• Journal of Environmental Health Sciences
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• v.46 no.4
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• pp.368-375
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• 2020

Objectives: Some animal studies have reported that methyl mercury causes developmental toxicities such as placental and fetal weight loss, but the mechanism is still unclear. This study aimed to investigate the developmental toxicities of methyl mercury, focusing on placental endocrine function and fetal growth retardation in rats. Methods: Positively same-time-mated female Sprague-Dawley rats were purchased on gestational day (GD) eight and treated with 0, 5, 10 and 20 ppm of methyl mercury (n=5) dissolved in tap water from GD eight through 19. During treatment, the drinking water (methyl mercury) intake and body weight of each pregnant rat was measured daily. On day 19, caesarean sections were performed and blood samples were collected. Developmental data such as placental and fetal weights, fetus numbers, and placental efficiency (fetal weight/placental weight) were also collected. Placental prolactin-growth hormone (PRL-GH) family, such as placental lactogen (PL) -Iv, II, and prolactin-like protein (PLP) -B, levels in serum were analyzed by ELISA. Also, placental tissues were assigned to histochemistry. Results: The mean cumulative methyl mercury exposure for the 5, 10, and 20 ppm groups were 2.37, 4.63, and 9.66 mg, respectively. The mean daily exposure of the 5, 10, and 20 ppm groups were 0.24, 0.47, and 0.97 mg, respectively. Maternal body weight increased in accordance with GD. There was no significant difference in weight gain among the experimental groups. Histopathologic changes were not observed in placental tissues among the experimental groups. However, mean placental and fetal weights were lower in the 10 and 20 ppm exposed groups compared to the control. Placental efficiency was also lower in the 10 and 20 ppm exposed groups compared to the control. Serum PL-Iv and II levels were lower in the 10 and 20 ppm exposed groups than the control, in accordance with the changing pattern of placental and fetal weights and placental efficiency. Conclusion: The inhibitory effects of methyl mercury on the serum levels of placental PRL-GH family such as PL-Iv and II may be secondary leads to the reduction of placental efficiency and fetal growth retardation in rats.

• Toxicological Research
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• v.16 no.1
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• pp.33-37
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• 2000

Hershberger assay is known as one of the in vivo-short-term scrrning assays for endocrine disrupting chemicals (EDCs), but this method is not a validated test system. In the present study, the establishment of Hershberger assay to detect EDCs was tried using a model substance, di(n-butyl)phthalate (DBP), a plasticizer for plastics. Thirty-six immature male rats were randomly assigned to six groups: DBP 0, 40, 200, and 1000mg/kg, a positive control (flutamide 20 mg/kg), and a combination group(DBP 1000mg/kg and testosterone 50 ug/kg). DBP and flutamide were administered by gavage to male rats from day 21 to 40 post partum. Testosterone was subcutaneously injected during the same period. We evaluated body weigth gain, weights of ventral prostate, seminal vesicle, and levator ani and bulvocavernous muscle, and serum concentrations of testosterone and lutenizing hormone in male rats. The weights of seminal vesicle and levator ani and bulvocavernous muscle of males receiving 1000mg/kg of DBP was significantly lower than controls. There was no effect of DBP-treatment on body weight gain, prostate weight, and hormone concentrations. In the positive control group, the weights of seminal vesicle and levator ani and bulvocavernous muscle of males receiving 20mg/kg of flutamide were significantly lower than controls. In the combination group, there was no effect of co-treatment of DBP and testosterone on all parameters effect against DBP. This method was found to be a useful short-term screening assay system for EDCs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5A
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• pp.433-440
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• 2007

In this paper, we have proposed WiBro receiver structure employing joint adaptive antenna scheme at the base station (BS) and the mobile station (MS) and evaluated its performance. In WiBro system, the joint use of transmit beamforming at BS and receive beamforming at MS can improve the SINR significantly and increase the capacity compared to the conventional system. Moreover, power allocation level can be decreased because channel variation can be reduced by using the jointly updated weight. However, it needsthe calculation of the antenna weights in every subcarrier for performance improvement. This could imply an enormous computational burden. However, the computational complexity can be reduced significantly by using the same set of the antenna weights for the adjacent subcarriers instead of calculating antenna weights for every subcarrier. We have analyzed the impact of subcarrier grouping for weight calculation on the system performance.

• Biomolecules & Therapeutics
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• v.8 no.1
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• pp.78-83
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• 2000

The objective of this study was to prevalidate the Organization for Economic Cooperation and Development's (OECD) rodent uterotrophic assay as a test method for screening of potential endocrine disrupting chemicals (EDCs). This study was conducted exactly as described in the OECD protocol documents. A positive control substance, 17$\alpha$-ethinyl estradiol (EE), was administered daily for three days to ovariectomized (OVX) Sprague-Dawley rats at various doses for determine the dose-response curve. Additionally, a pure antiestrogenic chemical, ZM189, 154 was administered to OVX rats at the same time EE to determine the effectiveness of the material against blocking the estrogenic effects of EE. At higher concentration of EE (10 $\mu\textrm{g}$/kg), a statistically significant difference in body weight gain and food consumption was observed compared to vehicle controls. In uterine responses, EE produced a dose-related increase in uterus weights compared to vehicle control. These increases were statistically significant at the >1.0 $\mu\textrm{g}$/kg doses. However, a similar dose-response relationship was not observed in vagina weight. A comparison of the two groups receiving ZM189,154 (0.1 and 1.0 mg/kg) with 0.3 $\mu\textrm{g}$/kg of EE and the group receiving only 0.3 $\mu\textrm{g}$/kg of EE showed dose-related decreases in uterus weights. However, statistical significance was shown in 1.0 mg/kg of ZM189,154. In conclusion, administration of EE produced a dose-related increase in uterine (wet and blotted) weights. Additionally, the 1.0mg/kg dose of ZM189,154 was effective in blocking the estrogenic activity of EE. These data suggest 3-day uterotrophic assay using OVX rats may serve as a good tool for EDCs screening.

• Journal of Physiology & Pathology in Korean Medicine
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• v.26 no.5
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• pp.693-698
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• 2012

This study was designed to investigate the effects of 1-fluoro-2,4 dinitrobenzene (DNFB) on Induction of dermatitis in mice. We investigated the effects of DNFB on induction of dermatitis in terms of changes in body weights, ear thickness, ear weight, spleen/body ratio, histopathological observation and cytokine productions in inflammed tissue of contact dermatitis (CD) mice. In our experiments, we induced CD by using two different methods. First, mice were sensitized and challenged on the back of each ear (topical induction). Second, mice were sensitized on shaved back and challenged back of each ear (systemic induction). In our results, average weights were lowered in both topical and systemic group. But, there was no statistical significance between topical and systemic group. Treatment with DNFB enlarged ear weights and thicknesses in both topical and systemic groups. In addition, both groups were showed almost same features such as immune cell infiltration, spongiosis and hyperplasia in histopathological observations. Finally production levels of TNF-a, IFN-g and IL-6 were markedly elevated in both topical and systemic groups. On the other hand, systemic induction was more effective in spleen/body ratio and IL-6 production compared to topical induction respectively. These results suggest that dermatitis model using DNFB was used as model of CD, not for atopic dermatitis model.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.4
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• pp.358-366
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• 2011

Multi-criteria ABC inventory classification, which aims to classify inventory items by considering more than one criterion, is one of the most widely employed techniques for inventory control. The weighted linear optimization (WLO) model proposed by Ramanathan (2006) solves the problem of multi-criteria ABC inventory classification by generating a set of criterion weights for each inventory item and assigning a normalized score to the item for ABC analysis. However, the WLO model has some limitations. First, many inventory items can share the same optimal score, which can hinder a precise classification of inventory items. Second, the model allows too much flexibility in weighting multiple criteria; each item is allowed to choose its own weights so that it can maximize its score. As a result, if an item dominates the others in terms of a certain criterion, it may be classified into a higher class regardless of other criteria by assigning an overwhelming weight to the criterion. Consequently, an item with a high value in an unimportant criterion and low values in others may be inappropriately classified as class A, leading to an inaccurate classification of inventory items. To overcome these shortcomings, we extend the WLO model by using the cross-efficiency method in data envelopment analysis. We claim that the proposed model can provide a more reasonable and accurate classification of inventory items by mitigating the adverse effect of flexibility in the choice of weights and yielding a unique ordering of inventory items.

• Journal of Korea Water Resources Association
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• v.48 no.9
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• pp.757-768
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• 2015

Recently, climate change has affected short time concentrated local rainfall and unexpected heavy rain which is increasingly causing life and property damage. In this research, arithmetic average analysis, weighted average analysis, and principal component analysis are used for predicting flood risk. This research is foundation for application of predicting flood risk based on annals of disaster and status of urban planning. Results obtained by arithmetic average analysis, weighted average analysis, and principal component analysis using many factors affect on flood are compared. In case of arithmetic average analysis, each factor has same weights though it is simple method. In case of weighted average analysis, correlation factors are complex by many variables and multicollinearty problem happen though it has different weights. For solving these problems, principal component analysis (PCA) is used because each factor has different weights and the number of variables is smaller than other methods by combining variables. Finally, flood risk assessment considering flood control ability and urban environment risk in former research is predicted.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3A
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• pp.199-207
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• 2009

In this paper, new vehicular load model is developed for reliability-based bridge design code. Rational load model and statistical properties of loads are important for developing reliability-based design code. In the previous paper, truck weight data collected at eight locations using WIM or BWIM system are analyzed to calculate the maximum truck weights for specified bridge lifetime. Probability distributions of upper 20% total truck weight are assumed as Extreme Type I (Gumbel Distribution) and 100 years maximum weights are estimated by linear regression. In this study, effects of multiple presence of trucks are analyzed. Probability of multiple presence of trucks are estimated and corresponding multiple truck weights are calculated using the same probability distribution function as in the previous paper. New vehicular live load model are proposed for span length from 10 m to 200 m. New model is compared with current Korean model and various load models of other countries.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.12
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• pp.1666-1673
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• 2011

The presence of sialoglycoproteins (SGPs) in the membranes from goat (Capra aegagrus hircus), buffalo (Bubalus bubalis bubalis) and pig (Sus scrofa domestica) erythrocytes was investigated by partial purification with a chloroform-methanol extraction method followed by Sodium dodecyl sulphate - Polyacrylamide gel electrophoresis in comparison to human (Homo sapiens) erythrocytes. The results show that mammalian erythrocytes possess clear differences in the SGPs numbers and molecular weights although all animals studied in this experiment are from the same class i.e. mammalia. The SGPs number in human, goat, buffalo and pig are four (PAS-1 to PAS-4), ten (PAS-GI to PAS-GX), seven (PAS-BI to PAS-BVII) and four (PAS-PI to PAS-IV) respectively as indicated by staining the polyacrylamide gel with sialoglycoprotein-specific Periodic acid-Schiff's (PAS) stain. The new SGPs could be observed only after the partial purification of membrane fractions named as PAS-HI with molecular weight (Mr) 190 kDa and PAS-HII 150 kDa in human, PAS-BIA in buffalo and PAS-PIA and PAS-PIVA in pig. The gels were also stained with Coomassie brilliant blue (CBB) and Silver stain to check the contamination of other membrane proteins in the purified fractions. The quantitative distribution of SGPs was also determined by densitometry. Present study indicates that there are some basic differences in mammalian erythrocyte membrane SGPs, especially with respect to their number and molecular weights indicating major structural variations.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.95-101
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• 1999

In this paper, a comparison between different methods to combine predictions from neural networks will be given. These methods are bagging, bumping, and balancing. Those are based on the analysis of the ensemble generalization error into an ambiguity term and a term incorporating generalization performances of individual networks. Neural Networks and AI machine learning models are prone to overfitting. A strategy to prevent a neural network from overfitting, is to stop training in early stage of the learning process. The complete data set is spilt up into a training set and a validation set. Training is stopped when the error on the validation set starts increasing. The stability of the networks is highly dependent on the division in training and validation set, and also on the random initial weights and the chosen minimization procedure. This causes early stopped networks to be rather unstable: a small change in the data or different initial conditions can produce large changes in the prediction. Therefore, it is advisable to apply the same procedure several times starting from different initial weights. This technique is often referred to as training ensembles of neural networks. In this paper, we presented a comparison of three statistical methods to prevent overfitting of neural network.