• Bulletin of the Korean Chemical Society
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• v.34 no.1
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• pp.29-30
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• 2013

• Journal of Korea Technology Innovation Society
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• v.17 no.1
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• pp.264-288
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• 2014

The competition for attracting outstanding HRST (human resource for science and technology) who can lead technological innovation is heating up all over the world. The various concepts, which are brain drain, brain gain, brain overflow, brain migration, brain circulation, are used to explain the international mobility of HRST. But the concept of brain scout is the more adequate for explaining in the case of outstanding HRST as the main cause of excessive competition for scouting. This study analyzed the critical factors of the determinants of Korean students in the USA who have intentions of returning to Korea in view of brain scout. As the first step in this study, potential factors and hypothesis are established by the interviews. As the second step, the major factors are examined by surveys and hypothesis testing. Also, a new model for decision-making is proposed which describes intentions of returning to Korea by logistic regression analysis and contributions of each factor derived from this study were compared. Finally, policy implications for attracting outstanding HRST and the limit of this study are discussed.

• Genomics & Informatics
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• v.13 no.2
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• pp.40-44
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• 2015

DNA microarray and next-generation sequencing provide data that can be used for the genetic analysis of multiple quantitative traits such as gene expression levels, transcription factor binding profiles, and epigenetic signatures. In particular, chromatin opening is tightly coupled with gene transcription. To understand how these two processes are genetically regulated and associated with each other, we examined the changes of chromatin accessibility and gene expression in response to genetic variation by means of quantitative trait loci mapping. Regulatory patterns commonly observed in yeast and human across different technical platforms and experimental designs suggest a higher genetic complexity of transcription regulation in contrast to a more robust genetic architecture of chromatin regulation.

• Journal of Computing Science and Engineering
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• v.7 no.2
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• pp.112-121
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• 2013

The last years have seen a rise of interest in using electroencephalography-based brain computer interfacing methodology for investigating non-medical questions, beyond the purpose of communication and control. One of these novel applications is to examine how signal quality is being processed neurally, which is of particular interest for industry, besides providing neuroscientific insights. As for most behavioral experiments in the neurosciences, the assessment of a given stimulus by a subject is required. Based on an EEG study on speech quality of phonemes, we will first discuss the information contained in the neural correlate of this judgement. Typically, this is done by analyzing the data along behavioral responses/labels. However, participants in such complex experiments often guess at the threshold of perception. This leads to labels that are only partly correct, and oftentimes random, which is a problematic scenario for using supervised learning. Therefore, we propose a novel supervised-unsupervised learning scheme, which aims to differentiate true labels from random ones in a data-driven way. We show that this approach provides a more crisp view of the brain states that experimenters are looking for, besides discovering additional brain states to which the classical analysis is blind.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2788-2808
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• 2023

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

• Journal of Mechanical Science and Technology
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• v.19 no.7
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• pp.1424-1431
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• 2005

In this study, head injury by impact force was evaluated by numerical analysis with 3-dimensional finite element (FE) model. Brain deformation by frontal head impact was analyzed to evaluate traumatic brain injury (TBI). The variations of head acceleration and intra-cranial pressure (ICP) during the impact were analyzed. Relative displacement between the skull and the brain due to head impact was investigated from this simulation. In addition, pathological severity was evaluated according to head injury criterion (HIC) from simulation with FE model. The analytic result of brain damage was accorded with that of the cadaver test performed by Nahum et al.(1977) and many medical reports. The main emphasis of this study is that our FE model was valid to simulate the traumatic brain injury by head impact and the variation of the HIC value was evaluated according to various impact conditions using the FE model.

• Bulletin of the Korean Chemical Society
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• v.32 no.6
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• pp.2015-2020
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• 2011

A series of isoxazol-indolin-2-one was designed for GSK-3${\beta}$ inhibitors as novel anticancer agents based on their binding mode analysis in GSK-3${\beta}$ crystal structure. Total 21 compounds were synthesized and evaluated for their inhibitory activity against two tumor cell lines (DU145 and HT29). Most of the synthesized compounds were potent with above 80% inhibitory activity at 100 ${\mu}M$, and several compounds were examined for inhibitory activity against GSK-3${\beta}$. Among them, 15(Z) ($R_1$=H, $R_2$=3-Cl-phenyl) was most active with 78% inhibition of tumor cell line (HT29) at 20 ${\mu}M$ and 72% inhibition of GSK-3${\beta}$ at 20 ${\mu}M$.

• Journal of radiological science and technology
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• v.39 no.3
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• pp.345-352
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• 2016

The purpose of this study was to needed basis of measure MRI CAD development for signal to noise ratio (SNR) by pulse sequence analysis from region of interest (ROI) in brain magnetic resonance imaging (MRI) contrast. We examined images of brain MRI contrast enhancement of 117 patients, from January 2005 to December 2015 in a University-affiliated hospital, Seoul, Korea. Diagnosed as one of two brain diseases such as meningioma and cysts SNR for each patient's image of brain MRI were calculated by using Image J. Differences of SNR among two brain diseases were tested by SPSS Statistics21 ANOVA test for there was statistical significance (p < 0.05). We have analysis socio-demographical variables, SNR according to sequence disease, 95% confidence according to SNR of sequence and difference in a mean of SNR. Meningioma results, with the quality of distributions in the order of T1CE, T2 and T1, FLAIR. Cysts results, with the quality of distributions in the order of T2 and T1, T1CE and FLAIR. SNR of MRI sequences of the brain would be useful to classify disease. Therefore, this study will contribute to evaluate brain diseases, and be a fundamental to enhancing the accuracy of CAD development.

• Molecules and Cells
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• v.40 no.7
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• pp.450-456
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• 2017

Mammalian physiology and behavior are regulated by an internal time-keeping system, referred to as circadian rhythm. The circadian timing system has a hierarchical organization composed of the master clock in the suprachiasmatic nucleus (SCN) and local clocks in extra-SCN brain regions and peripheral organs. The circadian clock molecular mechanism involves a network of transcription-translation feedback loops. In addition to the clinical association between circadian rhythm disruption and mood disorders, recent studies have suggested a molecular link between mood regulation and circadian rhythm. Specifically, genetic deletion of the circadian nuclear receptor Rev-$erb{\alpha}$ induces mania-like behavior caused by increased midbrain dopaminergic (DAergic) tone at dusk. The association between circadian rhythm and emotion-related behaviors can be applied to pathological conditions, including neurodegenerative diseases. In Parkinson's disease (PD), DAergic neurons in the substantia nigra pars compacta progressively degenerate leading to motor dysfunction. Patients with PD also exhibit non-motor symptoms, including sleep disorder and neuropsychiatric disorders. Thus, it is important to understand the mechanisms that link the molecular circadian clock and brain machinery in the regulation of emotional behaviors and related midbrain DAergic neuronal circuits in healthy and pathological states. This review summarizes the current literature regarding the association between circadian rhythm and mood regulation from a chronobiological perspective, and may provide insight into therapeutic approaches to target psychiatric symptoms in neurodegenerative diseases involving circadian rhythm dysfunction.

• Molecules and Cells
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• v.41 no.10
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• pp.881-888
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• 2018

During the cortical development, cells in the brain acquire somatic mutations that can be implicated in various neurodevelopmental disorders. There is increasing evidence that brain somatic mutations lead to sporadic form of epileptic disorders with previously unknown etiology. In particular, malformation of cortical developments (MCD), ganglioglioma (GG) associated with intractable epilepsy and non-lesional focal epilepsy (NLFE) are known to be attributable to brain somatic mutations in mTOR pathway genes and others. In order to identify such somatic mutations presenting as low-level in epileptic brain tissues, the mutated cells should be enriched and sequenced with high-depth coverage. Nevertheless, there are a lot of technical limitations to accurately detect low-level of somatic mutations. Also, it is important to validate whether identified somatic mutations are truly causative for epileptic seizures or not. Furthermore, it will be necessary to understand the molecular mechanism of how brain somatic mutations disturb neuronal circuitry since epilepsy is a typical example of neural network disorder. In this review, we overview current genetic techniques and experimental tools in neuroscience that can address the existence and significance of brain somatic mutations in epileptic disorders as well as their effect on neuronal circuitry.