• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.695-713
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• 2023

In coastal waters, phytoplankton,suspended particulate matter, and dissolved organic matter intricately and nonlinearly alter the reflectivity of seawater. Neural network technology, which has been rapidly advancing recently, offers the advantage of effectively representing complex nonlinear relationships. In previous studies, a three-stage neural network was constructed to extract the inherent optical properties of each component. However, this study proposes an algorithm that directly employs a deep neural network. The dataset used in this study consists of synthetic data provided by the International Ocean Color Coordination Group, with the input data comprising above-surface remote-sensing reflectance at nine different wavelengths. We derived inherent optical properties using this dataset based on a deep neural network. To evaluate performance, we compared it with a quasi-analytical algorithm and analyzed the impact of log transformation on the performance of the deep neural network algorithm in relation to data distribution. As a result, we found that the deep neural network algorithm accurately estimated the inherent optical properties except for the absorption coefficient of suspended particulate matter (R² greater than or equal to 0.9) and successfully separated the sum of the absorption coefficient of suspended particulate matter and dissolved organic matter into the absorption coefficient of suspended particulate matter and dissolved organic matter, respectively. We also observed that the algorithm, when directly applied without log transformation of the data, showed little difference in performance. To effectively apply the findings of this study to ocean color data processing, further research is needed to perform learning using field data and additional datasets from various marine regions, compare and analyze empirical and semi-analytical methods, and appropriately assess the strengths and weaknesses of each algorithm.

• Journal of Life Science
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• v.33 no.11
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• pp.887-896
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• 2023

The inflammatory response have been considered as one of important targets for cancer treatment because they play a key role during all steps of tumor development including initiation, promotion, malignant conversion and progression. To investigate the anti-inflammatory response during anti-tumor activity of an aqueous extracts of Ecklonia cava (AEC), alterations on the distribution of mast cells and the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), nuclear factor (NF)-κB, inflammasome compositional protein and inflammatory cytokines were examined in CT26 colon tumor-bearing BALB/cKorl syngeneic mice after administrating AEC for five weeks. After treatment of AEC, total weight of tumor and necrotic region of tumor section were significantly decreased compared to vehicle treated group. The number of infiltered mast cells was higher in AEC treated group than vehicle treated group, while the expression levels of COX-2 and iNOS were decreased in AEC treated group. Also, similar decrease pattern were detected in the expression levels of NF-κB, NLR family pyrin domain containing 3 (NLRP3), apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) and caspase-1 (Cas-1) after AEC treatment although the decrease rate was varied. Furthermore, the mRNA expressions of three inflammatory cytokines including tumor necrosis factor-α (TNF-α), interleukin-1α (IL-1α) and interleukin-6 (IL-6) were remarkably decreased in AEC treated group compared to vehicle treated group. These results suggest that inhibition of inflammatory response may be tightly associated with anti-tumor activity of AEC in CT26 colon tumor-bearing BALB/cKorl syngeneic mice.

• Journal of Life Science
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• v.33 no.11
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• pp.868-875
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• 2023

Kinesin-1 is a motor protein identified as the first member of the kinesin superfamily (KIF), which plays a role in intracellular cargo transport by acting as microtubule-dependent motor proteins within cells. Kinesin-1 consists of two heavy chains (KHCs, also known as KIF5s) and two light chains (KLCs). The 93 amino acids in the carboxyl (C)-terminal tail region of KIF5A are not homologous to the C-terminal tail region of KIF5B or the C-terminal tail region of KIF5C. In this study, we used a yeast two-hybrid screen to identify the binding proteins that interacted with the C-terminal region of KIF5A. We found an association between KIF5A and CUE domain containing 2 (CUEDC2), which is proposed to function as an adaptor protein involved in ubiquitination pathways and protein trafficking. CUEDC2 bound to the C-terminal region of KIF5A and did not interact with KIF5B (the motor of kinesin-1), KIF3A (the motor of kinesin-2), or kinesin light chain 1 (KLC1). KIF5A specifically bound to the C-terminal region of CUEDC2. Furthermore, KIF5A did not interact with another isoform: CUEDC1. In addition, glutathione S-transferase (GST) pull-downs showed that KIF5A directly bound GST-CUEDC2 but did not interact with GST-CUEDC1 and GST alone. When myc-KIF5A and EGFP-CUEDC2 were co-expressed in HEK-293T cells, CUEDC2 co-immunoprecipitated with kinesin-1, and myc-KIF5A and FLAG-CUEDC2 colocalized in the cells. These results suggest that in intracellular cargo transport by kinesin-1, CUEDC2 serves as an adaptor protein connecting kinesin-1 and cargo by binding to KIF5A.

• Journal of Marine Life Science
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• v.8 no.2
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• pp.136-149
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• 2023

Basic bioactivities (antioxidant, anti-inflammatory, antibacterial, anticancer, antiviral) were investigated from 370 strains of marine bacteria, fungi, and microalgae obtained from various marine environmental regions in Korea, and the activity results were obtained at the collection site, isolation source, and species level was compared. In the case of marine bacteria, strains belonging to the generally useful genera Streptomyces and Bacillus were observed to have particularly strong efficacy and useful resources were mainly isolated from marine sediments. In the case of marine fungi and microalgae, results showing strong species-specific activity were confirmed, and results showing efficacy-specific activity were also obtained. Based on these results, it is a research result that can facilitate priority access as a strategic material for industrial revitalization and the establishment of a strategy to secure resources based on usefulness when conducting research on chemicals that are selectively effective against specific diseases or when conducting resource-based research. In addition, we believe that by using these results as material for sale through the Marine BioBank (MBB), academia and industry can use them to help accelerate the revitalization of the marine bio industry.

• Analytical Science and Technology
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• v.36 no.6
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• pp.291-299
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• 2023

The benzo[a]pyrene in edible oils is extracted using methods such as Liquid-liquid, soxhlet and ultrasound-assisted extraction. However these extraction methods have significant drawbacks, such as long extraction time and large amount of solvent usage. To overcome these drawbacks, this study attempted to improve the current complex benzo[a]pyrene analysis method by applying the QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method that can be analyzed in a simple and short time. The QuEChERS method applied in this study includes extraction of benzo[a]pyrene into n-hexane saturated acetonitrile and n-hexane. After extraction and distribution using magnesium sulfate and sodium chloride, benzo[a]pyrene is analyzed by liquid chromatography with fluorescence detector (LC/FLR). As a result of method validation of the new method, the limit of detection (LOD) and quantification (LOQ) were 0.02 ㎍/kg and 0.05 ㎍/kg, respectively. The calibration curves were constructed using five levels (0.1~10 ㎍/kg) and coefficient (R²) was above 0.99. Mean recovery ratio was ranged from 74.5 to 79.3 % with a relative standard deviation (RSD) between 0.52 to 1.58 %. The accuracy and precision were 72.6~79.4 % and 0.14~7.20 %, respectively. All results satisfied the criteria ranges requested in the Food Safety Evaluation Department guidelines (2016) and AOAC official method of analysis (2023). Therefore, the analysis method presented in this study was a relatively simple pretreatment method compared to the existing analysis method, which reduced the analysis time and solvent use to 92 % and 96 %, respectively.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1553-1563
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• 2023

The Geostationary Ocean Color Imager-II (GOCI-II) is a satellite designed for ocean color observation, covering the Northeast Asian region and the entire disk of the Earth. It commenced operations in 2020, succeeding its predecessor, GOCI, which had been active for the previous decade. In this study, we aimed to enhance the atmospheric correction algorithm, a critical step in producing satellite-based ocean color data, by performing cross-calibration on the GOCI-II near-infrared (NIR) band using the GOCI NIR band. To achieve this, we conducted a cross-calibration study on the top-of-atmosphere (TOA) radiance of the NIR band and derived a vicarious calibration gain for two NIR bands (745 and 865 nm). As a result of applying this gain, the offset of two sensors decreased and the ratio approached 1. It shows that consistency of two sensors was improved. Also, the Rayleigh-corrected reflectance at 745 nm and 865 nm increased by 5.62% and 9.52%, respectively. This alteration had implications for the ratio of Rayleigh-corrected reflectance at these wavelengths, potentially impacting the atmospheric correction results across all spectral bands, particularly during the aerosol reflectance correction process within the atmospheric correction algorithm. Due to the limited overlapping operational period of GOCI and GOCI-II satellites, we only used data from March 2021. Nevertheless, we anticipate further enhancements through ongoing cross-calibration research with other satellites in the future. Additionally, it is essential to apply the vicarious calibration gain derived for the NIR band in this study to perform vicarious calibration for the visible channels and assess its impact on the accuracy of the ocean color products.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1591-1604
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• 2023

In ocean color remote sensing, atmospheric correction is a vital process for ensuring the accuracy and reliability of ocean color products. Furthermore, in recent years, the remote sensing community has intensified its requirements for understanding errors in satellite data. Accordingly, research is currently addressing errors in remote sensing reflectance (R_rs) resulting from inaccuracies in meteorological variables (total ozone, pressure, wind field, and total precipitable water) used as auxiliary data for atmospheric correction. However, there has been no investigation into the error in R_rs caused by the variability of the water vapor profile, despite it being a recognized error source. In this study, we used the Second Simulation of a Satellite Signal Vector version 2.1 simulation to compute errors in water vapor transmittance arising from variations in the water vapor profile within the GOCI-II observation area. Subsequently, we conducted an analysis of the associated errors in ocean color products. The observed water vapor profile not only exhibited a complex shape but also showed significant variations near the surface, leading to differences of up to 0.007 compared to the US standard 62 water vapor profile used in the GOCI-II atmospheric correction. The resulting variation in water vapor transmittance led to a difference in aerosol reflectance estimation, consequently introducing errors in R_rs across all GOCI-II bands. However, the error of R_rs in the 412-555 nm due to the difference in the water vapor profile band was found to be below 2%, which is lower than the required accuracy. Also, similar errors were shown in other ocean color products such as chlorophyll-a concentration, colored dissolved organic matter, and total suspended matter concentration. The results of this study indicate that the variability in water vapor profiles has minimal impact on the accuracy of atmospheric correction and ocean color products. Therefore, improving the accuracy of the input data related to the water vapor column concentration is even more critical for enhancing the accuracy of ocean color products in terms of water vapor absorption correction.

• Journal of Venture Innovation
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• v.5 no.3
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• pp.81-98
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• 2022

In the recent COVID-19 pandemic, urban agriculture is attracting attention as a healing concept. In 2020, 1,848,000 people participated in urban agriculture activities in Korea. Therefore, this study was conducted to empirically analyze the factors affecting the intention to participate in urban agriculture, which is rapidly increasing. The theoretical basis of this study is the experiential economy theory of Pine II and Gilmore and the experiential theory of Schmitt. As independent variables, a total of five variables were set as the four elements of Pine II and Gilmore's experiential economy theory, namely, educational, entertainment, escapist, and aesthetic experiences, and relational experience reclassified using Schmitt's theory. Interest was set as a mediating variable between these independent variables and the dependent variable, intention to participate in urban agriculture. For empirical analysis, data were collected through a survey. Based on the significant 314 samples of the collected data, the hypothesis was tested through statistical analysis. First, as a result of testing the influence relationship between the independent and dependent variables, educational, entertainment, and escapist experiences had a significant positive (+) effect on the intention to participate in urban agriculture. The impact of the influence was in the order of entertainment experience, escapist experience, and educational experience. There was no significant influence relationship between aesthetic experience, relational experience and intention to participate in urban agriculture. On the other hand, as a result of this study, interest introduced as a mediating variable was found to play a mediating role between entertainment, escapist, aesthetic experiences and intention to participate in urban agriculture. The mediating effect of interest was not tested between educational, relational experiences and intention to participate in urban agriculture. This study approached urban agriculture participation from the concept of healing and analyzes the factors affecting participation in urban agriculture activities empirically based on a theoretical framework by combining and analyzing the representative Pine II and Gilmore theories and Schmitt theories. It had academic significance. In addition, it was meaningful to suggest that the healing concept approach is directional in relation to urban agriculture by revealing that entertainment and escapist experiences are important influencing variables in decision-making to participate in urban agriculture in practice.