• Journal of Life Science
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• v.28 no.11
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• pp.1361-1368
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• 2018

Inflammation is the most common condition in the human body. Tissue damage triggers inflammation, together with vasodilation and increased blood flow at the inflamed site, resulting in edema. Inflammatory responses are also triggered by lipopolysaccharide (LPS), a Toll-like receptor Enterococcus faecalis, a gram-positive organism, has been reported to possess immunomodulatory and preventive activities; however, its use may present risks of sepsis and other systemic infections. Heat-killed Enterococcus faecalis (EF-2001) has been reported to induce antitumor activity, but its effects on inflammation are not known. In the present study, we investigated the effect of EF-2001 on LPS-induced macrophage inflammatory responses. EF-2001 treatment reduced nitric oxide (NO) production, indicating suppression of inflammatory reactions. EF-2001 showed no cytotoxicity in macrophages. Further investigation of the anti-inflammatory mechanism of EF-2001 indicated that EF-2001 reduced the LPS-induced expression of inducible nitric oxide synthase and cyclooxygenase-2. EF-2001 also reduced f the LPS induction of several inflammatory molecules involved in the nuclear factor-${\kappa}B$ ($NF-{\kappa}B$) and mitogen-activated protein kinase pathways, including ERK, JNK, and p38 phosphorylation, in a concentration-dependent manner. Additionally, EF-2001 inhibited Akt phosphorylation and increased the expression of the inhibitory ${\kappa}B$ ($I{\kappa}B$) protein, an inhibitor of $NF-{\kappa}B$. EF-2001 also inhibited the nuclear translocation of p65. These results suggest that EF-2001 has anti-inflammatory properties and may be useful for treating inflammatory diseases.

• Korean Journal of Environmental Agriculture
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• v.37 no.4
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• pp.251-259
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• 2018

BACKGROUND: Various culture media have been used for hydroponic cultures of horticultural plants under the smart greenhouses with natural and artificial light types. Management of the culture medium for the control of medium amounts and/or necessary components absorbed by plants during the cultivation period is performed with ICT (Information and Communication Technology) and/or IoT (Internet of Things) in a smart farm system. This study was conducted to develop the cloud-based data analysis system for effective management of culture medium applying to hydroponic culture and plant growth in smart greenhouses. METHODS AND RESULTS: Conventional inorganic Yamazaki and organic media derived from agricultural byproducts such as a immature fruit, leaf, or stem were used for hydroponic culture media. Component changes of the solutions according to the growth stage were monitored and plant growth was observed. Red and green lettuce seedlings (Lactuca sativa L.) which developed 2~3 true leaves were considered as plant materials. The seedlings were hydroponically grown in the smart greenhouse with fluorescent and light-emitting diodes (LEDs) lights of $150{\mu}mol/m^2/s$ light intensity for 35 days. Growth data of the seedlings were classified and stored to develop the relational database in the virtual machine which was generated from an open stack cloud system on the base of growth parameter. Relation of the plant growth and nutrient absorption pattern of 9 inorganic components inside the media during the cultivation period was investigated. The stored data associated with component changes and growth parameters were visualized on the web through the web framework and Node JS. CONCLUSION: Time-series changes of inorganic components in the culture media were observed. The increases of the unfolded leaves or fresh weight of the seedlings were mainly dependent on the macroelements such as a $NO_3-N$, and affected by the different inorganic and organic media. Though the data analysis system was developed, actual measurement data were offered by using the user smart device, and analysis and comparison of the data were visualized graphically in time series based on the cloud database. Agricultural management in data visualization and/or plant growth can be implemented by the data analysis system under whole agricultural sites regardless of various culture environmental changes.

• Korean Journal of Poultry Science
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• v.49 no.4
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• pp.211-220
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• 2022

Poultry are exposed to extremely high levels of oxidative stress as a consequence of the excessive production of reactive oxygen species (ROS) induced by endogenous and exogenous stressors, such as high-stocking densities, thermal stress, environmental and feed contamination, along with factors associated with intensive breeding systems. Oxidative stress promotes lipid peroxidation, DNA damage, and inflammation, which can have detrimental effects on the health of birds. During the course of evolution, birds have developed antioxidant defense mechanisms that contribute to maintaining homeostasis when exposed to endogenous and exogenous stressors. The primary antioxidant defense systems are enzymatic and non-enzymatic in nature and play roles in protecting cells from ROS attack. Recently, plant flavonoids, which have been established to reduce oxidative stress, have been attracting considerable attention as potential feed additives. Flavonoids are a group of polyphenolic compounds that can be stabilized by binding structural compounds with ROS, and can promote the elimination of ROS by inducing the expression of antioxidant enzymes. However, although flavonoids can contribute to reducing lipid peroxidation and thereby enhance the antioxidant capacity of birds, they have low solubility in the gastrointestinal tract, and consequently, it is necessary to develop a delivery technology that can facilitate the effect intestinal absorption of these compounds. Furthermore, it is important to determine the dietary levels of flavonoids by assessing the exact antioxidant effects in the gastrointestinal tract wherein the concentrations of dietary flavonoids are highest. It is also necessary to examine the expression of transcriptional factors and vitagenes associated with the efficient antioxidant effects induced by flavonoids. It is anticipated that the application of flavonoids as natural antioxidants will become a particularly important field in the poultry industry.

• Journal of Nutrition and Health
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• v.55 no.4
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• pp.462-475
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• 2022

Purpose: Allomyrina dichotoma larvae are one of the approved edible insects with nutritional value and various functional and medicinal properties. Previously we have demonstrated that the Allomyrina dichotoma larval extract (ADLE) ameliorates hepatic insulin resistance in high-fat diet (HFD)-induced diabetic mice through the activation of adenosine monophosphate-activated protein kinase (AMPK). This study investigated the effects of ADLE on insulin resistance in the skeletal muscle and explored mechanisms for enhancing the glucose uptake in palmitate (PAL)-treated C2C12 myotubes. Methods: To induce insulin resistance, the differentiated C2C12 myotubes were treated with PAL (0.5 mM) for 24 hours, and then treated with a 0.5 mg/ml concentration of ADLE, and the resultant effects were measured. The expression levels of glucose transporter-4 (GLUT4), AMPK, and the mitochondrial metabolism-related proteins were analyzed by western blotting. The mRNA expression levels of lipogenesis- related genes were determined by quantitative reverse-transcriptase PCR. Results: The exposure of C2C12 myotubes to 0.5 mg/ml of ADLE increased cell viability significantly compared to PAL-treated cells. ADLE upregulated the protein expression of GLUT4 and enhanced glucose uptake in the PAL-treated cells. ADLE increased the phosphorylated AMPK in both the PAL-treated C2C12 myotubes and HFD-treated skeletal muscle. The reduced expression levels of peroxisome-proliferator-activated receptor gamma co-activator-1 alpha (PGC1α) and uncoupling protein 3 (UCP3) due to the PAL and HFD treatment were reversed by the ADLE treatment. The citrate synthase activity was also significantly increased with the PAL and ADLE co-treatment. Moreover, the mRNA and protein expressions of fatty acid synthesis-related factors were reduced in the PAL and HFD-treated muscle cells, and this effect was significantly attenuated by the ADLE treatment. Conclusion: ADLE activates AMPK, which in turn induces mitochondrial metabolism and reduces fatty acid synthesis in C2C12 myotubes. Therefore, ADLE could be useful for preventing or treating insulin resistance of skeletal muscles in diabetes.

• Fashion & Textile Research Journal
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• v.24 no.6
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• pp.667-685
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• 2022

This paper aims to investigate 4D printing materials for soft robots. 4D printing is a targeted evolution of the 3D printed structure in shape, property, and functionality. It is capable of self-assembly, multi-functionality, and self-repair. In addition, it is time-dependent, printer-independent, and predictable. The shape-shifting behaviors considered in 4D printing include folding, bending, twisting, linear or nonlinear expansion/contraction, surface curling, and generating surface topographical features. The shapes can shift from 1D to 1D, 1D to 2D, 2D to 2D, 1D to 3D, 2D to 3D, and 3D to 3D. In the 4D printing auxetic structure, the kinetiX is a cellular-based material design composed of rigid plates and elastic hinges. In pneumatic auxetics based on the kirigami structure, an inverse optimization method for designing and fabricating morphs three-dimensional shapes out of patterns laid out flat. When 4D printing material is molded into a deformable 3D structure, it can be applied to the exoskeleton material of soft robots such as upper and lower limbs, fingers, hands, toes, and feet. Research on 4D printing materials for soft robots is essential in developing smart clothing for healthcare in the textile and fashion industry.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.1
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• pp.21-40
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• 2023

This study analyzed the cold air characteristics of the Nakdong Jeongmaek, which is advantageous for the formation of cold air that can flow into the city, in order to suggest the wind ventilation corridor plans, which have recently been increasing interest as a way to improve the urban thermal environment. In addition, based on the watershed analysis, specific cold-air watershed areas were established and management plans were suggested to expand the cold air function of the Nakdong Jeongmaek. As a result of the analysis of cold air in the Nakdong Jeongaek, cold air was strongly generated in the northern forest of the Jeongamek, and flowed into nearby cities along the valley topography. On average, the speed of cold air was high in cities located to the east of the Jeongmaek, while the height of cold air layer was high in cities located to the west. By synthesizing these cold air characteristics and watershed analysis results, the cold-air watershed area was classified into 8 zones, And the plans were proposed to preserve and strengthen the temperature reduction of the Jeongmaek by designating the zones as 'Conservation area of Cold-air', 'Management area of Cold-air', and 'Intensive management area of Cold-air'. In addition, in order to verify the temperature reduction of cold air, the effect of night temperature reduction effect was compared with the cold air analysis using weather observation data. As a result, the temperature reduction of cold air was confirmed because the night temperature reduction was large at the observation station with strong cold air characteristics. This study is expected to be used as basic data in establishing a systematic preservation and management plan to expand the cold air function of the Nakdong Jeongmaek.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.297-305
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• 2023

The recycled irrigation is a type of irrigation that uses downstream water to fulfill irrigation demand in the upstream agricultural areas; the used irrigation water returns back to the downstream. The recycled irrigation is advantageous for securing irrigation water for plant growth, but the returned water typically contains high levels of nutrients due to excess nutrients inputs during the agricultural activities, potentially deteriorating stream water quality. Therefore, quantitative assessment on the effect of the recycled irrigation on the stream water quality is required to establish strategies for effective irrigation water supply and water quality management. For this purpose, a watershed model is generally used; however no functions to simulate the effects of the recycled irrigation are provided in the existing watershed models. In this study, we used multi-reservoir model coupled with the Hydrological Simulation Program-Fortran (HSPF) to estimate the effect of the recycled irrigation on the stream water quality. The study area was the Gwangok stream watershed, a subwatershed of Gyeseong stream watershed in Changnyeong county, Gyeongsangnam-do. The HSPF model was built, calibrated, and used to produce time series data of flow and water quality, which were used as hypothetical observation data to calibrate the multi-reservoir model. The calibrated multi-reservoir model was used for simulating the recycled irrigation. In the multi-reservoir model, the Gwangok watershed consisted of two subsystems, irrigation and the Gwangok stream, and the reactions (plant uptake, adsorption, desorption, and decay) within each subsystem, and fluxes of water and materials between the subsystems, were modeled. Using the developed model, three scenarios with different combinations of the operating conditions of the recycled irrigation were evaluated for their effects on the stream water quality.

• Journal of the Korean Institute of Gas
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• v.27 no.3
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• pp.52-58
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• 2023

With the increasing awareness of the importance of carbon neutrality in response to global climate change, the utilization of hydrogen as a carbon-free fuel source is also growing. Hydrogen is commonly used in fuel cells (FC), but it can also be utilized in internal combustion engines (ICE) that are based on combustion. Particularly, ICEs that already have established infrastructure for production and supply can greatly contribute to the expansion of hydrogen energy utilization when it becomes difficult to rely solely on fuel cells or expand their infrastructure. However, a disadvantage of utilizing hydrogen through combustion is the potential generation of nitrogen oxides (NOx), which are harmful emissions formed when nitrogen in the air reacts with oxygen at high temperatures. In particular, for the EURO-7 exhaust regulation, which includes cold start operation, efforts to reduce exhaust emissions during the warm-up process are required. Therefore, in this study, the characteristics of nitrogen oxides and fuel consumption were investigated during the warm-up process of cooling water from room temperature to 88℃ using a 2-liter direct injection spark ignition (SI) engine fueled with hydrogen. One advantage of hydrogen, compared to conventional fuels like gasoline, natural gas, and liquefied petroleum gas (LPG), is its wide flammable range, which allows for sparser control of the excessive air ratio. In this study, the excessive air ratio was varied as 1.6/1.8/2.0 during the warm-up process, and the results were analyzed. The experimental results show that as the excessive air ratio becomes sparser during warm-up, the emission of nitrogen oxides per unit time decreases, and the thermal efficiency relatively increases. However, as the time required to reach the final temperature becomes longer, the cumulative emissions and fuel consumption may worsen.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.