• Journal of Yeungnam Medical Science
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• v.22 no.2
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• pp.221-240
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• 2005

Background: Doxorubicin has proved to be a useful chemotherapeutic agent especially for osteogenic sarcoma. It induces cancer cell death via apoptosis. Materials and Methods: To explore and analyze the changes of gene expression during doxorubicin induced apoptosis on human osteogenic sarcoma, Saos-2 cell, cDNA microarray was performed. After treatment with doxorubicin, total RNA was purified and expressed genes were investigated with a 17k human cDNA microarray. Results: For analysis of the cDNA microarray, the genes were filtered using the sum of the median value of Cy3 and Cy5 signal intensity of greater than 800. Expression of 264 genes was changed by more than 2 fold, and the expression of 35 genes was changed more than 3 fold after treatment with doxorubicin. The genes were primarily related to cell death, cell growth and maintenance, signal transduction, cellular component, transport, and metabolism. Conclusion: Treatment with doxorubicin induced expressional change of many genes. Some of the genes might be related with apoptosis directly or indirectly. Further study is now needed to characterize these genes.

• Kidney Research and Clinical Practice
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• v.36 no.1
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• pp.48-57
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• 2017

Background: Hepatic steatosis measured with controlled attenuation parameter (CAP) using transient elastography predicts metabolic syndrome in the general population. We investigated whether CAP predicted metabolic syndrome in chronic kidney disease patients. Methods: CAP was measured with transient elastography in 465 predialysis chronic kidney disease patients (mean age, 57.5 years). Results: The median CAP value was 239 (202-274) dB/m. In 195 (41.9%) patients with metabolic syndrome, diabetes mellitus was more prevalent (105 [53.8%] vs. 71 [26.3%], P < 0.001), with significantly increased urine albumin-to-creatinine ratio (184 [38-706] vs. 56 [16-408] mg/g Cr, P = 0.003), high sensitivity C-reactive protein levels (5.4 [1.4-28.2] vs. 1.7 [0.6-9.9] mg/L, P < 0.001), and CAP (248 [210-302] vs. 226 [196-259] dB/m, P < 0.001). In multiple linear regression analysis, CAP was independently related to body mass index (${\beta}=0.742$, P < 0.001), triglyceride levels (${\beta}=2.034$, P < 0.001), estimated glomerular filtration rate (${\beta}=0.316$, P = 0.001), serum albumin (${\beta}=1.386$, P < 0.001), alanine aminotransferase (${\beta}=0.064$, P = 0.029), and total bilirubin (${\beta}=-0.881$, P = 0.009). In multiple logistic regression analysis, increased CAP was independently associated with increased metabolic syndrome risk (per 10 dB/m increase; odds ratio, 1.093; 95% confidence interval, 1.009-1.183; P = 0.029) even after adjusting for multiple confounding factors. Conclusion: Increased CAP measured with transient elastography significantly correlated with and could predict increased metabolic syndrome risk in chronic kidney disease patients.

• Journal of Gastric Cancer
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• v.21 no.3
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• pp.268-278
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• 2021

Purpose: While several prognostic models for the stratification of death risk have been developed for patients with advanced gastric cancer receiving first-line chemotherapy, they have seldom been tested in the Chinese population. This study investigated the performance of these models and identified the optimal tools for Chinese patients. Materials and Methods: Patients diagnosed with metastatic or recurrent gastric adenocarcinoma who received first-line chemotherapy were eligible for inclusion in the validation cohort. Their clinical data and survival outcomes were retrieved and documented. Time-dependent receiver operating characteristic (ROC) and calibration curves were used to evaluate the predictive ability of the models. Kaplan-Meier curves were plotted for patients in different risk groups divided by 7 published stratification tools. Log-rank tests with pairwise comparisons were used to compare survival differences. Results: The analysis included a total of 346 patients with metastatic or recurrent disease. The median overall survival time was 11.9 months. The patients were different into different risk groups according to the prognostic stratification models, which showed variability in distinguishing mortality risk in these patients. The model proposed by Kim et al. showed relative higher predicting abilities compared to the other models, with the highest χ² (25.8) value in log-rank tests across subgroups, and areas under the curve values at 6, 12, and 24 months of 0.65 (95% confidence interval [CI]: 0.59-0.72), 0.60 (0.54-0.65), and 0.63 (0.56-0.69), respectively. Conclusions: Among existing prognostic tools, the models constructed by Kim et al., which incorporated performance status score, neutrophil-to-lymphocyte ratio, alkaline phosphatase, albumin, and tumor differentiation, were more effective in stratifying Chinese patients with gastric cancer receiving first-line chemotherapy.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.233-243
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• 2021

Photovoltaic panels are hazardous electronic waste that has heavy metal as one of the hazardous components. Each year, hazardous electronic waste is increasing worldwide and every heavy rainfall exposes the photovoltaic panel to become the source of heavy metal soil contamination. the development needs a monitoring technology for this hazardous exposure. this research use relationships between SAR temporal baseline and coherence of Sentinel-1 satellite to detected photovoltaic panel. Also, the photovoltaic plant detection tested using the difference between that photovoltaic panel and the other difference surface of coherence. The author tested the photovoltaic panel and its environment to calculate differences in coherence relationships. As a result of the experiment, the coherence of the photovoltaic panel, which is assumed to be a permanent scatterer, shows a bias that is biased toward a median value of 0.53 with a distribution of 0.50 to 0.65. Therefore, further research is needed to improve errors that may occur during processing. Additionally, the author found that the change detection using a temporal baseline is possible as the rate of reduction of coherence of photovoltaic panels differs from those of artificial objects such as buildings. This result could be an efficient way to continuously monitor regardless of weather conditions, which was a limitation of the existing optical satellite image-based photovoltaic panel detection research and to understand the spatial distribution in situations such as photovoltaic panel loss.

• Journal of Korean Society on Water Environment
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• v.35 no.4
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• pp.316-331
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• 2019

Carbon dioxide($CO_2$) emission from rivers to the atmosphere is a key component in the global carbon cycle. Most of the rivers are supersaturated with $CO_2$. At a global scale, the amount of $CO_2$ emission from rivers is reported to be five-fold greater than that from lakes and reservoirs, but relevant data are rare in Korea. The objectives of this study is to estimate the $CO_2$ net atmospheric flux(NAF) from the upstream of Gangjeong-Goryeong Weir(GGW), Dalseong Weir(DSW), Hapcheon-Changnyeong Weir(HCW), and Changnyeong-Haman Weir(CHW) located in Nakdong River South Korea) using field and laboratory experiments and to apply data mining techniques to develop parsimonious prediction models that can be used to estimate $CO_2$ NAF with physical and water quality variables that can be collected easily. As a result, the study sites were all heterotrophic systems that often released $CO_2$ to the atmosphere, except when the algal photosynthesis was active.The median $CO_2$ NAF was minimum $391.5mg-CO_2/m^2$ day at GGW and maximum $1472.7mg-CO_2/m^2$ day at DSW. The $CO_2$ NAF showed a negative correlation with pH and Chl-a since the overgrowth of the algae consumed $CO_2$ in the water and increased the pH. As the parsimonious multiple regression model and random forest model developed, this study showed an excellent performance with the $Adj.R^2$ value higher than 0.77 in all weirs. Thus, these methods can be used to estimate $CO_2$ NAF in the river even if there is no $pCO_2$ measurement data.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.493-503
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• 2019

This study presents a new method of estimating the size distribution of river bed gravel through image processing. The analysis was done in two steps; first the individual grain images were analyzed and then the grain particle segmentation of river-bed images were processed. In the first part of the analysis, the relationships (long axes, intermediate axes and projective areas) between grain features from images and those measured were compared. For this analysis, 240 gravel particles were collected at three river stations. All particles were measured with vernier calipers and weighed with scales. The measured data showed that river gravel had shape factors of 0.514~0.585. It was found that the weight of gravel had a stronger correlation with the projective areas than the long or intermediate axes. Using these results, we were able to establish an area-weight formula. In the second step, we calculated the projective areas of the river-bed gravels by detecting their edge lines using the ImageJ program. The projective areas of the gravels were converted to the grain-size distribution using the formula previously established. The proposed method was applied to 3 small- and medium- sized rivers in Korea. Comparisons of the analyzed size distributions with those measured showed that the proposed method could estimate the median diameter within a fair error range. However, the estimated distributions showed a slight deviation from the observed value, which is something that needs improvement in the future.

• Journal of IKEEE
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• v.22 no.4
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• pp.1140-1146
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• 2018

Recently, a methodology for analyzing complex bio-signals using a deep learning model has emerged among studies that recognize human emotions. At this time, the accuracy of emotion classification may be changed depending on the evaluation method and reliability depending on the kind of data to be learned. In the case of biological signals, the reliability of data is determined according to the noise ratio, so that the noise detection method is as important as that. Also, according to the methodology for defining emotions, appropriate emotional evaluation methods will be needed. In this paper, we propose a wavelet -based noise threshold setting algorithm for verifying the reliability of data for multimodal bio-signal data labeled Valence and Arousal and a method for improving the emotion recognition rate by weighting the evaluation data. After extracting the wavelet component of the signal using the wavelet transform, the distortion and kurtosis of the component are obtained, the noise is detected at the threshold calculated by the hampel identifier, and the training data is selected considering the noise ratio of the original signal. In addition, weighting is applied to the overall evaluation of the emotion recognition rate using the euclidean distance from the median value of the Valence-Arousal plane when classifying emotional data. To verify the proposed algorithm, we use ASCERTAIN data set to observe the degree of emotion recognition rate improvement.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.42.1-42.1
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• 2019

We observed 80 dense cores ($N(H_2)$ > $10^{22}cm^{-2}$) in the Orion molecular cloud complex which contains the Orion A (39 cores), B (26 cores), and ${\lambda}$ Orionis (15 cores) clouds. We investigate the behavior of the different molecular tracers and look for chemical variations of cores in the three clouds in order to systematically investigate the effects of stellar feedback. The most commonly detected molecular lines (with the detection rates higher than 50%) are $N_2H^+$, $HCO^+$, $H^{13}CO^+$, $C_2H$, HCN, and $H_2CO$. The detection rates of dense gas tracers, $N_2H^+$, $HCO^+$, $H^{13}CO^+$, and $C_2H$ show the lowest values in the ${\lambda}$ Orionis cloud. We find differences in the D/H ratio of $H_2CO$ and the $N_2H^+/HCO^+$ abundance ratios among the three clouds. Eight starless cores in the Orion A and B clouds exhibit high deuterium fractionations, larger than 0.10, while in the ${\lambda}$ Orionis cloud, no cores reveal the high ratio. These chemical properties could support that cores in the ${\lambda}$ Orionis cloud are affected by the photo-dissociation and external heating from the nearby H II region. An unexpected trend was found in the $[N_2H^+]/[HCO^+]$ ratio with a higher median value in the ${\lambda}$ Orionis cloud than in the Orion A/B clouds than; typically, the $[N_2H^+]/[HCO^+]$ ratio is lower in higher temperatures and lower column densities. This could be explained by a longer timescale in the prestellar stage in the ${\lambda}$ Orionis cloud, resulting in more abundant nitrogen-bearing molecules. In addition to these chemical differences, the kinematical difference was also found among the three clouds; the blue excess, which is an infall signature found in optically thick line profiles, is 0 in the ${\lambda}$ Orionis cloud while it is 0.11 and 0.16 in the Orion A and B clouds, respectively. This result could be another evidence of the negative feedback of active current star formation to the next generation of star formation.

• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.476-486
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• 2019

Objective : The functional information of $^{11}C$-methionine positron emission tomography (MET-PET) images can be applied for Gamma knife radiosurgery (GKR) and its image quality may affect defining the tumor. This study conducted the phantom-based evaluation for geometric accuracy and functional characteristic of diagnostic MET-PET image co-registered with stereotactic image in Leksell $GammaPlan^{(R)}$ (LGP) and also investigated clinical application of these images in metastatic brain tumors. Methods : Two types of cylindrical acrylic phantoms fabricated in-house were used for this study : the phantom with an array-shaped axial rod insert and the phantom with different sized tube indicators. The phantoms were mounted on the stereotactic frame and scanned using computed tomography (CT), magnetic resonance imaging (MRI), and PET system. Three-dimensional coordinate values on co-registered MET-PET images were compared with those on stereotactic CT image in LGP. MET uptake values of different sized indicators inside phantom were evaluated. We also evaluated the CT and MRI co-registered stereotactic MET-PET images with MR-enhancing volume and PET-metabolic tumor volume (MTV) in 14 metastatic brain tumors. Results : Imaging distortion of MET-PET was maintained stable at less than approximately 3% on mean value. There was no statistical difference in the geometric accuracy according to co-registered reference stereotactic images. In functional characteristic study for MET-PET image, the indicator on the lateral side of the phantom exhibited higher uptake than that on the medial side. This effect decreased as the size of the object increased. In 14 metastatic tumors, the median matching percentage between MR-enhancing volume and PET-MTV was 36.8% on PET/MR fusion images and 39.9% on PET/CT fusion images. Conclusion : The geometric accuracy of the diagnostic MET-PET co-registered with stereotactic MR in LGP is acceptable on phantom-based study. However, the MET-PET images could the limitations in providing exact stereotactic information in clinical study.

• Particle and aerosol research
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• v.16 no.4
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• pp.131-140
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• 2020

Air pollutants emitted from chimneys of coal-fired power plants are considered to be a major source of fine particulate matter in the atmosphere. In order to manage fine particle in the chimney of a coal-fired power plant, it is necessary to know the concentration of fine particle emitted in real time, but the current system is difficult. In this study, a real-time measurement system for chimney fine particle was developed, and measurements were performed on six coal-fired power plants. Through the measurements, the mass concentration distribution according to the particle size could be secured. All six chimneys showed bimodal distribution, and the count median diameters of each mode were 0.5 and 1.1 ㎛. In addition, it was compared with the gravimetric measurement method, and it was determined that the relative accuracy for PM10 was within 20%, and the value measured using the developed measuring instrument was reliable. Finally, three power plants were continuously measured for one month, and as a result of comparing the concentration of PM10 according to the amount of power generation, it was confirmed that the PM10 discharged from the chimney increased in the form of an exponential function according to the amount of power generation.