• Journal of the Society of Cosmetic Scientists of Korea
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• v.45 no.1
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• pp.37-47
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• 2019

Recently, due to the increase in skin diseases caused by particulate matter, endocrine disruptor and environmental changes, the trend of development of cosmetic materials has been shifting to the more safe and effective ingredients based on natural materials rather than existing synthetic compounds like steroids and antihistamines. This study aimed to develop a new natural cosmetic materials using oriental herbs such as Eucommiae cortex, Alpinia oxyphylla Miquel and Bombyx batryticatus. First, DPPH assay was performed to examine the antioxidative activity of the herbal extract (Mix) and 98.8% DPPH radical scavenging activity was confirmed at $400{\mu}g/mL$ concentration of it. In order to confirm the whitening efficacy of oriental herbal extracts(mix), the amount of melanin synthesized after stimulation of ${\alpha}-MSH$ with B16F10 cells was measured. Results showed that it was decreased to 27.1% comparing with the only ${\alpha}-MSH$ treated group, which confirmed the whitening efficacy. Also, both nitric oxide(NO) production and iNOS and COX-2 expression were significantly reduced in RAW264.7 macrophages activated by LPS in the presence of the extracts(Mix). The mRNA expression of the inflammatory cytokines such as $IL-1{\alpha}$, $IL-1{\beta}$, IL-6, and $TNF-{\alpha}$ was also analyzed to confirm the inhibition effect of the extracts on inflammation. Finally, to confirm the enhancement of skin barrier function, the expression of claudin 1 gene, a tight junction protein, was observed using human keratinocyte HaCaT cells and increased as concentration dependent manner. From these results, it is concluded that the oriental herbal extracts(Mix) containing Eucommiae cortex, Alpinia oxyphylla Miquel and Bombyx batryticatus is effective for antioxidant, anti-inflammation, skin whitening, and skin barrier and thus could be applied as a new natural cosmetic material.

• Journal of Food Hygiene and Safety
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• v.20 no.4
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• pp.258-266
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• 2005

Licorice Extract and Erythritol, food additives used in korea, are widely used in foods as sweetener. Its application for use in food is regulated by the standard and specification for food additives but official analytical method far determination of these sweetener in food has not been established. Accordingly, we has been carried out to set up analytical method of the glycyrrhizic acid in several foods by the way of thin layer chromatography and high performance liquid chromatography glycyrrhizic acid is qualitative anaylsis technique consists of clean-up with a sep-pak $C_{18}$ cartridge, separation of the sweeteners by Silica gel 60 F254 TLC plate using 1-butanol:4Nammonia solution:ethanol (50:20:10) as mobile solvent. Also, the quantitative analysis for glycyrrhizic acid, was performed using Capcell prk $C_{18}$ column at wavelength 254nm and DW:Acetonitrile (62:38 (pH2.5)) as mobile phase. and we has been carried out to set up analytical method of the erythritol in several foods by the way of high performance liquid chromatography. erythritol is qualitative anaylsis technique consists of clean-up with a DW and hexane. The quantitative analysis for erythritol, was performed using Asahipak NH2P-50 column, Rl and DW:Acetonitrile (25:75) as mobile phase. The glycyrrhizic acid results determined as glycyrrhizic acid in 105 items were as follows; N.D$\∼$48.7ppm for 18 items in soy sauce, N.D$\∼$5.3ppm for 12 items in sauce, N.D$\∼$988.93ppm for 15 items in health food, N.D$\∼$180.7ppm for 26 items in beverages, N.D$\∼$2.6ppm for 8 items in alcoholic beverages repectively and ND for 63 items in the ethers. The erythritol results determined as erythritol in 52 items were as follows; N.D$\∼$155.6ppm for 13 items in gm, N.D$\∼$398.1ppm for 12 items in health foods repectively and ND for 45 items in the others.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Microbiology and Biotechnology Letters
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• v.46 no.4
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• pp.360-371
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• 2018

Extracts and fractions of Anemarrhena asphodeloides Bunge were prepared and their physiological activities and components were analyzed. Antimicrobial activities of the ethyl acetate and aglycone fractions were $78{\mu}g/ml$ and $31{\mu}g/ml$, respectively, for Staphylococcus aureus and $156{\mu}g/ml$ and $125{\mu}g/ml$, respectively, for Pseudomonas aeruginosa. 1,1-Diphenyl-2-picrylhydrazyl free radical scavenging activities ($FSC_{50}$) of 50% ethanol extract, ethyl acetate fraction, and aglycone fraction of A. asphodeloides extracts were $146.2{\mu}g/ml$, $23.19{\mu}g/ml$, and $71.06{\mu}g/ml$, respectively. The total antioxidant capacity ($OSC_{50}$) in an $Fe^{3+}$-EDTA/hydrogen peroxide ($H_2O_2$) system were $17.5{\mu}g/ml$, $1.5{\mu}g/ml$, and $1.4{\mu}g/ml$, respectively. The cytoprotective effect (${\tau}_{50}$) in $^1O_2$-induced erythrocyte hemolysis was 181 min with $4{\mu}g/ml$ of the aglycone fraction. The ${\tau}_{50}$ of the aglycone fraction was approximately 4-times higher than that of (+)-${\alpha}$-tocopherol (${\tau}_{50}$, 41 min). Analysis of $H_2O_2$-induced damage of HaCaT cells revealed that the maximum cell viabilities for the 50% ethanol extract, ethyl acetate fraction, and aglycone fraction were 86.23%, 86.59%, and 89.70%, respectively. The aglycone fraction increased cell viability up to 11.53% at $1{\mu}g/ml$ compared to the positive control treated with $H_2O_2$. Analysis of ultraviolet B radiation-induced HaCaT cell damage revealed up to 41.77% decreased intracellular reactive oxygen species in the $2{\mu}g/ml$ aglycone fraction compared with the positive control treated with ultraviolet B radiation. The findings suggest that the extracts and fractions of A. asphodeloides Bunge have potential applications in the field of cosmetics as natural preservatives and antioxidants.

• Journal of Life Science
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• v.20 no.4
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• pp.528-534
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• 2010

Garlic (Allium sativum) has been well-known as a folk remedy for a variety of ailments since ancient times, and it is well documented that enhanced garlic consumption leads to a decrease in incidences of cancer. Tight junctions (TJs) are critical structures for the maintenance of cellular polarity, acting as paracellular permeability barriers and playing an essential role in regulating the diffusion of fluid, electrolytes and macromolecules through the paracellular pathway. Matrix metalloproteinases (MMPs) have been implicated as possible mediators of invasiveness and metastasis in some cancers. In this study, we investigated the potential effects of water extract of aged black garlic (ABG) on the correlation between tightening of TJs and anti-invasive activity in human gastric carcinoma AGS cells. The inhibitory effects of ABG on cell motility and invasiveness were found to be associated with increased tightness of TJs, which was demonstrated by an increase in transepithelial electrical resistance. Additionally, the activities of MMP-2 and -9 in AGS cells were inhibited by treatment with ABG, and this was also correlated with a decrease in the expression of their mRNA and proteins. Furthermore, RT-PCR and immunoblotting results indicated that ABG repressed the levels of the claudin proteins, major components of TJs that play a key role in the control and selectivity of paracellular transport. In conclusion, these results suggest that ABG treatment may inhibit tumor metastasis and invasion, and therefore may act as a dietary source to decrease the risk of developing cancer.

• Journal of Life Science
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• v.20 no.4
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• pp.589-596
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• 2010

We investigated antimicrobial activities of various pine (Pinus densiflora) needle extracts against antibiotic resistant strains of Staphylococcus aureus. Hot water extract showed the highest antimicrobial activity against normal and methicillin resistant Staphylococcus aureus (MRSA), however, it exhibited no antimicrobial activity against penicillin resistant S. aureus (PRSA). Hot water-hexane (HWH), hot water-ethanol (HWE), hexane, and ethanol extracts showed antimicrobial activity against S. aureus, PRSA and MRSA. Minimum inhibitory concentrations (MIC) of HWH, HWE, hexane, and ethanol extracts were 0.05, 0.05, 0.5 and 5 mg/ml, respectively, and HWH and HWE extracts showed the strongest antimicrobial activity among these extracts. Antimicrobial activities of pine needle extracts were stable after heating at $121^{\circ}C$ for 20 min. These results suggested that pine needle extracts can be used as an effective natural antimicrobial agent for food and medical industries.

• Biomolecules & Therapeutics
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• v.18 no.2
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• pp.205-210
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• 2010

Several studies have shown that plant-derived polyphenols reduce cardiovascular accidents in high-risk patients and the inhibition of platelet function may be responsible for part of this benefit. Lindera obtusiloba is widely used in traditional herbal medicine for the treatment of cardiovascular and inflammatory diseases. Therefore, the antiplatelet and antithrombotic activities of Lindera obtusiloba Extracts (LOE) on in vitro platelet aggregation, radical scavenging activity and in vivo murine pulmonary thrombosis were examined. LOE was able to directly scavenge the stable DPPH radical in a concentration-dependent manner and its $IC_{50}$ value was 3.9 ${\pm}$ 0.1 ${\mu}g$/ml. LOE significantly inhibited collagen- and ADP-induced platelet aggregation in a concentration-dependent manner and its $IC_{50}$ value is 0.9 ${\pm}$ 0.1 mg/ml and 0.4 ${\pm}$ 0.1 mg/ml respectively. The inhibitory effect of LOE was comparable to aspirin ($IC_{50}$ values were 1.0 ${\pm}$ 0.5 and 1.0 ${\pm}$ 0.7 mg/ml, respectively). Furthermore, oral administration of LOE suppressed the death of mice with pulmonary thrombosis induced by intravenous injection of collagen plus epinephrine. Taken together, our results suggest LOE may be a promising candidate for antithrombotic agent, and the antithrombotic effect of LOE may be due to, at least in part, antiplatelet activity.

• Journal of the East Asian Society of Dietary Life
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• v.21 no.5
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• pp.691-697
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• 2011

The purpose of this study was to determine the possibility of using Opuntia ficus-indica as a natural health food source. To accomplish this, the contents of general and antioxidative nutrient contents of Opuntia ficus-indica were measured. The carbohydrate, crude protein, crude fat and crude ash were 66.79%, 5.51%, 9.89% and 9.29%, respectively. The calorie contents of Opuntia ficusindica was 378.21 kcal. The content of total dietary fiber was 36.54%. The essential and non-essential amino acids contents were 1,635.14 mg and 3,012.68 mg, respectively. Potassium was the most abundant mineral followed by Ca, Mg, and Na, showing that Opuntia ficus-indica is an alkali material. The electron-donating activity (EDA) of Opuntia ficus-indica was 29.85~44.57%, and the activity was dependent on the sample concentration. Total phenolic content of Opuntia ficus-indica was 2.21 ${\mu}g$/mg, and total flavonoids content was estimated as 1.80 ${\mu}g$/mg. Opuntia ficus-indica extract showed the highest reducing power (OD 700=3.18) at a concentration of 6.25 mg/mL. Based on the above results, we determined that the Opuntia ficus-indica has potential antioxidant activities.

• Journal of Life Science
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• v.22 no.7
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• pp.958-963
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• 2012

The antioxidant activities of 6 solvent extracts of Artemisia capillaris were evaluated in a dintroflurobenzen (DNFB)-induced allergic mouse model. In vitro antioxidant activities were determined using DPPH and the FRAP test. Methanol (DPPH: 85.87%, FRAP: 1.772) and $dH_2O$ (DPPH: 60.69%, FRAP: 3.185) extracts showed the highest antioxidant activities compared with other solvents (ethyl acetate 41.81%, 0.407, hexane 8.37%, 0.328, etc.). In addition, we tested atopic dermatitis (AD)-like skin lesions in mice treated with DNFB. The methanol extract of A. capillaris on the AD-like skin lesions in DNFB-induced atopy inhibited ear thickness increases (47%) and the skin lesions (45%) compared with a positive control (methanol). The results suggest that they have potential as natural antioxidants and allergy-improving substances and that they may be valuable materials in the functional food or cosmeceutical industry.