• Journal of Korean Neurosurgical Society
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• v.63 no.3
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• pp.327-337
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• 2020

Junctional neurulation represents the most recent adjunct to the well-known sequential embryological processes of primary and secondary neurulation. While its exact molecular processes, occurring at the end of primary and the beginning of secondary neurulation, are still being actively investigated, its pathological counterpart -junctional neural tube defect (JNTD)- had been described in 2017 based on three patients whose well-formed secondary neural tube, the conus, is widely separated from its corresponding primary neural tube and functionally disconnected from corticospinal control from above. Several other cases conforming to this bizarre neural tube arrangement have since appeared in the literature, reinforcing the validity of this entity. The cardinal clinical, neuroimaging, and electrophysiological features of JNTD, and the hypothesis of its embryogenetic mechanism, form part of this review.

• Transactions of Materials Processing
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• v.9 no.6
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• pp.644-652
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• 2000

Tube hydroforming is recently drawing attention of automotive industries due to its several advantages over conventional methods. It can produce wide range of products such as subframes, engine cradles, and exhaust manifolds with cheaper production cost by reducing overall number of processes. h successful tube hydroforming depends on the reasonable combination of the internal pressure and axial load at the tube ends. This paper deals with the optimal process design of hydroforming process using the genetic algorithm and neural network. An optimization technique is used in order to minimize the tube thickness variation by determining the optimal loading path in the tube expansion forming and the tube T-shape forming process.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2095-2101
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• 2008

In this paper, we consider a performance improvement of neural network for predicting defect size of steam generator tube using early stopping. Usually, neural network is trained until MSE becomes less than a prescribed error goal. The smaller the error goal, the greater the prediction performance for the trained data. However, as the error goal is decreased, an over fitting is likely to start during supervised training of a neural network, which usually deteriorates the generalization performance. We propose that, for the prediction of an axisymmetric defect size, early stopping can be used to avoid the over-fitting. Through various experiments on the axisymmetric defect samples, we found that the difference bet ween the prediction error of neural network based on early stopping and that of ideal neural network is reasonably small. This indicates that the error goal used for neural network training for the prediction of defect size can be efficiently selected by early stopping.

• Proceedings of the KSAR Conference
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• 2004.06a
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• pp.275-275
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• 2004

To evaluate the potential of the stem cell therapy as a method for prenatal management of spinal open neural tube defect (ONTD), the influence of embryonic stem cells injected into the amniotic cavity on the re-closure capacity of spinal ONTD was investgated. Spinal neural tube was incised open for a length of 6 somites using chick embryos of Hamburger and Hamilton stage 18 or 19. (omitted)

• Journal of Korean Neurosurgical Society
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• v.64 no.3
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• pp.374-379
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• 2021

Recent case reports of junctional neural tube defect (JNTD) which is a peculiar type of spinal anomaly showing the functional disconnection of the primary and secondary neural tubes has risen interest in the process of junctional neurulation (the connection between the two neural tubes) during development. This article summarizes the clinical features of the JNTD and reviews the literature on the basic research on junctional neurulation.

• BMB Reports
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• v.49 no.8
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• pp.443-448
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• 2016

The arginylation branch of the N-end rule pathway is a ubiquitin-mediated proteolytic system in which post-translational conjugation of Arg by ATE1-encoded Arg-tRNA-protein transferase to N-terminal Asp, Glu, or oxidized Cys residues generates essential degradation signals. Here, we characterized the ATE1^−/− mice and identified the essential role of N-terminal arginylation in neural tube development. ATE1-null mice showed severe intracerebral hemorrhages and cystic space near the neural tubes. Expression of ATE1 was prominent in the developing brain and spinal cord, and this pattern overlapped with the migration path of neural stem cells. The ATE1^−/− brain showed defective G-protein signaling. Finally, we observed reduced mitosis in ATE1^−/− neuroepithelium and a significantly higher nitric oxide concentration in the ATE1^−/− brain. Our results strongly suggest that the crucial role of ATE1 in neural tube development is directly related to proper turn-over of the RGS4 protein, which participate in the oxygen-sensing mechanism in the cells.

• Journal of Life Science
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• v.19 no.10
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• pp.1346-1351
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• 2009

Recent evidence has shown that many pluripotetic neural crest cells are fate-restricted and that different fate-restricted crest cells emigrate from the neural tube at different times. Jin et al. (2001) identified the expression patterns of Wnts and its antagonists at the time that neural crest cells were being specified and suggested that Wnt signaling was involved in the segregation/differentiation of neural crest cells in the trunk in vitro. In this study, we evaluated the effects of Wnt signaling in avian neural crest lineage segregation. To accomplish this, Wnt signaling was disturbed at the time of neural crest segregation and differentiation by grafting Wnt-3a expressing cells and conducting dominant negative glycogen synthase kinase (dnGSK) electroporation. Stimulation of Wnt signaling induced neural crest lineage segregation and melanoblast specification, and increased the expression levels of genes known to be involved in neural crest development such as cadherin 7 and Slug, which suggests that they are involved in Wnt-induced neural crest lineage differentiation into melanoblasts.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.4
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• pp.302-310
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• 2010

In this paper, we studied Bagging neural network for predicting defect size of steam generator(SG) tube in nuclear power plant. Bagging is a method for creating an ensemble of estimator based on bootstrap sampling. For predicting defect size of SG tube, we first generated eddy current testing signals for 4 defect patterns of SG tube with various widths and depths. Then, we constructed single neural network(SNN) and Bagging neural network(BNN) to estimate width and depth of each defect. The estimation performance of SNN and BNN were measured by means of peak error. According to our experiment result, average peak error of SNN and BNN for estimating defect depth were 0.117 and 0.089mm, respectively. Also, in the case of estimating defect width, average peak error of SNN and BNN were 0.494 and 0.306mm, respectively. This shows that the estimation performance of BNN is superior to that of SNN.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2532-2537
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• 2009

For defect characterization in steam generator tubes in nuclear power plant, artificial neural network has been extensively used to classify defect types. In this paper, we study the effectiveness of Bagging for improving the performance of neural network for the classification of tube defects. Bagging is a method that combines outputs of many neural networks that were trained separately with different training data set. By varying the number of neurons in the hidden layer, we carry out computer simulations in order to compare the classification performance of bagging neural network and single neural network. From the experiments, we found that the performance of bagging neural network is superior to the average performance of single neural network in most cases.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.1
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• pp.63-70
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• 2010

In this paper, we study the structure of neural network for predicting defect size of steam generator tube. After extracting the features from the eddy current testing (ECT) signals, multi-layer neural networks are used to predict the defect size. In order to maximize the prediction performance for the defect size, we should carefully choose the structure of neural networks, especially the number of neurons in the hidden layer. In this paper, it is shown that, for the prediction of defect size, the number of neurons in the hidden layer can be efficiently determined by using cross-validation.