• Title, Summary, Keyword: Neural tube

Search Result 94, Processing Time 0.037 seconds

Junctional Neural Tube Defect

  • Eibach, Sebastian;Pang, Dachling
    • Journal of Korean Neurosurgical Society
    • /
    • v.63 no.3
    • /
    • pp.327-337
    • /
    • 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.

A Study on Optimal Process Design of Hydroforming Process with n Genetic Algorithm and Neural Network (Genetic Algorithm과 Neural Network을 이용한 Tube Hydroforming의 성형공정 최적화에 대한 연구)

  • 양재봉;전병희;오수익
    • Transactions of Materials Processing
    • /
    • v.9 no.6
    • /
    • pp.644-652
    • /
    • 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.

  • PDF

A performance improvement of neural network for predicting defect size of steam generator tube using early stopping (조기학습정지를 이용한 원전 SG세관 결함크기 예측 신경회로망의 성능 향상)

  • Jo, Nam-Hoon
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.57 no.11
    • /
    • pp.2095-2101
    • /
    • 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.

Enhancement of Re-closure Capacity by the Intra-amniotic Injection of Human Embryonic Stem Cells in Surgically Induced Spinal Open Neural Tube Defects in Chick Embryos

  • Lee, Gun-Soup;Lee, Do-Hun;Kim, Eun-Young;Wang, Kyu-Chang;Lee, Won-Don;Park, Sepill;Lim, Jin-Ho
    • Proceedings of the KSAR Conference
    • /
    • /
    • pp.275-275
    • /
    • 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)

  • PDF

Ablation of Arg-tRNA-protein transferases results in defective neural tube development

  • Kim, Eunkyoung;Kim, Seonmu;Lee, Jung Hoon;Kwon, Yong Tae;Lee, Min Jae
    • BMB Reports
    • /
    • v.49 no.8
    • /
    • pp.443-448
    • /
    • 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.

The Effects of Wnt Signaling on Neural Crest Lineage Segregation and Specification (Wnt signaling이 neural crest lineage segregation과 specification에 미치는 영향)

  • Song, Jin-Su;Jin, Eun-Jung
    • Journal of Life Science
    • /
    • v.19 no.10
    • /
    • pp.1346-1351
    • /
    • 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.

A Study on Bagging Neural Network for Predicting Defect Size of Steam Generator Tube in Nuclear Power Plant (원전 증기발생기 세관 결함 크기 예측을 위한 Bagging 신경회로망에 관한 연구)

  • Kim, Kyung-Jin;Jo, Nam-Hoon
    • Journal of the Korean Society for Nondestructive Testing
    • /
    • v.30 no.4
    • /
    • pp.302-310
    • /
    • 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.

Classification Performance Improvement of Steam Generator Tube Defects in Nuclear Power Plant Using Bagging Method (Bagging 방법을 이용한 원전SG 세관 결함패턴 분류성능 향상기법)

  • Lee, Jun-Po;Jo, Nam-Hoon
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.58 no.12
    • /
    • pp.2532-2537
    • /
    • 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.

A Study on Fault Diagnosis of Boiler Tube Leakage based on Neural Network using Data Mining Technique in the Thermal Power Plant (데이터마이닝 기법을 이용한 신경망 기반의 화력발전소 보일러 튜브 누설 고장 진단에 관한 연구)

  • Kim, Kyu-Han;Lee, Heung-Seok;Jeong, Hee-Myung;Kim, Hyung-Su;Park, June-Ho
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.66 no.10
    • /
    • pp.1445-1453
    • /
    • 2017
  • In this paper, we propose a fault detection model based on multi-layer neural network using data mining technique for faults due to boiler tube leakage in a thermal power plant. Major measurement data related to faults are analyzed using statistical methods. Based on the analysis results, the number of input data of the proposed fault detection model is simplified. Then, each input data is clustering with normal data and fault data by applying K-Means algorithm, which is one of the data mining techniques. fault data were trained by the neural network and tested fault detection for boiler tube leakage fault.

Predicting the axial compressive capacity of circular concrete filled steel tube columns using an artificial neural network

  • Nguyen, Mai-Suong T.;Thai, Duc-Kien;Kim, Seung-Eock
    • Steel and Composite Structures
    • /
    • v.35 no.3
    • /
    • pp.415-437
    • /
    • 2020
  • Circular concrete filled steel tube (CFST) columns have an advantage over all other sections when they are used in compression members. This paper proposes a new approach for deriving a new empirical equation to predict the axial compressive capacity of circular CFST columns using the Artificial Neural Network (ANN). The developed ANN model uses 5 input parameters that include the diameter of circular steel tube, the length of the column, the thickness of steel tube, the steel yield strength and the compressive strength of concrete. The only output parameter is the axial compressive capacity. Training and testing the developed ANN model was carried out using 219 available sets of data collected from the experimental results in the literature. An empirical equation is then proposed as an important result of this study, which is practically used to predict the axial compressive capacity of a circular CFST column. To evaluate the performance of the developed ANN model and the proposed equation, the predicted results are compared with those of the empirical equations stated in the current design codes and other models. It is shown that the proposed equation can predict the axial compressive capacity of circular CFST columns more accurately than other methods. This is confirmed by the high accuracy of a large number of existing test results. Finally, the parametric study result is analyzed for the proposed ANN equation to consider the effect of the input parameters on axial compressive strength.