• Journal of Life Science
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• v.23 no.11
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• pp.1317-1324
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• 2013

High-molecular weight glutenin subunits (HMW-GS) have been shown to play a crucial role in determining the processing properties of the wheat grain. We have produced marker-free transgenic rice plants containing a wheat Glu-1Bx7 gene encoding the HMG-GS from the Korean wheat cultivar 'Jokyeong' using the Agrobacterium-mediated co-transformation method. The Glu-1Bx7-own promoter was inserted into a binary vector for seed-specific expression of the Glu-1Bx7 gene. Two expression cassettes comprised of separate DNA fragments containing only Glu-1Bx7 and hygromycin phosphotransferase II (HPTII) resistance genes were introduced separately to the Agrobacterium tumefaciens EHA105 strain for co-infection. Each EHA105 strain harboring Glu-1Bx7 or HPTII was infected to rice calli at a 3:1 ratio of Glu-1Bx7 and HPTII, respectively. Then, among 216 hygromycin-resistant $T_0$ plants, we obtained 24 transgenic lines with both Glu-1Bx7 and HPTII genes inserted into the rice genome. We reconfirmed integration of the Glu-1Bx7 gene into the rice genome by Southern blot analysis. Transcripts and proteins of the wheat Glu-1Bx7 were stably expressed in the rice $T_1$ seeds. Finally, the marker-free plants harboring only the Glu-1Bx7 gene were successfully screened at the $T_1$ generation.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.45 no.2
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• pp.175-184
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• 2019

The epidermal growth factor (EGF) has a intrinsic function of inducing growth and proliferation of cells through interacting with cell membrane receptors in human epidermis and dermis layer. These functions of EGF are used as a main ingredient for wound healing medicines and anti-aging cosmetics. As a cosmetic ingredient, the EGF has a problem in exhibiting its natural efficacy due to the lack of the ability to penetrate through the stratum corneum, which is known as the skin barrier. In this study, a recombinant human epidermal growth factor ($MTD_{151}-EGF$) fused with the macromolecule transduction domain $(MTD)_{151}$ with the skin penetration ability was developed to improve the skin penetration efficiency of the EGF. Expression of $MTD_{151}-EGF$ was performed in E. coli transformed with a vector encoding the $MTD_{151}-EGF$ gene and then purified. The purified $MTD_{151}-EGF$ was evaluated using cell proliferation assay, cytotoxicity test and skin penetration test by franz diffusion cell assay and artificial skin. Cell proliferation activity of $MTD_{151}-EGF$ purified to high purity of 99% or above was equivalent to the EGF or better, and cytotoxicity was not observed. In addition, the $MTD_{151}-EGF$ showed an excellent penetration efficiency compared to the EGF in the skin penetration test with EGF and $MTD_{151}-EGF$ labeled by FITC in an artificial skin penetration model. Based on the quantitative analysis of the penetrating substance using franz diffusion cell assay, the amount of penetration was about 16 times more than that of EGF. These results can be regarded as an effective alternative to improve the existing physical transdermal penetration method related to the use of various active ingredients for cosmetics.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.1-22
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• 2021

Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.