• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.4
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• pp.13-30
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• 2020

This study aims to analyze the qualitative and quantitative effects of environmental policies in terms of green space management using logistic regression model(LRM). Landsat satellite imageries in 1985, 1992, 2000, 2008, and 2015 are classified using a hybrid-classification method. Based on these classified maps, logistic regression model having a deforestation tendency of the past is built. Binary green space change map is used for the dependent variable and four explanatory variables are used: distance from green space, distance from settlements, elevation, and slope. The green space map of 2008 and 2015 is predicted using the constructed model. The conservation effect of Ulsan's environmental policies is quantified through the numerical comparison of green area between the predicted and real data. Time-series analysis of green space showed that restoration and destruction of green space are highly related to human activities rather than natural land transition. The effect of green space management policy was spatially-explicit and brought a significant increase in green space. Furthermore, as a result of quantitative analysis, Ulsan's environmental policy had effects of conserving and restoring 111.75㎢ and 175.45㎢ respectively for the periods of eight and fifteen years. Among four variables, slope was the most determinant factor that accounts for the destruction of green space in the city. This study presents logistic regression model as a way of evaluating the effect of environmental policies that have been practiced in the city. It has its significance in that it allows us a comprehensive understanding of the effect by considering every direct and indirect effect from other domains, such as air and water, on green space. We conclude discussing practicability of implementing environmental policy in terms of green space management with the focus on a non-statutory plan.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2023.04a
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• pp.55-55
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• 2023

With the COVID-19 pandemic, there is increasing interest in anti-obesity strategies. According to the National Statistical Office, the obesity rate in Korea was 38.3% in 2020 and 37.1% in 2021. Obesity is a risk factor for several severe diseases, including stroke, heart disease, type 2 diabetes, and certain types of cancer. Pinus rigida × Pinus taeda is a hybrid of Pinus rigida Mill and Pinus taeda Linn, and its cones are considered a by-product. Although previous studies have investigated their pharmacological effects on antioxidant activity and protection against oxidative DNA damage, few researchers have explored their potential as functional natural materials. Therefore, we evaluated the anti-obesity effects of the cone of ethyl acetate fraction of P. rigida × P. taeda (ERT), specifically its ability to inhibit lipid accumulation. Our analysis showed that ERT contains phytochemicals (catechin and caffeic acid) which are known to improve immune function and inhibit cell damage. ERT inhibited lipid droplet accumulation at the cellular levels through Oil Red O staining. Furthermore, ERT suppressed the expression of adipogenic transcription factors (PPARγ and CEBP/α) as well as downstream lipogenic target genes (FAS and SREBP-1) thereby inhibiting adipogenesis. ERT also down-regulated key adipogenic markers, including aP2α, while inducing the phosphorylation of AMPK. It has been reported that PPARγ and CEBP/α are expressed in the early stages of adipose differentiation, while SREBP-1 is expressed in the late stage. Therefore, our findings suggest that ERT activates AMPK signaling pathways, which inhibits adipogenic transcription factors (PPARγ, C/EBPα, and SREBP1) and lipogenic genes (FAS and aP2α), thereby blocking lipid accumulation and preventing obesity and related disorders. ERT showed potential as a new resource for developing a functional material for anti-obesity agents.

• Imaging Science in Dentistry
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• v.52 no.4
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• pp.383-391
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• 2022

Purpose: Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods: Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results: The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics(i.e., dice coefficient and intersection-over-union [IoU] score). MultiLabel U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion: Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.334-334
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• 2022

Improvement of old-fashioned rain gauge systems for automatic, timely, continuous, and accurate precipitation observation is highly essential for weather/climate prediction and natural hazards early warning, since the occurrence frequency and intensity of heavy and extreme precipitation events (especially floods) are recently getting more increase and severe worldwide due to climate change. Although rain gauge accuracy of 0.1 mm is recommended by the World Meteorological Organization (WMO), the traditional rain gauges in both weighting and tipping bucket types are often unable to meet that demand due to several existing technical limitations together with higher production and maintenance costs. Therefore, we aim to introduce a newly developed and cost-effective hybrid rain gauge system at 0.1 mm accuracy that combines advantages of weighting and tipping bucket types for continuous, automatic, and accurate precipitation observation, where the errors from long-term load cells and external environmental sources (e.g., winds) can be removed via an automatic drainage system and artificial intelligence-based data quality control procedure. Our rain gauge system consists of an instrument unit for measuring precipitation, a communication unit for transmitting and receiving measured precipitation signals, and a database unit for storing, processing, and analyzing precipitation data. This newly developed rain gauge was designed according to the weather instrument criteria, where precipitation amounts filled into the tipping bucket are measured considering the receiver's diameter, the maximum measurement of precipitation, drainage time, and the conductivity marking. Moreover, it is also designed to transmit the measured precipitation data stored in the PCB through RS232, RS485, and TCP/IP, together with connecting to the data logger to enable data collection and analysis based on user needs. Preliminary results from a comparison with an existing 1.0-mm tipping bucket rain gauge indicated that our developed rain gauge has an excellent performance in continuous precipitation observation with higher measurement accuracy, more correct precipitation days observed (120 days), and a lower error of roughly 27 mm occurred during the measurement period.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.317-317
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• 2022

Seakso 5, a maize hybrid, was applied for variety in 2021 by Gangwon Agricultural Research and Extension Services in Korea. This variety was bred to produce a purple extract of com husk. It shows purple color in the husk and cob and contains abundant anthocyanins. In this study, to obtain basic data on purple com 'Seakso 5' husk extract (PCHE), a husk extract was prepared and the contents of vitamins, amino acids and fatty acids were analyzed. The Seakso 5 husks were extracted with 30% alcohol and concentrated, after adding dextrin to the concentrate, it was spray-dried to prepare an extract. The PCHE were analyzed for vitamins, amino acids, and fatty acids by referring to the Food Code. As a result, three types of vitamins were detected, and their contents were vitamin B₁ 0.14 mg/100g, vitamin B₂ 2.30 mg/100g, and vitamin C 11.32 mg/100g. A total of 15 amino acids were detected in the PCHE, including aspartic acid and glutamic acid, and the content of aspartic acid was the highest at 3.02 mg/g. Simultaneous analysis of fatty acids Among 37 types, saturated fatty acids were palmitic acid (C16:0), stearic acid (C18:0), monounsaturated fatty acids were oleic acid (C18:1(n-9)), polyunsaturated fatty acids were linoleic acid(C18:2(n-6)c) and a-Linolenic Acid (C18:3(n-3)) were detected. Among the detected fatty acids, the content of palmitic acid was the highest at 40.75 mg/100g. In the future, These analysis results will be used as reference data for temporary food ingredient applications by the Ministry of Food and Drug Safety.

• Korean Journal of Plant Taxonomy
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• v.31 no.4
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• pp.343-358
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• 2001

We have examined the pedicel pubescence and other major morphological characters of 11 putative hybrid populations of Aconitum subgn. Aconitum at Mt. Sobaek to understand their origin. These populations show very complicated patterns of variation in pedicel pubescence ; they contain individuals having pedicels (1) completely glabrous, (2) with few micropapillate curved hairs near the receptacle, (3) moderately pubescent with micropapillate curved hairs from middle to upper portion, (4) sparsely pubescent with a mixture of micropapillate curved hairs and smooth-surfaced spreading glandular hairs near the receptacle, and (5) moderately pubescent with both types of hairs but from middle to upper portion. All five types of individuals co-occur in most populations with varying proportions. These results, in conjunction with evidence from the analysis of other major morphological characters, suggest that the populations at Mt. Sobaek were derived from the multiple hybridization events involving A. kusnezoffii, A. japonicum subsup. napiforme, and A. jaluense subsp. jaluense. In addition, the absence of "typical" forms of these putative parental taxa in Mt. Sobaek area suggests that repeated introgression has probably occurred in these populations.

• Journal of KIISE
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• v.44 no.12
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• pp.1290-1295
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• 2017

Document classification, a task of classifying the category of each document based on text, is one of the fundamental areas for natural language processing. Document classification may be used in various fields such as topic classification and sentiment classification. Neural network models for document classification can be divided into two categories: word-level models and character-level models that treat words and characters as basic units respectively. In this study, we propose a neural network model that combines character-level and word-level models to improve performance of document classification. The proposed model extracts the feature vector of each word by combining information obtained from a word embedding matrix and information encoded by a character-level neural network. Based on feature vectors of words, the model classifies documents with a hierarchical structure wherein recurrent neural networks with attention mechanisms are used for both the word and the sentence levels. Experiments on real life datasets demonstrate effectiveness of our proposed model.

• Journal of Ginseng Research
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• v.36 no.3
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• pp.298-307
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• 2012

Panax ginseng has been cultivated for centuries, and nine commercial cultivars have been registered in Korea. However, these nine elite cultivars are grown in less than 10% of ginseng fields, and there is no clear authentication system for each cultivar even though their values are higher than those of local landraces. Here, we have developed 19 microsatellite markers using expressed gene sequences and established an authentication system for all nine cultivars. Five cultivars, 'Chunpoong', 'Sunpoong', 'Gumpoong', 'Sunun', and 'Sunone', can each be identified by one cultivar-unique allele, gm47n-a, gm47n-c, gm104-a, gm184-a (or gm129-a), and gm175-c, respectively. 'Yunpoong' can be identified by the co-appearance of gm47n-b and gm129-c. 'Sunhyang' can be distinguished from the other eight cultivars by the co-appearance of gm47n-b, gm129-b, and gm175-a. The two other cultivars, 'Gopoong' and 'Cheongsun', can be identified by their specific combinations of five marker alleles. This marker set was successfully utilized to identify the cultivars among 70 ginseng individuals and to select true F1 hybrid plants between two cultivars. We further analyzed the homogeneity of each cultivar and phylogenetic relationships among cultivars using these markers. This marker system will be useful to the seed industry and for breeding of ginseng.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.106-106
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• 2017

The soybean [Glycine max (L.) Merr.] is one of the most important crop resources worldwide as food and forage. It is also important and valuable that to hold crop resources to have high genetic diversities. Recently, a core collection has been constructed in many plants to preserve the genetic resources of various plants. A core collection is small population to represent the genetic diversity of the total collection, and is of strategic importance as they allow the use of a small part of a germplasm collection that is representative of the total collection. Here, we developed the core collection consisting of 816 accessions by using approximately 180,000 (180K) single nucleotide polymorphisms (SNPs) developed in previous study. In addition, we performed genetic diversity and population structure analysis to construct the core collection from entire 4,392 collections. there were excluded sample call rates less than 93% and duplicated samples more than 99.9% according to genotype analysis using 180K SNPs from entire collections. Furthermore, we were also excluded natural hybrid resources which Glycine max and Glycine soja are mixed in half through population structure analysis. As a result, we are constructed the core collection of genetic diversity that reflects 99% of the entire collections, including 430 cultivated soybeans (Glycine max) and 386 wild soybeans (Glycine soja). The core collection developed in this study should be to provide useful materials for both soybean breeding programs and genome-wide association studies.

• Journal of Microbiology and Biotechnology
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• v.12 no.5
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• pp.807-812
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• 2002

In order to analyze the cellular proteins which interact with core protein of hepatitis C virus (HCV), a yeast two-hybrid screening technique was employed. A carboxyl terminus truncated core protein, which contained amino acid residues from the 1st to 120th, was used as a bait to screen cellular proteins. The expression library prepared from HeLa cell was screened and 400 positive clones were selected. The 75 clones from the positive clones were sequenced and analyzed by undergoing the Blast search. Interestingly, 7 out of the 75 clones encoded E7 antigen of human papilloma virus (HPV). We studied in detail the Interaction between the truncated version of HCV core and E7 antigen in vitro. The core$_{120}$ protein expressed in chimeric form with G57 was able to bring down the E7 protein of HPV type 18 expressed in bacteria. It is therefore suggested that the core of HCV might affect the interaction between E7 and a normal cellular tumor suppressor, known as Rb protein.