• Culinary science and hospitality research
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• v.24 no.3
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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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• v.46 no.4
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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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• v.22 no.1
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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.

• Korean Journal of Cognitive Science
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• v.17 no.4
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• pp.375-398
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• 2006

Humans learn by observing, hearing, imitating, doing, and feeling. The brain(cortex) is the central tore of this process. The recent rapid progress of brain science and the active interdisciplinary collaboration between brain science and cognitive science opens a new possibility. That is a new research Held called 'Brain-Based learning Science', 'Edutational Neuroscienre', or 'NeuroEduration' This study reviews the nature and basic assumptions of brain-based learning science, current directions in educational neuroscience research, the neuro-myths, educational implications of neuroscience, and a possibility of making a meaningful connection between brain science and education. Also the future prospects and limitations of the brain-based learning science are discussed.

• 한국초전도학회:학술대회논문집
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• 2009.07a
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• pp.72-72
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• 2009

• Knowledge Management Research
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• v.6 no.1
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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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• v.18 no.1
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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.

• Journal of East-West Nursing Research
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• v.28 no.1
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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.

• Journal of Biomedical Engineering Research
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• v.31 no.1
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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.

• Journal of Biomedical Engineering Research
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• v.26 no.3
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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.