• Journal of Korean Neurosurgical Society
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• 제63권3호
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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.

• 소성∙가공
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• 제9권6호
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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.

• 전기학회논문지
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• 제57권11호
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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.

• 한국동물번식학회:학술대회논문집
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• 한국동물번식학회 2004년도 춘계학술발표대회
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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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• 제64권3호
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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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• 제49권8호
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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.

• 생명과학회지
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• 제19권10호
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• pp.1346-1351
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• 2009

Neural crest는 신경계의 발생과정에서 생긴 특정화된 외배엽으로서 말초신경계(peripheral nervous system)의 모든 sensory cells과 peripheral cells, unipolar spinal ganglion cell, cranial sensory ganglia, peripheral nerve의 neurolemmal sheath cells, ganglia의 capsule cells, sympathetic ganglia, chromaffin cells, pigment cell 등의 자율신 경계의 대부분의 세포로 분화 한다. 최근pluripotetic neural crest cells의 운명이 이미 제한되어 있으며, 이러한 fate-restricted crest cells이 neural tube에서 emigration된다고 보고된바 있다. 또한 본 연구자는 Wnt와 Wnt의 antagonist가 neural crest cell의 specification이 일어나는 시기에 발현하여, neural crest cell의 segregation과 differentiation에 직접적으로 관여함을 밝혔다. 이를 보다 명확히 규명하기 위해, 본 연구에서는 neural tube에 Wnt-3a expressing cell의 grafting 혹은 dominant negative GSK construct의 electroporation을 통해 Wnt signaling을 modulation 하여 downstream mediator를 조사하였다. Wnt signaling의 stimulation은 neural crest cell의 melanoblast 로의 commitment를 유도하였으며, 이와 더불어 cadherin 7과 slug의 발현을 조절함을 확인하였다.

• 비파괴검사학회지
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• 제30권4호
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• pp.302-310
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• 2010

본 논문에서는 원자력 발전소 증기발생기 세관에 발생할 수 있는 결함의 크기측정에 사용되는 Bagging 신경회로망에 대한 연구를 수행하였다. Bagging은 부트스트랩(bootstrap) 샘플링에 기반을 둔 추정기 앙상블을 생성하는 방법이다. 증기발생기 세관의 결함 크기측정을 위하여 다양한 폭과 깊이를 갖는 4가지 결함패턴의 eddy current testing 신호를 생성하였다. 그 다음, 단일 신경회로망(single neural network; SNN)과 Bagging 신경회로망(Bagging neural network; BNN)을 구성하여 각 결함의 폭과 깊이를 추정하였다. SNN과 BNN 추정성능은 최대오차를 이용해서 측정하였다. 실험결과, 결함 깊이 추정시의 SNN과 BNN 최대오차는 0.117mm와 0.089mm 이었다. 또한, 결함 폭 추정 시에는 SNN과 BNN 최대오차는 0.494mm와 0.306mm 이었다. 이러한 실험결과는 BNN 추정성능이 SNN 추정성능보다 우수하다는 것을 보여준다.

• 전기학회논문지
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• 제58권12호
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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.

• 조명전기설비학회논문지
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• 제24권1호
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• pp.63-70
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• 2010

본 논문에서는 원자력발전소 증기세관 크기 예측을 위한 신경회로망 구조에 대해서 연구한다. 와류탐상 시험(ECT) 신호로부터 특징을 추출한 후, 결함크기 예측을 위해서 다층퍼셉트론 신경회로망을 이용한다. 결함크기 예측성능을 최대화하기 위해서는 신경회로망의 구조, 특히 은닉층 내의 뉴런의 개수를 신중히 결정하여야 한다. 본 논문에서는, 결함크기 예측을 위한 은닉층 내의 뉴런의 개수를 교차검증을 이용하여 매우 효과적으로 결정할 수 있음을 보인다.