• Journal of Microbiology and Biotechnology
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• v.23 no.8
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• pp.1159-1166
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• 2013

Orientia tsutsugamushi, a gram-negative bacterium, causes severe acute febrile illness in humans. Despite this danger, the route of infection, infectivity, and protective mechanisms of the host's immune response to O. tsutsugamushi are unclear. Dendritic cells (DCs) are one of the most important cell types in bridging the innate and adaptive immune responses. In this study, we observed that O. tsutsugamushi infects and replicates in monocyte-derived DCs (MODCs). During infection and replication, the expressions of the cytokines IL-12 and TNF-${\alpha}$, as well as the co-stimulatory molecules CD80, CD83, CD86, and CD40, were increased in MODCs. When O. tsutsugamushi-treated MODCs were co-cultured with autologous $CD4^+$ T cells, they enhanced production of IFN-${\gamma}$, a major Th1 cytokine. Collectively, our results show that O. tsutsugamushi can replicate in MODCs and can simultaneously induce MODC maturation and increase proinflammatory cytokine levels in MODCs that subsequently activate $CD4^+$ T cells.

• Analytical Science and Technology
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• v.34 no.4
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• pp.172-179
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• 2021

Oleanane-type triterpenoids exist as secondary metabolites in various plants. In particular, soyasaponin, an oleanane-type triterpenoid, is abundant in the hypocotyl of soybean, one of the most widely cultivated crops in the world. Depending on their chemical structure, soyasaponins are categorized as group A saponins or group DDMP (2,3-dihydro-2,5-dihydroxy-6-methyl-4H-pyran-4-one) saponins. The different soyasaponin chemical structures present different health functionalities and taste characteristics. However, conventional phenotype screening of soybean requires a substantial amount of time for functionality of soyasaponins. Therefore, we attempted to use liquid chromatography with a photodiode array detector and tandem mass spectrometry (LC-PDA/MS/MS) for accurately predicting the phenotype and chemical structure of soyasaponins in the hypocotyl of five common soybean natural mutants. In this method, the aglycones (soyasapogenol A [SS-A] and soyasapogenol B [SS-B]) were detected after acid hydrolysis. These results indicated that the base peak and fragmentation differ depending on the chemical structure of soyasaponin with aglycone. Thus, a fragmentation database can help predict the chemical structure of soyasaponins in soyfoods and plants.

• Journal of the Korean earth science society
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• v.41 no.4
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• pp.391-404
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• 2020

This study identified and examined earth science education department programs in Korea. Major courses provided by 11 universities and their course requirements were analyzed, and the main research results are as follows. First, many basic courses, other major requisite, and elective courses are provided in geology, astronomy, and atmospheric science. oceanography, geophysics, earth environmental science, and natural disaster and energy resources had fewer major requisite courses provided in addition to basic courses, and few elective courses were offered. Second, many courses in science education focused on earth science, while others focused on general science and there were few courses that covered education theory regarding the specific subject. Third, science course application requirements emphasized the understanding of science in general or of earth science specifically. From the above results, additional studies are proposed to reflect on the current state and supplement these programs.

• Journal of Science Education
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• v.47 no.1
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• pp.37-51
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• 2023

This study explored the factors influencing major satisfaction and academic satisfaction of science education major students at the College of Education using machine learning models, random forest, gradient boosting model, and SHAP. Analysis results showed that the performance of the gradient boosting model was better than that of the random forest, but the difference was not large. Factors influencing major satisfaction include 'satisfaction with science teachers in high school corresponding to the subject of one's major', 'motivation for teaching job', and 'age'. Through the SHAP value, the influence of variables was identified, and the results were derived for the group as a whole and for individual analysis. The comprehensive and individual results could be complementary with each other. Based on the research results, implications for ways to support pre-service science teachers' major and academic satisfaction were proposed.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.249-255
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• 2017

Antarctica isimportant area in order to understand climate change. In addition, this area is complex region where indicate warming and cooling trend according to previous studies. Therefore, it is necessary to understand the long-term variability of Antarctic energy budget. Net radiation, one of energy budget factor, is affected by albedo, and albedo cause negative radiative forcing. It is necessary to analyze a relationship between albedo and net radiation in order to analyze relationship between two factors in Antarctic climate changes and ice-albedo feedback. In thisstudy, we calculated net radiation using satellite data and performed an analysis of long-term variability of net radiation over Antarctica. In addition we analyzed correlation between albedo. As a results, net radiation indicates a negative value in land and positive value in ocean during study periods. As an annual changes, oceanic trend indicates an opposed to albedo. Time series pattern of net radiation is symmetrical with albedo. Correlation between the two factors indicate a negative correlation of -0.73 in the land and -0.32 in the ocean.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.741-746
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• 2016

Albedo is one of the climate variables that modulate absorption of solar energy, and its retrieval is important process for climate change study. High spatial resolution and long-term consistent periods are important considerations in order to efficiently use the retrieved albedo data. This study retrieved surface broadband albedo based on Landsat 8 as high resolution which is consistent with Landsat 7. First of all, we analyzed consistency of Landsat 7 channel and Landsat 8 channel. As a result, correlation coefficient(R) on all channels is average 0.96. Based on this analysis, we used multiple linear regression model using Landsat 7 albedo, which is being used in many studies, and Landsat 8 reflectance channel data. The regression coefficients of each channel calculated by regression analysis were used to derive a formula for converting the Landsat 8 reflectance channel data to broadband albedo. After Landsat 8 albedo calculated using the derived formula is compared with Landsat 7 albedo data, we confirmed consistency of two satellite using Root Mean Square Error (RMSE), R-square ($R^2$) and bias. As a result, $R^2$ is 0.89 and RMSE is 0.003 between Landsat 7 albedo and Landsat 8 albedo.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.333-338
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• 2017

We compared sea ice concentration(SIC) and sea ice extent(SIE) using EUMETSAT Ocean and Sea Ice Satellite Application Facilities(OSI SAF) and NASA Team(NT) sea ice algorithm in the Arctic during 1980-2010 to investigate the difference between sea ice data applied different algorithms. SIC and SIE of the two data showed different consistency by season and by sea area. Seasonally, SIC of OSI SAF was 0.85 % overall, 0.48 % in spring, 0.97 % in summer, 1.38 % in autumn and 0.66 % in winter higher than NT SIC. By sea area, OSI SAF SIC was 2.7 %, SIE was $198,000km^2$ higher than NT in Arctic Ocean, but in Lincoln Sea, OSI SAF SIC was 2.3 %, SIE was $20,000km^2$ lower than NT.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.77-86
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• 2023

Ship detection is widely used in areas such as maritime security, maritime traffic, fisheries management, illegal fishing, and border control, and ship detection is important for rapid response and damage minimization as ship accident rates increase due to recent increases in international maritime traffic. Currently, according to a number of global and national regulations, ships must be equipped with automatic identification system (AIS), which provide information such as the location and speed of the ship periodically at regular intervals. However, most small vessels (less than 300 tons) are not obligated to install the transponder and may not be transmitted intentionally or accidentally. There is even a case of misuse of the ship'slocation information. Therefore, in this study, ship detection was performed using high-resolution optical satellite images that can periodically remotely detect a wide range and detectsmallships. However, optical images can cause false-alarm due to noise on the surface of the sea, such as waves, or factors indicating ship-like brightness, such as clouds and wakes. So, it is important to remove these factors to improve the accuracy of ship detection. In this study, false alarm wasreduced, and the accuracy ofship detection wasimproved by removing wake.As a ship detection method, ship detection was performed using machine learning-based random forest (RF), and convolutional neural network (CNN) techniquesthat have been widely used in object detection fieldsrecently, and ship detection results by the model were compared and analyzed. In addition, in this study, the results of RF and CNN were combined to improve the phenomenon of ship disconnection and the phenomenon of small detection. The ship detection results of thisstudy are significant in that they improved the limitations of each model while maintaining accuracy. In addition, if satellite images with improved spatial resolution are utilized in the future, it is expected that ship and wake simultaneous detection with higher accuracy will be performed.

• Proceedings of the Korean Society of Crop Science Conference
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• 2006.04a
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• pp.506-507
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• 2006

• Asian Journal of Innovation and Policy
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• v.5 no.3
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• pp.251-274
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• 2016

Practical methodology for categorizing collaborative disciplines or research in a quantitative manner is presented by developing a Correlation Matrix of Major Disciplines (CMMD) using bibliometric data collected between 2009 and 2014. First, 21 major disciplines in science and engineering are defined based on journal publication frequency. Second, major disciplines using a comparing discipline correlation matrix is created and correlation score using CMMD is calculated based on an analyzer function that is given to the matrix elements. Third, a correlation between the major disciplines and 14 research fields using CMMD is calculated for validation. Collaborative researches are classified into three groups by partially accepting the definition of pluri-discipline from peer review manual, European Science Foundation, inner-discipline, inter-discipline and cross-discipline. Applying simple categorization criteria identifies three groups of collaborative research and also those results can be visualized. Overall, the proposed methodology supports the categorization for each research field.