development. Meanwhile, content-based video analysis using visual features has been the main source for video information retrieval and browsing. Content in video can be represented with content-based analysis techniques, which can extract various features from audio-visual data such as frames, shots, colors, texture, or shape. Moreover, similarity between videos can be measured through content-based analysis. However, a movie that is one of typical types of video data is organized by story as well as audio-visual data. This causes a semantic gap between significant information recognized by people and information resulting from content-based analysis, when content-based video analysis using only audio-visual data of low level is applied to information retrieval of movie. The reason for this semantic gap is that the story line for a movie is high level information, with relationships in the content that changes as the movie progresses. Information retrieval related to the story line of a movie cannot be executed by only content-based analysis techniques. A formal model is needed, which can determine relationships among movie contents, or track meaning changes, in order to accurately retrieve the story information. Recently, story-based video analysis techniques have emerged using a social network concept for story information retrieval. These approaches represent a story by using the relationships between characters in a movie, but these approaches have problems. First, they do not express dynamic changes in relationships between characters according to story development. Second, they miss profound information, such as emotions indicating the identities and psychological states of the characters. Emotion is essential to understanding a character's motivation, conflict, and resolution. Third, they do not take account of events and background that contribute to the story. As a result, this paper reviews the importance and weaknesses of previous video analysis methods ranging from content-based approaches to story analysis based on social network. Also, we suggest necessary elements, such as character, background, and events, based on narrative structures introduced in the literature. We extract characters' emotional words from the script of the movie Pretty Woman by using the hierarchical attribute of WordNet, which is an extensive English thesaurus. WordNet offers relationships between words (e.g., synonyms, hypernyms, hyponyms, antonyms). We present a method to visualize the emotional pattern of a character over time. Second, a character's inner nature must be predetermined in order to model a character arc that can depict the character's growth and development. To this end, we analyze the amount of the character's dialogue in the script and track the character's inner nature using social network concepts, such as in-degree (incoming links) and out-degree (outgoing links). Additionally, we propose a method that can track a character's inner nature by tracing indices such as degree, in-degree, and out-degree of the character network in a movie through its progression. Finally, the spatial background where characters meet and where events take place is an important element in the story. We take advantage of the movie script to extracting significant spatial background and suggest a scene map describing spatial arrangements and distances in the movie. Important places where main characters first meet or where they stay during long periods of time can be extracted through this scene map. In view of the aforementioned three elements (character, event, background), we extract a variety of information related to the story and evaluate the performance of the proposed method. We can track story information extracted over time and detect a change in the character's emotion or inner nature, spatial movement, and conflicts and resolutions in the story.

• Management & Information Systems Review
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• v.35 no.1
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• pp.85-116
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• 2016

The present study aims to explore the causes of employment difficulty on the basis of job applicant's interest from P-E (person-environment) fit perspective. Our approach relied on a textual analytic method to reveal insights from their situational interests in a job search during the change of labor market. Thus, to investigate the type of major interests and psychological responses, user-generated texts in a social community were collected for analysis between January 1, 2013 through December 31, 2015 by crawling the online-community in regard to job seeking and sharing information and opinions. The results of topic analysis indicated user's primary interests were divided into four types: perception of vocation expectation, employment pre-preparation behaviors, perception of labor market, and job-seeking stress. Specially, job applicants put mainly concerns of monetary reward and a form of employment, rather than their work values or career exploration, thus youth job applicants expressed their psychological responses using contextualized language (e.g., slang, vulgarisms) for projecting their unstable state under uncertainty in response to environmental changes. Additionally, they have perceived activities in the restricted preparation (e.g., certification, English exam) as determinant factors for success in employment and suffered form job-seeking stress. On the basis of these findings, current unemployment matters are totally attributed to the absence of pursing the value of vocation and job in individuals, organizations, and society. Concretely, job seekers are preoccupied with occupational prestige in social aspect and have undecided vocational value. On the other hand, most companies have no perception of the importance of human resources and have overlooked the needs for proper work environment development in respect of stimulating individual motivation. The attempt in this study to reinterpret the effect of environment as for classifying job applicant's interests in reference to linguistic and psychological theories not only helps conduct a more comprehensive meaning for understanding social matters, but guides new directions for future research on job applicant's psychological factors (e.g., attitudes, motivation) using topic analysis.

• Tuberculosis and Respiratory Diseases
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• v.50 no.1
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• pp.94-105
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• 2001

Background : Examining the biological susceptibility of Mycobacterium tuberculosis to pyrazinamide (PZA) in vitro is very difficult as PZA is inactive under normal culture conditions. The biological susceptibility test, an enzyme assay for Pzase activity, and a genetic test for pncA gene mutations, were performed in order to predict PZA resistance. Methods : 28 cultured clinical isolates of Mycobacterium tuberculosis were tested. The biological susceptibility was performed by the absolute concentration method using Lowenstein-Jensen media. The PZase activity was tested by means of Wayne's method. A 710-bp region includes the entire open reading frame of pncA was amplified and sequenced. Results : All six strains with positive PZase activity exhibited no pncA mutations with one strain showing a false resistance in the biological susceptibility test. Among the 22 strains with no PZase activity, 21 exhibited showed pncA mutations. In the biological susceptibility test, 20 strains were resistant, and one was susceptible, and the other flied to test. The mutation types varied with ten missense, one silent and one nonsense mutation 1 slipped-strand mispairing, and 6 frameshift mutations. Three strains had an adenine to guanine mutation at position -11 upstream of the start codon. Conclusion : The mutation at the pncA promotor region is frequent at -11 upstream position. Automatic sequencing of pncA is a useful tool for rapid and accurate detection of PZA resistant M. tuberculosis, and for demonstrating the epidemiological relatedness of the PZA resistant M. tuberculosis strains.

• Journal of Wetlands Research
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• v.21 no.4
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• pp.384-391
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• 2019

Nutrients in stormwater runoff have raised concerns regarding water quality degradation in the recent years. Low impact development (LID) technologies are types of nature-based solutions developed to address water quality problems and restore the predevelopment hydrology of a catchment area. Two LID facilities, infiltration trench (IT) and infiltration planter (IP), are known for their high removal rate of nutrients through sedimentation and vegetation. Long-term monitoring was conducted to assess the performance and cite the advantages and disadvantages of utilizing the facilities in nutrient removal. Since a strong ionic bond exists between phosphorus compounds and sediments, reduction of total phosphorus (TP) (more than 76%), in both facilities was associated to the removal of total suspended solids (TSS) (more than 84%). The efficiency of nitrogen in IP is 28% higher than IT. Effective nitrification occurred in IT and particulate forms of nitrogen were removed through sedimentation and media filters. Decrease in ammonium- nitrogen (NH₄-N) and nitrite-nitrogen (NO₂-N), and increase in nitrate-nitrogen (NO₃-N) fraction forms indicated that effective nitrification and denitrification occurred in IP. Hydrologic factors such as rainfall depth and rainfall intensity affected nutrient treatment capabilities of urban stormwater LID facilities The greatest monitored rainfall intensity of 11 mm/hr for IT yielded to 34% and 55% removal efficiencies for TN and TP, respectively, whereas, low rainfall intensities below 5 mm resulted to 100 % removal efficiency. The greatest monitored rainfall intensity for IP was 27 mm/hr, which still resulted to high removal efficiencies of 98% and 97% for TN and TP, respectively. Water quality assessment showed that both facilities were effective in reducing the amount of nutrients; however, IP was found to be more efficient than IT due to its additional provisions for plant uptake and larger storage volume.

• Journal of Korean Home Economics Education Association
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• v.20 no.1
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• pp.45-62
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• 2008

The purpose of this study was to investigate middle school home economics(HE) teachers' perception and needs on self supervision related to HE subject matter, Using the methods of survey and interview, 177 samples were collected. For collected surveys, mean value, standard deviation, frequency, percentage analysis were performed by using an SPSS/Win (ver10.1) program. The results of this study were as follows. First, the middle school HE teachers recognized that self supervision related to HE subject matter was absolutely needed to expand the improvement of techniques for teaching instructions and the width of knowledge on the studies on textbook. Second, the middle school HE teachers recognized the necessary parts of self supervision related to HE subject matter as HE teaching-learning methods, the studies on textbook contents, and HE education philosophy in order. Third, the middle school HE teachers recognized that it would be helpful in improving their HE class and expertise in order of field survey, participation in various training programs, utilization of mass media, participation in societies for researches and meetings and information sharing with co-teachers among the types of self supervision. Fourth, the middle school HE teachers needed the reduction in miscellaneous duties, less pressure for time, restoration of teachers' desire, support of physical resources (improvement of various environments such as classrooms and special rooms), economic support and various support programs (expanding the opportunities to participate in training and society and establishment of a database for relevant materials, etc.) to facilitate self supervision. As such, the middle school HE teachers' overall recognition on HE-related self supervision became significantly higher. To enhance the HE-related expertise, however, it would be necessary to conduct concrete and active support for HE education, philosophical area and the studies on textbook contents as well as the teaching-learning methods for HE in which teachers' demand was high. In addition, the HE teachers wanted to have an easy and quick access to various HE-related data; therefore, it would be urgent to summarize scattered relevant data and support the HE teachers more systematically.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.77-92
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• 2014

Recently, numerous documents including unstructured data and text have been created due to the rapid increase in the usage of social media and the Internet. Each document is usually provided with a specific category for the convenience of the users. In the past, the categorization was performed manually. However, in the case of manual categorization, not only can the accuracy of the categorization be not guaranteed but the categorization also requires a large amount of time and huge costs. Many studies have been conducted towards the automatic creation of categories to solve the limitations of manual categorization. Unfortunately, most of these methods cannot be applied to categorizing complex documents with multiple topics because the methods work by assuming that one document can be categorized into one category only. In order to overcome this limitation, some studies have attempted to categorize each document into multiple categories. However, they are also limited in that their learning process involves training using a multi-categorized document set. These methods therefore cannot be applied to multi-categorization of most documents unless multi-categorized training sets are provided. To overcome the limitation of the requirement of a multi-categorized training set by traditional multi-categorization algorithms, we propose a new methodology that can extend a category of a single-categorized document to multiple categorizes by analyzing relationships among categories, topics, and documents. First, we attempt to find the relationship between documents and topics by using the result of topic analysis for single-categorized documents. Second, we construct a correspondence table between topics and categories by investigating the relationship between them. Finally, we calculate the matching scores for each document to multiple categories. The results imply that a document can be classified into a certain category if and only if the matching score is higher than the predefined threshold. For example, we can classify a certain document into three categories that have larger matching scores than the predefined threshold. The main contribution of our study is that our methodology can improve the applicability of traditional multi-category classifiers by generating multi-categorized documents from single-categorized documents. Additionally, we propose a module for verifying the accuracy of the proposed methodology. For performance evaluation, we performed intensive experiments with news articles. News articles are clearly categorized based on the theme, whereas the use of vulgar language and slang is smaller than other usual text document. We collected news articles from July 2012 to June 2013. The articles exhibit large variations in terms of the number of types of categories. This is because readers have different levels of interest in each category. Additionally, the result is also attributed to the differences in the frequency of the events in each category. In order to minimize the distortion of the result from the number of articles in different categories, we extracted 3,000 articles equally from each of the eight categories. Therefore, the total number of articles used in our experiments was 24,000. The eight categories were "IT Science," "Economy," "Society," "Life and Culture," "World," "Sports," "Entertainment," and "Politics." By using the news articles that we collected, we calculated the document/category correspondence scores by utilizing topic/category and document/topics correspondence scores. The document/category correspondence score can be said to indicate the degree of correspondence of each document to a certain category. As a result, we could present two additional categories for each of the 23,089 documents. Precision, recall, and F-score were revealed to be 0.605, 0.629, and 0.617 respectively when only the top 1 predicted category was evaluated, whereas they were revealed to be 0.838, 0.290, and 0.431 when the top 1 - 3 predicted categories were considered. It was very interesting to find a large variation between the scores of the eight categories on precision, recall, and F-score.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• The Korean Journal of Blood Transfusion
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• v.23 no.2
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• pp.115-126
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• 2012

Background: Umbilical cord (UC) is a promising source of mesenchymal stromal cells (MSCs). We compared the characteristics of MSCs from cryopreserved UC with those from fresh tissues, and demonstrated the possibility of UC cryopreservation for acquisition of MSCs from cryopreserved UC. Methods: Each UC was sliced into two types ($1{\sim}2mm^3$ vs. 0.5 cm), and cryopreserved in liquid nitrogen using different media (autologous cord blood plasma, aCBP vs. RPMI 1640). A fresh aliquot of $1{\sim}2mm^3$-sized UC was used as control tissue. After one week, the cryopreserved tissues were thawed and cultured. For the 0.5 cm UC, a slicing step into $1{\sim}2mm^3$ was needed. Cell count, viability, proliferative activity, and surface antigens were determined from harvested MSCs. Several growth factors (EGF, IGF-1, PDGF, TGF-${\beta}$, bFGF, and VEGF), were measured from the culture supernatant. Results: Eleven UC were enrolled in the study. Efficiencies of obtaining MSCs were higher in cryopreserved UC using RPMI 1640, compared with use of aCBP; the same result was observed for 0.5 cm sized UC, compared with $1{\sim}2mm^3$ sized UC. No difference in proliferative activity was observed between MSCs from fresh and cryopreserved UC. The amount of growth factors in culture supernatant using RPMI 1640 was larger than that of fresh tissues. Conclusion: We obtained growth factors from the supernatant as well as MSCs from cryopreserved UC. As with a cord blood bank, in the future, cryopreservation of UC for acquisition of both MSCs and growth factors would be possible in a time of need.