• Journal of Ginseng Research
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• v.30 no.3
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• pp.117-127
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• 2006

Ginseng is a medicinal herb widely used in Asian countries. Dendritic cells(DCs) play a pivotal role in the initiation of T cell-mediated immune responses, making them an attractive cellular adjuvant for use in cancer vaccines. In this study, we examined the effects of Red-ginseng(water extract, edible and fermented ethyl alcohol extract, crude saponin) on the DCs phenotypic and functional maturation. Immature DCs were cultured in the presence of GM-CSF and IL-4, and the generated immature DCs were stimulated by water extract, edible and fermented ethyl alcohol extract, crude saponin and LPS, respectively, for 24hours. The expression of surface co-stimulatory molecules, including MHC(major histocompatibility complex) class II, CD40, CD80 and CD86, was increased on DCs that were stimulated with crude saponin, but antigen-uptake capacity was decreased. The antigen-presenting capacity of Red-ginseng extracts-treated DCs as analyzed by allogeneic T cells proliferation and IL-2, $IFN-{\gamma}$ production was increased. Furthermore, $CD4^+$ and $CD8^+$ syngeneic T cell(OVA-specific) proliferation and $IFN-{\gamma}$ production was significantly increased. However, $CD4^+$ syngeneic T cell secreted higher levels of IL-2 in responding but not $CD8^+$ syngeneic T cell. These results indicate the immunomodulatory properties of Red-ginseng extracts, which might be therapeutically useful in the control of cancers and immunodeficient diseases through the up-regulation of DCs maturation.

• Journal of the Korean Vacuum Society
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• v.10 no.1
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• pp.44-50
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• 2001

Metal oxide semiconductor (MOS) structures containing nanocrystals are fabricated by using rapid thermal oxidations of amorphous silicon films. The amorphous films are deposited either by electron beam deposition method or by electron beam deposition assisted by Ar ion beam during deposition. Post oxidation of e-beam deposited film results in relatively small hysteresis of capacitance-voltage (C-V) and the flat band voltage shift, $\DeltaV_{FB}$ is less than 1V indicative of the formation of low density nanocrystals in $SiO_2$ near $SiO_2$/Si interface. By contrast, we observe very large hysteresis in C-V characteristics for oxidized ion-beam assisted e-beam deposited sample. The flat band voltage shift is larger than 22V and the hysteresis becomes even broader as increasing injection times of holes at accumulation condition and electrons at inversion condition. The result indicates the formation of slow traps in $SiO_2$ near $SiO_2$/Si interface which might be related to large density nanocrystals. Roughly estimated trap density is $1{\times}10^{13}cm^{-2}$. Such a large hysteresis may be explained in terms of the activation of adatom migration by Ar ion during deposition. The activated migration may increase nucleation rate of Si nuclei in amorphous Si matrix. During post oxidation process, nuclei grow into nanocrystals. Therefore, ion beam assistance during deposition may be very feasible for MOS structure containing nanocrystals with large density which is a basic building block for single electron memory device.

• 한국사회정책
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• v.24 no.1
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• pp.33-66
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• 2017

Based on the premise that the systematic career process of workers in the general labor market was one of core elements of successful achievements and their establishment both at the individual and organizational level, this study set out to conduct empirical analysis of factors influencing the subjective career success of disabled workers in competitive employment at the multi-dimensional levels of individuals and organizations(corporations) and thus provide practical implications for the career management directionality of their successful vocational life with data based on practical and statistical accuracy. For those purposes, the investigator administered a structured questionnaire to 126 disabled workers at 48 companies in Seoul, Gyeonggi, Chungcheong, and Gangwon and collected data about the individual and organizational characteristics. Then the influential factors were analyzed with the multilevel analysis technique by taking into consideration the organizational effects. The analysis results show that organizational characteristics explained 32.1% of total variance of subjective career success, which confirms practical implications for the importance of organizational variables and the legitimacy of applying the multilevel model. The significant influential factors include the degree of disability, desire for growth, self-initiating career attitude and value-oriented career attitude at the individual level and the provision of disability-related convenience, career support, personnel support, and interpersonal support at the organizational level. The latter turned out to have significant moderating effects on the influences of subjective career success on the characteristic variables at the individual level. Those findings call for plans to increase subjective career success through the activation of individual factors based on organizational effects. The study thus proposed and discussed integrated individual-corporate practice strategies including setting up a convenience support system by reflecting the disability characteristics, applying a worker support program, establishing a frontier career development support system, and providing assistance for a human network.

• Journal of Nutrition and Health
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• v.55 no.4
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• pp.462-475
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• 2022

Purpose: Allomyrina dichotoma larvae are one of the approved edible insects with nutritional value and various functional and medicinal properties. Previously we have demonstrated that the Allomyrina dichotoma larval extract (ADLE) ameliorates hepatic insulin resistance in high-fat diet (HFD)-induced diabetic mice through the activation of adenosine monophosphate-activated protein kinase (AMPK). This study investigated the effects of ADLE on insulin resistance in the skeletal muscle and explored mechanisms for enhancing the glucose uptake in palmitate (PAL)-treated C2C12 myotubes. Methods: To induce insulin resistance, the differentiated C2C12 myotubes were treated with PAL (0.5 mM) for 24 hours, and then treated with a 0.5 mg/ml concentration of ADLE, and the resultant effects were measured. The expression levels of glucose transporter-4 (GLUT4), AMPK, and the mitochondrial metabolism-related proteins were analyzed by western blotting. The mRNA expression levels of lipogenesis- related genes were determined by quantitative reverse-transcriptase PCR. Results: The exposure of C2C12 myotubes to 0.5 mg/ml of ADLE increased cell viability significantly compared to PAL-treated cells. ADLE upregulated the protein expression of GLUT4 and enhanced glucose uptake in the PAL-treated cells. ADLE increased the phosphorylated AMPK in both the PAL-treated C2C12 myotubes and HFD-treated skeletal muscle. The reduced expression levels of peroxisome-proliferator-activated receptor gamma co-activator-1 alpha (PGC1α) and uncoupling protein 3 (UCP3) due to the PAL and HFD treatment were reversed by the ADLE treatment. The citrate synthase activity was also significantly increased with the PAL and ADLE co-treatment. Moreover, the mRNA and protein expressions of fatty acid synthesis-related factors were reduced in the PAL and HFD-treated muscle cells, and this effect was significantly attenuated by the ADLE treatment. Conclusion: ADLE activates AMPK, which in turn induces mitochondrial metabolism and reduces fatty acid synthesis in C2C12 myotubes. Therefore, ADLE could be useful for preventing or treating insulin resistance of skeletal muscles in diabetes.

• The Korean Journal of Nuclear Medicine
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• v.39 no.6
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• pp.421-429
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• 2005

Purpose: It has been postulated that dopamine release in the striatum underlies the reinforcing properties of nicotine. Substantial evidence in the animal studies demonstrates that nicotine interacts with dopaminergic neuron and regulates the activation of the dopaminergic system. The aim of this study was to visualize the dopamine release by smoking in human brain using PET scan with $[^{11}C]raclopride$. Materials and Methods: Five male non-smokers or ex-smokers with an abstinence period longer than 1 year (mean age of $24.4{\pm}1.7$ years) were enrolled in this study $[^{11}C]raclopride$, a dopamine D2 receptor radioligand, was administrated with bolus-plus-constant infusion. Dynamic PET was performed during 120 minutes ($3{\times}20s,\;2{\times}60s,\;2{\times}120s,\;1{\times}180s\;and\;22{\times}300s$). following the 50 minute-scanning, subjects smoked a cigarette containing 1 mg of nicotine while in the scanner. Blood samples for the measurement of plasma nicotine level were collected at 0, 5, 10, 15, 20, 25, 30, 45, 60, and 90 minute after smoking. Regions for striatal structures were drawn on the coronal summed PET images guided with co-registered MRI. Binding potential, calculated as (striatal-cerebellar)/cerebellar activity, was measured under equilibrium condition at baseline and smoking session. Results: The mean decrease in binding potential of $[^{11}C]raclopride$ between the baseline and smoking in caudate head, anterior putamen and ventral striatum was 4.7%, 4.0% and 7.8%, respectively. This indicated the striatal dopamine release by smoking. Of these, the reduction in binding potential in the ventral striatum was significantly correlated with the cumulated plasma level of the nicotine (Spearman's rho=0.9, p=0.04). Conclusion: These data demonstrate that in vivo imaging with $[^{11}C]raclopride$ PET could measure nicotine-induced dopamine release in the human brain, which has a significant positive correlation with the amount or nicotine administered bt smoking.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.