• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.33 no.5
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• pp.445-454
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• 2007

Background: Phosphatidic acid(PA), an important second messenger, is involved in inflammation. Notably, cell-cell interactions via adhesion molecules playa central role in inflammation. This thesis show that PA induces expression of intercellular adhesion molecule-1(ICAM-1) on macrophages and describe the signaling pathways. Materials and methods: Macrophages were cultured in the presence of 10% FBS and assayed cell to cell adhesion using HUVEC. For the gene and protein analysis, RT-PCR, Western blot and flow cytometry were performed. In addition, overexpressed cell lines for dominant negative PKC-${\delta}$ mutant established and tested their effect on the promoter activity and expression of ICAM-1 protein by PA. Results: PA-activated macrophages significantly increased adhering to human umbilical vein endothelial cell and this adhesion was mediated by ICAM-1. Pretreatment with rottlerin(PKC-${\delta}$ inhibitor) or expression of a dominant negative PKC-${\delta}$ mutant, but not Go6976(classical PKC-${\alpha}$ inhibitor) and myristoylated PKC-${\xi}$ inhibitor, attenuated PA-induced ICAM-1 expression. The p38 mitogen-activated protein kinase(MAPK) inhibitor blocked PA-induced ICAM-1 expression in contrast, ERK upstream inhibitor didn't block ICAM-1. Conclusion: These data suggest that PA-induced ICAM-1 expression and cell-cell adhesion in macrophages requires PKC-${\delta}$ activation and that PKC-${\delta}$ activation is triggers to sequential activation of p38 MAPK.

• Food Engineering Progress
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• v.13 no.4
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• pp.320-325
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• 2009

The effects of adding additives such as vital gluten, gum, emulsifier, and enzyme to rice flour on baking quality were examined. The effects of different gums on the pasting and dough properties of rice flour containing vital gluten were studied using a Rapid Visco Analyzer (RVA) and a Brabender farinograph. The RVA peak, breakdown, and final viscosities decreased with the addition of gums, while setback viscosity increased. The farinogram showed that rice flour supplemented with gums such as tara gum, guar gum, and locust bean gum (LBG) increased water absorption and dough stability, yielding strengthened dough similar to wheat flour dough. The addition of guar or tara gum/sodium stearoyl lactylate (SSL)/fungal $\alpha$-amylase (AMYL) or glucose oxidase (GO) blend improved the volume and reduced the crumb firmness of rice bread prepared from rice flour containing 14% vital gluten. Therefore, the combined addition of gum, emulsifier and enzyme into rice flour significantly improved the rice bread quality, allowing the decrease of the vital gluten level in rice bread formula.

• Food Engineering Progress
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• v.21 no.1
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• pp.1-11
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• 2017

Dyslipidemia, defined as elevated triglyceride (TG), total- and LDL-C, and/or decreased HDL-C levels, is considered a principal risk factor for cardiovascular disease. The low-density lipoprotein receptor (LDLR) family has been considered a key player in the prevention of dyslipidemia. The LDLR family consists of cytoplasmic membrane proteins and plays an important role not only in ligand-receptor binding and uptake, but also in various cell signaling pathways. Emerging reports state that various functional ingredients dynamically modulate the function of the LDLR family. For instance, oats stimulated the LDLR function in vivo, resulting in decreased body weight and improved serum lipid profiles. The stimulation of LRP6 by functional ingredients in vitro activated the Wnt/${\beta}-catenin$ pathway, subsequently suppressing the intracellular TG via inhibition of SREBP1, $PPAR{\gamma}$, and $C/EBP{\alpha}$. Furthermore, the extract of Cistanchetubulosa enhanced the expression of the mRNA of VLDLR, followed by a reduction in the serum cholesterol level. In addition, fermented soy milk diminished TG and total cholesterol levels while increasing HDL-C levels via activation of LRP1. To summarize, modulating the function of the LDLR family by diverse functional ingredients may be a potent therapeutic remedy for the treatment of dyslipidemia and cardiovascular diseases.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.748-756
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• 2022

Thanks to the competition of AlphaGo and Sedol Lee, machine learning has received world-wide attention and huge investments. The performance improvement of computing devices greatly contributed to big data processing and the development of neural networks. Artificial intelligence not only imitates human beings in many fields, but also seems to be better than human capabilities. Although humans' creation is still considered to be better and higher, several artificial intelligences continue to challenge human creativity. The quality of some creative outcomes by AI is as good as the real ones produced by human beings. Sometimes they are not distinguishable, because the neural network has the competence to learn the common features contained in big data and copy them. In order to confirm whether artificial intelligence can express the inherent characteristics of different arts, this paper proposes a new neural network model called Humming. It is an experimental model that combines vgg16, which extracts image features, and DeepJ's architecture, which excels in creating various genres of music. A dataset produced by our experiment shows meaningful and valid results. Different results, however, are produced when the amount of data is increased. The neural network produced a similar pattern of music even though it was a different classification of images, which was not what we were aiming for. However, these new attempts may have explicit significance as a starting point for feature transfer that will be further studied.

• Food Science of Animal Resources
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• v.33 no.3
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• pp.411-416
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• 2013

This study aimed to examine the mechanisms underlying the effects of Garcinia cambogia extract on the adipogenic differentiation of 3T3-L1 cells and long-chain saturated fatty acid-induced lipotoxicity of HepG2 cells. 3T3-L1 preadipocytes, mouse embryonic fibroblast-adipose like cell line, were treated with MDI solution (0.5 mM IBMX, 1 ${\mu}M$ dexamethasone, 10 ${\mu}g/mL$ insulin) to generate a cellular model of adipocyte differentiation. Using this cellular model, the anti-obesity effect of Garcinia cambogia extract was evaluated. MDI-induced lipid accumulation and expression of adipogenesis-related genes were detected by Oil red O staining, Nile Red staining, and Western blot analysis. Effects Garcinia cambogia extract on palmitate-induced lipotoxicity was also analyzed by MTT assay, LDH release, and DAPI staining in HepG2 cells. Garcinia cambogia extract significantly suppressed the adipogenic differentiation of preadipocytes and intracellular lipid accumulation in the differentiating adipocytes. Garcinia cambogia extract also markedly inhibited the expression of peroxisome proliferator- activated receptor ${\gamma}2$ ($PPAR{\gamma}2$), CCAT/enhancer-binding protein ${\alpha}$ ($C/EBP{\alpha}$), and adipocyte protein aP2 (aP2). In addition, Garcinia cambogia extract significantly attenuated palmitate-induced lipotoxicity in HepG2 cells. Palmitateinduced cellular damage and reactive aldehydes were also significantly reduced in the presence of Garcinia cambogia extract. These findings suggest that the Garcinia cambogia extract inhibits the adipogenic differentiation of 3T3-L1 preadipocytes, probably by regulating the expression of multiple genes associated with adipogenesis such as $PPAR{\gamma}2$, $C/EBP{\alpha}$, aP2, and thereby modulating fatty acid-induced lipotoxicity to reduce cellular injury in hepatocytes.

• Journal of Sasang Constitutional Medicine
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• v.8 no.1
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• pp.187-246
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• 1996

The purpose of this study is to evaluate and standardize the four scales of Questionnaire for the Sasang Constitution ClassificationII (QSCCII). QSCCII is newly prepared by statistical item analysis and is designed to examine its diagnostic discriminability. QSCCII is administered to 1366 random informants. From the survey, we could get the data for the standardization. The criteria of standardization are based on the data from 265 informants who are examined by professionals. Collected data are analyzed by internal consistency, variation analysis(ANOVA), Duncan test and discrimination analysis of SPSS PC+ V4.0 program. The results are as follows 1) The reliability of four scales for QSCCII is relatively valid. The internal consistency of Tae-yang(太陽) scale is Cronbach's ${\alpha}=0.5708$. That of So-yang(少陽) scale is ${\alpha}=0.5708$. That of Tae-eum(太陰) scale is ${\alpha}=0.5922$. That of So-eum(少陰) scale is ${\alpha}=0.6319$. 2) There is a significant difference between each group through variation analysis of four scales. 3) The process of standardization is based on the average value and standard deviation with respect to age and sex difference of each criteria. 4) This study suggests a source of standardization of Sasang Constitution Classification by providing norms in which the differences of age, sex, and number of items are taken into deep consideration. QSCCII, therefore, can be applied to every age(the 10's to the 60's) and sex groups. 5) The recalculation of the raw-score to standard value (T-score) shows that the diagnostic discriminability (Hit-ratio : 70.08%) of QSCCII brings about 37% improvement than proportional chance criteria(33.33%). Especially, Hit-ratios of Tae-eum In(74.5%) and So-eum In(70.8%) are higher than that of So-yang In(60.0%). 6) QSCC has discriminability only to male informants. Compared with QSCC, however, QSCCII has relatively efficient discriminability both to male and female informants. 7) These results would be a demonstration of the fact that the QSCCII could be used as a tool for sasang constitution classification.

• The Korean Journal of Air & Space Law and Policy
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• v.32 no.2
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• pp.155-188
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• 2017

On the one hand, the 4th Industrial Revolution provides a positive opportunity to build a new civilization paradigm for mankind. However, on the other hand, due to the 4th Industrial Revolution, artificial intelligence such as 'Goggle Alpha Go' revolutionized and even the human ability was replaced with a 'Silicon Chip' as the opportunity to communicate decreases, the existence of human beings is weakened. And there is a growing concern that the number of violent crimes, such as psychopath, which hunts humans as games, will increase. Moreover, recent international terrorism is being developed in a form similar to 'Psychopathic Violent-Crime' that indiscriminately attacks innocent people. So, the probability that terrorist organizations abuse the positive effects provided by the Fourth Industrial Revolution as means of terrorism is increasing. Therefore, the paradigm of aviation terrorism is expected to change in a way that attacks airport facilities and users rather than aircraft. Because airport facilities are crowded, and psychopathic terrorists are easily accessible. From this point of view, our counter terrorism system of aviation has many weak points in various aspects such as: (1) limitations of counter-terrorism center (2) inefficient on-site command and control system (3) separated organization for aviation security consultation (4) dispersed information collection function in government (5) vulnerable to cyber attack (6) lack of international cooperation network for aviation terrorism. Consequently, it is necessary to improve the domestic counter terrorism system of aviation so as to preemptively respond to the international terrorism. This study propose the following measures to improve the aviation security system by (1) create 'Aviation Special Judicial Police' (2) revise the anti-terrorism law and aviation security law (3) Strengthening the ability respond to terrorism in cyberspace (4) building an international cooperation network for aviation terrorism.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• The Journal of the Convergence on Culture Technology
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• v.4 no.2
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• pp.197-204
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• 2018

One of the hottest issues in our 21st century is AI. Just as the automation of manual labor has been achieved through the Industrial Revolution in the agricultural society, the intelligence information society has come through the SW Revolution in the information society. With the advent of Google 'Alpha Go', the computer has learned and predicted its own machine learning, and now the time has come for the computer to surpass the human, even to the world of Baduk, in other words, the computer. Machine learning ML (machine learning) is a field of artificial intelligence. Machine learning ML (machine learning) is a field of artificial intelligence, which means that AI technology is developed to allow the computer to learn by itself. The time has come when computers are beyond human beings. Many companies use machine learning, for example, to keep learning images on Facebook, and then telling them who they are. We also used a neural network to build an efficient energy usage model for Google's data center optimization. As another example, Microsoft's real-time interpretation model is a more sophisticated translation model as the language-related input data increases through translation learning. As machine learning has been increasingly used in many fields, we have to jump into the AI industry to move forward in our 21st century society.

• Journal of Oriental Neuropsychiatry
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• v.27 no.3
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• pp.169-184
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• 2016

Objectives: To investigate the neurophysiological effect of acupuncture treatment on insomnia patients using quantitative electroencephalography (QEEG) and standardized Low Resolution Brain Electromagnetic Tomography method (sLORETA).Background: Insomnia is one of the commonly encountered symptoms in primary medical care. Recent studies of acupuncture for insomnia reported that the acupuncture groups showed significant improvements compared with the control groups. However, the neurophysiological mechanism of acupuncture in the treatment of insomnia has not been revealed and a few studies have measured the effect of acupuncture treatment using QEEG.Methods: Participants who had some problems in initiating or maintaining sleep, or had non-restorative sleep for more than 3 days a week and ISI scores above 8 and below 21 were treated by acupuncture for 2 weeks (3 times a week, total 6 times). We assessed the effectiveness of acupuncture for insomnia by the PSQI (Pittsburgh Sleep Quality Index) at baseline and at 2 weeks after the end of treatment (4th week). Also, we performed EEG and analysed the EEG data at baseline and at the end of treatment (2nd week) on the linked ears montage using the Neuroguide software program and sLORETA.Results: Thirty-two participants were enrolled and 2 participants dropped out because of personal reasons. Among the 30 participants, EEGs of 12 participants were included in the analysis of QEEG and sLORETA. Total score on the ISI and PSQI was significantly decreased after acupuncture treatment. The number of electrodes exceeding the range of 90% (±1.65) or 95% (±1.96) in the z scored absolute power of beta was significantly decreased after acupuncture treatment. There was no significant change in brain activation between pre- and post-acupuncture using sLORETA.Conclusions: The deviation of absolute power compared to the normative database was significantly decreased after acupuncture treatment in the alpha and beta ranges. Therefore, we suggest that acupuncture treatment for insomnia might be effective through the central nervous system especially in the brain. There are many limitations to drawing any conclusion. Further studies are needed in the future to overcome these limitations.