• Proceedings of the Plant Resources Society of Korea Conference
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• 2018.04a
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• pp.3-3
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• 2018

For centuries, Panax ginseng Meyer (Korean ginseng) has been widely used as a medicinal herb in Korea, China, and Japan. Ginsenosides are a class of triterpene saponins and recognized as the bioactive components in Korean ginseng. Ginsenosides, which can be classified broadly as protopanaxadiols (PPD), protopanaxatriols (PPT), and oleanolic acids, have been shown to flaunt a vast array of pharmacological activities such as immune-modulatory, anti-inflammatory, anti-tumor, anti-diabetic, and antioxidant effects. In recent years, a number of ginseng and ginsenoside researches have increasingly gained wide attention owing to its unique pharmacological properties. Although good efficacies of ginsenosides have been reported, lack of target specific delivery into tumor sites, low solubility, and low bioavailability due to modifications in gastro-intestinal environments limit their biomedical application in clinical trials. As a result to this major challenge, nanotechnology and drug delivery techniques play a significant role to solve this problematic issue. Thus, we reported the preparation of poly-ethylene glycol (PEG) and glycol chitosan (GC) functionalized to ginsenoside (Compound K and PPD) conjugates via hydrolysable ester bonds with improved aqueous solubility and pH-dependent drug release. In vitro cytotoxicity assays revealed that PEG-CK, and PPD-CK conjugates exhibited lower cytotoxicity compared to bare CK and PPD in HT29 cells. However, GC-CK conjugates exhibited higher and similar cytotoxicity in HT29 and HepG2 cells. Furthermore, GC-CK-treated RAW264.7 cells did not exhibit significant cell death at higher concentration of treatment which supports the biocompatibility of the polymer conjugates. They also inhibited nitric oxide production in lipopolysaccharide (LPS)-induced RAW64.7 cells. In addition to polymer-ginsenoside conjugates, silver (AgNps) and gold nanoparticles (AuNps) have been successfully synthesized by green chemistry using different m. The biosynthesized nanoparticles demonstrated antimicrobial efficacy, anticancer, anti-inflammatory, antioxidant activity, biofilm inhibition, and anticoagulant effect. Special interest on the effective delivery methods of ginsenoside to treatment sites is the focus of metal nanoparticle research.In short, nano-sizing of ginsenoside results in an increased water solubility and bioavailability. The use of nano-sized ginsenoside and P. ginseng mediated metallic nanoparticles is expected to be effective on medical platform against various diseases in the future.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1246-1246
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• 2001

The quality of meat is highly variable in many properties. This variability originates from both animal production and meat processing. At the pre-slaughter stage, animal factors such as breed, sex, age contribute to this variability. Environmental factors include feeding, rearing, transport and conditions just before slaughter (Hildrum et al., 1995). Meat can be presented in a variety of forms, each offering different opportunities for adulteration and contamination. This has imposed great pressure on the food manufacturing industry to guarantee the safety of meat. Tissue and muscle speciation of flesh foods, as well as speciation of animal derived by-products fed to all classes of domestic animals, are now perhaps the most important uncertainty which the food industry must resolve to allay consumer concern. Recently, there is a demand for rapid and low cost methods of direct quality measurements in both food and food ingredients (including high performance liquid chromatography (HPLC), thin layer chromatography (TLC), enzymatic and inmunological tests (e.g. ELISA test) and physical tests) to establish their authenticity and hence guarantee the quality of products manufactured for consumers (Holland et al., 1998). The use of Near Infrared Reflectance Spectroscopy (NIRS) for the rapid, precise and non-destructive analysis of a wide range of organic materials has been comprehensively documented (Osborne et at., 1993). Most of the established methods have involved the development of NIRS calibrations for the quantitative prediction of composition in meat (Ben-Gera and Norris, 1968; Lanza, 1983; Clark and Short, 1994). This was a rational strategy to pursue during the initial stages of its application, given the type of equipment available, the state of development of the emerging discipline of chemometrics and the overwhelming commercial interest in solving such problems (Downey, 1994). One of the advantages of NIRS technology is not only to assess chemical structures through the analysis of the molecular bonds in the near infrared spectrum, but also to build an optical model characteristic of the sample which behaves like the “finger print” of the sample. This opens the possibility of using spectra to determine complex attributes of organic structures, which are related to molecular chromophores, organoleptic scores and sensory characteristics (Hildrum et al., 1994, 1995; Park et al., 1998). In addition, the application of statistical packages like principal component or discriminant analysis provides the possibility to understand the optical properties of the sample and make a classification without the chemical information. The objectives of this present work were: (1) to examine two methods of sample presentation to the instrument (intact and minced) and (2) to explore the use of principal component analysis (PCA) and Soft Independent Modelling of class Analogy (SIMCA) to classify muscles by quality attributes. Seventy-eight (n: 78) beef muscles (m. longissimus dorsi) from Hereford breed of cattle were used. The samples were scanned in a NIRS monochromator instrument (NIR Systems 6500, Silver Spring, MD, USA) in reflectance mode (log 1/R). Both intact and minced presentation to the instrument were explored. Qualitative analysis of optical information through PCA and SIMCA analysis showed differences in muscles resulting from two different feeding systems.

• Restorative Dentistry and Endodontics
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• v.30 no.2
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• pp.112-120
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• 2005

This study was done to evaluate whether there were any differences in microleakage of class V composite restorations according to restoration site and cavity size. Total sixty-four restorations were made in molar teeth using Esthet-X. Small ($2\;{\times}\;2\;{\times}\;1.5\;mm$) and large ($4{\times}2{\times}1.5\;mm$) restorations were made at the buccal/lingual surface and the proximal surface each. After 1,000 times of thermocycling ($5^{\circ}\;-\;55^{\circ}C$), resin replica was made and the percentage of marginal gap to the whole periphery of the restoration was estimated from SEM evaluation. Thermocycled tooth was dye penetrated with $50\%$ silver nitrate solution. After imbedding in an auto-curing resin, it was serially ground with a thickness of 0.25 mm. Volumetric microleakage was estimated after reconstructing three dimensionally. Two-way ANOVA and independent T-test for dye volume, Mann-Whitney U test for the percentage of marginal gap, Spearman's rho test for the relationship between two techniques were used, The results were as follows : 1. The site and size of the restoration affected on the microleakage of restoration. Namely, much more leakage was seen in the proximal and the large restorations rather than the buccal/lingual and the small restorations. 2. Close relationship was found between two techniques (Correlation coefficient = 0.614/ P = 0.000). Within the limits of this study, it was noted that proximal and the large restorations leaked more than buccal/lingual and the small restorations. Therefore, it should be strictly recommended large exposure of margins should be avoided by reducing unnecessary tooth reduction.

• 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.