• Journal of Physiology & Pathology in Korean Medicine
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• v.28 no.4
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• pp.403-410
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• 2014

The purpose of this study was to examine the anti-depressive effects of OnDam-Tang with addition of Linderae Radix (ODT-L) on the animal model of depression induced by chronic immobilization stress. Depression model was made by chronic immobilization stress for 2 hours for 21 days. And we performed forced swimming test, analysis of the neurotransmitter and immunohistochemical staining, measured expression levels of serotonin in the brain. ODT-L has decreased immobilization time in forced swimming test. ODT-L has increased amount of melatonin in the brain. ODT-L has increased expression levels of serotonin in the brain. ODT-L prevented damage in the hippocampal region. ODT-L has reduced the expression level of CRF receptors in in hippocampus region. These results suggest that ODT-L may have anti-depressive effects on depression.

• YAKHAK HOEJI
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• v.54 no.4
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• pp.300-308
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• 2010

Adrenoceptor has been considered to be an important target in psychiatric disorders. Based on x-ray structures of bovine rhodopsin, we established homology model of ${\alpha}_{2c}$-adrenoceptor (ADA2C_rat) and then analyzed docking from binding model of receptor-ligand complex with high-active compound No.29 in tricyclic isoxazole analogues (1-30). We observed that the N (1.907 $\AA$) and O (1.712 $\AA$) atoms of isoxazole ring on the docked ligand (No.29) formed H-bonding interaction with O-H of Ser5.32 and carmeron phenyl ring centroid of tricyclic isoxazole formed $\pi-\pi$ interaction at 3.342 $\AA$ distance with phenyl ring centroid of Phe6.52. According to predictions of blood-brain distribution (logBB) through penetration of blood-brain barrie (BBB) and polar surface area (PSA) of the ligands, the high-active compound No.29 has values of logBB=-0.203, PSA=67.50, respectively. These results suggest that the high-active compound No.29 is a novel anti-depressant with the characteristics such as dopamine and serotonin.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.1.1-5
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• 1994

In the single sweep record of event-related potential (ERP), the peak latency of P300, which is one of the most prominent positive peaks in the ERP record, might fluctuate according to the recording conditions. The fluctuation of the peak latency (measurement fluctuation) is the summation of the fluctuation caused by physiological factor (physiological fluctuation) and one by noise of background EEG (noise fluctuation). We propsed a method for estimating the interval of the physiological fluctuation based on a limited number of single sweep records. The noise fluctuation was estimated by using the relationship between the signal-to-noise (SN) ratio and the noise fluctuation based on the P300 model and the background EEG model. The interval estimate of the physiological fluctuation were obtained by subtracting the interval estimate of the noise fluctuation from that of the measurement fluctuation. The proposed method was tested by using simulation data of ERP and applied to actual ERP and data of normal subjects, and gave satisfactory results.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.603-613
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• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.531-541
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• 2018

Road freezing caused by snowfall during wintertime causes traffic congestion and many accidents. To prevent such problems, we developed, in this study, a system to predict road freezing based on weather forecast data and the freezing generation modules. The weather forecast data were obtained from a high-resolution model with 1 km resolution for Jeju Island from 00:00 KST on December 1, 2017, to 23:00 KST on February 28, 2018. The results of the weather forecast data show that index of agreement (IOA) temperature was higher than 0.85 at all points, and that for wind speed was higher than 0.7 except in Seogwipo city. In order to evaluate the results of the freezing predictions, we used model evaluation metrics obtained from a confusion matrix. These metrics revealed that, the Imacho module showed good performance in precision and accuracy and that the Karlsson module showed good performance in specificity and FP rate. In particular, Cohen's kappa value was shown to be excellent for both modules, demonstrating that the algorithm is reliable. The superiority of both the modules shows that the new system can prevent traffic problems related to road freezing in the Jeju area during wintertime.

• Physical Therapy Rehabilitation Science
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• v.11 no.3
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• pp.304-310
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• 2022

Objective: Mild cognitive impairment (MCI) is a condition in which cognitive and executive functions are reduced, and older adults with MCI are ten times more likely to develop dementia than healthy older adults. Expression of brain-derived neurotrophic factor (BDNF) through aerobic exercise is associated with increased cognitive and executive functions. in this review, randomized controlled trials (RCTs) on the effects of aerobic exercise on BDNF in individuals with mild cognitive impairment are summarized and qualitatively and quantitatively analyzed to suggest the necessity of aerobic exercise. Design: a systematic review and meta-analysis. Methods: RCTs were searched for changes in BDNF through aerobic exercise using four international databases. Quality assessment and quantitative analysis were performed using RevMan 5.4. Quantitative analysis was quantified with a standardized mean difference (SMD) and presented as a random effect model. Results: Three RCTs evaluated BDNF in 123 patients with MCI. There was a significant improvement in the experimental group that performed aerobic exercise compared to the control group. The results analyzed using the random effects model were SMD = 0.48. Conclusions: In this review, we reported the effects and mechanisms of aerobic exercise in individuals with MCI. As a result of synthesizing RCTs that performed aerobic exercise, a significant increase in BDNF was confirmed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2082-2102
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• 2024

Accurate segmentation of magnetic resonance (MR) images is crucial for providing doctors with effective quantitative information for diagnosis. However, the presence of weak boundaries, intensity inhomogeneity, and noise in the images poses challenges for segmentation models to achieve optimal results. While deep learning models can offer relatively accurate results, the scarcity of labeled medical imaging data increases the risk of overfitting. To tackle this issue, this paper proposes a novel fuzzy c-means (FCM) model that integrates a deep learning approach. To address the limited accuracy of traditional FCM models, which employ Euclidean distance as a distance measure, we introduce a measurement function based on the skewed normal distribution. This function enables us to capture more precise information about the distribution of the image. Additionally, we construct a regularization term based on the Kullback-Leibler (KL) divergence of high-confidence deep learning results. This regularization term helps enhance the final segmentation accuracy of the model. Moreover, we incorporate orthogonal basis functions to estimate the bias field and integrate it into the improved FCM method. This integration allows our method to simultaneously segment the image and estimate the bias field. The experimental results on both simulated and real brain MR images demonstrate the robustness of our method, highlighting its superiority over other advanced segmentation algorithms.

• Anxiety and mood
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• v.2 no.1
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• pp.3-8
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• 2006

Anxiety has been suggested to be related to many neurotransmitters in brain, such as norepinephrine, serotonin, dopamine, cholecystokinin, and gamma-amino butyric acid. There are many studies to examine the relationship between anxiety and norepinephrine, and norepinephrine seems to be clearly related to the development of anxiety. We suggest that future studies to explore the pathophysiology of anxiety should be necessary, which include studies on antianxiety drugs, genetic studies, animal model studies, and brain imaging studies.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1022-1028
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• 2013

It is well knowns that based on the CSP (Common Spatial Pattern) algorithm, the linear projection of an EEG (Electroencephalography) signal can be made to spaces that optimize the discriminant between two patterns. Sharing disadvantages from linear time invariant systems, CSP suffers from the non-stationary nature of EEGs causing the performance of the classification in a BCI (Brain-Computer Interface) system to drop significantly when comparing the training data and test data. The author has suggested a simple idea based on the parallel model of CSP filters to improve the performance of BCI systems. The model was tested with a simple CSP algorithm (without any elaborate regularizing methods) and a perceptron learning algorithm as a classifier to determine the improvement of the system. The simulation showed that the parallel model could improve classification performance by over 10% compared to conventional CSP methods.

• Korean Journal of Veterinary Service
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• v.36 no.2
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• pp.67-71
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• 2013

Neonatal infection caused by Cronobacter species can result in serious illnesses such as bacteremia, septicemia, meningitis, and death in at-risk infants who are orally fed contaminated reconstituted powdered infant formulas. The objective of this study was to compare the virulence among three Cronobacter species strains by using an animal model for human neonatal Cronobacter species infections. We acquired timed-pregnant ICR mice and all owed them to give birth naturally. On postnatal day 3, each pup was administered orally a total dose of $1{\times}10^7$ CFU Cronobacter species strain 3439, CDC 1123-79, and 3231. Mice were observed twice daily for morbidity and mortality. At postnatal day 10, the remaining pups were euthanized, and brain, liver, and cecum were excised and analyzed for the presence of Cronobacter species. Cronobacter species were isolated from cecum and other tissues in inoculated mice. In the tissues of Cronobacter species infected mice, meningitis and gliosis were detected in the brain. In this study, we identified the virulence among Cronobacter species strains by using a neonatal mice model which was a very effective animal model for human neonatal Cronobacter species infections.