• Sleep Medicine and Psychophysiology
• /
• v.27 no.1
• /
• pp.24-31
• /
• 2020

Objectives: Subjective-objective discrepancy of sleep (SODS) is a common symptom and one of the major phenotypes of insomnia. A distorted perception of sleep deficit might be related to abnormal brain reactivity to insomnia-related stimuli. We aimed to investigate differences in brain activation to insomnia-related stimuli vs. general anxiety-inducing stimuli among insomnia patients with SODS, insomnia patients without SODS, and healthy controls (HCs). Methods: All participants were evaluated for subjective sleep status using a sleep diary and questionnaires; occult sleep disorders and objective sleep status were assessed using polysomnography and actigraphy. Task functional magnetic resonance imaging was performed during insomnia-related stimuli (Ins) and general anxiety-inducing stimuli (Gen). Brain reactivity to Ins versus Gen was compared among insomnia with SODS, insomnia without SODS, and HC groups, and a combined insomnia disorder group (ID, insomnia with and without SODS) was also compared with HCs. Results: In the insomnia with SODS group compared to the insomnia without SODS group, the right precuneus and right supplementary motor areas showed significantly increased BOLD signals in response to Ins versus Gen. In the ID group compared to the HC group, the left anterior cingulate cortex showed significantly increased BOLD signals in response to Ins versus Gen. Conclusion: The insomnia with SODS and ID groups showed higher brain activity in response to Ins versus Gen, while this was not observed in the insomnia without SODS and HC groups, respectively. These results suggest that insomnia patients with sleep misperception are more sensitive to sleep-related threats than general anxiety-inducing threats.

• Tuberculosis and Respiratory Diseases
• /
• v.60 no.1
• /
• pp.72-75
• /
• 2006

Background : This report reviews our experience with persistent air leaks in the peumothorax that were not considered candidates for surgical treatment in order to evaluate the efficacy and risks of the OK-432 plus autoblood or OK-432 pleurodesis. Material & Methods : From March 2004 to July 2005, 8 consecutive patients who had an air leak in the pneumothorax over 5 days and had been treated with OK-432 plus autoblood or OK-432 pleurodesis. The patients were not considered candidates for surgical treatments because the chest CT findings revealed severe chronic lung disease with multiple bullae and/or bullous changes. A prolonged air leak with/without dead space was treated with either OK-432 plus autoblood or OK-432 pleurodesis. The efficacy and side effects of OK-432 pleurodesis were assessed by determining the duration of the air leak, the number of pleurodesis, the patients' symptoms, measurements of the white blood cell count and the c-reactive protein level. Results : All of eight patients were male and the mean age was $72.4{\pm}8.5$. The mean number of pleurodesis was $1.9{\pm}1.1$ and the mean duration of the air leak was $4.6{\pm}4.6days$ after pleurodesis. Side effects after pleurodesis were encountered in 7 patients, which included a chilling sensation in 7 cases, chest pain in 5 cases, headache in 3 cases, local heat sensation in 2 cases, and fever in 1 case. Leukocytosis was observed in 6 patients, and the mean of WBC count and CRP were $14500{\pm}2100$ and $21.9{\pm}11.4mg/dL$, respectively. Conclusion : Either OK-432 plus autoblood or OK-432 pleurodesis has acceptable side effects, and can be considered a treatment option for persistent air leaks in the pneumothorax that are not candidates for surgical treatment.

• Journal of Internet Computing and Services
• /
• v.21 no.6
• /
• pp.23-31
• /
• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.