• Clinical and Experimental Pediatrics
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• v.48 no.7
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• pp.779-788
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• 2005

Purpose : Echinacea, a traditional plant medicine has been used as immune-stimulant. Recent studies have revealed that extract of Echinacea has immunostimulatory effects on human blood mononuclear cells. This study was designed for the purpose of screening the genes associated with immunologic effects of Echinacea on monocytes and dendritic cells using a cDNA microarray chip. Methods : $CD14^+$ monocyte cells were cultured for one day with Echinacea extract(final concentration : $50{\mu}g/mL$) in experiment 1, but were cultured without Echinacea in experiment 2. The gene expression of these cultured monocytes was analyzed using the cDNA microarray chip. Dendritic cells produced from $CD14^+$ monocyte were cultured for five days with GM-CSF and IL-4, and then cultured for one day with Echinacea in experiment 3, but were done without Echinacea in experiment 4. Results : In experiments 1 and 2, there were 17 significantly expressed genes with average expression ratios above 2.5, including interferon gamma-inducible protein 30(IFI 30), CDC(cell-division-cylcle)-like kinase 2(CLK 2), syndecan binding protein(syntenin), superoxide dismutase 2, etc. In experiments 3 and 4, there were 24 gene, with significantly expressed genes were 24 genes, which were insulin-like growth factor 2(somatomedin A), methyl-CpG binding domain protein 3, IFI 30, small inducible cytokine subfamily A, member 22, etc. The genes encoding CD44, IFI 30, mannose receptor C type 1(MRC 1), chemokine receptor 7(CCR 7), CLK 2, syntenin and cytochrome C oxidase subunit VIII were significantly expressed in both monocytes and dendritic cells cultured with Echinacea. Conclusion : This study employed a cDNA microarray chip to elicit the immune-associated gene profile; the expression was enhanced by Echinacea in CD14+ monocytes and dendritic cells. Thus we laid the basis for the quantitative and functional analysis of genes induced by Echinacea in monocytes and monocyte-derived dendritic cells.

• Journal of Plant Biotechnology
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• v.43 no.3
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• pp.359-366
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• 2016

Tradescantia is a perennial plant in the family of Commelinaceae. It is known to be sensitive to radiation. In this study, Tradescantia BNL 4430 was irradiated with gamma radiation at doses of 50 to 1,000 mGy in a phytotron equipped with a $^{60}Co$ radiation source at Korea Atomic Energy Research Institute, Korea. At 13 days after irradiation, we extracted RNA from irradiated floral tissues for RNA-seq. Transcriptome assembly produced a total of 77, 326 unique transcripts. In plantlets exposed to 50, 250, 500, and 1000 mGy, the numbers of up-regulated genes with more than 2-fold of expression compared that in the control were 116, 222, 246, and 308, respectively. Most of the up-regulated genes induced by 50 mGy were heat shock proteins (HSPs) such as HSP 70, indicating that protein misfolding, aggregation, and translocation might have occurred during radiation stress. Similarly, highly up-regulated transcripts of the IQ-domain 6 were induced by 250 mGy, KAR-UP oxidoreductase 1 was induced by 500 mGy, and zinc transporter 1 precursor was induced by 1000 mGy. Reverse transcriptase (RT) PCR and quantitative real time PCR (qRT-PCR) further validated the increased mRNA expression levels of selected genes, consistent with DEG analysis results. However, 2.3 to 97- fold higher expression activities were induced by different doses of radiation based on qRT-PCR results. Results on the transcriptome of Tradescantia in response to radiation might provide unique identifiers to develop in situ monitoring kit for measuring radiation exposure around radiation facilities.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.1
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• pp.54-63
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• 2010

Baicalin, a flavonoid, was shown to have diverse effects such as anti-inflammatory, anti-cancer, anti-viral, anti-bacterial and others. Recently, we found that the baicalin inhibits adipogenesis through the modulations of anti-adipogenic and pro-adipogenic factors of the adipogenesis pathway. In the present study, we further characterized the molecular mechanism of the anti-adipogenic effect of baicalin using microarray technology. Microarray analyses were conducted to analyze the gene expression profiles during the differentiation time course (0 day, 2 day, 4 day and 7 day) in 3T3-L1 cells with or without baicalin treatment. We identified a total of 3972 genes of which expressions were changed more than 2 fold. These 3972 genes were further analyzed using hierarchical clustering analysis, resulting in 20 clusters. Four clusters among 20 showed clearly up-regulated expression patterns (cluster 8 and cluster 10) or clearly down-regulated expression patterns (cluster 12 and cluster 14) by baicalin treatment for over-all differentiation period. The cluster 8 and cluster 10 included many genes which enhance cell proliferation or inhibit adipogenesis. On the other hand, the cluster 12 and cluster 14 included many genes which are related with proliferation inhibition, cell cycle arrest, cell growth suppression or adipogenesis induction. In conclusion, these data provide detailed information on the molecular mechanism of baicalin-induced inhibition of adipogenesis.

• Korean Journal of Psychosomatic Medicine
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• v.25 no.2
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• pp.176-184
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• 2017

Objectives : Middle aged women with depression often experience hot flush symptoms. It is still unclear about the association between depression and hot flush symptoms. Therefore, we investigated hormonal profile, functional somatic symptoms, coping styles and attitude for menopause between depressed women with hot flush and those without hot flush. Methods : This study included 33 depressed patients with hot flush symptoms and 33 depressed patients without hot flush symptoms. Hot flush was confirmed through prospective daily symptom ratings for a week. Subjects' coping styles were assessed by the stress coping checklist. Somatic symptoms were assessed by the Patient Health Questionnaire-15(PHQ-15). Sex hormonal levels were assayed by a radioimmunoassay. The Brief World Health Organization Quality of Life Assessment Instrument(WHOQOL-BREF) was used to evaluate quality of life. Results : In coping style, depressed women with hot flush symptoms used less 'problem-centered coping'($13.15{\pm}3.17$) and 'search for social support'($11.83{\pm}2.84$) than those without hot flush symptoms($15.17{\pm}3.1$, p=0.028 ; $14.25{\pm}3.22$, p=0.009 ; respectively). Depressed women with hot flush symptoms showed more negative attitude toward post-menopause, but its statistical significance was marginally insufficient(p=0.059). We did not find any group differences in sex hormonal levels and somatic complaints assessed by the PHQ-15. The score of social relationship domain of WHOQOL-BREF was significantly lower in subjects with hot flush symptoms($8.62{\pm}2.04$) than subjects without hot flush symptoms($9.71{\pm}1.65$ ; p=0.044). Conclusions : Among middle aged women with depression, the manifestation of hot flush symptoms was associated with coping styles and attitude for menopausal transition. Hot flush symptoms in depressed women negatively influence quality of life, so clinicians actively perform therapeutic approach in case of depressed patients with hot flush symptoms. In case of depressed patients who present hot flushes, cognitive behavior therapy or stress management might be an effective treatment option in company with antidepressants or hormonal treatment. Later, longitudinal study will be needed to evaluate risk factor, cause and effect associated with hot flush and depression.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.