• Journal of Chest Surgery
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• v.36 no.3
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• pp.131-135
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• 2003

Bovine pericardium fixed in glutaraldehyde solution (GA) has been one of the most popular surgical bioprosthesis, however, late calcific degeneration after implantation remains to be solved. To mitigate calcific degeneration, we posttreated the bovine pericardium with amino acids after GA fixation. Material and Method: 40 small pieces of bovine pericardia were fixed in 0.625% GA solution with 4 g/L $MgCl_26H_2O$as a control group (group 1). 40 pieces fixed in the same GA solution were posttreated with 2% chitosan solution (group 2) and the other 40 pieces posttreated with 8% glutamate (group 3). These were implanted into the belly of forty Fisher 344 rats subdermally and extracted at f month, 2 months, 3 months and 4 months after implantation. Result: With atomic absorption spectrophotometry we measured the deposited calcium amount and the results were as follows; 2.01 $\pm$0.13 mg/g in group 1, 2.34$\pm$0.73 mg/g in group 2, 2.49$\pm$0.15 mg/g in group 3 at 1 month after implantation, and 3.57$\pm$0.15 mg/g in group 1, 3.52$\pm$0.92 mg/g in group 2, 3.46$\pm$0.12 mg/g in group 3 at the second month. But 5.45$\pm$0.42 mg/g in group 1, 3.22 $\pm$1.31 mg/g in group 2 and 4.20$\pm$0.55 mg/g in group 3 at the 3rd month, which have statistical significance in group 2 (p<0.05). Finally at 4th month, 6.01$\pm$1.21 mg/g in group 1, 3.78$\pm$1.82 mg/g in group 2, 3.92$\pm$0.92 mg/g in group 3, which also have statistical significance (p < 0.05). Conclusion: This means posttreatment with 2% chitosan shows meaningful calcium mitigation effects after 3rd month on subcutaneously implanted bovine pericardium in the rat models but 8% glutamate shows mitigation effect after 4months in this experiment.

• Economic and Environmental Geology
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• v.50 no.4
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• pp.257-266
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• 2017

Batch experiments were performed to develop the method for the pH reduction of recycled aggregate by using $scCO_2$ (supercritical $CO_2$), maintaining the pH of extraction water below 9.8. Three different aggregate types from a domestic company were used for the $scCO_2$-water-recycled aggregate reaction to investigate the low pH maintenance of aggregate during the reaction. Thirty five gram of recycled aggregate sample was mixed with 70 mL of distilled water in a Teflon beaker, which was fixed in a high pressurized stainless steel cell (150 mL of capacity). The inside of the cell was pressurized to 100 bar and each cell was located in an oven at $50^{\circ}C$ for 50 days and the pH and ion concentrations of water in the cell were measured at a different reaction time interval. The XRD and SEM-EDS analyses for the aggregate before and after the reaction were performed to identify the mineralogical change during the reaction. The extraction experiment for the aggregate was also conducted to investigate the pH change of extracted water by the $scCO_2$ treatment. The pH of the recycled aggregate without the $scCO_2$ treatment maintained over 12, but its pH dramatically decreased to below 7 after 1 hour reaction and maintained below 8 for 50 day reaction. Concentration of $Ca^{2+}$, $Si^{4+}$, $Mg^{2+}$ and $Na^+$ increased in water due to the $scCO_2$-water-recycled aggregate reaction and lots of secondary precipitates such as calcite, amorphous silicate, and hydroxide minerals were found by XRD and SEM-EDS analyses. The pH of extracted water from the recycled aggregates without the $scCO_2$ treatment maintained over 12, but the pH of extracted water with the $scCO_2$ treatment kept below 9 of pH for both of 50 day and 1 day treatment, suggesting that the recycled aggregate with the $scCO_2$ treatment can be reused in real construction sites.

• Journal of Life Science
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• v.32 no.11
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• pp.872-881
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• 2022

In this study, we optimized the composition of the indole-3-acetic acid (IAA) production medium using response surface methodology on Pantoea agglomerans SRCM 119864 isolated from soil. IAA-producing P. aglomerans SRCM 119864 was identified by 16S rRNA gene sequencing. There are 11 intermediate components known to affect IAA production, hence the effect of each component on IAA production was investigated using a Plackett-Burman design (PBD). Based on the PBD, sucrose, tryptone, and sodium chloride were selected as the main factors that enhanced the IAA production at optimal L-tryptophan concentration. The predicted maximum IAA production (64.34 mg/l) was obtained for a concentration of sucrose of 13.38 g/l, of tryptone of 18.34 g/l, of sodium chloride of 9.71 g/l, and of L-tryptophan of 6.25 g/l using a the hybrid design experimental model. In the experiment, the nutrient broth medium supplemented with 0.1% L-tryptophan as the basal medium produced 45.24 mg/l of IAA, whereas the optimized medium produced 65.40 mg/l of IAA, resulting in a 44.56% increase in efficiency. It was confirmed that the IAA production of the designed optimal composition medium was very similar to the predicted IAA production. The statistical significance and suitability of the experimental model were verified through analysis of variance (ANOVA). Therefore, in this study, we determined the optimal growth medium concentration for the maximum production of IAA, which can contribute to sustainable agriculture and increase crop yield.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.3
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• pp.67-75
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• 2009

Purpose: As to the analytical method of data, the various programs in which it is used for the quantitative rating of the Tl-201 myocardial perfusion SPECT are reported that there is a difference. Therefore, the measured value error of the mutual program is expected to be generated even if the quantitative analysis is made against data of the same patient. Using quantitative index that able to represent myocardial perfusion defect level, we aimed to determine correlation among three myocardial perfusion analysis programs 4DM (4DMSPECT), QPS (Quantitative Perfusion SPECT), ECT (Emory Cardiac Toolbox) that be used generally in most departments of Nuclear Medicine. Materials and Methods: We analyzed the 145 patients who were examined by Tl-201 gated myocardial perfusion SPECT in department of nuclear medicine at Asan Mediacal Center from December 1th 2008 to February 14th 2008. We sorted as normal group and abnormal group. Normal group consist of 80 patients (Male/Female=38/42, age:$65.1{\pm}9.9$) who have low possibility of cardiovascular disease. And abnormal group consist of 65 patients (Male/Female=45/20, age:$63.0{\pm}8.7$) who were diagnosed cardiovascular disease with reversible perfusion defect or fixed perfusion defect through myocardial perfusion SPECT results. Using the 4DM, QPS, and ECT programs, the total defect extent (TDE) such as LAD, LCX, RCA and the summed stress score (SSS) have been analysed for their correlations and statistical comparison with the paried t-test for the quantitative indices analysed from each group. Results: The correlation of 4DM:QPS, QPS:ECT, ECT:4DM each group result from 145 patients is 0.84, 0.86, 0.82 at SSS, 0.87, 0.84, 0.87 at TDE, and both index showed good correlation. In paired t-test and Bland-Altman analysis results showed no statistically significant difference in the comparison of QPS:ECT at the mean SSS and TDE, 4DM:QPS, ECT:4DM comparative analysis results showed statistically significant difference at SSS and TDE index. The correlation of 4DM:QPS, QPS:ECT, ECT:4DM program results from abnormal group (65 patients) is 0.72, 0.72, 0.70 at SSS and 0.77, 0.70, 0.77 at TDE and TDE and SSS has a good correlation. In abnormal group, paired t-test and Bland-Altman analysis results showed no statistically significant difference at QPS:ECT SSS (p=0.89) and TDE (p=0.23) comparison, 4DM:QPS, ECT:4DM comparative analysis results showed statistically significant difference at SSS and TDE index (p<0.01). In normal group (80 patients), paired t-test and Bland-Altman analysis results showed no statistically significant difference at QPS:ECT SSS (p=0.95) and TDE (p=0.73) comparison. And 4DM:QPS, ECT:4DM comparative analysis results showed statistically significant difference at SSS and TDE index (p<0.01). Conclusions: The perfusion defect of the Tl-201 myocardial perfusion SPECT was analyzed in not only the patient in whom it has the cardiovascular disease but also the patient in whom the possibility of the cardiovascular disease is few. In the comparison of the all group research, the mean of the TDE and SSS, 4DM was lower than QPS and ECT progrms. Each program showed good correlation and the results showed statistically significant difference. However, in this way, it is determined to be compatible about the analysis value in which the large-scale side between the programs uses each program a difference in a clinical in the Bland-Altman analyzed result in spite of the good correlation and cannot use. but, this analyzed result will be able to be usefully used as the reference material for the clinical read and is expected.

• The Journal of Korean Academy of Prosthodontics
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• v.47 no.2
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• pp.108-118
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• 2009

Statement of problem: Volume stability, microstructure reproducibility and fluidity along with compatibility with dental stone must be in consideration in order to use tissue conditioner as a material for functional impression. There are few studies concerning the influence of time factor in oral condition on surface roughness of the stone and optimal retention period in the oral cavity considering such changes in surface roughness. Purpose: The purpose of this study was to find out the influence of various kinds of tissue conditioner, its powder/liquid ratio and immersion time on surface roughness of the stone. Material and methods: Materials used in this study were the three kinds of tissue conditioners(Coe-Comfort, Visco-Gel, Soft-Liner) and were grouped into three: group R-mixed with standard powder/liquid ratio that was recommended by the manufacturers, group M-mixed with 20% more powder, group L-mixed with 20% less powder. Specimens were made with the size of 20 mm diameter and 2 mm width. Each tissue conditioner specimens were subdivided into 5 groups according to the immersion time(0 hour, 1 day, 3 days, 5 days, 7 days), completely immersed into artificial saliva and were stored under $37^{\circ}C$. Specimens of which the given immersion time elapsed were taken out and were poured with improved stone, making the stone specimens. Surface roughness of the stone specimens was measured by a profilometer. Results: Within the limitation of this study, the following results were drawn. 1. Major influencing factor on surface roughness of the stone model made from tissue conditioner was the retention period(contribution ratio($\rho$)=62.86%, P<.05) of the tissue conditioner in oral cavity to make functional impression. 2. In case of Coe-Comfort, higher mean surface roughness value of the stone model with statistical significance was observed compared to that of Soft-Liner and Visco-Gel as immersion time changes(P<.05). 3. In case of group L(less), higher mean surface roughness value of the stone model with statistical significance was observed compared to that of R(recommended) and M(more) group as immersion time changes(P<.05). Conclusion: We may conclude that as the retention period of time in oral cavity influences surface roughness of the stone model the most and as the kind of tissue conditioner and its P/L ratio may influence also, clinician should well understand the optimal retention period in oral cavity and choose the right tissue conditioner for the functional impression, thus making the functional impression with tissue conditioner usefully.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.