• Interdisciplinary Bio Central
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• v.3 no.4
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• pp.15.1-15.11
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• 2011

Background: Histone deacetylase (HDAC) 8 is one of its family members catalyzes the removal of acetyl groups from N-terminal lysine residues of histone proteins thereby restricts transcription factors from being expressed. Inhibition of HDAC8 has become an emerging and effective anti-cancer therapy for various cancers. Application computational methodologies may result in identifying the key components that can be used in developing future potent HDAC8 inhibitors. Results: Facilitating the discovery of novel and potential chemical scaffolds as starting points in the future HDAC8 inhibitor design, quantitative structure-activity relationship models were generated with 30 training set compounds using genetic function approximation (GFA) and Bayesian algorithms. Six GFA models were selected based on the significant statistical parameters calculated during model development. A Bayesian model using fingerprints was developed with a receiver operating characteristic curve cross-validation value of 0.902. An external test set of 54 diverse compounds was used in validating the models. Conclusions: Finally two out of six models based on their predictive ability over the test set compounds were selected as final GFA models. The Bayesian model has displayed a high classifying ability with the same test set compounds and the positively and negatively contributing molecular fingerprints were also unveiled by the model. The effectively contributing physicochemical properties and molecular fingerprints from a set of known HDAC8 inhibitors were identified and can be used in designing future HDAC8 inhibitors.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

• Biomolecules & Therapeutics
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• v.6 no.3
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• pp.289-295
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• 1998

The bioequivalence of two nilvadipine products was evaluated in 16 normal male volunteers (age 22-32 yr, body weight 57-80 kg) following sidle oral dose. Test product was Overca $l_{R}$ tablet (Choong-Wae Pharm. Corp., Korea) and reference product was Nivadi $l_{R}$ tablet (Hyundai Pharm. Corp., Korea). Both products contain 4 mg of nilvadipine. One tablet of the test or the reference product was administered to the volunteers, respectively, by randomized two period cross-over study (2$\times$2 Latin square method). The determination of nilvadipine was accomplished using a validated capillary column GC with electron-capture detection. As a result of the assay validation, the quantiflcation of nilvadipine in human plasma by this technique was possible down to 0.5 ng/ml using 1 ml of plasma. Absolute overall recovery from five replicate analyses of nilvadipine-spiked sample were 88.4$\pm$ 10.24% (mean$\pm$ 5.D.) for human plasma of 10 ng/ml. The coefficients of variation (C.V.) were less than 20% and the actual concentration of nilvadipine measured by GC ranged from 80 to 99% in all plasma. Average drug concentrations at each sampling time and pharmacokinetic parameters calculated were not significantly different between two products (p>0.05); the area under the curve from time zero to 8 hr (AUCo-$_{8 hr}$) (22.8$\pm$5.90 vs 22.2$\pm$6.10 ng . hr/ml), maximum plasma concentration ( $C_{max}$) (10.0$\pm$2.85 vs 9.3$\pm$3.28 ng/ml) and time to reach maximum plasma concentration ( $T_{max}$) (1.2$\pm$0.31 vs 1.3 $\pm$0.47 hr). The differences of mean AU $Co_{8hr}$ $C_{max}$, and $T_{max}$ between the two products (2.25, 7.65, and 10.30%, respectively) were less than 20%. The power (1-$\beta$) and treaeent difference (7) for AU $Co_{8hr}$, and $C_{max}$ were more than 0.8 and less than 0.2, respectively. Although the power for Tmax was under 0.8, Tm\ulcorner of the two products was not significantly different from each other (p>0. 05). These results suggest that the bioavailability of Overeat tablet is not significantly different from that of Nivadil tablet. Therefore, two products are bioequivalent based on the current results.sults.lts.lts.lts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.654-670
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• 2020

Workers at construction sites are easily exposed to many dangers and accidents involving falls, tripping, and missteps on stairs. However, researches on construction site monitoring system to prevent work-related injuries are still insufficient. The purpose of this study was to develop a wearable textile pressure insole sensor and examine its effectiveness in managing the real-time safety of construction workers. The sensor was designed based on the principles of parallel capacitance measurement using conductive textile and the monitoring system was developed by C# language. Three separate experiments were carried out for performance evaluation of the proposed sensor: (1) varying the distance between two capacitance plates to examine changes in capacitance charges, (2) repeatedly applying 1 N of pressure for 5,000 times to evaluate consistency, and (3) gradually increasing force by 1 N (from 1 N to 46 N) to test the linearity of the sensor value. Five subjects participated in our pilot test, which examined whether ascending and descending the stairs can be distinguished by our sensor and by weka assessment tool using k-NN algorithm. The 10-fold cross-validation method was used for analysis and the results of accuracy in identifying stair ascending and descending were 87.2% and 90.9%, respectively. By applying our sensor, the type of activity, weight-shifting patterns for balance control, and plantar pressure distribution for postural changes of the construction workers can be detected. The results of this study can be the basis for future sensor-based monitoring device development studies and fall prediction researches for construction workers.

• Journal of Korean Academy of Nursing Administration
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• v.14 no.3
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• pp.344-351
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• 2008

Purpose: The aim of this study was to testify whether clinical performance examination (CPX) was useful to evaluate comprehensive performance for nursing students just prior to graduation. Method: A cross-sectional descriptive study was designed to examine the usefulness. A total of 61 nursing students whose requirement credits were completed for graduation from a C University in G-city, at December, 5, 2007. Data were analyzed by Pearson's Correlation Coefficient and Spearman's rank Correlation Coefficient. Results: This study showed that both of the finals scores with paper and pens and the clinical practicum scores were not correlated with the CPX scores (r=-.031, p=.811; r=.028, p=.831). Consistency of scores between faculties and standardized patients was moderate (r=.752, p=.000). Conclusion: CPX was considered as a different and innovative evaluation from previous testing systems to test the various aspects of performance including knowledge, skill and attitude. Therefore, CPX under high raters' consistency was useful to test nursing students's final performance. Further study would be needed to develop the standard of CPX system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1718-1724
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• 2017

Recently, Smart plugs for real time monitoring of household appliances based on IoT(Internet of Things) have been activated. Through this, consumers are able to save energy by monitoring real-time energy consumption at all times, and reduce power consumption through alarm function based on consumer setting. In this paper, we measure the alternating current from a wall power outlet for real-time monitoring. At this time, the current pattern for each household appliance was classified and it was experimented with deep learning to determine which product works. As a result, we used a cross validation method and a bootstrap verification method in order to the classification performance according to the type of appliances. Also, it is confirmed that the cost function and the learning success rate are the same as the train data and test data.

• Journal of Biosystems Engineering
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• v.27 no.2
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• pp.133-142
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• 2002

This is a fundamental study to develop a sensor to detect weeds in paddy field using machine vision adopted spectralphotometric technique in order to use the sensor to spread herbicide selectively. A set of spectral reflectance data was collected from dry and wet soil and leaves of rice and 6 kinds of weed to select desirable wavelengths to classify soil, rice and weeds. Stepwise variable selection method of discriminant analysis was applied to the data set and wavelengths of 680 and 802 m were selected to distinguish plants (including rice and weeds) from dry and wet soil, respectively. And wavelengths of 580 and 680 nm were selected to classify rice and weeds by the same method. Validity of the wavelengths to distinguish the plants from soil was tested by cross-validation test with built discriminant function to prove that all of soil and plants were classified correctly without any failure. Validity of the wavelengths for classification of rice and weeds was tested by the same method and the test resulted that 98% of rice and 83% of weeds were classified correctly. Feasibility of CCD color camera to detect weeds in paddy field was tested with the spectral reflectance data by the same statistical method as above. Central wavelengths of RGB frame of color camera were tried as tile effective wavelengths to distingush plants from soil and weeds from plants. The trial resulted that 100% and 94% of plants in dry soil and wet soil, respectively, were classified correctly by the central wavelength or R frame only, and 95% of rice and 85% of weeds were classified correctly by the central wavelengths of RGB frames. As a result, it was concluded that CCD color camera has good potential to be used to detect weeds in paddy field.

• Journal of Korean Critical Care Nursing
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• v.12 no.2
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• pp.1-12
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• 2019

Purpose : The purpose of this study was to validate the Korean version of Partners In Health scale (PIH-K) which is used to measure the self-management of patients with chronic illnesses in Korea. Methods : Translation of the 12-item PIH-K was conducted according to the World Health Organization guidelines. Data from 306 participants who took medicines over 3 months by doctor's prescription were collected from October to November 2017. Validity such as content validity, construct validity, and concurrent validity were conducted using content validity index (CVI), exploratory and confirmatory factor analyses (CFA). To evaluate concurrent validity, the correlation coefficients between the PIH-K and concurrent scales (Self-As-Carer Inventory) were calculated. The reliability of the PIH-K was examined using the internal consistency and test-retest reliability tests. Results : The CVI of the PIH-K was 0.91. According to the CFA, factor loadings for four factors ranged from .64 to .97, which explained 67.5% of the total variance. The PIH-K was significantly correlated with concurrent variables such as those on the Self-As-Carer Inventory. The Cronbach's ${\alpha}$ was .86 and the intraclass correlation coefficient for the two-week test-retest reliability was .88. Conclusion : Findings show that the PIH-K is reliable and valid in measuring self-management of patients with chronic illnesses.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.153-166
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• 2021

A main goal of pharmacogenomics studies is to predict individual's drug responsiveness based on high dimensional genetic variables. Due to a large number of variables, feature selection is required in order to reduce the number of variables. The selected features are used to construct a predictive model using machine learning algorithms. In the present study, we applied several hybrid feature selection methods such as combinations of logistic regression, ReliefF, TurF, random forest, and LASSO to a next generation sequencing data set of 400 epilepsy patients. We then applied the selected features to machine learning methods including random forest, gradient boosting, and support vector machine as well as a stacking ensemble method. Our results showed that the stacking model with a hybrid feature selection of random forest and ReliefF performs better than with other combinations of approaches. Based on a 5-fold cross validation partition, the mean test accuracy value of the best model was 0.727 and the mean test AUC value of the best model was 0.761. It also appeared that the stacking models outperform than single machine learning predictive models when using the same selected features.

• Geomechanics and Engineering
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• v.27 no.6
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• pp.551-560
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• 2021

Due to the low strength and high compressibility characteristics, the loess deposits tunnels are prone to large deformations and collapse. An accurate stability evaluation for loess deposits is of considerable significance in deformation control and safety work during tunnel construction. 37 groups of representative data based on real loess deposits cases were adopted to establish the stability evaluation model for the tunnel project in Yan'an, China. Physical and mechanical indices, including water content, cohesion, internal friction angle, elastic modulus, and poisson ratio are selected as index system on the stability level of loess. The data set is randomly divided into 80% as the training set and 20% as the test set. Firstly, principal component analysis (PCA) is used to convert the five index system to three linearly independent principal components X1, X2 and X3. Then, the principal components were used as input vectors for probabilistic neural network (PNN) to map the nonlinear relationship between the index system and stability level of loess. Furthermore, Leave-One-Out cross validation was applied for the training set to find the suitable smoothing factor. At last, the established model with the target smoothing factor 0.04 was applied for the test set, and a 100% prediction accuracy rate was obtained. This intelligent classification method for loess deposits can be easily conducted, which has wide potential applications in evaluating loess deposits.