• Journal of Yeungnam Medical Science
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• v.41 no.4
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• pp.261-268
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• 2024

Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.

• Journal of Korean society of Dental Hygiene
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• v.12 no.2
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• pp.335-343
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• 2012

Objectives : The aim of this investigation was to evaluate the remineralization effect of Bamboo salt and NaF+Bamboo salt solutions on bovine enamel formed incipient artificial enamel caries by microcomputed tomography (micro CT). Methods : Experimental solutions were distilled water(negative control), 2% sodium fluoride solution (2% NaF group), 3% bamboo salt solution (3% BS group) and the solution mixed 2% sodium fluoride solution and 3% bamboo salt solution (2% NaF+3% BS group). Specimens were prepared from extracted bovine teeth and divided into 4 groups of 10 specimens each by randomized blocks according to density. Then the specimens surface were divided equally into three parts to observe sound enamel area, incipient enamel carious area and remineralized enamel area. Only one-third of specimen surface was coated with nail varnish and these were exposed to a lactate carbopol buffer system for 72 hours. Then one-half of the demineralized enamel area was coated with nail varnish. The specimens were carried out under pH cycling model for 14 days as follows; samples were immersed in each experimental solution for 2 mins 3 times per day, demineralized for 4 hours and in mixed saliva for the remaining hours. After pH cycling, density was measured using micro CT. Results : All experimental groups showed remineralization effects except for negative control group(p<0.05). The differences of density after experimental solution treatment were statistically significant difference among 4 groups (p<0.01). The density difference values of groups were $0.04{\pm}0.01$ in negative control group, $0.19{\pm}0.01$ in 2% NaF group, $0.14{\pm}0.01$ in 3% BS group, and $0.21{\pm}0.01$ in 2% NaF+3% BS group. Conclusions : The bamboo salt solution showed remineralization effects on incipient artificial enamel caries and the solution mixed sodium fluoride solution and bamboo salt solution showed more remineralization effects than the bamboo salt solution. Thus, it is suggested that bamboo salt can be used as remineralization agent in incipient enamel caries lesion.

• Journal of the Korean Society of Radiology
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• v.83 no.3
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• pp.473-485
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• 2022

The incidence of neurodegenerative diseases in the older population has increased in recent years. A considerable number of studies have been performed to characterize these diseases. Imaging analysis is an important biomarker for the diagnosis of neurodegenerative disease. Objective and reliable assessment and precise detection are important for the early diagnosis of neurodegenerative diseases. Artificial intelligence (AI) using brain MRI applied to the study of neurodegenerative diseases could promote early diagnosis and optimal decisions for treatment plans. MRI-based AI software have been developed and studied worldwide. Representatively, there are MRI-based volumetry and segmentation software. In this review, we present the development process of brain volumetry analysis software in neurodegenerative diseases, currently used and developed AI software for neurodegenerative disease in the Republic of Korea, probable uses of AI in the future, and AI software limitations.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2008.10a
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• pp.43-51
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• 2008

Currently an increasing number of urban tunnels with small overburden are excavated according to the principle of the New Austrian Tunneling Method (NATM). For rational management of tunnels from planning to construction and maintenance stages, prediction, control and monitoring of displacements of and around the tunnel have to be performed with high accuracy. Computational method tools, such as finite element method, have been and are indispensable tool for tunnel engineers for many years. It is, however, a commonly acknowledged fact that determination of input parameters, especially material properties exhibiting nonlinear stress-strain relationship, is not an easy task even for an experienced engineer. Use and application of the acquired tunnel information is important for prediction accuracy and improvement of tunnel behavior on construction. Artificial Neural Network (ANN) model is a form of artificial intelligence that attempts to mimic behavior of human brain and nervous system. The main objective of this paper is to perform the deformation analysis in NATM tunnel by means of numerical simulation and artificial neural network (ANN) with field database. Developed ANN model can achieve a high level of prediction accuracy.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.7
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• pp.929-934
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• 2001

The physiological role of brain somatostatin in the central regulation of feed intake in sheep was investigated through a continuous intracerebroventricular (ICV) infusion of somatostastin 1-28 (SRIF) at a small dose of $5{\mu}g/0.2ml/hr$ for 98.5 hours from day 1 to day 5. Sheep (n=5) were fed for 2 hours once a day, and water and 0.5 M NaCI solution were given ad libitum. Feed, water and salt intake were measured during ICV infusion of artificial cerebrospinal fluid (CSF) and SRIF. The feed intake during SRIF infusion on days 2 to 5 increased significantly compared to that during CSF infusion. Water intake, when compared to that during CSF infusion, only increased significantly on day 4. NaCI intake during SRIF infusion was not different from that during CSF infusion. Mean arterial blood pressure (MAP) and heart rate during SRIF infusion were not different from those during CSF infusion. The plasma concentrations of Na, K, Cl, osmolality and total protein during SRIF infusion were also not different from those values during CSF infusion.There are two possible mechanisms, that is, the suppression of brain SRIF on feed suppressing hormones and the direct actions on brain mechanisms controlling feed intake, explaining how SRIF works in the brain to bring about increases in feed intake in sheep fed on hay. The results indicate that brain SRIF increases feed intake in sheep fed on hay.

• Current Optics and Photonics
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• v.2 no.6
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• pp.499-507
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• 2018

For the spatiotemporally aligned observation of photothrombosis induction and transient variations of in vivo brain stroke, we developed a novel photothrombosis inducing system compatible to a magnetic resonance imaging (MRI) system using nonmagnetic stereotaxic equipment. From the spatial point of view, the system provides a more reliable level of reproducibility of the photothrombosis in each brain. From the temporal point of view, from T1- and T2-weighted in vivo MR (magnetic resonance) images, the transient variations such as incidence, location, and size of the thrombosis are measured quantitatively. In addition, the final variation is observed in the ex vivo brain by TTC (Triphenyltetrazolium chloride) staining based on histological assay and utilized for the verification of the MR images. From the experimental result of the rat brain, the proposed system shows more reliable characteristics for transient variations of brain strokes.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.37-44
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• 2021

Automatic classification of brain MRI images play an important role in early diagnosis of brain tumors. In this work, we present a deep learning-based brain tumor classification model in MRI images using ensemble of deep features. In our proposed framework, three different deep features from brain MR image are extracted using three different pre-trained models. After that, the extracted deep features are fed to the classification module. In the classification module, the three different deep features are first fed into the fully-connected layers individually to reduce the dimension of the features. After that, the output features from the fully-connected layers are concatenated and fed into the fully-connected layer to predict the final output. To evaluate our proposed model, we use openly accessible brain MRI dataset from web. Experimental results show that our proposed model outperforms other machine learning-based models.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.238-244
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• 2019

In this study, we compared and analyzed the performance of each Convolution Neural Network (CNN) by implementing the CNN that reflected the characteristics of the cerebral structure, in order to analyze the CNN that was used for the prediction of cybersickness, and provided the performance varying depending on characteristics of the brain. Dizziness has many causes, but the most severe symptoms are considered attributable to vestibular dysfunction associated with the brain. Brain waves serve as indicators showing the state of brain activities, and tend to exhibit differences depending on external stimulation and cerebral activities. Changes in brain waves being caused by external stimuli and cerebral activities have been proved by many studies and experiments, including the thesis of Martijn E. Wokke, Tony Ro, published in 2019. Based on such correlation, we analyzed brain wave data collected from dizziness-inducing environments and implemented the dizziness predictive artificial neural network reflecting characteristics of the cerebral structure. The results of this study are expected to provide a basis for achieving optimal performance of the CNN used in the prediction of dizziness, and for predicting and preventing the occurrence of dizziness under various virtual reality (VR) environments.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.1
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• pp.35-40
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• 2001

The physiological role of brain neuropeptide Y (NPY) in the central regulation of grass intake in sheep was investigated through a continuous intracerebroventricular (ICV) infusion of NPY at a dose of $5{\mu}g/0.2ml/hr$ for 98.5 hours from day 1 to day 5. Sheep (n=5) were fed for 2 hours once a day, and water and 0.5 M NaCl solution were given ad libitum. Feed intake during ICV NPY infusion increased significantly compared to that during ICV artificial cerebrospinal fluid (CSF) infusion. Water and NaCl intake during ICV NPY infusion remained unchanged. Mean arterial blood pressure (MAP) and plasma osmolality during ICV NPY infusion were not significantly different from those during ICV CSF infusion. On the other hand, plasma glucose concentration during ICV NPY infusion increased significantly compared to that during ICV CSF infusion. The results suggest that brain NPY acts as a hunger factor in brain mechanisms controlling feeding to increase grass intake in sheep.

• Applied Microscopy
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• v.46 no.4
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• pp.184-187
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• 2016

Neuronal connectivity determines brain function. Therefore, understanding the full map of brain connectivity with functional annotations is one of the most desirable but challenging tasks in science. Current methods to achieve this goal are limited by the resolution of imaging tools and the field of view. Macroscale imaging tools (e.g., magnetic resonance imaging, diffusion tensor images, and positron emission tomography) are suitable for large-volume analysis, and the resolution of these methodologies is being improved by developing hardware and software systems. Microscale tools (e.g., serial electron microscopy and array tomography), on the other hand, are evolving to efficiently stack small volumes to expand the dimension of analysis. The advent of mesoscale tools (e.g., tissue clearing and single plane ilumination microscopy super-resolution imaging) has greatly contributed to filling in the gaps between macroscale and microscale data. To achieve anatomical maps with gene expression and neural connection tags as multimodal information hubs, much work on information analysis and processing is yet required. Once images are obtained, digitized, and cumulated, these large amounts of information should be analyzed with information processing tools. With this in mind, post-imaging processing with the aid of many advanced information processing tools (e.g., artificial intelligence-based image processing) is set to explode in the near future, and with that, anatomic problems will be transformed into informatics problems.