• Journal of Life Science
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• v.33 no.2
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• pp.169-175
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• 2023

As a protective defensive mechanism against ultraviolet (UV) light exposure in skin tissue, melanocytes produce the pigment melanin. Tyrosinase plays a key role in melanin production in melanocytes. However, the overproduction of melanin can lead to lesions, such as freckles and dark spots. Thus, it is clinically important to find a modulating molecule to control melanogenesis by regulating tyrosinase expression and/or activity. It is known that catechin, a plant flavonoid, can reduce melano- genesis through the downregulation of tyrosinase expression. Here, we tested whether catechin derivatives isolated from the stem bark of Ulmus parvifolia have an effect on melanin production by regulating tyrosinase in mouse melanoma cells and in vitro mushroom tyrosinase. The catechin derivatives used in this study included C5A, C7A, C7G, and C7X. Treatments using these catechin derivatives reduced melanin production in mouse melanoma B16F10 cells in which melanogenesis was stimulated by α-MSH. Notably, the anti-melanogenic effects of catechin derivatives were similar to those of kojic acid, a well-known anti-melanogenic molecule. Both C5A and C7A directly inhibited the activity of tyrosinase isolated from mushrooms in vitro. Furthermore, our in silico computational simulation showed that these two compounds were expected to bind to the active site of tyrosinase, which is similar to kojic acid. In addition, all four catechin derivatives reduced tyrosinase protein expression. In summary, our results showed that catechin derivatives can reduce melanogenesis by regulating tyrosinase activity or expression. Thus, this study suggests that catechin derivatives isolated from U. parvifolia can be novel modulators of melanin production.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.517-527
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• 2023

This study aimed to examine the possibility of upcycling extracts of Angelica keiskei and Oenanthe javanica juice by-products through comparing enzyme extraction (EE) and complex extraction (CE) methods to increase the extraction yield and flavor of materials. A higher extraction yield was obtained for free amino acid content with EE and CE for A. keiskei and O. javanica juice by-products, respectively, and a higher extraction efficiency was achieved with juice by-products than with extracts prepared from raw materials before juice production. The content of major amino acids varied depending on the extraction method used. When used according to the characteristics of the extract, their use as a functional material was confirmed along with improvement in the flavor of the food. Consistently high extraction yields for organic acid and sugar levels were obtained with CE in A. keiskei and O. javanica juice by-products. The DPPH radical scavenging ability and TPC were consistently high with CE in A. keiskei and O. javanica juice by-products; the increase in extracted content was likely because of the reaction between the ethanol used for CE and the phenolic compounds. However, because the antioxidant capacity of the juice by-product extracts was somewhat lower than that of the extracts from raw materials before juice production, the amount used should be reviewed. The TFC was found to be higher in extracts obtained with EE than with CE for A. keiskei juice by-products; however, no significant difference was observed between EE and CE in the O. javanica juice by-products. Through this study, the taste compounds and antioxidant properties of extracts obtained from juice by-products produced after the production of A. keiskei and O. javanica green juice were analyzed, and the availability of high value-added materials was confirmed. Based on these research results, expanding specific R&D for practical use should be explored.

• Clinical and Experimental Pediatrics
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• v.48 no.3
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• pp.298-305
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• 2005

Purpose : We performed this study to evaluate the mean serum levels of insulin-like growth factor (IGF)-I, insulin-like growth factor binding protein(IGFBP)-2 and IGFBP-3 in healthy Korean children according to age and sex. Methods : Ninety two healthy children, consisting of 42 boys and 50 girls, were classified into five groups according to age : neonate; infancy; early childhood; late childhood; and adolescence. We measured serum levels of IGF-I, IGFBP-2 and IGFBP-3 by enzyme-linked immunosorbent assay(ELISA) and analysed the serum levels according to sex and age group. Results : For boys, the mean serum levels of IGF-I(ng/mL) in neonate, infancy, early childhood, late childhood and adolescence were $41.1{\pm}3.6$, $70.9{\pm}33.7$, $103.5{\pm}97.2$, $89.8{\pm}46.5$ and $51.4{\pm}27.8$, respectively. Those of IGFBP-2(ng/mL) were $8.2{\pm}3.4$, $5.8{\pm}0.4$, $9.3{\pm}4.0$, $9.5{\pm}1.1$ and $7.0{\pm}0.5$, respectively. Those of IGFBP-3(ng/mL) were $559.2{\pm}215.2$, $1,333.3{\pm}692.5$, $2,254.6{\pm}1,513.8$, $2,447.1{\pm}1,464.2$, $1,533.6{\pm}807.4$, respectively. For girls, the mean serum levels of IGF-I(ng/mL) according to five age groups were $53.3{\pm}9.5$, $99.3{\pm}45.8$, $69.6{\pm}51.1$, $106.2{\pm}67.0$ and $145.1{\pm}127.8$, respectively. Those of IGFBP-2 (ng/mL) were $9.1{\pm}7.4$, $5.3{\pm}0.9$, $6.9{\pm}2.0$, $10.5{\pm}3.0$ and $7.9{\pm}1.3$, respectively. Those of IGFBP-3(ng/mL) were $858.2{\pm}433.4$, $1,834.8{\pm}851.3$, $1,404.3{\pm}570.2$, $2,203.5{\pm}899.4$ and $2,029.3{\pm}1,316.7$, respectively. There were significant positive correlations observed between IGF-I and IGFBP-3 levels(r=0.589, P=0.000). Conclusion : IGF-I and IGFBP-3 levels increased as children get older. The peak level of IGFBP-3 was observed in late childhood for both boys and girls, suggesting a current trend of children reaching peak growth velocity before adolescence. The IGFBP-2 level was higher in neonates compare to infancy, suggesting that IGFBP-2 is an important substance for fetal growth.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.71-88
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• 2017

Language models were originally developed for speech recognition and language processing. Using a set of example sentences, a language model predicts the next word or character based on sequential input data. N-gram models have been widely used but this model cannot model the correlation between the input units efficiently since it is a probabilistic model which are based on the frequency of each unit in the training set. Recently, as the deep learning algorithm has been developed, a recurrent neural network (RNN) model and a long short-term memory (LSTM) model have been widely used for the neural language model (Ahn, 2016; Kim et al., 2016; Lee et al., 2016). These models can reflect dependency between the objects that are entered sequentially into the model (Gers and Schmidhuber, 2001; Mikolov et al., 2010; Sundermeyer et al., 2012). In order to learning the neural language model, texts need to be decomposed into words or morphemes. Since, however, a training set of sentences includes a huge number of words or morphemes in general, the size of dictionary is very large and so it increases model complexity. In addition, word-level or morpheme-level models are able to generate vocabularies only which are contained in the training set. Furthermore, with highly morphological languages such as Turkish, Hungarian, Russian, Finnish or Korean, morpheme analyzers have more chance to cause errors in decomposition process (Lankinen et al., 2016). Therefore, this paper proposes a phoneme-level language model for Korean language based on LSTM models. A phoneme such as a vowel or a consonant is the smallest unit that comprises Korean texts. We construct the language model using three or four LSTM layers. Each model was trained using Stochastic Gradient Algorithm and more advanced optimization algorithms such as Adagrad, RMSprop, Adadelta, Adam, Adamax, and Nadam. Simulation study was done with Old Testament texts using a deep learning package Keras based the Theano. After pre-processing the texts, the dataset included 74 of unique characters including vowels, consonants, and punctuation marks. Then we constructed an input vector with 20 consecutive characters and an output with a following 21st character. Finally, total 1,023,411 sets of input-output vectors were included in the dataset and we divided them into training, validation, testsets with proportion 70:15:15. All the simulation were conducted on a system equipped with an Intel Xeon CPU (16 cores) and a NVIDIA GeForce GTX 1080 GPU. We compared the loss function evaluated for the validation set, the perplexity evaluated for the test set, and the time to be taken for training each model. As a result, all the optimization algorithms but the stochastic gradient algorithm showed similar validation loss and perplexity, which are clearly superior to those of the stochastic gradient algorithm. The stochastic gradient algorithm took the longest time to be trained for both 3- and 4-LSTM models. On average, the 4-LSTM layer model took 69% longer training time than the 3-LSTM layer model. However, the validation loss and perplexity were not improved significantly or became even worse for specific conditions. On the other hand, when comparing the automatically generated sentences, the 4-LSTM layer model tended to generate the sentences which are closer to the natural language than the 3-LSTM model. Although there were slight differences in the completeness of the generated sentences between the models, the sentence generation performance was quite satisfactory in any simulation conditions: they generated only legitimate Korean letters and the use of postposition and the conjugation of verbs were almost perfect in the sense of grammar. The results of this study are expected to be widely used for the processing of Korean language in the field of language processing and speech recognition, which are the basis of artificial intelligence systems.

• Cartoon and Animation Studies
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• s.50
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• pp.349-382
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• 2018

China is a great civilization which is a combination of various ethnic groups with long history change. As one of these important components of traditional culture, the lucky shape has been going through the ideological upheaval of the history change of China. Up to now, it has become the important parts which can stimulate the emotion of Chinese nation. The lucky shape becomes the basis of the rich traditional culture by long history of the Chinese nation. Even say it is the centre of this traditional culture resource. The lucky shape is a way of expressing the Chinese history and national emotions. It is the important part of people's living habits, emotion, as well as the cultural background. What's more, it has the value of beliefs of Surname totem. Meanwhile, it also has the function of passing on information. The symbol of information finally was created by the being of lucky shape to indicate its conceptual content. There are various kinds of lucky shapes. It will have its limitations when researching all kinds of them professionally. So, here the lucky shape of FISH will be researched. The shape of fish is the first good shape created by the Chinese nation. It is about 6000 years. Its special shape and lucky meaning embody the peculiar inherent culture and intension of the Chinese nation. It's the important component of the Chinese traditional culture. The traditional shape of fish was focused on the continuation of history and the patterns recognition, etc. It seldom indicated the meaning of the shape into the using of the modern design. So by searching the lucky meaning & the way of fish shape, the purpose of the search is to explore the real analysis of value of the fish shape in the modern enterprise identity design. The way of search is through the development of the history, the evolvement and the meaning of lucky of the traditional fish shape to analyse the symbolic meaning and the cultural meaning from all levels in nation, culture, art and life, etc. And by using the huge living example of the enterprise identity design of the traditional shape of the fish to analyse that how it works in positive way by those enterprise which is based on the trust with good image. In the modern Chinese enterprise identity design, the lucky image will be reinterpreted in the modern way. It will be proofed by the national perceptual knowledge of the consumer and the way of enlarge the goodwill of corporate image. It will be the conclusion. The traditional fish shape is the important core of modern design.So this search is taken through the instance of the design of enterprise image of the traditional fish shape to analysis the idea of the majority Chinese people of the traditional luck and the influence of corporation which based on trust and credibility. In modern image design of Chinese corporation, the auspicious sign reappear. The question survey is taken by people through the perceptual knowledge of the consumer and the cognition the enterprise image. According the result, people can speculate the improvement of consumer's recognition and the possibility of development of traditional concept.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Journal of the Korean Home Economics Association
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• v.37 no.6
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• pp.11-22
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• 1999

This study focused on the relationship of social skills and social support from family and friends to adjustment between children and adolescents. Subjects were enrolled in the fifth, sixth, 1st, & 2nd grades of elementary and junior high schools. The instruments were Teenage Inventory of Social Skills, Perceived Social Support from Family & Friends, Child Depression Inventory, and Antisocial Behavior Scale. Results indicated that there were positive relations between social skills and social support from family and friends. The more social support from family children and adolescents had, the less depression and antisocial behavior they reported. For depression, children and adolescents showed a significant sex difference. In the case of antisocial behavior, only adolescents revealed a significant sex difference. Depression was explained by social support from family most for both children and adolescents. Antisocial behavior was explained by social skills most especially for children. The results discussed in the context of the effects of social skills and social support on emotional and behavioral adjustments.

• The Korean Society of Law and Medicine
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• v.23 no.4
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• pp.29-74
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• 2022

As social disasters occur under the Disaster Management Act, which can damage the people's "life, body, and property" due to the rapid spread and spread of unexpected COVID-19 infectious diseases in 2020, information collected through inspection and reporting of infectious disease pathogens (Article 11), epidemiological investigation (Article 18), epidemiological investigation for vaccination (Article 29), artificial technology, and prevention policy Decision), (3) It was used as an important basis for decision-making in the context of an infectious disease crisis, such as promoting vaccination and understanding the current status of damage. In addition, medical policy decisions using infectious disease data contribute to quarantine policy decisions, information provision, drug development, and research technology development, and interest in the legal scope and limitations of using infectious disease data has increased worldwide. The use of infectious disease data can be classified for the purpose of spreading and blocking infectious diseases, prevention, management, and treatment of infectious diseases, and the use of information will be more widely made in the context of an infectious disease crisis. In particular, as the serious stage of the Disaster Management Act continues, the processing of personal identification information and sensitive information becomes an important issue. Information on "medical records, vaccination drugs, vaccination, underlying diseases, health rankings, long-term care recognition grades, pregnancy, etc." needs to be interpreted. In the case of "prevention, management, and treatment of infectious diseases", it is difficult to clearly define the concept of medical practicesThe types of actions are judged based on "legislative purposes, academic principles, expertise, and social norms," but the balance of legal interests should be based on the need for data use in quarantine policies and urgent judgment in public health crises. Specifically, the speed and degree of transmission of infectious diseases in a crisis, whether the purpose can be achieved without processing sensitive information, whether it unfairly violates the interests of third parties or information subjects, and the effectiveness of introducing quarantine policies through processing sensitive information can be used as major evaluation factors. On the other hand, the collection, provision, and use of infectious disease data for research purposes will be used through pseudonym processing under the Personal Information Protection Act, consent under the Bioethics Act and deliberation by the Institutional Bioethics Committee, and data provision deliberation committee. Therefore, the use of research purposes is recognized as long as procedural validity is secured as it is reviewed by the pseudonym processing and data review committee, the consent of the information subject, and the institutional bioethics review committee. However, the burden on research managers should be reduced by clarifying the pseudonymization or anonymization procedures, the introduction or consent procedures of the comprehensive consent system and the opt-out system should be clearly prepared, and the procedure for re-identifying or securing security that may arise from technological development should be clearly defined.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.227-252
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• 2018

The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.