• 한국조리학회지
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• 제24권3호
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• pp.144-155
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• 2018

This study was conducted to explore the direction of culinary education based brain-based education with analysis of comprehensive research. Questionnaire was completed by frequency analysis, factor analysis, reliability analysis and regression analysis by using SPSS 21. The purpose of this study was to investigate the educational system for creative development through cooking sources and to develop brain-based learning theory, and thus to generate the characteristics and effects of the practice in culinary educational context. The basic principles of brain- based learning are brain plasticity, emotional brain, and ecological brain. Students need to be able to enrich their understanding of social interaction so that social brain's function will be activated through consistent and high-quality feedback. Likewise, students should be capable of collecting everything what they have learned. Defining main ideas and goal of the lesson, four factors were derived from development of competency, personality, application, and diversity. Regarding to the result of this study, the implications for the development of a brain-base program were suggested.

• Applied Microscopy
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• 제46권4호
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• pp.179-183
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• 2016

Mapping brain structural and functional connections through the whole brain is essential for understanding brain mechanisms and the physiological bases of brain diseases. Although region specific structural or functional deficits cause brain diseases, the changes of interregional connections could also be important factors of brain diseases. This review will introduce common neuroimaging modalities, including structural magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion tensor imaging, and other recent neuroimaging analyses methods, such as voxel-based morphometry, cortical thickness analysis, local gyrification index, and shape analysis for structural imaging. Tract-Based Spatial Statistics, TRActs Constrained by UnderLying Anatomy for diffusion MRI, and independent component analysis for fMRI also will also be introduced.

• Molecules and Cells
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• 제22권1호
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• pp.119-125
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• 2006

The rat is an accepted model for studying human psychiatric/neurological disorders. We provide a protocol for total soluble protein extraction using trichloroacetic acid/acetone (TCA/A) from rat (female) whole brain, 10 brain regions and the pituitary gland, and show that two-dimensional gel electrophoresis (2-DGE) using precast immobilized pH (4-7) gradient (IPG) strip gels (13 cm) in the first dimension yields clean silver nitrate stained protein profiles. Though TCA/A precipitation may not be "ideal", the important choice here is the selection of an appropriate lysis buffer (LB) for solubilizing precipitated proteins. Our results reveal enrichment of protein spots by use of individual brain regions rather than whole brain, as well as the presence of differentially expressed spots in their proteomes. Thus individual brain regions provide improved protein coverage and are better suited for differential protein detection. Moreover, using a phosphoprotein-specific dye, ingel detection of phosphoproteins was demonstrated. Representative high-resolution silver nitrate stained proteome profiles of rat whole brain total soluble protein are presented. Shortcomings apart (failure to separate membrane proteins), gel-based proteomics remains a viable option, and 2-DGE is the method of choice for generating high-resolution proteome maps of rat brain and brain regions.

• 인지과학
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• 제17권4호
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• pp.375-398
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• 2006

인간은 보고, 듣고, 따라하고, 행하고, 느끼면서 배운다. 이 모든 과정에 뇌가 관여한다. 최근 뇌과학 연구의 비약적 발전과 뇌과학 연구와 인지과학 연구의 활발한 협력은 '뇌기반 학습과학(Brain-Based Learning Science)', '교육신경과학(Educational Neuroscience)', 혹은 신경교육학(Neuro-Education)이라 불리우는 새로운 연구 분야를 탄생시켰다. 뇌기반 학습과학은 기존의 '학습과학'을 넘어서 실제 학습과 교육환경에 적지 않은 변화를 줄 것으로 기대되는 만큼 그 가능성에 대해 회의적인 입장도 공존하고 있다. 이 논문에서는 뇌기반 학습과학의 정의와 기본가정을 살펴보고, 인지신경과학의 최신 연구가 어떻게 이루어지고 있는지를 소개하고 이러한 연구결과에서 교육적 함의를 도출해 보고자 하였다. 또한 신경계에 대한 신화적 사고와 그 문제점을 열거하고, 뇌과학과 학습-교육 현장의 연계 가능성 및 향후 뇌기반 학습과학의 전망과 한계에 대해 논의하였다.

• 한국초전도학회:학술대회논문집
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• 한국초전도학회 2009년도 Korea Superconductivity Society Meeting 2009
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• pp.72-72
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• 2009

• 지식경영연구
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• 제6권1호
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• pp.85-103
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• 2005

The purpose of this study was to identify the relationship between fixation and brain preference. Based on the hemisphere asymmetric theory and fixation, two hypotheses were articulated. They were: 1) Right-brain preference is negatively related to divergent fixation. 2) Left-brain preference is negatively related to convergent fixation. A self-reporting scale for measuring the brain preference with 42 items were developed for the study based on functional characteritics of left and right hemisphere. Samples were collected from 579 college students in K University. Regression analysis showed that right-brain preference was negatively associated with divergent fixation. In the relationship between left-brain preference and convergent fixation, mixed results were produced. Research implication were discussed at the end of the study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.105-125
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• 2024

Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.

• 동서간호학연구지
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• 제28권1호
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• pp.49-56
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• 2022

Purpose: This study was conducted to identify the factors affecting the attitudes toward brain death organ donation among nurses and doctors in an operating room. Methods: A descriptive research was used. The participants included 90 nurses and 30 doctors who had experience of operating organ transplantation for brain death organ donation. Data were collected from March 12 to May 23, 2020 in the one tertiary general hospital. The outcome measures were perception and attitude of death and attitude towards brain death organ donation. Results: Attitudes toward brain death organ donation was influenced by type of occupation, intention of organ donation and attitude toward death. In addition, the explanatory power of the total variance was 52.1%. Conclusions: Based on the results, it is necessary to prepare an intervention to improve awareness of the brain death and the brain death organ donation.

• 대한의용생체공학회:의공학회지
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• 제31권1호
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• pp.1-13
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• 2010

There are a great numbers of disabled individuals who cannot freely move or control specific parts of their body because of serious neurological diseases such as spinal cord injury, amyotrophic lateral sclerosis, brainstem stroke, and so on. Brain-computer interfaces (BCIs) can help them to drive and control external devices using only their brain activity, without the need for physical body movements. Over the past 30 years, several Bel research programs have arisen and tried to develop new communication and control technology for those who are completely paralyzed. Thanks to the rapid development of computer science and neuroimaging technology, new understandings of brain functions, and most importantly many researchers' efforts, Bel is now becoming 'practical' to some extent. The present review article summarizes the current state of electroencephalogram (EEG)-based Bel, which have been being studied most widely, with specific emphasis on its basic concepts, system developments, and prospects for the future.

• 대한의용생체공학회:의공학회지
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• 제26권3호
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• pp.129-132
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• 2005

It is one of the most important issues to determine a target brain image that gives a common coordinate system for a constructing population-based brain atlas. The purpose of this study is to provide a simple and reliable procedure that determines the target brain image among the group based on the inherent structural information of three-dimensional magnetic resonance (MR) images. It uses only 11 lines defined automatically as a feature vector representing structural variations based on the Talairach coordinate system. Average characteristic vector of the group and the difference vectors of each one from the average vector were obtained. Finally, the individual data that had the minimum difference vector was determined as the target. We determined the target brain image by both our algorithm and conventional visual inspection for 20 healthy young volunteers. Eighteen fiducial points were marked independently for each data to evaluate the similarity. Target brain image obtained by our algorithm showed the best result, and the visual inspection determined the second one. We concluded that our method could be used to determine an appropriate target brain image in constructing brain atlases such as disease-specific ones.