• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.522-526
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• 2007

Since newly emerged disease, the Severe Acute Respiratory Syndrome (SARS), spread from Asia to North America and Europe rapidly in 2003, many researchers have tried to determine where the virus came from. In the phylogenetic point of view, SARS virus has been known to be one of the genus Coronavirus, but, the overall conservation of SARS virus sequence was not highly similar to that of known coronaviruses. The natural reservoirs of SARS-CoV are not clearly determined, yet. In the present study, the genomic sequences of SARS-CoV were analyzed by bioinformatics techniques such as multiple sequence alignment and phylogenetic analysis methods as well multivariate statistical analysis. All the calculating processes, including calculations of the relative synonymous codon usage (RSCU) and other genomic parameters using 30,305 coding sequences from the two genera, Coronavirus, and Lentivirus, and one family, Orthomyxoviridae, were performed on SMP cluster in KISTI, Supercomputing Center. As a result, SARS_CoV showed very similar RSCU patterns with feline coronavirus on the both axes of the correspondence analysis, and this result showed more agreeable results with serological results for SARS_CoV than that of phylogenetic result itself. In addition, SARS_CoV, human immunodeficiency virus, and influenza A virus commonly showed the very low RSCU differences among each synonymous codon group, and this low RSCU bias might provide some advantages for them to be transmitted from other species into human beings more successfully. Large-scale genomic analysis using bioinformatics techniques may be useful in genetic epidemiology field effectively.

• Journal of Marine Life Science
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• v.6 no.2
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• pp.97-105
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• 2021

Tegillarca granosa, is one of the most important fishery resources throughout Asia. However, due to industrialization factories, marine environmental pollution, and global warming, the marine fishery production has drop sharply. In order to understand the genetic factors of the blood clam, which is a major fishery resource on the southern coast of Korea, the whole genome of blood clam was studied. The assembled genome of T. granosa was 915.4 Mb, and 19 chromosomes were identified. 25,134 genes were identified, and 22,745 genes were functionally annotated. As a result of performing gene gain and loss analysis between the blood clam genome and eight other types of shellfish, it was confirmed that 725 gene groups were expanded, and 479 gene groups were contracted. The homeobox gene cluster of blood clam showed a well-preserved genetic structure within lophotrochozoan ancestor. T. granosa genome showed high similarity between three hemoglobin genes with Scarpharca broughtonii. The blood clam genome will provide information for the genetic and physiological characteristics of blood clam adaptation, evolution, and the development of aquaculture industry.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.