• Ocean Science Journal
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• v.42 no.2
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• pp.69-79
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• 2007

A vertical transect with 4 km length was established for the macrofaunal survey on the Chokchon macrotidal flat in Kyeonggi Bay, Incheon, Korea, 1994. Tidal elevation (m) and sediment mean grain size $(\phi)$ were inversely predicted by the transfer functions from the faunal assemblages. Three methods: weighted average using optimum value (WA), tolerance weighted version of the weighted average (WAT) and maximum likelihood calibration (MLC) were employed. Estimates of tidal elevation and mean grain size obtained by using the three different methods showed positively corresponding trends with the observations. The estimates of MLC were found to have the minimum value of sum of squares due to errors (SSE). When applied to the previous data $(1990\sim1992)$, each of three inference models exhibited high predictive power. This result implied there are visible relationships between species composition and faunas' critical environmental factors. Although a potential significance of the two major abiotic factors was re-affirmed, a weak tendency of biological interaction was detected from faunal distribution patterns across the flat. In comparison to the spatial and temporal patterns of the estimates, it was suggested that sediment characteristics were the primary factors regulating the distribution of macrofaunal assemblages, rather than tidal elevation, and the species composition may be sensitively determined by minute changes in substratum properties on a tidal flat.

• Toxicological Research
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• v.22 no.4
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• pp.423-429
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• 2006

Recent reports on the photocarcinogenicity and photogerotoxicity of many compounds led to an increasing awareness for the need of a standard approach to test for photogenotoxicity. The comet assay has been recently validated as a sensitive and specific test system for the quantification of DNA damage. Thus, the objectives of this study are to investigate the utility of photo-comet assay for detecting photo-mutagens, and to evaluate its ability to predict rodent photo-carcinogenicity. Photo-comet assays were performed using L5178Y $Tk^{+/-}$ mouse lymphoma cells on five test substances (8-methoxypsoralen, chlorpromazine, lomefloxacin, anthracene and retinoic acid) that demonstrated positive results in photocarcinogenicity tests. For the best discrimination between the test substance-mediated DNA damage and the undesirable DNA damage caused by direct UV absorption, a UV dose-response of the cells in the absence of the test substances was firstly fnalized. Out of 5 test substances, positive comet results were obtained for chlorpromazine, lomefloxacin, anthracene and retinoic acid while 8-methoxypsoralen found negative. An investigation into the predictive value of this photo-comet assay for determining the photocarcinogenicity showed that photo-comet assay has relatively high sensitivity. Therefore, the photo-comet assay with mammalian cells seems to be a good and sensitive predictor of the photocarcinogenic potential of new substances.

• Biomolecules & Therapeutics
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• v.3 no.3
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• pp.242-244
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• 1995

In vitro cell culture system has been proposed as a promising alternative model to in vivo skin irritation test. These studies were performed to screen the cytotoxicity effects of surfactants using normal human skin fibroblasts. Cell membrane integrity assessed by the leakage of lactate dehydrogenase (LDH) and mitochondrial integrity by MTT [3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium bromides reduction test were affected in a dose dependent manner. The irritation potential of surfactants to human skin patch test, and the changes of capillary permeability by rabbit intradermal safety test were assessed as in vivo methods. Our results suggest that LDH leakage assay and MTT reduction test using cultured human fibroblasts could be predictive for the irritancy of various surfactants in human, and LDH assay is superior correlated with in vivo test (r=0.886) to MTT test with in vivotest (r=0.757).

• Journal of Gastric Cancer
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• v.14 no.1
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• pp.1-6
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• 2014

Aerobic glycolysis has been the most important hypothesis in cancer metabolism. It seems to be related to increased bioenergetic and biosynthetic needs in rapidly proliferating cancer cells. To this end, F-18 fluorodeoxyglucose (FDG), a glucose analog, became widely popular for the detection of malignancies combined with positron emission tomography/computed tomography (PET/CT). Although the potential roles of FDG PET/CT in primary tumor detection are not fully established, it seems to have a limited sensitivity in detecting early gastric cancer and mainly signet ring or non-solid types of advanced gastric cancer. In evaluating lymph node metastases, the location of lymph nodes and the degree of FDG uptake in primary tumors appear to be important factors affecting the diagnostic accuracy of PET/CT. In spite of the limited sensitivity, the high specificity of PET/CT for lymph node metastases may play an important role in changing the extent of lymphadenectomy or reducing futile laparotomies. For peritoneal metastases, PET/CT seems to have a poorer sensitivity but a better specificity than CT. The roles of PET/CT in the evaluation of other distant metastases are yet to be known. Studies including primary tumors with low FDG uptake or peritoneal recurrence seem suffer from poorer diagnostic performance for the detection of recurrent gastric cancer. There are only a few reports using FDG PET/CT to predict response to neoadjuvant or adjuvant chemotherapy. A complete metabolic response seems to be predictive of more favorable prognosis.

• Computers and Concrete
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• v.16 no.5
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

• Journal of the Korean housing association
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• v.22 no.3
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• pp.113-122
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• 2011

This study aimed to develop AI- (Artificial Intelligence) based thermal control logics and test their performance for identifying the optimal thermal control method in buildings. For this objective, a conventional Two-Position On/Off logic and two AI-based variable logics, which applied ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System), have developed. Performance of each logic was tested in a typical two-story residential building in U.S.A. using the computer simulation incorporating MATLAB and IBPT (International Building Physics Toolbox). In the analysis of the test results, AI-based control logic presented the advanced thermal comfort with stability compared to the conventional logic while they did not show significant energy saving effects. In conclusion, the predictive and adaptive AI-based control logics have a potential to maintain interior air temperature more comfortably, and the findings in this study could be a solid foundation for identifying the optimal thermal control method in buildings.

• Genomics & Informatics
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• v.6 no.3
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• pp.136-141
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• 2008

A large number of studies have been performed to identify biomarkers that will allow efficient detection and determination of the precise status of a patient’s disease. The use of microarrays to assess biomarker status is expected to improve prediction accuracies, because a whole-genome approach is used. Despite their potential, however, patient samples can differ with respect to biomarker status when analyzed on different platforms, making it more difficult to make accurate predictions, because bias may exist between any two different experimental conditions. Because of this difficulty in experimental standardization of microarray data, it is currently difficult to utilize microarray-based gene sets in the clinic. To address this problem, we propose a method that predicts disease status using gene expression data that are transformed by their ranks, a concept that is easily applied to two datasets that are obtained using different experimental platforms. NCI and colon cancer datasets, which were assessed using both Affymetrix and cDNA microarray platforms, were used for method validation. Our results demonstrate that the proposed method is able to achieve good predictive performance for datasets that are obtained under different experimental conditions.

• Journal of Environmental Health Sciences
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• v.35 no.1
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• pp.45-52
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• 2009

In recent years, pharmaceuticals in the aquatic environment have become a matter of increasing public concern. Environmental risk assessment (ERA), including an exposure assessment, is considered the best scientifically based approach for evaluating the potential effects of pharmaceuticals on ecosystems. Computerized exposure models constitute an important tool in predicting environmental exposures of pharmaceuticals. This paper presents the applicability of an exposure model by comparing measured data of selected pharmaceuticals with predicted environmental concentrations from an exposure model. $PhATE^{TM}$ (Pharmaceutical Assessment and Transport Evaluation) model developed by the Pharmaceutical Research and Manufacturers of America (PhRMA) was adapted to run simulations for the Keum River. A set of 7 pharmaceuticals of high production in Korea was modeled. The PECs generated by the $PhATE^{TM}$ model that were then compared to the measured concentrations. The $PhATE^{TM}$ model predicted concentrations for 7 pharmaceuticals including acetaminophen, acetylsalicylic acid, erythromycin, ibuprofen, lincomycin, mefenamic acid, and naproxen were in good agreement with actual measured concentrations, which demonstrated the utility of $PhATE^{TM}$ as a predictive tool. In conclusion, $PhATE^{TM}$, although it does not intend to accurately represent reality, could be utilized for rapid predictions of the environmental concentrations of pharmaceuticals.

• Bulletin of the Korean Chemical Society
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• v.30 no.1
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• pp.67-76
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• 2009

The development of a variety of methods like AM1, PM3, PM5 and DFT now allows the calculation of atomic and molecular properties with high precision as well as the treatment of large molecules with predictive power. In this paper, these methods have been used to calculate a number of quantum chemical descriptors (like Klopman atomic softness in terms of $E_n^{\ddag}\;and\;E_m^{\ddag}$, chemical hardness, global softness, electronegativity, chemical potential, electrophilicity index, heat of formation, total energy etc.) for 75 alkanes to predict their boiling point values. The 3D modeling, geometry optimization and semiempirical & DFT calculations of all the alkanes have been made with the help of CAChe software. The calculated quantum chemical descriptors have been correlated with observed boiling point by using multiple linear regression (MLR) analysis. The predicted values of boiling point are very close to the observed values. The values of correlation coefficient ($r^2$) and cross validation coefficient ($r_{cv}^2$) also indicates the generated QSPR models are valuable and the comparison of all the methods indicate that the DFT method is most reliable while the addition of Klopman atomic softness $E_n^{\ddag}$ in DFT method improves the result and provides best correlation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.26-35
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• 2005

This paper is intended to compare the hazard rate estimations from Bayesian approach and maximum likelihood estimate(MLE) method. Hazard rate frequently involves unknown parameters and it is common that those parameters are estimated from observed data by using MLE method. Such estimated parameters are appropriate as long as there are sufficient data. Due to various reasons, however, we frequently cannot obtain sufficient data so that the result of MLE method may be unreliable. In order to resolve such a problem we need to rely on the judgement about the unknown parameters. We do this by adopting the Bayesian approach. The first one is to use a predictive distribution and the second one is a method called Bayesian estimate. In addition, in the Bayesian approach, the prior distribution has a critical effect on the result of analysis, so we introduce the method using computerized-simulation to elicit an effective prior distribution. For the simplicity, we use exponential and gamma distributions as a likelihood distribution and its natural conjugate prior distribution, respectively. Finally, numerical examples are given to illustrate the potential benefits of the Bayesian approach.