• Journal of Life Science
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• v.32 no.12
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• pp.962-970
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• 2022

Nonalcoholic steatohepatitis (NASH) is the progressive stage of nonalcoholic fatty liver disease (NAFLD) that highly increases the risk of cirrhosis and liver cancer, and there are few therapeutic options available in the clinic. Poricoic acid (PoA), a component of Poria cocos Wolf, has a wide range of pharmacological activities; however, little is known about its effects on NASH. The preventive effects of PoA on NASH were examined in vivo and in vitro by analyzing triglyceride synthesis, inflammation and fibrosis. In the high fat and methionine-choline deficient diet (HFMCD)-induced NASH mice, PoA reduced the liver weight and the levels of alanine aminotransferase and aspartate aminotransferase compared with non-treated HFMCD group. The staining with Oil Red O and hematoxylin and eosin revealed that PoA administration reduced red staining and the size of lipid droplet. qPCR analysis showed that PoA also reduced the expression of genes related to triglyceride synthesis. Further, immunostaining with CD68 and qPCR analysis revealed that PoA reduced the staining with CD68 and the expression of inflammatory genes induced by HFMCD. Moreover, PoA reduced the staining with sirius red and antibody of α-smooth muscle actin and also reduced the expression of genes related to fibrosis. The treatment of PoA to AML12 cells reduced the increase in triglyceride amount and expression of genes associated with triglyceride synthesis, inflammation and fibrosis. Taken together, our study indicate that PoA has therapeutic effect on NASH through preventing triglyceride synthesis, inflammation and fibrosis.

• Research in Plant Disease
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• v.28 no.2
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• pp.82-91
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• 2022

Recently, several in vitro studies have reported antiviral activity of agents of systemic acquired resistance against plant virus infection, but the approach has not been applied in a wide range of agricultural fields. The objective of this study was to evaluate the inhibitory effect of the exogenous application of salicylic acid (SA), chitosan (CH), or eugenol (EG) in tomato yellow leaf curl virus (TYLCV) infection of greenhouse-grown tomato plants. In vitro, the initial time of symptom was observed in TYLCV-infected plants (VP) of the resistant cultivar 'Superdotaerang' at 12 days post inoculation (dpi) after application of antiviral agents. At 32 dpi, the disease rate of TYLCV in the CHT+VP (0.1% chitosan and virus infected control) treated plants was 87.5%, lower than that of the other treatment. However, the virus content in the CHT+VP treated plants was higher than those of the other treatments, and SA, EG, and CH did not show significant effect on plant height or shoot and root fresh weight. Our results from summer-cultivated greenhouse-grown tomatoes show that none of the tested agents had an inhibitory activity on viral infection or yield of tomato 'Dotaerangsola'cultivar. In contrast, all treated 'TY Giants' cultivars that possessed TYLCV resistance genes Ty-1 and Ty-3a did not show typical symptoms and the virus content was remarkably lower than those in the TYLCV treated plants in 'Superdotaerang'. The results of this research indicated that the planting of resistant tomato cultivars was effective method instead of using SA, EG, and CH (known as resistance-inducing factors for control) of TYLCV in the field.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Korean Journal of Environment and Ecology
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• v.36 no.6
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• pp.582-591
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• 2022

This study was conducted to eliminate Myocastor coypusMolina, 1782 (Nutria) from Jeju Special Self-Governing Province, South Korea. Habitat identification and eradication were carried out from September to November 2013, and a survey was carried out until June 2022 to check whether the eradication was successful. The habitat was identified with unmanned cameras, interviews, and literature surveys, and the capture was performed using the trapping method with food to attract nutria to the habitat area. The study area for the follow-up survey, which was set relatively wide considering nutria's home range, included wetlands and rivers within 4.0 km² of the habitat area (eradication area). As a result, nutria's habitat was confirmed only at Songdang Ranch, Songdang-ri, of Jeju Island, with traces of habitat (footprints, excrement, and burrows) confirmed in waterways and ponds within the pasture. Eight individuals were captured, including four females, three males, and one individual in too advanced a state of decay to identify the sex. The follow-up survey thoroughly investigated the habitat and its surroundings, focusing on three areas with permanent water, Seongeup Reservoir, Cheonmi Creek, and Molsuni Pond, but no signs of habitat were identified. Therefore, it is determined that nutria inhabiting Jeju Island has been completely eradicated. It is believed that the successful eradication of nutria in the Jeju Special Autonomous Region was possible due to a synergy between 1) the eradication of nutria at the beginning of the settlement phase through rapid capture after confirming the nutria habitat and 2) the delayed expansion period because of rare presence of wetlands, where water is constantly stagnant, on Jeju Island. These results imply that quickly identifying the ecological characteristics of the species and preventing disturbances before they or at the beginning of the ecological disturbance through control and eradication at the initial stage of settlement before the expansion stage is an effective measure to cope with the influx of alien species.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

• The Journal of KAIRB
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• v.2 no.1
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• pp.23-31
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• 2020

Purpose: To obtain opinions from Korean Institutional Review Board (IRB) members' self-evaluation on ability to conduct fairness review of clinical trial protocol with presence of conflict of interest and from investigators and IRB members on financial conflict of interest through surveying. Methods: IRB members and researchers in 9 different hospitals were asked to answer survey questions via email. Results: Responders were 115 personnel (IRB Chair/vice 18, medical member 30, non-medical member 28, and researcher 39) from 9 centers. Compared to IRB medical members, IRB chair/vice respondents scored higher with statistically significance on 10 point scale (8.44±1.381 vs. 7.30±1.685, p=0.005) when asked to self-evaluate fairness reviewing a protocol proposed by an investigator from the same department and a protocol from the company that supports the scientific committee of responders. When reviewing a protocol proposed by a hospital director, non-medical members scored statistically significantly higher than medical-members (7.47±1.76 vs. 8.07±2.70, p=0.034). When asked about the limitation of labor fee for principal investigator on phase 3 Human clinical trials of the Investigational new drug, while the responses range was wide, 60% answered that labor cost of principal investigator should be less than 30% of total budget for clinical trials with a budget of 100 million won. 51.3% answered that there is no need to disclose the labor cost of the principal investigator in the consent form. Since every investigator can be influenced unconsciously by conflict of interest, the answer that 'responder agrees that there is need for management' was the most chosen answer (IRB member 61.8%, investigator 64.1%, multiple answers allowed). Conclusion: Considering scores on questions of fairness by IRB members were between 7.23-8.56 on scale of 0 to 10 point when IRB members were asked about reviewing a clinical trial protocol, it cannot be said with absolute certainty that there is no issue regarding fairness in the review process. Therefore, there should be more ways to safeguard fairness for these issues. There is a need that the disclosure amount of honorarium from sponsor should be lower than 100 million Korean won. Considering the results of the survey in which respondents expressed their thoughts, it is likely that more education on the concept of conflict of interest is needed.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.274-283
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• 2023

Buffer zones, an example of nature-based solutions, offer wide range of environmental, social and economic benefits due to their multifunctionality when applied to watershed areas promoting blue-green connectivity. This study evaluated the effects of buffer zone application to the water quality of Yongdam reservoir tributaries. Particularly, the challenges and improvement of the buffer zone design were identified and suggested, respectively. Water and soil samples were collected from a total of six sites in Yongdam reservoir from September 2021 to April 2022. Water quality analyses revealed that among the sites monitored, downstream of Sangjeonmyeon Galhyeonri (SG_W_D2) was found to have the highest concentration for water quality parameters turbidity, total suspended solids (TSS), chemical oxygen demand (COD), total phosphorus (TP) and total nitrogen (TN). This finding was attributed to the algal bloom observed during the sampling conducted in September and October 2021. It was found through the soil analyses that high TN and TP concentrations were also observed in all the agricultural land uses implying that nutrient accumulation in agricultural areas are high. Highest TN concentration was found in the agricultural area of Jeongcheonmyeon Wolpyeongri (JW_S_A) followed by Jucheonmyeon Sinyangri (JS_S_A) while the lowest TN concentration was found in the original soil of Sangjeonmyeon Galhyeonri (SG_S_O). Among the types of buffer zones identified in this study, Type II-A, Type II-B and Type III were found to have blue-green connectivity. However, initially, blue-green connectivity in these buffer zone types were not considered leading to poor design and poor performance. As such, improvement in the design considering blue-green network and renovation must be considered to optimize the performance of these buffer zones. The findings in this study is useful for designing buffer zones in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.387-397
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• 2023

Plant height is a growth parameter that provides visible insights into the plant's growth status and has a high correlation with yield, so it is widely used in crop breeding and cultivation research. Investigation of the growth characteristics of crops such as plant height has generally been conducted directly by humans using a ruler, but with the recent development of sensing and image analysis technology, research is being attempted to digitally convert growth measurement technology to efficiently investigate crop growth. In this study, the canopy height of rice grown at various nitrogen fertilization levels was measured using a laser scanner capable of precise measurement over a wide range, and a comparative analysis was performed with the actual plant height. As a result of comparing the point cloud data collected with a laser scanner and the actual plant height, it was confirmed that the estimated plant height measured based on the average height of the top 1% points showed the highest correlation with the actual plant height (R² = 0.93, RMSE = 2.73). Based on this, a linear regression equation was derived and used to convert the canopy height measured with a laser scanner to the actual plant height. The rice growth curve drawn by combining the actual and estimated plant height collected by various nitrogen fertilization conditions and growth period shows that the laser scanner-based canopy height measurement technology can be effectively utilized for assessing the plant height and growth of rice. In the future, 3D images derived from laser scanners are expected to be applicable to crop biomass estimation, plant shape analysis, etc., and can be used as a technology for digital conversion of conventional crop growth assessment methods.

• Journal of Nutrition and Health
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• v.56 no.6
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• pp.730-741
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• 2023

Purpose: The 2020 Dietary Reference Intakes for Koreans (KDRIs) serves as a foundation for daily nutrient and energy recommendations aiming to enhance public health and prevent chronic diseases. They act as guidelines for maintaining proper nutrition and overall health. Using KDRIs is crucial for promoting healthier lifestyles and making informed dietary choices. Thus, this study explores the influence of a nutrition education program, based on the 2020 KDRIs, on the nutrition knowledge and dietary habits of undergraduates in Gyeongsangnam-do and Gyeonggi-do. Methods: The nutrition education program, designed with diverse instructional materials, was executed across a wide range of universities. The education group (n = 75) engaged in the program for a 6-week instructional period, while the control group (n = 53) underwent the survey without participating in the education program. Nutrition Quotient (NQ) and knowledge assessments were administered to both groups immediately before and after the instructional period. Results: Within the education group, the nutrition education program positively impacted responses to NQ practice items, including knowledge of nutrition, daily intake, and portion sizes (p < 0.05). In contrast, there were no significant differences between the before and after responses of the control group for most survey items. Post-program evaluations showed significantly higher self-assessment scores and increased satisfaction levels (p < 0.05), with the satisfaction rate for the education program using the 2020 KDRIs reaching 99.2%. Conclusion: This study has demonstrated the positive impact of an effective nutrition education program. However, there is a need for the continuous development and implementation of nutrition education programs to sustain these outcomes and further enhance the nutritional education experience.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.