• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.130-138
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• 2023

The selection of the correct examination variables for diagnosing heart disease provides many benefits, including faster diagnosis and lower cost of examination. The selection of inspection variables can be performed by referring to the data of previous examination results so that future investigations can be carried out by referring to these selected variables. This paper proposes a model for selecting examination variables using an Artificial Bee Swarm Optimization method by considering the variables of accuracy and cost of inspection. The proposed feature selection model was evaluated using the performance parameters of accuracy, area under curve (AUC), number of variables, and inspection cost. The test results show that the proposed model can produce 24 examination variables and provide 95.16% accuracy and 97.61% AUC. These results indicate a significant decrease in the number of inspection variables and inspection costs while maintaining performance in the excellent category.

• ETRI Journal
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• v.46 no.3
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• pp.513-525
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• 2024

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.

• Research in Plant Disease
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• v.22 no.3
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• pp.168-177
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• 2016

A forecast model for estimating the infection risk of bacterial canker caused by Pseudomonas syringae pv. actinidiae on kiwifruit leaves in Korea was developed using the generic infection model of Magarey et al. (2005). Two-way contingency table analysis was carried out to evaluate accuracy of forecast models including the model developed in this study for estimating the infection of bacterial canker on kiwifruit using the weather and disease data collected from three kiwifruit orchards at Seogwipo in 2015. All the tested models had more than 80% of probability of detection indicating that all the tested models could be effective to manage the disease. The model developed in this study showed the highest values in proportion of correct (51.1%), probability of detection (90.9%), and critical success index (47.6%). It indicated that the model developed in this study would be the best model for estimating the infection of bacterial wilt on kiwifruit leaves in Korea. The model developed in this study could be used for a part of decision support system for managing bacterial wilt on kiwifruit leaves and help growers to reduce the loss caused by the disease in Korea.

• Korean Journal Plant Pathology
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• v.3 no.2
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• pp.131-136
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• 1987

Rates of lesion development over time and disease gradients over distance for blast disease on the two rice varieties, Brazos and M-20 1 were significantly affected by two different cultural conditions, upland and flooded conditions. Flooding rice field plots lowered the rates of lesion increase and flattened the disease gradients for both varieties. Despite absence of statistically significant differences in the rate of lesion increase between four sampled distances from infection focus, rate of lesion development tended to be slightly greater as distance from the infection focus increases. Rate of lesion increase was greater with more susceptible variety M-201 than with Brazos. Disease gradient was steeper for M-201 than for Brazos. As blast disease progressed, disease gradients became flattened regardless of variety due to the infections originated from secondary foci. Between two empirical disease gradient models examined, Kiyosawa & Shiyomi model was fitted better over Gregory model. Rates of blast isopath movement under upland conditions were calculated as approximately 0.2m/day and 0.4 m/day for Brazos and M-201, respectively. The results in this study suggest that differences in varietal resistance to blast could be detected by measuring disease gradient as efficiently as by measuring infection rate.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.5483-5487
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• 2013

Background: Associations between Arg399Gln, Arg194Trp and Arg280His polymorphisms of the XRCC1 gene and risk of differentiated thyroid carcinoma (DTC) have been widely studied but the findings are contradictory. Methods: We performed a meta-analysis in the present study using STATA 11.0 software to clarify any associations. Electronic literature databases and reference lists of relevant articles revealed a total of 10, 6 and 6 published studies for the Arg399Gln, Arg194Trp and Arg280His polymorphisms, respectively. Results: No significant associations were observed between Arg399Gln and DTC risk in all genetic models within the overall and subgroup meta-analyses, while the Trp/Trp vs Arg/Arg and recessive model of the Arg194Trp polymorphism was associated with DTC susceptibility, and the dominant model of Arg280His polymorphism contributed to DTC susceptibility in Caucasians. Conclusions: Our meta-analysis suggests that XRCC1 Arg194Trp may be a risk factor for DTC development.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.322-330
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• 2021

In modern aquaculture, mass mortality is a very important issue that determines the success of aquaculture business. If a fish disease is not detected at an early stage in the farm, the disease spreads quickly because the farm is a closed environment. Therefore, early detection of diseases is crucial to prevent mass mortality of fish raised in farms. Recently deep learning-based automatic identification of fish diseases has been widely used, but there are many difficulties in identifying objects due to insufficient images of fish diseases. Therefore, this paper suggests a method to generate a large number of fish disease images by synthesizing normal images and disease images using SinGAN deep learning model in order to to solve the lack of fish disease images. We generate images from the three most frequently occurring Paralichthys Olivaceus diseases such as Scuticociliatida, Vibriosis, and Lymphocytosis and compare them with the original image. In this study, a total of 330 sheets of scutica disease, 110 sheets of vibrioemia, and 110 sheets of limphosis were made by synthesizing 10 disease patterns with 11 normal halibut images, and 1,320 images were produced by quadrupling the images.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.2
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• pp.374-380
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• 2009

Samryungbaikchul-san(SRBCS) has been used in oriental medicine for the treatments of gastrointestinal and neurological disorders. Here, potential protective function of SRBCS was investigated in neural tissues in Alzheimer's disease(AD) mouse model. In primary cultured cells from the spinal cord of newborn rats, treatment of ${\beta}$-amyloid peptide elevated cell counts positive to glial fibrillary acidic protein(GFAP) or caspase 3 immunoreactivity, but the co-treatment of SRBCS reduced positive cell counts. In vivo administration of scopolamine, an inhibitor of muscarinic receptor, resulted in increases in the number of glial fibrillary acidic protein(GFAP) and caspase 3-positive cells in hippocampal subfields, which was then decreased by the treatment of SRBCS or acetylcholinesterase inhibitor galathamine. The present data suggest that SRBCS may play a protective role in damaged neural tissues caused by scopolamine treatments in mice.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup2
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• pp.7-9
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• 2016

In order to help smokers quit easier, China has started to provide quitline service since 2004. There are two models for Chinese quitline service-the National Quitline Model, which provides only cessation service to smokers, and the 12320 Hotline Model, which integrates cessation counseling into public health hotline service and is currently adapted in public health hotlines in 28 provinces. A protocol of 4 counseling calls is used by 12320 Hotline. Three-month abstinence rate for clients is about 20%. The fact that most smokers who attempted quit don't seek cessation help or quitline service is not well known by the public are major constraints for quitline service in China. Effective advocating campaign should be implemented to propagate quitline. Diverse protocols targeting different subpopulation will also need to be developed to better service the public.

• Molecules and Cells
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• v.46 no.2
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• pp.120-129
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• 2023

Recent technical advances have enabled unbiased transcriptomic and epigenetic analysis of each cell, known as "single-cell analysis". Single-cell analysis has a variety of technical approaches to investigate the state of each cell, including mRNA levels (transcriptome), the immune repertoire (immune repertoire analysis), cell surface proteins (surface proteome analysis), chromatin accessibility (epigenome), and accordance with genome variants (eQTLs; expression quantitative trait loci). As an effective tool for investigating robust immune responses in coronavirus disease 2019 (COVID-19), many researchers performed single-cell analysis to capture the diverse, unbiased immune cell activation and differentiation. Despite challenges elucidating the complicated immune microenvironments of chronic inflammatory diseases using existing experimental methods, it is now possible to capture the simultaneous immune features of different cell types across inflamed tissues using various single-cell tools. In this review, we introduce patient-based and experimental mouse model research utilizing single-cell analyses in the field of chronic inflammatory diseases, as well as multi-organ atlas targeting immune cells.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.271-280
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• 2014

Foot-and-mouth Disease (FMD) is a highly infectious and fatal viral livestock disease that affects cloven-hoofed animals domestic and wild and the FMD outbreak in Korea in 2010/2011 was a disastrous incident for the country and the economy. Thus, efforts at the national level are put to prevent foot-and-mouth disease and to reduce the damage in the case of outbreak. As one of these efforts, it is useful to study the spread of the disease by using probabilistic model. In fact, after the FMD epidemic in the UK occurred in 2001, many studies have been carried on the spread of the disease using a variety of stochastic models as an effort to prepare future outbreak of FMD. However, for the FMD outbreak in Korea occurred in 2010/2011, there are few study by utilizing probabilistic model. This paper assumes a stochastic spatial-temporal susceptible-infectious-removed (SIR) epidemic model for the 2010/2011 FMD outbreak to understand spread of the disease. Since data on infections of FMD disease during 2010/2011 outbreak of Aniaml and Plant Quarantine Agency and on the livestock farms from the nationwide census in 2011 of Statistics Korea do not have detail informations on address or missing values, we generate detail information on address by randomly allocating farms within corresponding Si/Gun area. The kernel function is estimated using the infection data and by using simulations, the susceptibility and transmission of the spatial-temporal stochastic SIR models are determined.