• The Plant Pathology Journal
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• v.21 no.2
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• pp.111-118
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• 2005

Weather data for disease forecasts are usually derived from automated weather stations (AWS) that may be dispersed across a region in an irregular pattern. We have developed an alternative method to simulate local scale, high-resolution weather and plant disease in a grid pattern. The system incorporates a simplified mesoscale boundary layer model, LAWSS, for estimating local conditions such as air temperature and relative humidity. It also integrates special models for estimating of surface wetness duration and disease forecasts, such as the grapevine downy mildew forecast model, DMCast. The system can recreate weather forecasts utilizing the NCEP/NCAR reanalysis database, which contains over 57 years of archived and corrected global upper air conditions. The highest horizontal resolution of 0.150 km was achieved by running 5-step nested child grids inside coarse mother grids. Over the Finger Lakes and Chautauqua Lake regions of New York State, the system simulated three growing seasons for estimating the risk of grape downy mildew with 1 km resolution. Outputs were represented as regional maps or as site-specific graphs. The highest resolutions were achieved over North America, but the system is functional for any global location. The system is expected to be a powerful tool for site selection and reanalysis of historical plant disease epidemics.

• Proceedings of the Korean Society of Applied Pharmacology
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• 2007.11a
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• pp.19-27
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• 2007

An important potential of metabolomics-based approach is the possibility to develop fingerprints of diseases or cellular responses to classes of compounds with known common biological effect. Such fingerprints have the potential to allow classification of disease states or compounds, to provide mechanistic information on cellular perturbations and pathways and to identify biomarkers specific for disease severity and drug efficacy. Metabolic profiles of biological fluids contain a vast array of endogenous metabolites. Changes in those profiles resulting from perturbations of the system can be observed using analytical techniques, such as NMR and MS. $^1H$ NMR was used to generate a molecular fingerprint of serum or urinary sample, and then pattern recognition technique was applied to identity molecular signatures associated with the specific diseases or drug efficiency. Several metabolites that differentiate disease samples from the control were thoroughly characterized by NMR spectroscopy. We investigated the metabolic changes in human normal and clinical samples using $^1H$ NMR. Spectral data were applied to targeted profiling and spectral binning method, and then multivariate statistical data analysis (MVDA) was used to examine in detail the modulation of small molecule candidate biomarkers. We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences between healthy and disease population. Such metabolic signatures could provide diagnostic markers for a disease state or biomarkers for drug response phenotypes.

• Molecules and Cells
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• v.42 no.10
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• pp.729-738
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• 2019

Autophagy is an important process for protein recycling. Oligomerization of p62/SQSTM1 is an essential step in this process and is achieved in two steps. Phox and Bem1p (PB1) domains can oligomerize through both basic and acidic surfaces in each molecule. The ZZ-type zinc finger (ZZ) domain binds to target proteins and promotes higher-oligomerization of p62. This mechanism is an important step in routing target proteins to the autophagosome. Here, we determined the crystal structure of the PB1 homo-dimer and modeled the p62 PB1 oligomers. These oligomer models were represented by a cylindrical helix and were compared with the previously determined electron microscopic map of a PB1 oligomer. To accurately compare, we mathematically calculated the lead length and radius of the helical oligomers. Our PB1 oligomer model fits the electron microscopy map and is both bendable and stretchable as a flexible helical filament.

• Journal of Climate Change Research
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• v.9 no.2
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• pp.133-142
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• 2018

Climate change will affect not only the crop productivity but also the pattern of rice disease epidemics in Korea. Impact assessments for the climate change are conducted using various climate change scenarios from many global climate models (GCM), such as a scenario from a best GCM or scenarios from multiple GCMs, or a combination of both. Here, we evaluated the feasibility of using a climate change scenario from the best GCM for the impact assessment on the potential epidemics of a rice leaf blast disease in Korea, in comparison to a multi?model ensemble (MME) scenario from multiple GCMs. For this, this study involves analyses of disease simulation using an epidemiological model, EPIRICE?LB, which was validated for Korean rice paddy fields. We then assessed likely changes in disease epidemics using the best GCM selected for individual agro?climatic zones and MME scenarios constructed by running 11 GCMs. As a result, the simulated incidence of leaf blast epidemics gradually decreased over the future periods both from the best GCM and MME. The results from this study emphasized that the best GCM selection approach resulted in comparable performance to the MME approach for the climate change impact assessment on rice leaf blast epidemic in Korea.

• Journal of Korean Neurosurgical Society
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• v.62 no.1
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• pp.10-26
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• 2019

Magnetic resonance-guided focused ultrasound (MRgFUS) is an emerging new technology with considerable potential to treat various neurological diseases. With refinement of ultrasound transducer technology and integration with magnetic resonance imaging guidance, transcranial sonication of precise cerebral targets has become a therapeutic option. Intensity is a key determinant of ultrasound effects. High-intensity focused ultrasound can produce targeted lesions via thermal ablation of tissue. MRgFUS-mediated stereotactic ablation is non-invasive, incision-free, and confers immediate therapeutic effects. Since the US Food and Drug Administration approval of MRgFUS in 2016 for unilateral thalamotomy in medication-refractory essential tremor, studies on novel indications such as Parkinson's disease, psychiatric disease, and brain tumors are underway. MRgFUS is also used in the context of blood-brain barrier (BBB) opening at low intensities, in combination with intravenously-administered microbubbles. Preclinical studies show that MRgFUS-mediated BBB opening safely enhances the delivery of targeted chemotherapeutic agents to the brain and improves tumor control as well as survival. In addition, BBB opening has been shown to activate the innate immune system in animal models of Alzheimer's disease. Amyloid plaque clearance and promotion of neurogenesis in these studies suggest that MRgFUS-mediated BBB opening may be a new paradigm for neurodegenerative disease treatment in the future. Here, we review the current status of preclinical and clinical trials of MRgFUS-mediated thermal ablation and BBB opening, described their mechanisms of action, and discuss future prospects.

• Journal of Ginseng Research
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• v.40 no.1
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• pp.38-46
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• 2016

Background: Blind and excessive application of fertilizers was found during the cultivation of Panax notoginseng in fields, as well as increase in root rot disease incidence. Methods: Both "3414" application and orthogonal test designs were performed at Shilin county, Yunnan province, China, for NPK (nitrogen, phosphorus, and potassium) and mineral fertilizers, respectively. The data were used to construct the one-, two-, and three-factor quadratic regression models. The effect of fertilizer deficiency on root yield loss was also analyzed to confirm the result predicted by these models. A pot culture experiment was performed to observe the incidence rate of root rot disease and to obtain the best range in which the highest yield of root and saponins could be realized. Results: The best application strategy for NPK fertilizer was $0kg/667m^2$, $17.01kg/667m^2$, and $56.87kg/667m^2$, respectively, which can produce the highest root yield of 1,861.90 g (dried root of 100 plants). For mineral fertilizers, calcium and magnesium fertilizers had a significant and positive effect on root yield and the content of four active saponins, respectively. The severity of root rot disease increased with the increase in soil moisture. The best range of soil moisture varied from 0.56 FC (field capacity of water) to 0.59 FC, when the highest yield of root and saponins could be realized as well as the lower incidence rate of root disease. Conclusion: These results indicate that the amount of nitrogen fertilizer used in these fields is excessive and that of potassium fertilizer is deficient. Higher soil moisture is an important factor that increases the severity of the root rot disease.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.25-34
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• 2020

In this paper, we empirically compare the effectiveness of training models to recognize beauty-related skin disease using supervised deep learning algorithms. Recently, deep learning algorithms are being actively applied for various fields such as industry, education, and medical. For instance, in the medical field, the ability to diagnose cutaneous cancer using deep learning based artificial intelligence has improved to the experts level. However, there are still insufficient cases applied to disease related to skin beauty. This study experimentally compares the effectiveness of identifying beauty-related skin disease by applying deep learning algorithms, considering CNN, ResNet, and SE-ResNet. The experimental results using these training models show that the accuracy of CNN is 71.5% on average, ResNet is 90.6% on average, and SE-ResNet is 95.3% on average. In particular, the SE-ResNet-50 model, which is a SE-ResNet algorithm with 50 hierarchical structures, showed the most effective result for identifying beauty-related skin diseases with an average accuracy of 96.2%. The purpose of this paper is to study effective training and methods of deep learning algorithms in consideration of the identification for beauty-related skin disease. Thus, it will be able to contribute to the development of services used to treat and easy the skin disease.

• The Plant Pathology Journal
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• v.22 no.3
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• pp.278-282
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• 2006

Bacillus vallismortis EXTN-1, a biocontrol agent in cucumber, tomato and potato was tested in rice pathosystem against rice fungal pathogens viz. Magnaporthe grisea, Rhizoctonia solani and Cochliobolus miyabeanus. Apart from increasing the yield in the bacterized plants (11.6-12.6% over control), the study showed that EXTN1 is effective in bringing about disease suppression against all the tested fungal pathogens. EXTN-l treatment resulted in 52.11% reduction in rice blast, 83.02% reduction in sheath blight and 11.54% decrease in brown spot symptoms. As the strain is proven as an inducer for systemic resistance based on PR gene expression in Arabidopsis and tobacco models, it is supposed that a similar mechanism works in rice, bringing about disease suppression. The strain could be used as a potent biocontrol and growth-promoting agent in rice cropping system.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.328-330
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• 2004

In this paper, ECG based cardiac disease diagnosis models are developed. Conventionally, ECG monitoring equipments can only measure and store ECG signals and they always require medical doctor's diagnosis actions which are not desirable for continuous ambulatory monitoring and diagnosis healthcare systems. In this paper, two kinds of neural based self cardiac disease diagnosis engines are developed and tested for four kinds of diseases, sinus bradycardia, sinus tachycardia, left bundle branch block and right bundle branch block. For diagnosis engines, error backpropagation neural network (BP) and probabilistic neural network (PNN) were applied. Five signal features including heart rate, QRS interval, PR interval, QT interval, and T wave types were selected for diagnosis characteristics. To show the validity of proposed diagnosis engine, MIT-BIH database were used to test. Test results showed that BP based diagnosis engine has 71% of diagnosis accuracy which is superior to accuracy of PNN based diagnosis engine. However, PNN based diagnosis engine showed superior diagnosis accuracy for complex-disease diagnoses than BP based diagnosis engine.

• Tuberculosis and Respiratory Diseases
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• v.80 no.1
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• pp.1-10
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• 2017

Chronic obstructive pulmonary disease (COPD) is a critical condition with high morbidity and mortality. Although several medications are available, there are no definite treatments. However, recent advances in the understanding of stem and progenitor cells in the lung, and molecular changes during re-alveolization after pneumonectomy, have made it possible to envisage the regeneration of damaged lungs. With this background, numerous studies of stem cells and various stimulatory molecules have been undertaken, to try and regenerate destroyed lungs in animal models of COPD. Both the cell and drug therapies show promising results. However, in contrast to the successes in laboratories, no clinical trials have exhibited satisfactory efficacy, although they were generally safe and tolerable. In this article, we review the previous experimental and clinical trials, and summarize the recent advances in lung regeneration therapy for COPD. Furthermore, we discuss the current limitations and future perspectives of this emerging field.