• Journal of Genetic Medicine
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• v.5 no.1
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• pp.15-20
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• 2008

Purpose : Large exon deletions in the DMD gene are found in about 60% of DMD/BMD patients. Multiplex PCR has been employed to detect the deletion mutation, which frequently generates noise PCR products due to the presence of multiple primers in a single reaction as well as the stringency of PCR conditions. This often leads to a false-negative or false-positive result. To address this problematic issue, we introduced the dual primer oligonucleotide (DPO) system. DPO contains two separate priming regions joined by a polydeoxyinosine linker that results in high PCR specificity even under suboptimal PCR conditions. Methods : We tested 50 healthy male controls, 50 patients with deletion mutation as deletion-positive patient controls, and 20 patients with no deletions as deletion-negative patient controls using DPO-multiplex PCR. Both the presence and extent of deletion were verified by simplex PCR spanning the promoter region (PM) and 18 exons including exons 3, 4, 6, 8, 12, 13, 17, 19, 43-48, 50-52, and 60 in all 120 controls. Results : DPO-multiplex PCR showed 100% sensitivity and specificity for the detection a deletion. However, it showed 97.1% sensitivity and 100% specificity for determining the extent of deletions. Conclusion : The DPO-multiplex PCR method is a useful molecular test to detect large deletions of DMD for the diagnosis of patients with DMD/BMD because it is easy to perform, fast, and cost-effective and has excellent sensitivity and specificity.

• Korean Journal of Food Science and Technology
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• v.42 no.1
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• pp.8-13
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• 2010

Bisphenol A diglycidyl ether (BADGE) and bisphenol F diglycidyl ether (BFDGE) were obtained by a polymerization reaction of epichlorohydrin (ECH) with bisphenol A (BPA) or bisphenol F (BPF). These compounds are commonly used as monomers or additives such as a polymerization stabilizer and a hydrochloric acid scavenger of epoxy resin, polyvinyl chloride (PVC)-containing organosols and polyester lacquers, that are applied to the internal surface of most canned foods to impart chemical resistance. The unreacted BADGE, BFDGE and their reaction products migrating from epoxy resin, PVC-containing organosol and/or polyester lacquer-based food packaging materials into the foods have recently become an issue of great concern because of increased customer demand for safety. This study was conducted to develop a rapid and sensitive simultaneous analysis method based on HPLC/FLD and HPLC/APCI-mass and to evaluate the concentration of BADGE, BFDGE and their metabolites, BADGE $H_2O$, BADGE $2H_2O$, BADGE HCl, BADGE 2HCl, BADGE HCl $H_2O$, BFDGE $H_2O$, BFDGE $2H_2O$, BFDGE HCl, BFDGE 2HCl and BFDGE HCl $H_2O$ for 133 canned food samples. The method provided a linearity of 0.9997-0.9999, a limit of detection of $0.01-0.13\;{\mu}g/mL$, a limit of quantitation of $0.03-0.44\;{\mu}g/mL$ and a recovery (%) of 85.64-118.18. The number of samples containing BADGE, BFDGE or their metabolites were: 28/133 (21.1%), with levels of 0.400-0.888 mg/kg being observed for aqueous foods (19/133) and 0.093-0.506 mg/kg being observed for oily foods (9/133).

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• Analytical Science and Technology
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• v.35 no.3
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• pp.136-142
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• 2022

According to media reports, the carcasses of euthanized abandoned dogs were processed at high temperature and pressure to make powder, and then used as feed materials (meat and bone meal), raising the possibility of residuals in the feed of the anesthetic ketamine and dexmedetomidine used for euthanasia. Therefore, a simultaneous analysis method using QuEChERS combined with high-performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry was developed for rapid residue analysis. The method developed in this study exhibited linearity of 0.999 and higher. Selectivity was evaluated by analyzing blank and spiked samples at the limit of quantification. The MRM chromatograms of blank samples were compared with those of spiked samples with the analyte, and there were no interferences at the respective retention times of ketamine and dexmedetomidine. The detection and quantitation limits of the instrument were 0.6 ㎍/L and 2 ㎍/L, respectively. The limit of quantitation for the method was 10 ㎍/kg. The results of the recovery test on meat and bone meal, meat meal, and pet food showed ketamine in the range of 80.48-98.63 % with less than 5.00 % RSD, and dexmedetomidine in the range of 72.75-93.00 % with less than 4.83 % RSD. As a result of collecting and analyzing six feeds, such as meat and bone meal, prepared at the time the raw material was distributed, 10.8 ㎍/kg of ketamine was detected in one sample of meat and bone meal, while dexmedetomidine was found to have a concentration below the limit of quantitation. It was confirmed that the detected sample was distributed before the safety issue was known, and thereafter, all the meat and bone meal made with the carcasses of euthanized abandoned dogs was recalled and completely discarded. To ensure the safety of the meat and bone meal, 32 samples of the meat and bone meal as well as compound feed were collected, and additional residue investigations were conducted for ketamine and dexmedetomidine. As a result of the analysis, no component was detected. However, through this investigation, it was confirmed that some animal drugs, such as anesthetics, can remain without decomposition even at high temperature and pressure; therefore, there is a need for further investigation of other potentially hazardous substances not controlled in the feed.

• Korean Journal of Environmental Biology
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• v.40 no.3
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• pp.316-324
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• 2022

Ethyl formate (EF) is a potent fumigant replacing methyl bromide. The use of EF is limited to a quarantine process. Appling EF to agricultural field as a safe insecticide in greenhouse give us valuable benefits including less residual concern. In this regard, residual pattern after EF fumigation in greenhouse should be undertaken. In the previous study, we have established agricultural control concentration of EF to control pests in a greenhouse. EF was fumigated at 5 g m^-3 level for 2 h. The concentration of EF inside a greenhouse was analyzed to be 4.1-4.3 g m^-3 at 30 min after fumigation. To prepare an analytical method for residues in cucumber crops and soil in the greenhouse, the limit of detection(LOD) of the method was 100ng g^-1 and the limit of quantitation(LOQ) of this method was 300 ng g^-1. R² values of calibration curves for crops and soil were 0.991-0.997. In samples collected immediately after ventilation, EF concentration was determined to be below LOQ level. In addition, EF level was below LOQ in samples collected at 3 h after ventilation except that leaf samples of melon during the flowering period showed a level of 1,068.9 ng g^-1. Taken together, these results indicate that EF used in quarantine can be applied to agricultural fields without residual issue as an effective fumigant for insect pest control.

• 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.