• Korean Journal of Breeding Science
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• v.49 no.3
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• pp.129-140
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• 2017

This study was carried out to develop of environmental risk assessments and the biosafety guide for Vitamin E enhanced transgenic soybean at LMO (Living Modified Organism) isolation field. In LMO quarantine area of National Institute of Agricultural Sciences, insect species diversities and population densities on vitamin E enhanced transgenic soybean and non-GM soybeans (Willams 82 and Seoritae) were investigated. A total of 17,717 individuals of 77 species from 8 orders were collected in LMO isolation field. In three type soybeans field, total of 5,250 individuals in Vitamin E enhanced transgenic soybean, 5,510 individuals in Willams 82, and 6,957 individuals in Seoritae were collected, respectively. There was no difference between the population densities of insect pests, natural enemies and other insects on Vitamin E enhanced transgenic soybean and Willams 82, while natural enemies density on Seoritae was higher than on Vitamin E enhanced transgenic soybean, but insect pests density on Vitamin E enhanced transgenic soybean was higher. These results provided the insects diversity for risk assessment survey of Vitamin E enhanced transgenic soybean and suggested that the guideline could be useful to detect LMO crops.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.296-304
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• 2018

The objective of this study was to analyze the impact of climate change on rice yield and quality. Experiments were conducted using SPAR(Soil-Plant-Atmosphere-Research) chambers, which was designed to create virtual future climate conditions, in the National Institute of Crop Science, Jeonju, Korea, in 2016. In the future climate conditions($+2.8^{\circ}C$ temp, 580 ppm $CO_2$) of year 2051~2060 according to RCP 8.5 scenario, elevated temperature and $CO_2$ accelerated the heading date by about five days than the present climate conditions, resulted in a high temperature environment during grain filling stage. Rice yield decreased sharply in the future climate conditions due to the high temperature induced poor ripening. And the spikelet numbers, ripening ratio, and 1000-grain weight of brown rice were significantly decreased compared to control. The rice grain quality was also decreased sharply, especially due to the increased immature grains. In the future climate conditions, expression of starch biosynthesis-related genes such as granule-bound starch synthase(GBSSI, GBSSII, SSIIa, SSIIb, SSIIIa), starch branching enzyme(BEIIb) and ADP-glucose pyrophosphorylase(AGPS1, AGPS2, AGPL2) were repressed in developing seeds, whereas starch degradation related genes such as ${\alpha}-amylase$(Amy1C, Amy3D, Amy3E) were induced. These results suggest that the reduction in yield and quality of rice in the future climate conditions is likely caused mainly by the poor grain filling by high temperature. Therefore, it is suggested to develop tolerant cultivars to high temperature during grain filling period and a new cropping system in order to ensure a high quality of rice in the future climate conditions.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• Journal of Ginseng Research
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• v.43 no.4
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• pp.600-605
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• 2019

Background: The leaves and roots of Panax ginseng are rich in ginsenosides. However, the chemical compositions of the leaves and roots of P. ginseng differ, resulting in different medicinal functions. In recent years, the aerial parts of members of the Panax genus have received great attention from natural product chemists as producers of bioactive ginsenosides. The aim of this study was the isolation and structural elucidation of novel, minor ginsenosides in the leaves of P. ginseng and evaluation of their antiinflammatory activity in vitro. Methods: Various chromatographic techniques were applied to obtain pure individual compounds, and their structures were determined by nuclear magnetic resonance and high-resolution mass spectrometry, as well as chemical methods. The antiinflammatory effect of the new compounds was evaluated on lipopolysaccharide-stimulated RAW 264.7 cells. Results and conclusions: Two novel, minor triterpenoid saponins, ginsenoside $LS_1$ (1) and 5,6-didehydroginsenoside $Rg_3$ (2), were isolated from the leaves of P. ginseng. The isolated compounds 1 and 2 were assayed for their inhibitory effect on nitric oxide production in LPS-stimulated RAW 264.7 cells, and Compound 2 showed a significant inhibitory effect with $IC_{50}$ of $37.38{\mu}M$ compared with that of NG-monomethyl-L-arginine ($IC_{50}=90.76{\mu}M$). Moreover, Compound 2 significantly decreased secretion of cytokines such as prostaglandin $E_2$ and tumor necrosis factor-${\alpha}$. In addition, Compound 2 significantly suppressed protein expression of inducible nitric oxide synthase and cyclooxygenase-2. These results suggested that Compound 2 could be used as a valuable candidate for medicinal use or functional food, and the mechanism is warranted for further exploration.

• The Journal of Korean Academy of Prosthodontics
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• v.57 no.2
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• pp.95-101
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• 2019

Purpose: The aim of this study is to evaluate the effects of various primers on the microtensile bond strength (${\mu}TBS$) of resin cements to cobalt-chromium (Co-Cr) dental casting alloy. Materials and methods: Four adhesive primers (Universal primer, Metal primer II, Alloy primer, and Metal/Zirconia primer) and two resin cements (Panavia F2.0, G-CEM LinkAce) were tested. One hundred fifty Co-Cr beams were prepared from Co-Cr ingots via casting ($6mm\;ength{\times}1mm\;width{\times}1mm\;thick$). The metal beams were randomly divided into ten groups according to the adhesive primers and resin cements used; the no-primer groups served as the control (n = 15). After sandblasting with aluminum oxide ($125{\mu}m$ grain), the metal and resin cements were bonded together using a silicone mold. Prior to testing, all metal-resin beams were examined under stereomicroscope, and subjected to the ${\mu}TBS$ test. The mean value of each group was analyzed via one-way ANOVA with Tukey's test as post hoc (${\alpha}=.05$) using SPSS software. Results: The mean ${\mu}TBS$ of all groups was ranged from 20 to 28 MPa. There is no statistically significant difference between groups (P > .05). Mixed failure, which is the combination of adhesive and cohesive failures, is the most prevalent failure mode in both the Panavia F2.0 and G-Cem LinkAce groups. Conclusion: The ${\mu}TBS$ of all tested groups are relatively high; however, the primers used in this study result in no favorable effect in the ${\mu}TBS$ of Panavia F2.0 and G-Cem LinkAce resin cement to Co-Cr alloy.

• The Journal of Korean Academy of Prosthodontics
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• v.57 no.2
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• pp.110-117
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• 2019

Purpose: This study was to evaluate the wear resistance of 3D printed, milled, and conventionally cured provisional resin materials. Materials and methods: Four types of resin materials made with different methods were examined: Stereolithography apparatus (SLA) 3D printed resin (S3P), digital light processing (DLP) 3D printed resin (D3P), milled resin (MIL), conventionally self-cured resin (CON). In the 3D printed resin specimens, the build orientation and layer thickness were set to $0^{\circ}$ and $100{\mu}m$, respectively. The specimens were tested in a 2-axis chewing simulator with the steatite as the antagonist under thermocycling condition (5 kg, 30,000 cycles, 0.8 Hz, $5^{\circ}C/55^{\circ}C$). Wear losses of the specimens were calculated using CAD software and scanning electron microscope (SEM) was used to investigate wear surface of the specimens. Statistical significance was determined using One-way ANOVA and Dunnett T3 analysis (${\alpha}=.05$). Results: Wear losses of the S3P, D3P, and MIL groups significantly smaller than those of the CON group (P < .05). There was no significant difference among S3P, D3P, and MIL group (P > .05). In the SEM observations, in the S3P and D3P groups, vertical cracks were observed in the sliding direction of the antagonist. In the MIL group, there was an overall uniform wear surface, whereas in the CON group, a distinct wear track and numerous bubbles were observed. Conclusion: Within the limits of this study, provisional resin materials made with 3D printing show adequate wear resistance for applications in dentistry.

• Korean Journal of Psychosomatic Medicine
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• v.28 no.2
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• pp.177-184
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• 2020

Objectives : Postpartum depression is known to occur in 10-15% of mothers. The concentration of cytokine varies depending on stress, depression, pregnancy and general medical conditions. We hypothesized that the concentration of cytokines may be related to reproduction and childbirth, and that women with postpartum depression would show alterations in cytokines levels. Methods : A total of 104 pregnant women were selected as subjects, and 60 non-pregnant women were selected as normal controls. Symptoms of depression were evaluated in the pregnant study subjects using the diagnostic criteria outlined in the Edinburgh Postnatal Depression Scale (EPDS). The pregnant subjects were divided into three groups perinatal non-depression controls (n=61), postpartum depression-recovery (n=18), and postpartum depression (n=25). Results : The plasma concentration of TGF-β1, IGF-1 was higher in the pregnant group than in non-pregnant controls (TGF-β1 ; p<0.01, IGF-1 ; p=0.026). At 24 weeks of pregnancy and 6 weeks of delivery, there were no significant differences in the plasma concentration of TGF-β1, IGF-1, β-NGF, IL-2, IL-4, IL-6, IFN-γ, TNF-α between the three groups. There was no statistically significant difference in all three groups during the course of depression in pregnant women. Conclusions : This study found significant difference in plasma cytokines concentrations between non-pregnant controls and perinatal non-depression controls.

• Journal of Life Science
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• v.31 no.3
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• pp.356-365
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• 2021

Recently, we sequenced the entire genome of a freshwater agar-degrading bacterium Cellvibrio sp. KY-GH-1 (KCTC13629BP) to explore genetic information encoding agarases that hydrolyze agarose into monomers 3,6-anhydro-L-galactose (L-AHG) and D-galactose. The KY-GH-1 strain appeared to possess nine β-agarase genes and two α-neoagarobiose hydrolase (α-NABH) genes in a 77-kb agarase gene cluster. Based on these genetic information, the KY-GH-1 strain-caused agarose degradation into L-AHG and D-galactose was predicted to be initiated by both endolytic GH16 and GH86 β-agarases to generate NAOS (NA4/NA6/NA8), and further processed by exolytic GH50 β-agarases to generate NA2, and then terminated by GH117 α-NABHs which degrade NA2 into L-AHG and D-galactose. More recently, by employing E. coli expression system with pET-30a vector we obtained three recombinant His-tagged GH50 family β-agarases (GH50A, GH50B, and GH50C) derived from Cellvibrio sp. KY-GH-1 to compare their enzymatic properties. GH50A β-agarase turned out to have the highest exolytic β-agarase activity among the three GH50 isozymes, catalyzing efficient NA2 production from the substrate (agarose, NAOS or AOS). Additionally, we determined that GH117A α-NABH, but not GH117B α-NABH, could potently degrade NA2 into L-AHG and D-galactose. Sequentially, we examined the enzymatic characteristics of GH50A β-agarase and GH117A α-NABH, and assessed their efficiency for NA2 production from agarose and for production of L-AHG and D-galactose from NA2, respectively. In this review, we describe the benefits of recombinant GH50A β-agarase and GH117A α-NABH originated from Cellvibrio sp. KY-GH-1, which may be useful for the enzymatic hydrolysis of agarose for mass production of L-AHG and D-galactose.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• 한국노년학
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• v.41 no.2
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• pp.197-215
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• 2021

This study aims to evaluate Person Centered Care practice and characteristics of care services in Korean long-term care facilities using Dementia Care Mapping as a tool. DCM, systematic observational evaluation tool for measuring dementia patients' QOL, was transformed into self-report rating scale. The process of transforming DCM into a scale of 34 items involves operationalization of DCM concepts and it's adaptation into Korean long-term care practices. Review by research team of Bradford university was added to maintain DCM concept and meaning in this scale. The scale with Cronbach alpha of .88 was surveyed on 343 care workers. Survey result shows PCC value practiced by them is 3.77(of 5 likert scale) and values on each categories of PCC reveal the characteristics of care in Korean facilities; attachment(4.02), comfort(3.95), inclusion(3.89), identity(3.67) and occupation(3.41). Dementia care in Korean facilities focuses on recipients'safety, comfort but lacks individualistic care and the meaningful and fulfilling occupation for patients. Looking at the organizational and individual factors influencing DCM values, the small facilities showed higher PCC values and there are no significant difference in PCC values between public and private facilities. Managers and care workers with career of 1~2 years showed higher PCC values compared to other career ranks and lengthes. This study suggests care practice should be centered on personhood of patients in long-term care facilities, for which introduction of unit care and education of PCC for service providers including support personnel are needed. DCM and Korean DCM scale developed in this study are suggested for the PCC-based assessment on care quality.