• Progress in Medical Physics
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• v.13 no.4
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• pp.209-217
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• 2002

There have been many studies on the application of the reciprocal advantages of multimodality image to define accurate target volume in the Process of radiation treatment planning. For the proper use of the multimodality images, the registration works between different modality images should be performed in advance. In this study, we selected chamfer matching method and mutual information method as most popular methods in recent image registration studies considering the registration accuracy and clinical practicality. And the two registration methods were analyzed to deduce the optimal registration method according to the characteristics of images. Lung phantom of which multimodality images could be acquired was fabricated and CT, MRI and SPECT images of the phantom were used in this study. We developed the registration program which can perform the two registration methods properly and analyzed the registration results which were produced by the developed program in many different images' conditions. Although the overall accuracy of the registration in both chamfer matching method and mutual information method was acceptable, the registration errors in SPECT images which had lower resolution and in degraded images of which data were removed in some part were increased when chamfer matching method was applied. Especially in the case of degraded reference image, chamfer matching methods produce relatively large errors compared with mutual information method. Mutual information method can be estimated as more robust registration method than chamfer matching method in this study because it did not need the prerequisite works, the extraction of accurate contour points, and it produced more accurate registration results consistently regardless of the images' characteristics. The analysis of the registration methods in this study can be expected to provide useful information to the utilization of multimodality images in delineating target volume for radiation treatment planning and in many other clinical applications.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Food Hygiene and Safety
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• v.29 no.3
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• pp.234-240
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• 2014

Fluxapyroxad is classified as carboxamide fungicide that inhibits succinate dehydrogenase in complex II of mitochondrial respiratory chain, which results in inhibition of mycelial growth within the fungus target species. This study was carried out to assure the safety of fluxapyroxad residues in agricultural products by developing an official analytical method. A new, reliable analytical method was developed and validated using High Performance liquid Chromatograph-UV/visible detector (HPLC-UVD) for the determination of fluxapyroxad residues. The fluxapyroxad residues in samples were extracted with acetonitrile, partitioned with dichloromethane, and then purified with silica solid phase extraction (SPE) cartridge. Correlation coefficient($R^2$) of fluxapyroxad standard solution was 0.9999. The method was validated using apple, pear, peanut, pepper, hulled rice, potato, and soybean spiked with fluxapyroxad at 0.05 and 0.5 mg/kg. Average recoveries were 80.6~114.0% with relative standard deviation less than 10%, and limit of detection (LOD) and limit of quantification (LOQ) were 0.01 and 0.05 mg/kg, respectively. All validation parameters were followed with Codex guideline (CAC/GL 40). LC-MS (Liquid Chromatograph-Mass Spectrometer) was also applied to confirm the analytical method. Base on these results, this method was found to be appropriate fluxapyroxad residue determination and can be used as the official method of analysis.

• Journal of Periodontal and Implant Science
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• v.35 no.3
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• pp.661-674
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• 2005

The purpose of this study was to quantify and compare the level of MMP-2, MMP-8 in the healthy, inflammed gingival tissue and inflammed gingival tissue associated with type 2 DM. We investigate whether expression of MMP-2, MMP-8 is increased by chronic periodontitis associated with type 2 DM. Gingival tissue samples were obtained during periodontal surgery or tooth extraction. Based on patient's systemic condition & clinical criteria of gingiva, each gingival samples were divided into three groups. Group l(n=8) is clinically healthy gingiva without bleeding and no evidence of bone resorption or periodontal pockets, obtained from 8 systemically healthy patients. Group 2(n=8) is inflammed gingiva from patients with chronic periodontitis. Group 3(n=8) is inflammed gingiva from type 2 diabetic patients with chronic periodontitis. Tissue samples were prepared and analyzed by Western blotting. The quantification of MMP-2, MMP-8 was performed using a densitometer and statistically analyzed by ANOVA. MMP-2, MMP-8 was expressed in all samples including healthy gingiva and increased in group 3 compared to group 1 and 2, and showed that significant variation was observed between group 1 & 3 in MMP-8 results. In conclusion, this study demonstrated that human gingival tissue with chronic periodontitis associated to type 2 diabetes showed slightly elevated MMP-2, MMP-8 levels compared to healthy gingiva and non-diabetic inflamed gingiva.

• Korean Journal of Environmental Agriculture
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• v.40 no.4
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• pp.353-358
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• 2021

BACKGROUND: Prochloraz has been widely used as an imidazole fungicide on fruits and vegetables in Korea. Analytical approaches to evaluate prochloraz residues in herbal medicine are required for their safety management. In this study, we developed a GC-ECD method for quantitative determination of prochloraz in Platycodi Radix. The metabolite 2,4,6-trichlorophenol (2,4,6-T) was used as a target compound to evaluate total prochloraz residues as it is categorized to a representative residue definition of prochloraz. All residues containing 2,4,6-T were converted to 2,4,6-T and subjected to GC-ECD. METHODS AND RESULTS: In order to verify the applicability, the method was optimized for determining prochloraz and it metabolite 2,4,6-T in Platycodi Radix. Prochloraz and its metabolite 2,4,6-T residuals were extracted using acetone. The extract was diluted with and partitioned directly into dichloromethane to remove polar co-extractives in the aqueous phase. The extract was decomposed to 2,4,6-T, and then the partitioned ion-associate was finally purified by optimized aminopropyl solid-phase extraction (SPE). The limits of quantitation of the method (MLOQs) were 0.04 mg/kg and 0.02 mg/kg, respectively for prochloraz and 2,4,6-T, considering the maximum residue level (MRL) of prochloraz as 0.05 mg/kg in Platycodi Radix. Recovery tests were carried out at two levels of concentration (MLOQ, 10 MLOQ) and resulted in good recoveries (82.1-89.7%). Good reproducibilities were obtained (coefficient of variation < 2.8%), and the linearities of calibration curves were reasonable (r² > 0.9986) in the range of 0.005-0.5 ㎍/mL. CONCLUSION(S): The method developed in this study was successfully validated to meet the guidelines required for quantitative determination of pesticides in herbal medicine. Thus, the method could be useful to monitor prochloraz institutionally in herbal medicine.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.3
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• pp.309-315
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• 2019

Cervical cancer is the fourth most common malignant neoplasm in women worldwide. Most cases of cervical cancer are caused by an infection by the human papillomavirus. Molecular diagnostic methods have emerged to detect the HPV for sensitivity, specificity, and objectivity. In particular, real-time PCR has been introduced to acquire a more sensitive target DNA or RNA. RNA extraction and complementary DNA synthesis are proceeded before performing real-time PCR targeting RNA. To identify an adequate and sensitive cDNA synthesis kit, this study evaluated the two commonly used kits for cDNA synthesis. The results show that the $R^2$ and efficiency (%) of the two cDNA synthesis kits were similar in the cervical cancer cell lines. On the other hand, the Takara kit compared to Invitrogen kit showed P<0.001 in the $10^2$ and $10^3$ SiHa cell count. The Takara kit compared to the Invitrogen kit showed P<0.001 in the $10^1$ and $10^2$ HeLa cell count. Furthermore, 8, 4, 2, 1, and 0.5 ml of forty exfoliated cell samples were used to compare the cDNA synthesis kits. The Takara kit compared to the Invitrogen kit showed P<0.01 in 8, 4, and 1 ml and P<0.05 in 0.5 mL. The study was performed to identify the most appropriate cDNA synthesis kit and suggests that a cDNA synthesis kit could affect the real-time PCR results.

• Korean Journal of Environmental Agriculture
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• v.40 no.2
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• pp.108-117
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• 2021

BACKGROUND: Dichlobentiazox is a newly registered pesticide in Korea as a triazole fungicide and requires establishment of an official analysis method for the safety management. Therefore, the aim of this study was to determine the residual analysis method of dichlobentiazox for the five representative agricultural products. METHODS AND RESULTS: Three QuEChERS methods were applied to establish the extraction method, and the EN method was finally selected through the recovery test. In addition, various adsorbent agents were applied to establish the clean-up method. As a result, it was found that the recovery of the tested pesticide was reduced when using the d-SPE method with PSA and GCB, but C₁₈ showed an excellent recovery. Therefore this method was established as the final analysis method. For the analysis, LC-MS/MS was used with consideration of the selectivity and sensitivity of the target pesticide and was operated in MRM mode. The results of the recovery test using the established analysis method and inter laboratory validation showed a valid range of 70-120%, with standard deviation and coefficient of variation of less than 3.0% and 11.6%, respectively. CONCLUSION: Dichlobentiazox could be analyzed with a modified QuEChERS method, and the method determined would be widely available to ensure the safety of residual pesticides in Korea.

• Korean Journal of Environmental Agriculture
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• v.41 no.2
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• pp.82-94
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• 2022

BACKGROUND: Spiropidion and its metabolite are tetramic acid insecticide and require the establishment of an official analysis method for the safety management because they are newly registered in Korea. Therefore, this study was to determine the analysis method of residual spiropidion and its metabolite for the five representative agricultural products. METHODS AND RESULTS: Three QuEChERS methods (original, AOAC, and EN method) were applied to optimize the extraction method, and the EN method was finally selected by comparing the recovery test and matrix effect results. Various adsorbent agents were applied to establish the clean up method. As a result, the recovery of spiropidion was reduced when using the dispersive-SPE method with MgSO₄, primary secondary amine (PSA), graphitized carbon black (GCB) and octadecyl (C₁₈) in soybean. Color interference was minimized by selecting the case including GCB and C18 in addition to MgSO₄. This method was established as the final analysis method. LC-MS/MS was used for the analysis by considering the selectivity and sensitivity of the target pesticide and the analysis was performed in MRM mode. The results of the recovery test using the established analysis method and inter laboratory validation showed a valid range of 79.4-108.4%, with relative standard deviation and coefficient of variation were less than 7.2% and 14.4%, respectively. CONCLUSION(S): Spiropidion and its metabolite could be analyzed with a modified QuEChERS method, and the established method would be widely available to ensure the safety of residual insecticides in Korea.

• Journal of Food Hygiene and Safety
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• v.38 no.3
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• pp.79-88
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• 2023

Trifludimoxazin is a triazinone herbicide that inhibits the synthesis of protoporphyrinogen oxidase (PPO). The lack of PPO damages the cell membranes, leading to plant cell death. An official analytical method for the safety management of trifludimoxazin is necessary because it is a newly registered herbicide in Korea. Therefore, this study aimed to develop a residual analysis method to detect trifludimoxazin in five representative agricultural products. The EN method was established as the final extraction method by comparing the recovery test and matrix effect with those of the QuEChERS method. Various sorbent agents were used to establish the clean-up method, and no differences were observed among them. MgSO₄ and PSA were selected as the final clean-up conditions. We used LC-MS/MS considering the selectivity and sensitivity of the target pesticide and analyzed the samples in the MRM mode. The recovery test results using the established analysis method and inter-laboratory validation showed a valid range of 73.5-100.7%, with a relative standard deviation and coefficient of variation less than 12.6% and 14.5%, respectively. Therefore, the presence of trifludimoxazin can be analyzed using a modified QuEChERS method, which is widely available in Korea to ensure the safety of residual insecticides.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.