• Archives of Plastic Surgery
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• v.32 no.4
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• pp.503-510
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• 2005

If we could predict the necrosis of the flap caused by reperfusion injury, we can minimize the necrosis of the flap by taking appropriate action before necrosis begins. In this study, we examined whether we can predict the survival of flap under reperfusion injury or not, by measuring laser doppler flow meter values. We divided the group into the control and experimental groups corresponding to 6, 8, 9, 10, and 12hours after reperfusion(hours after ligation of auricular central artery). In each group, we examined necrotic change, perfusion unit (PU), serum superoxide dismutase (SOD), glutathione peroxidase, angiography and pathologic findings. No necrosis was observed in the 6 and 8 hours group but 8, 18, 20 hours after ligation, necrosis was observed, Also in each of 9, 10 and 12 hours group (each group consisted of 20 flaps), necrosis were noted. According to the above data, the critical time of necrosis in the auricular skin flap model lies between about 8 to 9 hours. Comparing the PU between the necrosis and non-necrosis groups, the former group showed a mean 39.57 PU increase after 60 min of reperfusion, and the latter group showed a mean increase of 21.21 PU. We can conclude that better flow can dilute oxygen free radical into systemic circulation, and this means less injuries are caused on vessels. Our study implies that if blood flow increase is less than 30 PU, intensive care is needed to save the flap. Additionally, we found significant decrease of serum SOD and glutathione peroxidase in the necrotic group. Therefore, monitoring these serum markers will be helpful in predicting reperfusion injury and supplementing these enzymes could be helpful to save the flap. The laser doppler flow meter is thought to be helpful in clinical circumstances for evaluating the circulation of the flap after the operation. However, more accumulation of clinical studies should be necessary establishing useful clinical data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.581-588
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• 2017

This study was conducted to determine which factors would predict metabolic syndrome (MetS) perception and exercise by applying a machine learning classifier, or Extreme Gradient Boosting algorithm (XGBoost) from July 2014 to December 2015. Data were obtained from the Korean Community Health Survey (KCHS), representing different community-dwelling Korean adults 19 years and older, from 2009 to 2013. The dataset includes 370,430 adults. Outcomes were categorized as follows based on the perception of MetS and physical activity (PA): Stage 1 (no perception, no PA), Stage 2 (perception, no PA), and Stage 3 (perception, PA). Features common to all questionnaires for the last 5 years were selected for modeling. Overall, there were 161 features, categorical except for age and the visual analogue scale (EQ-VAS). We used the Extreme Boosting algorithm in R programming for a model to predict factors and achieved prediction accuracy in 0.735 submissions. The top 10 predictive factors in Stage 3 were: age, education level, attempt to control weight, EQ mobility, nutrition label checks, private health insurance, EQ-5D usual activities, anti-smoking advertising, EQ-VAS, education in health centers for diabetes, and dental care. In conclusion, the results showed that XGBoost can be used to identify factors influencing disease prevention and management using healthcare bigdata.

• Korean Journal of Environmental Agriculture
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• v.36 no.4
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• pp.293-298
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• 2017

BACKGROUND: Pyrethroids (PYRs) are a widely used insecticide in agriculture and household area. In mammals, PYRs such as deltamethrin is metabolized to 3-phenoxybenzoic acid (3-PBA) in liver that is mainly excreted in urine. This study is designed to single exposure of deltamethrin to rats in a dose-dependent manner and identify the correlation between deltamethrin exposure and its metabolite (3-PBA) in urine. METHODS AND RESULTS: Exposure levels of deltamethrin were control (0 mg/kg bw), low (0.0705 mg/kg bw), medium (0.705 mg/kg bw) and high (7.05 mg/kg bw) dose. Low concentration was derived by ussing Korea predictive operator exposure model (KoPOEM). Dermal exposure persisted for 6 h, and urine specimens were collected for 24 h. The urine matrix was removed after a series of procedures and 3-PBA was analyzed by gas chromatography/mass spectrometry. CONCLUSION: There was a strong correlation ($R^2=0.83$) between the amount of oral exposure to delta me thrin and urinary levelof3-PBAexcreted. In dermal exposure groups of deltamethrin except high-dose, also there was a good correlation between urinary 3-PBA and deltamethrin exposure, but not stronger than in oral deltamethrin exposure groups. Based on these results, therefore, the amount of 3-PBA in urine can be used as a good monitoring indicator that reflexing the exposure level of deltamethrin to human body.

• Journal of Korean Society on Water Environment
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• v.28 no.5
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• pp.704-716
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• 2012

This paper provides a detailed account of pilot testing conducted at South Lake Tahoe (California), the Ddukdo (Seoul) water treatment plant (WTP) and the Bokjung (Seongnam) WTP between February, 2010, and February, 2012. The objectives were first, to characterize the reactions of ozone with hydrogen peroxide (Peroxone) for Han River water following sand filtration, second to determine empirical ozone and hydrogen peroxide doses to remove a taste-and-odor surrogate 2-methylisoborneol (MIB) using an advanced oxidation process (AOP) configuration and third, to determine the optimum dosing configuration to reduce residual ozone to a safe level at the exit of the process. The testing was performed in a real-time plant environment at both low- and high seasonal water temperatures. Experimental results including ozone decomposition rates were dependent on temperature and pH, consistent with data reported by other researchers. MIB in post-sand-filtration water was spiked to 40-50 ng/L, and in all cases, it was reduced to below the specified target level (7 ng/liter) and typically non-detect (ND). It was demonstrated that Peroxone could achieve both MIB removal and low effluent ozone residual at ozone+hydrogen peroxide doses less than those for ozone alone. An empirical predictive model, suitable for use by design engineers and operating personnel and for incorporation in plant control systems was developed. Due to a significant reduction in the ozone reaction/decomposition at low winter temperatures, results demonstrate the hydrogen peroxide can be "pre-conditioned" in order to increase initial reaction rates and achieve lower ozone residuals. Results also indicate the method, location and composition of hydrogen peroxide injection is critical to successful implementation of Peroxone without using excessive chemicals or degrading performance.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.

• Journal of Food Hygiene and Safety
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• v.24 no.3
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• pp.232-237
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• 2009

This study was conducted to develop a model for describing the effect of storage temperature (4, 10, 15, 20, 25, 30 and $35^{\circ}C$) on the growth of Escherichia coli O157 : H7 in ready-to-eat (RTE) lettuce treated with or without (control) alkaline electrolyzed water (AIEW). The growth curves were well fitted with the Gompertz equation, which was used to determine the specific growth rate (SGR) and lag time (LT) of E. coli O157 : H7 ($R^2$ = 0.994). Results showed that the obtained SGR and LT were dependent on the storage temperature. The growth rate increased with increasing temperature from 4 to $35^{\circ}C$. The square root models were used to evaluate the effect of storage temperature on the growth of E. coli O157 : H7 in lettuce samples treated without or with AIEW. The coefficient of determination ($R^2$), adjusted determination coefficient ($R^2_{Adj}$), and mean square error (MSE) were employed to validate the established models. It showed that $R^2$ and $R^_{Adj}$ were close to 1 (> 0.93), and MSE calculated from models of untreated and treated lettuce were 0.031 and 0.025, respectively. The results demonstrated that the overall predictions of the growth of E. coli O157: H7 agreed with the observed data.

• Journal of Life Science
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• v.24 no.12
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• pp.1339-1344
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• 2014

Inflammatory bowel disease is an immune disorder associated with chronic mucosal inflammation and severe ulceration in the gastrointestinal tract. Antibodies against proinflammatory cytokines, including TNF${\alpha}$, are currently used as promising therapeutic agents against the disease. Stabilization of the transcript is a crucial post-transcriptional process in the expression of proinflammatory cytokines. In the present study, we assessed the expression and histological distribution of the HuR protein, an important transcript stabilizer, in tissues from experimental animals and patients with Crohn's disease. The total and cytosolic levels of the HuR protein were enhanced in the intestinal epithelia from dextran sodium sulfate (DSS)-treated mice compared to those in control tissues from normal mice. Moreover, the expression of HuR was very high only in the mucosal and glandular epithelium, and the relative localization of the protein was sequestered in the lower parts of the villus during the DSS insult. The expression of HuR was significantly higher in mucosal lesions than in normal-looking areas. Consistent with the data from the animal model, the expression of HuR was confined to the mucosal and glandular epithelium. These results suggest that HuR may contribute to the post-transcriptional regulation of proinflammatory genes during early mucosal insults. More mechanistic investigations are warranted to determine the potential use of HuR as a predictive biomarker or a promising target against IBD.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Sleep Medicine and Psychophysiology
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• v.21 no.1
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• pp.5-13
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• 2014

Objectives: Depression, sleep complaints and cognitive impairments are commonly observed in the elderly. Elderly subjects with depressive symptoms have been found to show both poor cognitive performances and sleep disturbances. However, the relationship between sleep complaints and cognitive dysfunction in elderly depression is not clear. The aim of this study is to identify the association between sleep disturbances and cognitive decline in late-life depression. Methods: A total of 282 elderly people who underwent nocturnal polysomnography in a sleep laboratory were enrolled in the study. The Korean version of the Neuropsychological Assessment Battery developed by the Consortium to Establish a Registry for Alzheimer's Disease (CERAD-K) was applied to evaluate cognitive function. Depressive symptoms were assessed with the geriatric depression scale (GDS) and subjective sleep quality was measured using the Pittsburg sleep quality index (PSQI). Results: The control group ($GDS{\leq}9$) when compared with mild ($10{\leq}GDS{\leq}16$) and severe ($17{\leq}GDS$) depression groups, had significantly different scores in the Trail making test part B (TMT-B), Benton visual retention test part A (BVRT-A), and Stroop color and word test (SCWT)(all tests p<0.05). The PSQI score, REM sleep duration, apnea-hypopnea index and oxygen desaturation index were significantly different across the three groups (all indices, p<0.05). A stepwise multiple regression model showed that educational level, age and GDS score were predictive for both TMT-B time (adjusted $R^2$=35.6%, p<0.001) and BVRT-A score (adjusted $R^2$=28.3%, p<0.001). SCWT score was predicted by educational level, age, apnea-hypopnea index (AHI) and GDS score (adjusted $R^2$=20.6%, p<0.001). Poor sleep quality and sleep structure alterations observed in depression did not have any significant effects on cognitive deterioration. Conclusion: Older adults with depressive symptoms showed mild sleep alterations and poor cognitive performances. However, we found no association between sleep disturbances (except sleep apnea) and cognitive difficulties in elderly subjects with depressive symptoms. It is possible that the impact of sleep disruptions on cognitive abilities was hindered by the confounding effect of age, education and depressive symptoms.