• Journal of Life Science
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• v.19 no.4
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• pp.449-456
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• 2009

Murine Neuro-2a (N2a) cells have been widely used for the investigation of neuronal differentiation, trophic interaction and neurotoxic effects of various compounds and their associated mechanisms. N2a cells have many genomic variations such as gains or losses in DNA copy number, similar to other neuroblastoma cells, and no systematic or high-resolution studies of their genome-wide chromosomal aberrations have been reported. Presently, we conducted a systematic genome-wide determination of chromosomal aberrations in N2a cells using a high-throughput, oligonucleotide array-based comparative genomic hybridization (oaCGH) technique. A hidden Markov Model was employed to assign each genomic oligonucleotide to a DNA copy number state: double loss, single loss, normal, gain, double gain and amplification. Unlike most neuroblastoma cells, Mycn amplification was not observed in N2a cells. In addition, these cells showed gain only in the neuron-derived neurotrophic factor (NF), while other neurotrophic factors such as glial line-derived NF and brain-derived NF presented normal copy numbers. Chromosomes 4, 8, 10, 11 and 15 displayed more than 1000 aberrational oligonucleotides, while chromosomes 3, 17, 18 and 19 displayed less than 20. The largest region of gain was located on chromosome 8 and its size was no less than 26.7 Mb (Chr8:8427841-35162415), while chromosome 4 had the longest region of single deletion, with a size of 15.1 Mb (Chr4:73265785-88374165).

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2006.06a
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• pp.39-41
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• 2006

A modifiedPosner cue-target paradigm in which neutral, positive and negative (threat) words were presented in peripheral location for cue was used to investigate the difference of engagement component of attention across emotional valence and anxiety level of participants. Results showed an interaction effect between anxiety level of participants and emotional valence of cue in valid trial. This indicates that the engage component of attention is not encapsulated and influenced by anxiety level of participant.

• Annual Conference on Human and Language Technology
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• 2004.10d
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• pp.229-231
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• 2004

본 연구는 다(多)음절 한국어 단어재인에서의 어휘접근단위(unit of lexical access)를 알아보고자 했다. 이를 위해 Taft(1987)가 영어 어휘접근단위를 알아보고자 했을 때 사용한 실험 패러다임을 이용하였다. 실험 결과 반응시간에서는 조건간 통계적으로 유의미한 차이를 보이지 않았지만, BOSS 조건의 반응시간이 짧은 경향성을 보였고, 반응률에 있어서도 BOSS를 지지하는 결과를 보여주었다. 물론, 반응 오류가 많은 등 Taft(1987)의 패러다임을 한국어에 적용하기에 부적절했던 점이 있었지만, 적어도 다음절 단어 어휘접근 시 BOSS가 역할을 하고 있다는 것은 알아 볼 수 있었다.

• Smart Structures and Systems
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• v.15 no.4
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• pp.931-947
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• 2015

This paper focuses on the design of an intelligent control system. The used techniques are based on Neuro Fuzzy approaches applied to a magnetorheological damper in order to reduce the vibrations over footbridges; it has been applied to the Science Museum Footbridge of Valladolid, particularly. A model of the footbridge and of the damper has been built using different simulation tools, and a successful comparison with the real footbridge and the real damper has been carried out. This simulated model has allowed the reproduction of the behaviour of the footbridge and damper when a pedestrian walks across the footbridge. Once it is determined that the simulation results are similar to real data, the control system is introduced into the model. In this sense, different strategies based on Neuro Fuzzy systems have been studied. In fact, an ANFIS (Artificial Neuro Fuzzy Inference System) method has also been used, in addition to an alternative Neuro Fuzzy approach. Several trials have been carried out, using both techniques, obtaining satisfactory results after using these techniques.

• Geomechanics and Engineering
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• v.22 no.5
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• pp.397-414
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• 2020

In this paper, practical predictive models for soil shear strength parameters are proposed. As cohesion and internal friction angle are of essential shear strength parameters in any geotechnical studies, we try to predict them via artificial neural network (ANN) and neuro-imperialism approaches. The proposed models was based on the result of a series of consolidated undrained triaxial tests were conducted on reinforced sandy soil. The experimental program surveys the increase in internal friction angle of sandy soil due to addition of polypropylene fibers with different lengths and percentages. According to the result of the experimental study, the most important parameters impact on internal friction angle i.e., fiber percentage, fiber length, deviator stress, and pore water pressure were selected as predictive model inputs. The inputs were used to construct several ANN and neuro-imperialism models and a series of statistical indices were calculated to evaluate the prediction accuracy of the developed models. Both simulation results and the values of computed indices confirm that the newly-proposed neuro-imperialism model performs noticeably better comparing to the proposed ANN model. While neuro-imperialism model has training and test error values of 0.068 and 0.094, respectively, ANN model give error values of 0.083 for training sets and 0.26 for testing sets. Therefore, the neuro-imperialism can provide a new applicable model to effectively predict the internal friction angle of fiber-reinforced sandy soil.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.167-168
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• 2018

The importance of business problem-solving creativity (BPSC) becomes crucial much more as competitive situations go on in the market. However, how to assess the BPSC remains an unsolved research issue yet in the literature. In this sense, this study proposes an exploratory analysis of the BPSC from the view of neuro-science experiments called fNIRS. The fNIRS represents a functional near-infrared spectroscopy, a new type of neuro-science research paradigm. This study proposes an exploratory level of how to conduct the fNIRS-based experiments to analyze the BPSC.

• Journal of Sensor Science and Technology
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• v.9 no.6
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• pp.440-447
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• 2000

This paper describes a practical system to classify material temperature responses by composition of curve fitting and neuro-fuzzy inference. There are problems with a classification system which utilizes temperature responses. It requires too much time to approach the steady state of temperature response and it has to be filtered to remove the noise which occurs in experiments. Thus, this paper proposes a practical method using curve fitting only for transient state to remove the above problems of time and noise. Using the neuro-fuzzy system, the thermal conductivity of the material can be inferred on various ambient temperatures. So the material can be classified via its inferred thermal conductivity. To realize the system, we designed a contact sensor which has a similar structure with human finger, implemented a hardware system, and developed a classification software of curve fitting and neuro-fuzzy algorithm.

• Molecules and Cells
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• v.40 no.12
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• pp.945-953
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• 2017

We report the biological functions of a zebrafish homologue of RING-finger protein 152 (rnf152) during embryogenesis. rnf152 was initially identified as a brain-enriched E3 ligase involved in early embryogenesis of zebrafish. Expression of rnf152 was ubiquitous in the brain at 24 hpf but restricted to the eyes, midbrain-hindbrain boundary (MHB), and rhombomeres at 48 hpf. Knockdown of rnf152 in zebrafish embryos caused defects in the eyes, MHB, and rhombomeres (r1-7) at 24 hpf. These defects in rnf152-deficient embryos were analyzed by whole-mount in situ hybridization (WISH) using neuroD, deltaD, notch1a, and notch3 probes. NeuroD expression was abolished in the marginal zone, outer nuclear layer (ONL), inner nuclear layer (INL), and ganglion cell layer (GCL) of the eyes at 27 hpf. Furthermore, deltaD and notch1a expression was remarkably reduced in the ONL, INL, subpallium, tectum, cerebellum, and rhombomeres (r1-7) at 24 hpf, whereas notch3 expression was reduced in the tectum, cerebellum, and rhombomeres at 24 hpf. Finally, we confirmed that expression of Notch target genes, her4 and ascl1a, also decreased significantly in these areas at 24 hpf. Thus, we propose that Rnf152 is essential for development of the eyes, midbrain and hindbrain, and that Delta-Notch signaling is involved.

• Journal of radiological science and technology
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• v.39 no.3
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• pp.323-328
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• 2016

The spatial dose distribution was measured with ionization chamber as preliminary study to evaluate operator dose and to study dose reduction during neuro-interventional procedures. The zone of operators was divided into four area (45, 135, 225, and 315 degree).We supposed that operator exist on the four area and indicated location of critical organs(eyes, breast, gonad). The spatial doses were measured depending on distance( 80, 100, 120, and 140 cm) and location of critical organs. The spatial doses of area of 225 degree were 114.5 mR/h (eyes location), 143.1 mR/h (breast location) and 147 mR/h (gonad location) in 80 cm. When changed location of x-ray generator, spatial dose increased in $18.1{\pm}10.5%$, averagely. We certified spatial dose in the operator locations, Using the results of this study, It is feasible to protect operator from radiation in neuro-interventional procedures.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.239-247
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• 2019

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.