• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.6
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• pp.903-911
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• 2011

In this study, Bacillus polyfermenticus CJ6 harboring antibacterial activity was isolated from meju. The antibacterial activity of Bacillus polyfermenticus CJ6 was stable in the pH range of 3.0~9.0, but it disappeared after culture at $70^{\circ}C$ for 24 hr. Antibacterial activity was inactivated by proteinase K, protease, and ${\alpha}$-chymotrypsin, indicating its proteinaceous nature. The growth inhibitory effects of B. polyfermenticus CJ6 culture on food-borne pathogens such as Staphylococcus aureus, Salmonella Typhi, Listeria monocytogenes, and Escherichia coli O157:H7 were examined in this study. Approximately 6~6.2 log CFU/mL of each pathogen was co-cultured with B. polyfermenticus CJ6 in a 50 mL culture volume for 24 hr. Growth of S. aureus and L. monocytogenes was completely inhibited after 3 hr of incubation. Growth of S. Typhi and E. coli O157:H7 was also completely inhibited after 6 hr of incubation. The antibacterial compounds from B. polyfermenticus CJ6 were purified by solid phase extraction (C18 Sep-pak cartridge), recycling preparative HPLC, and analytical HPLC. Ultra-high performance liquid chromatography and electrospray ionization tandem mass spectrometry analysis were used to identify the purified antibacterial compounds, which were confirmed to be five peptides (757.4153 Da, 750.3444 Da, 1024.5282 Da, 1123.6083 Da, and 1617.8170 Da).

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of dental hygiene science
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• v.18 no.2
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• pp.85-96
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• 2018

The purpose of this study is to propose learning objectives in social dental hygiene by analyzing and reviewing learning objectives in oral health administration area of the existing public oral health. This study is a cross-sectional study. The subjects of the study selected with convenience extraction were 15 members of the social dental hygiene subcommittee of the Korean Society of Dental Hygiene Science. Data collection was conducted by self-filling questionnaire. The research tool is from 48 items of A division in the book of learning objectives in the dental hygienist national examination, and this study classified each of them into 'dental hygiene job relevance', 'dental hygiene competency relevance', 'timeliness', and 'value discrimination of educational goal setting' to comprise 192 items. Also, to collect expert opinions, this study conducted Delphi survey on 7 academic experts. Statistical analysis was performed using the IBM SPSS Statistics ver. 23.0 program (IBM Co., Armonk, NY, USA). Recoding was performed according to the degree of relevance of each learning objective and frequency analysis was performed. This study removed 18 items from the whole learning objectives in the dental hygienist national examination in the oral health administration area of public oral health. Fifteen revisions were made and 15 existing learning objectives were maintained. Forty-five learning objectives were proposed as new social dental hygiene learning objectives. The topics of learning objectives are divided into social security and medical assistance, oral health care system, oral health administration, and oral health policy. As a result of this study, it was necessary to construct the learning objectives of social dental hygiene in response to changing situation at the time. The contents of education should be revised in order of revision of learning objectives, development of competency, development of learning materials, and national examination.