• Tuberculosis and Respiratory Diseases
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• v.50 no.4
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• pp.426-436
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• 2001

Background : Chronic obstructive pulmonary disease(COPD) is one of the major contributors to morbidity and mortality among the adult population. Cigarette smoking(CS) is undoubtedly the single most important factor in the pathogenesis of COPD. However, its mechanism is unclear. The current hypothesis regarding the pathogenesis of COPD postulates that an imbalance between proteases and antiproteases leads to the destructive changes in the lung parenchyma. This study had two aims. First, to evaluate the effect of CS exposure on histologic changes of the lung parenchyme, and second, to evaluate the effect of CS exposure on the expression of the gelatinolytic enzymes in BAL fluid cells in guinea pigs. Methods : Two groups of five guinea pigs were exposed to the whole smoke of 20 commercial cigarettes per day, 5 hours/day, 5 days/week, for 6weeks, and 12 weeks, respectively, using a smoking apparatus. Five age-matched guinea pigs exposed to room air were used as controls. Five or more sections were microscopically extamined(${\times}400$) and the number of cellular infiltration of the alveolar wall was measured in order to evaluate the effect of CS exposure on the histologic changes of lung parenchyme. The statistical significance was analyzed by a linear regression method. To evaluate the expression of the gelatinolytic enzymes in intraalveolar cells, BAL fluid was obtained and the intraalveolar cells were separated by centrifugation (500 g for 10 min at $4^{\circ}C$). Two sets of culture plates were loaded with $1{\times}10^6$ intraalveolar cells. One plate, contained O.1mM EDTA, a inhibitor of matrix metalloproteases(MMPs), and the other plate had no EDTA. Both plates were incubated for 48 hours at $37^{\circ}C$. After incubation, gelatinolytic protease expression in the supernatants was analyzed by gelatin zymography. Results : At the end of CS exposure, the level of blood carboxy Hb had increased significantly(4.1g/dl in control group, 24g/dl immediately after CS exposure, 18g/dl 30 min after CS exposure, 15g/dl 1 hour after CS exposure). Alveolar inflammatory cells were identified in the CS exposed guinea pigs. The number of alveolar cellular cells observed in a microscopic field ($400{\times}$) was $121.4{\pm}7.2$, $158.0{\pm}20.2$, $196.8{\pm}32.8$, in the control, the 6 weeks, and the 12 weeks group, respectively. The increased extent of inflammatory cellular infiltration of the lung parenchema showed a statistically significant linear relationship with the duration of CS exposure(p=0.001, $r^2=0.675$). Several types of gelatinolytic enzymes in the intraalveolar cells of CS exposed guinea pigs were expressed, of which some were inhibited by EDT A. However, the gelatinolytic enzymes were not expressed in the control groups. Conclusion : CS exposure increases inflammatory cellular infiltration of the alveolar wall and the expression of gelatinolytic proteases in guinea pigs. EDTA inhibits some of the gelatinolytic proteases. These findings suggest a possibility that CS exposure may increase MMP expression in the lungs of guinea pigs.

• Journal of dental hygiene science
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• v.17 no.6
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• pp.523-532
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• 2017

The purpose of this study was to provide basic data to standardize the clinical dental hygiene curriculum, based on analysis of current clinical dental hygiene curricula in Korea. We emailed questionnaires to 12 schools to investigate clinical dental hygiene curricula, from February to March, 2017. We analyzed the clinical dental hygiene curricula in 5 schools with a 3-year program and in 7 schools with a 4-year program. The questionnaire comprised nine items on topics relating to clinical dental hygiene, and four items relating to the dental hygiene process and oral prophylaxis. The questionnaire included details regarding the subject name, the grade/semester/credit system, course content and class hours, the number of senior professors, and the number of patients available for dental hygiene clinical training purposes. In total, there were 96 topics listed in the curricula relating to clinical dental hygiene training, and topics varied between the schools. There was an average of 20.4 topic credits, and more credits and hours were allocated to the 4-year program than to the 3-year program. On average, the ratio of students to professors was 21.4:1. Course content included infection control, concepts for dental hygiene processes, dental hygiene assessment, intervention and evaluation, case studies, and periodontal instrumentation. An average of 2 hours per patient was spent on dental hygiene practice, with an average of 1.9 visits. On average, student clinical training involved 19 patients and 26.6 patients in the 3-year and 4-year programs, respectively. The average participation time per student per topic was 38.0 hours and 53.1 hours, in the 3-year and 4-year programs, respectively. Standardizing the clinical dental hygiene curricula in Korea will require consensus guidelines on topics, the number of classes required to achieve core competencies as a dental hygienist, and theory and practice time.

• Journal of Life Science
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• v.19 no.11
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• pp.1529-1537
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• 2009

To elucidate further the antitumor effects of a natural L-arginine analogue, L-canavanine, the mechanism underlying apoptogenic activity of L-canavanine and its modulation by protein tyrosine kinase $p56^{lck}$ was investigated in human Jurkat T cells. When the cells were treated with 1.25 to 2.5 mM L-canavanine for 36 h, several apoptotic events including mitochondrial membrane potential (${\Delta\Psi}m$) loss, activation of caspase-9, -3, -8, and -7, poly (ADP-ribose) polymerase (PARP) degradation, and DNA fragmentation were induced without alteration in the levels of Fas or FasL. These apoptotic changes were more significant in $p56^{lck}$-deficient Jurkat clone JCaM1.6 than in $p56^{lck}$-positive Jurkat clone E6.1. The L-canavanine-induced apoptosis observed in $p56^{lck}$-deficient JCaM1.6 cells was significantly reduced by introducing $p56^{lck}$ gene into JCaM1.6 cells by stable transfection. Treatment of JCaM1.6/lck cells with L-canavanine caused a transient 1.6-fold increase in the kinase activity of $p56^{lck}$. Both FADD-positive wild-type Jurkat T cell clone A3 and FADD-deficient Jurkat T cell clone I2.1 exhibited a similar susceptibility to the cytotoxicity of L-canavanine, excluding involvement of Fas/FasL system in triggering L-canavanine-induced apoptosis. The L-canavanine-induced apoptotic sub-$G_1$ peak and activation of caspase-3, -8, and -7 were abrogated by pan-caspase inhibitor (z-VAD-fmk), whereas L-canavanine-induced activation of caspase-9 was not affected. These results demonstrated that L-canavanine caused apoptosis of Jurkat T cells via the loss of ${\Delta\Psi}m$, and the activation of caspase-9, -3, -8, and -7, leading to PARP degradation, and that the $p56^{lck}$ kinase attenuated the ${\Delta\Psi}m$ loss and activation of caspases, and thus contributed as a negative regulator to L-canavanine-induced apoptosis.

• Journal of Korean Home Economics Education Association
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• v.19 no.4
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• pp.37-56
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• 2007

The purpose of this study is to provide basic resources to the first-grade Home Economics Regular Teacher Qualification Program (FGHERTQP) in order to improve its operation plans. For the study, the three methods were carried out: an analysis on the curricula of FGHERTQP over six years since 2000, a questionnaire asking their satisfaction degrees and needs on the programs which was answered by the home economics teachers who were the participants of FGHERTQP, and several statistical analyses such as a descriptive-test, a $X^2$-test, a t-test, and one way ANOVA by using SPSS Win ver 10.0. The results of the study were as follows; Firstly, FGHERTQP has been operated ten times by five training centers during resent six years. Subject matters ($1{\sim}7$), whole numbers of lectures ($11{\sim}29$), and their allotted working hours ($111{\sim}136$) vary with individual training centers and operation years. Secondly, when using 5 point likert scales, Contents and Methods of evaluation marked 3.08 which were the lowest scores, and Qualification Training in General marked 3.72 which was the highest score among five fields of Qualification Training in General, Contents, Organizations, Methods and Evaluation. The overall scores were low. Thirdly, in needs analysis on offering subject matters, the participants wanted to study the field of home economics education more than that of subject contents. Looking about the highest needs classified by domains, Food Principles & Meal Management showed the highest in Foods. And Consumer Issues in Clothing & Textiles in Textiles, Upcoming Housing Cultures in Housing, Family Relationship in Child Development & Family Relationship, Juveniles and their daily life as a consumer in Family & Consumer Resources Management. Fourthly, training centers' lectures available had a significant influence on the satisfaction degrees according to general characteristic variations of the participants. That is, as a training center offers more lectures in the field of subject education than those of subject contents, the participants showed higher satisfaction degrees (p<.05).

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Tuberculosis and Respiratory Diseases
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• v.44 no.6
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• pp.1296-1307
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• 1997

Background : Erdosteine is a thiol derivative developed for the treatement of chronic obstructive bronchitis, including acute infective exacerbation of chronic bronchitis. Erdosteine has mucomodulating and antioxidant properties and especially exhibits excellent gastrointestinal tolerability. Methods : The study was conducted as a prospective evaluation, with 2 comparative groups orally treated with erdosteine 300mg (bid.) or ambroxol 30mg (b.i.d.) for 7 days and the design of trial was double-blind. The treatments have been assigned randomly to patients (n=80) with acute or chronic bronchitis. The primary end-point used to determine efficacy in this study was subjective symptoms including expectorating frequence, expectoration volume, expectorating difficulty, expectoration viscosity, cough intensity and dyspnea. The secondary end-points of efficacy was the result of arterial blood gas analysis and pulmonary function test. Safety was evaluated with adverse drug reactions and laboratory tests monitoring. 61 patients was included in the efficacy analysis, due to the fact that 19 patients drop-out for different reasons. The obtained values have been analyzed with paired Hest., ANOVA test., multivariate $t^2$-test, repeated measures analysis of covariance, two sample t-test, loglinear-logit model analysis, Fisher's exact test. Results : 1) There was no significant difference on demographic data and vital signs between erdosteine and ambroxol treated groups. 2) The comparison between erdosteine and ambroxol treated groups showed no significant difference in improvement of each symptom in spite of the more favorable efficacy obtained with erdosteine. No difference on the contrary was observed for arterial blood gas analysis and pulmonary function test. 3) As safety is concerned, no clinical significant changes in laboratory test and symptom were induced in erdosteine and ambroxol treated group and two patients in ambroxol treated group drop-out for adverse reactions in symptom. 4) In the evaluation of final clinical efficacy, erdosteine improved more effectively patient's overall symptoms {very good effect (11/31), good effect (12/31), moderate effect (6/31), no effect (2/31), aggravation (0/31)} than ambroxol {very good effect (6/30), good effect (14/30), moderate effect (5/30), no effect (4/30), aggravation (2/30)}. And the probability of symptomatic improvement by erdosteine compared to ambroxol was 2.5 times. (p<0.05). Conclusion : This study showed that erdosteine was clinically effective and safe drug for treatment of acute and chronic bronchitis.

• Asian Journal of Turfgrass Science
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• v.18 no.4
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• pp.179-191
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• 2004

This study was conducted to find out the effect of sodding and seeding time and rate of seed mixtures on the establishment of cool-season turfgrasses by evaluating the turf coverage rates for two years. In fall planting, the required establishment period of full coverage($100\%$) was 1.5 months with a rolled turf sodding(Kentucky bluegrass $100\%$, Kentucky bluegrass $80\%$+perennial ryegrass $20\%$). The $100\%$ turf establishment was achieved in 7 months with Perennial ryegrass $100\%$, and 7.5 months by seeding with Kentucky bluegrass $100\%$(KB 100), Kentucky bluegrass $80\%$+perennial ryegrass $20\%$(KB80+PR20), Kentucky bluegrass $70\%$+perennial ryegrass $30\%$(KB70+PR30). In spring planting, the establishment periods far sod with KB 100 or KB80+PR20 were taken one month. However, in the case of seeding, the establishment periods were 3 months, 3.5 months, 3.5 months and 4 months with PR100, KB80+PR20, KB70+PR30, and KB 100, respectively Comparing the turf establishment vigor between fall and spring planting, the vigor was higher In spring planting than in fall planting in both sodding and . seeding. In the case of spring planting, the most proper time for turf establishment was tested on April, May, and June trials. The effect was significant in establishment vigor. The result showed highest on April planting. On May and June trials, establishment vigors were decreased gradually As the mixture rate of PR increased, ryegrass, establishment vigor was decreased with the rates. These results indicated that perennial ryegrass has relatively less tolerant to summer heat than Kentucky bluegrass. Number of shoots in 95 days after seeding was higher in KB100 by 16,600 per $m^2$ than in PR100 by 12,400 per $m^2$, while the lowest number showed in KB50+PR50 by 3,300 per $m^2$. Those in KB80:PR20, KB70:PR30 were 6,700 and 4,900 per $m^2$, respectively. The ratios of tillers according to mixture rates between Kentucky bluegrass and perennial ryegrass were KB80:PR20=87:13, KB70:PR30=78:22, and KB50:PR50=48:52. According to results in this study, Ideal seeding time might be spring (April) than in fall (September), and proper mixture rate was $80\%$ of Kentucky bluegrass with $20\%$ of perennial ryegrass.

• The Korean Journal of Nuclear Medicine
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• v.37 no.4
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• pp.219-228
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• 2003

Purpose: The amount of salvaged myocardium is an important prognostic factor in patients with acute myocardial infarction (MI). We investigated if early Tl-201 SPECT imaging could be used to predict the salvaged myocardium and functional recovery in acute MI after primary PTCA. Materials and Methods: In 36 patients with first acute MI treated with primary PTCA, serial echocardiography and Tl-201 SPECT imaging ($5.8{\pm}2.1$ days after PTDA) were performed. Regional wall motion and perfusion were quantified with on 16-segment myocardial model with 5-point and 4-point scaling system, respectively. Results: Wall motion was improved in 78 of the 212 dyssynergic segments on 1 month follow-up echocardiography and 97 on 7 months follow-up echocardiography, which were proved to be salvaged myocardium. The areas under receiver operating characteristic curves of Tl-201 perfusion score for detecting salvaged myocardial segments were 0.79 for 1 month follow-up and 0.83 for 7 months follow-up. The sensitivity and specificity of Tl-201 redistribution images with optimum cutoff of 40% of peak thallium activity for detecting salvaged myocardium were 84.6% and 55.2% for 1 month follow-up, and 87.6% and 64.3% for 7 months follow-up, respectively. There was a linear relationship between the percentage of peak thallium activity on early redistribution imaging and the likelihood of segmental functional improvement 7 months after reperfusion. Conclusion: Tl-201 myocardial perfusion SPECT imaging performed early within 10 days after reperfusion can be used to predict the salvaged myocardium and functional recovery with high sensitivity during the 7 months following primary PTCA in patients with acute MI.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.