• Journal of Life Science
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• v.33 no.1
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• pp.15-24
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• 2023

To examine the antitumor effect of proso millet grains, whether proso millet grains exert apoptotic activity against human cancer cells was investigated. When the cytotoxicity of 80% ethanol (EtOH) extract of proso millet grains was tested against various cancer cells using MTT assay, more potent cytotoxicity was observed against human breast cancer MDA-MB-231 cells than against other cancer cells. When the EtOH extract was evaporated to dryness, dissolved in water, and then further fractionated by sequential extraction using four organic solvents (n-hexane, methylene chloride, ethyl acetate, and n-butanol), the BuOH fraction exhibited the highest cytotoxicity against MDA-MB-231 cells. Along with the cytotoxicity, TUNEL-positive apoptotic nucleosomal DNA fragmentation and several apoptotic responses including BAK/BAX activation, mitochondria membrane potential (Δψm) loss, mitochondrial cytochrome c release into the cytosol, activation of caspase-8/-9/-3, and degradation of poly (ADP-ribose) polymerase (PARP) were detected. However, human normal mammary epithelial MCF-10A cells exhibited a significantly lesser extent of sensitivity compared to malignant MDA-MB-231 cells. Irrespective of Fas-associated death domain (FADD)-deficiency or caspase-8-deficiency, human T acute lymphoblastic leukemia Jurkat cells displayed similar sensitivities to the cytotoxicity of BuOH fraction, excluding an involvement of extrinsic apoptotic mechanism in the apoptosis induction. These results demonstrate that the cytotoxicity of BuOH fraction from proso millet grains against human breast cancer MDA-MB-231 cells is attributable to intrinsic apoptotic cell death resulting from BAK/BAX activation, and subsequent mediation of mitochondrial damage-dependent activation of caspase cascade.

• Journal of Life Science
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• v.33 no.4
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• pp.357-362
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• 2023

This report looks at an agar-degrading marine bacterium and characterization of its agarase. Agar-degrading marine bacterium JS-1 was isolated with Marine agar 2216 media from seawater from the seashore of Sojuk-do, Changwon in Gyeongnam Province, Korea. The agar-degrading bacterium was named as Agarivorans sp. JS-1 by phylogenetic analysis based on 16S rRNA gene sequencing. The extracellular agarase was prepared from the culture media of Agarivorans sp. JS-1 and used for characterization. Relative activities at 20℃, 30℃, 35℃, 40℃, 45℃, 50℃, 55℃, and 60℃ were 70%, 74%, 78%, 83%, 87%, 100%, 74%, and 66%, respectively. Relative activities at pH 5, 6, 7, and 8 were 91%, 100%, 90%, and 89%, respectively. Its extracellular agarase showed maximum activity (207 units/l) at pH 6.0 and 50℃ in 20 mM Tris-HCl buffer. The residual activity after heat treatment at 20℃, 30℃, and 50℃ for 30 minutes was 90%, 70%, and 50% or more, respectively. After a 2-hour heat treatment at 20℃, 30℃, 35℃, 40℃, and 45℃, the residual activity was 80%, 68%, 65%, 63%, and 57%, respectively. At 50℃ and above, after heat treatment for 30 minutes, the residual activity was below 60%. Thin layer chromatography analysis suggested that Agarivorans sp. JS-1 produces extracellular β-agarases as they hydrolyze agarose to produce neoagarooligosaccharides such as neoagarohexaose (20.6%), neoagarotetraose (58.5%), and neoagarobiose (20.9%). Agarivorans sp. JS-1 and its thermotolerant β-agarase would be useful in the production of neoagarooligosaccharides, showing functional activity such as inhibition of bacterial growth and delay of starch degradation.

• Journal of Food Hygiene and Safety
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• v.38 no.2
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• pp.69-78
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• 2023

This study aimed to investigate the protective effect of enzymatically modified stevia (EMS) on C2C12 cell-based model of dexamethasone (DEX)-induced muscle atrophy to provide baseline data for utilizing EMS in functional health products. C2C12 cells with DEX-induced muscle atrophy were treated with EMS (10, 50, and 100 ㎍/mL) for 24 h. C2C12 cells were treated with EMS and DEX to test their effects on cell viability and myotube formation (myotube diameter and fusion index), and analyze the expression of muscle strengthening or degrading protein markers. Schisandra chinensis Extract, a common functional ingredient, was used as a positive control. EMS did not show any cytotoxic effect at all treatment concentrations. Moreover, it exerted protective effects on C2C12 cell-based model of DEX-induced muscle atrophy at all concentrations. In addition, the positive effect of EMS on myotube formation was confirmed based on the measurement and comparison of the fusion index and myotube diameter when compared with myotubes treated with DEX alone. EMS treatment reduced the expression of muscle cell degradation-related proteins Fbx32 and MuRF1, and increased the expression of muscle strengthening and synthesis related proteins SIRT1 and pAkt/Akt. Thus, EMS is a potential ingredient for developing functional health foods and should be further evaluated in preclinical models.

• Journal of Ginseng Research
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• v.47 no.3
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• pp.408-419
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• 2023

Background: Ginsenoside compound K (CK), the main active metabolite in Panax ginseng, has shown good safety and bioavailability in clinical trials and exerts neuroprotective effects in cerebral ischemic stroke. However, its potential role in the prevention of cerebral ischemia/reperfusion (I/R) injury remains unclear. Our study aimed to investigate the molecular mechanism of ginsenoside CK against cerebral I/R injury. Methods: We used a combination of in vitro and in vivo models, including oxygen and glucose deprivation/reperfusion induced PC12 cell model and middle cerebral artery occlusion/reperfusion induced rat model, to mimic I/R injury. Intracellular oxygen consumption and extracellular acidification rate were analyzed by Seahorse multifunctional energy metabolism system; ATP production was detected by luciferase method. The number and size of mitochondria were analyzed by transmission electron microscopy and MitoTracker probe combined with confocal laser microscopy. The potential mechanisms of ginsenoside CK on mitochondrial dynamics and bioenergy were evaluated by RNA interference, pharmacological antagonism combined with co-immunoprecipitation analysis and phenotypic analysis. Results: Ginsenoside CK pretreatment could attenuate mitochondrial translocation of DRP1, mitophagy, mitochondrial apoptosis, and neuronal bioenergy imbalance against cerebral I/R injury in both in vitro and in vivo models. Our data also confirmed that ginsenoside CK administration could reduce the binding affinity of Mul1 and Mfn2 to inhibit the ubiquitination and degradation of Mfn2, thereby elevating the protein level of Mfn2 in cerebral I/R injury. Conclusion: These data provide evidence that ginsenoside CK may be a promising therapeutic agent against cerebral I/R injury via Mul1/Mfn2 mediated mitochondrial dynamics and bioenergy.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.297-303
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• 2023

Pediatric Computed Tomography (CT) examinations can often result in exam failures or the need for frequent retests due to the difficulty of cooperation from young patients. Deep Learning Image Reconstruction (DLIR) methods offer the potential to obtain diagnostically valuable images while reducing the retest rate in CT examinations of pediatric patients with high radiation sensitivity. In this study, we investigated the possibility of applying DLIR to reduce artifacts caused by respiration or motion and obtain clinically useful images in pediatric chest CT examinations. Retrospective analysis was conducted on chest CT examination data of 43 children under the age of 7 from P Hospital in Gyeongsangnam-do. The images reconstructed using Filtered Back Projection (FBP), Adaptive Statistical Iterative Reconstruction (ASIR-50), and the deep learning algorithm TrueFidelity-Middle (TF-M) were compared. Regions of interest (ROI) were drawn on the right ascending aorta (AA) and back muscle (BM) in contrast-enhanced chest images, and noise (standard deviation, SD) was measured using Hounsfield units (HU) in each image. Statistical analysis was performed using SPSS (ver. 22.0), analyzing the mean values of the three measurements with one-way analysis of variance (ANOVA). The results showed that the SD values for AA were FBP=25.65±3.75, ASIR-50=19.08±3.93, and TF-M=17.05±4.45 (F=66.72, p=0.00), while the SD values for BM were FBP=26.64±3.81, ASIR-50=19.19±3.37, and TF-M=19.87±4.25 (F=49.54, p=0.00). Post-hoc tests revealed significant differences among the three groups. DLIR using TF-M demonstrated significantly lower noise values compared to conventional reconstruction methods. Therefore, the application of the deep learning algorithm TrueFidelity-Middle (TF-M) is expected to be clinically valuable in pediatric chest CT examinations by reducing the degradation of image quality caused by respiration or motion.

• Korean Journal of Heritage: History & Science
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• v.55 no.4
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• pp.180-193
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• 2022

The East Nine Royal Tombs is a representative place in the Royal Tombs of Joseon (a World Heritage Site). It consists of 1,289 stone artifacts including 979 related stone structures, 310 stone statues, and objects. Most of the stone structures in the East Nine Royal Tombs are composed of biotite granite, but some tombs are composed of light red granite. As a result of magnetic susceptibility measurement, the average data from Geonwolleung to Mongneung, excluding Hyeolleung, were similar, so it is estimated that stones were obtained from the same quarry. In the case of Sungneung, Sureung, and Gyeongneung, the range of susceptibility measurement is widely distributed. It assumed that the newly produced stones were mixed in the moving and construction process. Also, stones might be gathered from different quarries. As a result of a conservation status investigation, both the mound member and the ridge stone had the highest damage rate due to peeling and granular decomposition according to surface weathering. In the case of surface discoloration, yellowing and soils were found in the burial mound members. Yellowing, blackening, and soil were identified in the ridge stone structures. Bio-degradation is the major factor of deterioration of the East Nine Royal Tombs and the conservation status of the tombs were detected as grades 4 to 5. It seems that it is easy for the environment of the royal tombs to form soil for the microorganisms and fine conditions for continuous moisture. In the case of structures, they are in relatively good condition. As a result of a comprehensive damage rating for each tomb, the overall condition is good, but the Geonwolleung Royal Tomb and Hyeolleung Tomb, which were created in the early period, had relatively high weathering ratings. Stone objects in East Nine Royal Tombs have lost many pieces and gateway members due to surface deterioration. Also, secondary damage is ongoing. Each damage factor of the stone artifacts of the East Nine Royal Tombs combines to cause various and continuous damages. Therefore, it is necessary to establish regular conservation status data of the stone artifacts for efficient management after processing as well as conservation treatment of the royal tombs, and specific management manuals and systems. This study investigated the conservation status of stone structures in the East Nine Royal Tombs, a World Heritage Site, and systematically classified them to provide priority and necessity for conservation processing. We look forward to establishing a plan for the conservation and management of the East Nine Royal Tombs with this database in the future.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.297-309
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• 2023

The degradation of coastal ecosystems and fishery environments is accelerating due to the recent phenomenon of invertebrate grazers. To effectively monitor and implement preventive measures for this phenomenon, the adoption of remote sensing-based monitoring technology for extensive maritime areas is imperative. In this study, we compared and analyzed the robustness of deep learning-based object detection modelsfor detecting and monitoring invertebrate grazersfrom underwater videos. We constructed an image dataset targeting seven representative species of invertebrate grazers in the coastal waters of South Korea and trained deep learning-based object detection models, You Only Look Once (YOLO)v7 and YOLOv8, using this dataset. We evaluated the detection performance and speed of a total of six YOLO models (YOLOv7, YOLOv7x, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x) and conducted robustness evaluations considering various image distortions that may occur during underwater filming. The evaluation results showed that the YOLOv8 models demonstrated higher detection speed (approximately 71 to 141 FPS [frame per second]) compared to the number of parameters. In terms of detection performance, the YOLOv8 models (mean average precision [mAP] 0.848 to 0.882) exhibited better performance than the YOLOv7 models (mAP 0.847 to 0.850). Regarding model robustness, it was observed that the YOLOv7 models were more robust to shape distortions, while the YOLOv8 models were relatively more robust to color distortions. Therefore, considering that shape distortions occur less frequently in underwater video recordings while color distortions are more frequent in coastal areas, it can be concluded that utilizing YOLOv8 models is a valid choice for invertebrate grazer detection and monitoring in coastal waters.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3A
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• pp.297-307
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• 2010

Recently, maintenance engineering and technology for civil and building structures have begun to draw big attention and actually the number of structures that need to be evaluate on structural safety due to deterioration and performance degradation of structures are rapidly increasing. When stiffness is decreased because of deterioration of structures and member cracks, dynamic characteristics of structures would be changed. And it is important that the damaged areas and extent of the damage are correctly evaluated by analyzing dynamic characteristics from the actual behavior of a structure. In general, typical measurement instruments used for structure monitoring are dynamic instruments. Existing dynamic instruments are not easy to obtain reliable data when the cable connecting measurement sensors and device is long, and have uneconomical for 1 to 1 connection process between each sensor and instrument. Therefore, a method without attaching sensors to measure vibration at a long range is required. The representative applicable non-contact methods to measure the vibration of structures are laser doppler effect, a method using GPS, and image processing technique. The method using laser doppler effect shows relatively high accuracy but uneconomical while the method using GPS requires expensive equipment, and has its signal's own error and limited speed of sampling rate. But the method using image signal is simple and economical, and is proper to get vibration of inaccessible structures and dynamic characteristics. Image signals of camera instead of sensors had been recently used by many researchers. But the existing method, which records a point of a target attached on a structure and then measures vibration using image processing technique, could have relatively the limited objects of measurement. Therefore, this study conducted shaking table test and field load test to verify the validity of the method that can measure multi-point displacement responses of structures using image processing technique.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.497-508
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• 2023

Agricultural reservoirs are crucial structures for water resources monitoring especially in Korea where the resources are seasonally unevenly distributed. Optical and Synthetic Aperture Radar (SAR) satellites, being utilized as tools for monitoring the reservoirs, have unique limitations in that optical sensors are sensitive to weather conditions and SAR sensors are sensitive to noises and multiple scattering over dense vegetations. In this study, we tried to improve water body detection accuracy through optical-SAR data fusion, and quantitatively analyze the complementary effects. We first detected water bodies at Edong, Cheontae reservoir using the Compact Advanced Satellite 500(CAS500), Kompsat-3/3A, and Sentinel-2 derived Normalized Difference Water Index (NDWI), and SAR backscattering coefficient from Sentinel-1 by K-means clustering technique. After that, the improvements in accuracies were analyzed by applying K-means clustering to the 2-D grid space consists of NDWI and SAR. Kompsat-3/3A was found to have the best accuracy (0.98 at both reservoirs), followed by Sentinel-2(0.83 at Edong, 0.97 at Cheontae), Sentinel-1(both 0.93), and CAS500(0.69, 0.78). By applying K-means clustering to the 2-D space at Cheontae reservoir, accuracy of CAS500 was improved around 22%(resulting accuracy: 0.95) with improve in precision (85%) and degradation in recall (14%). Precision of Kompsat-3A (Sentinel-2) was improved 3%(5%), and recall was degraded 4%(7%). More precise water resources monitoring is expected to be possible with developments of high-resolution SAR satellites including CAS500-5, developments of image fusion and water body detection techniques.