• IMMUNE NETWORK
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• v.21 no.1
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• pp.11.1-11.10
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• 2021

Coronavirus causes an infectious disease in various species and crosses the species barriers leading to the outbreak of zoonotic diseases. Due to the respiratory diseases are mainly caused in humans and viruses are replicated and excreted through the respiratory tract, the nasal fluid and sputum are mainly used for diagnosis. Early diagnosis of coronavirus plays an important role in preventing its spread and is essential for quarantine policies. For rapid decision and prompt triage of infected host, the immunochromatographic assay (ICA) has been widely used for point of care testing. However, when the ICA is applied to an expectorated sputum in which antigens are present, the viscosity of sputum interferes with the migration of the antigens on the test strip. To overcome this limitation, it is necessary to use a mucolytic agent without affecting the antigens. In this study, we combined known mucolytic agents to lower the viscosity of sputum and applied that to alpha and beta coronavirus, porcine epidemic diarrhea virus (PEDV) and Middle East respiratory syndrome coronavirus (MERS-CoV), respectively, spiked in sputum to find optimal pretreatment conditions. The pretreatment method using tris(2-carboxyethyl)phosphine (TCEP) and BSA was suitable for ICA diagnosis of sputum samples spiked with PEDV and MERS-CoV. This sensitive assay for the detection of coronavirus in sputum provides an useful information for the diagnosis of pathogen in low respiratory tract.

• IMMUNE NETWORK
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• v.21 no.6
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• pp.39.1-39.18
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• 2021

The high virulent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that emerged in China at the end of 2019 has generated novel coronavirus disease, coronavirus disease 2019 (COVID-19), causing a pandemic worldwide. Every country has made great efforts to struggle against SARS-CoV-2 infection, including massive vaccination, immunological patients' surveillance, and the utilization of convalescence plasma for COVID-19 therapy. These efforts are associated with the attempts to increase the titers of SARS-CoV-2 neutralizing Abs (nAbs) generated either after infection or vaccination that represent the body's immune status. As there is no standard therapy for COVID-19 yet, virus eradication will mainly depend on these nAbs contents in the body. Therefore, serological nAbs neutralization assays become a requirement for researchers and clinicians to measure nAbs titers. Different platforms have been developed to evaluate nAbs titers utilizing various epitopes sources, including neutralization assays based on the live virus, pseudovirus, and neutralization assays utilizing recombinant SARS-CoV-2 S glycoprotein receptor binding site, receptor-binding domain. As a standard neutralization assay, the plaque reduction neutralization test (PRNT) requires isolation and propagation of live pathogenic SARS-CoV-2 virus conducted in a BSL-3 containment. Hence, other surrogate neutralization assays relevant to the PRNT play important alternatives that offer better safety besides facilitating high throughput analyses. This review discusses the current neutralization assay platforms used to evaluate nAbs, their techniques, advantages, and limitations.

• Clinical Nutrition Research
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• v.12 no.2
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• pp.138-153
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• 2023

The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40-69 years from the Korea National Health and Nutrition Examination Survey (2013-2018). We set MetS (3-5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = -2.0545] and saturated fatty acid [β = -2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.891-899
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• 2020

Objective: To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT. Materials and Methods: We retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455). Results: All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level [per CT scan: rib fracture(s): yes/no]. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement. Conclusion: We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.

• Steel and Composite Structures
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• v.51 no.4
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• pp.441-456
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• 2024

This paper aims to develop Machine Learning (ML) algorithms to predict the buckling resistance of cold-formed steel (CFS) channels with restrained flanges, widely used in typical CFS sheathed wall panels, and provide practical design tools for engineers. The effects of cross-sectional restraints were first evaluated on the elastic buckling behaviour of CFS channels subjected to pure axial compressive load or bending moment. Feedforward multi-layer Artificial Neural Networks (ANNs) were then trained on different datasets comprising CFS channels with various dimensions and properties, plate thicknesses, and restraining conditions on one or two flanges, while the elastic distortional buckling resistance of the elements were determined according to the Finite Strip Method (FSM). To develop less biased networks and ensure that every observation from the original dataset has the chance of appearing in the training and test set, a K-fold cross-validation technique was implemented. In addition, the hyperparameters of the ANNs were tuned using a grid search technique to provide ANNs with optimum performances. The results demonstrated that the trained ANNs were able to predict the elastic distortional buckling resistance of CFS flange-restrained elements with an average accuracy of 99% in terms of coefficient of determination. The developed models were then used to propose a simple ANN-based design formula for the prediction of the elastic distortional buckling stress of CFS flange-restrained elements. Finally, the proposed formula was further evaluated on a separate set of unseen data to ensure its accuracy for practical applications.

• Geophysics and Geophysical Exploration
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• v.8 no.4
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• pp.270-279
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• 2005

Under the research project supported by Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), we have conducted the development of GPR systems for landmine detection. Until 2005, we have finished development of two prototype GPR systems, namely ALIS (Advanced Landmine Imaging System) and SAR-GPR (Synthetic Aperture Radar-Ground Penetrating Radar). ALIS is a novel landmine detection sensor system combined with a metal detector and GPR. This is a hand-held equipment, which has a sensor position tracking system, and can visualize the sensor output in real time. In order to achieve the sensor tracking system, ALIS needs only one CCD camera attached on the sensor handle. The CCD image is superimposed with the GPR and metal detector signal, and the detection and identification of buried targets is quite easy and reliable. Field evaluation test of ALIS was conducted in December 2004 in Afghanistan, and we demonstrated that it can detect buried antipersonnel landmines, and can also discriminate metal fragments from landmines. SAR-GPR (Synthetic Aperture Radar-Ground Penetrating Radar) is a machine mounted sensor system composed of B GPR and a metal detector. The GPR employs an array antenna for advanced signal processing for better subsurface imaging. SAR-GPR combined with synthetic aperture radar algorithm, can suppress clutter and can image buried objects in strongly inhomogeneous material. SAR-GPR is a stepped frequency radar system, whose RF component is a newly developed compact vector network analyzers. The size of the system is 30cm x 30cm x 30 cm, composed from six Vivaldi antennas and three vector network analyzers. The weight of the system is 17 kg, and it can be mounted on a robotic arm on a small unmanned vehicle. The field test of this system was carried out in March 2005 in Japan.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6521-6526
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• 2015

Background: This study aimed toanalyze the risk behavior for cervical cancer (CC) and the human papillomavirus (HPV) prevalence and resolution among women who received care through the private healthcare network of a municipality in southern Brazil. Materials and Methods: This descriptive and retrospective study was conducted with 25 women aged 20 to 59 years who received care through the private healthcare network and were treated at a specialty clinic in the period from January to December 2012 in a municipality in Northwest Parana, Southern Brazil. Data from medical records with cytological and HPV results were used. Following treatment, these women were followed-up and reassessed after 6 months. Data were statistically analyzed using the t-test and chi-squared test at a 5% significance level. Results: The mean age of the studied women was $27.8{\pm}7.75$ years old, and the majority were married, with paid employment and were non-smokers. The mean age at menarche was $13.0{\pm}0.50$ years old, and the mean age at first intercourse was $17.5{\pm}1.78$ years, with only 8.0% (2) initiating sexual activity at an age ${\leq}15$ years old. The majority had 1 to 2 children (60.0%), while 88.0% reported having had one sexual partner in their lifetime, and all the women were sexually active. A total of 68.0% used a hormonal contraceptive method. All the women had leukorrhea and pain and were infected by a single HPV type. Regarding the lesion grade, 80.0% showed high risk and 20.0% low risk. The most prevalent high-risk HPV strain was 16. Conclusions: These findings provide relevant information on HPV risk factors and infection, as well as the treatment and 6-month follow-up results, in economically and socially advantaged women with no traditional risk factors, corroborating previous reports that different risk factors may be described in different populations. Thus, this study reinforces the fact that even women without the traditional risk factors should undergo HPVmonitoring and assessment to determine the persistence of infection, promoting early diagnosis of the lesions presented and appropriate treatment to thus prevent the occurrence of CC.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.246-257
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• 2018

The purpose of this study was to investigate the effects of physical health status and social support on depression and quality of life among the elderly in G City. This is a descriptive research study of 497 elderly residents in 45 senior citizen centers in G city; the data were collected from March 5 to 30, 2018. Data were analyzed using the IBM SPSS/win 24.0 program by t-test, ANOVA and multiple regression analysis. In physical health status, the chronic disease score was $1.35{\pm}0.91$, the functional status score was $1.80{\pm}4.45$, and the subjective health score was $3.14{\pm}1.13$. The average score for social support in the emotional network was $5.71{\pm}1.13$. In the sub-region of the social network, the score for frequency of contact with relatives was $2.92{\pm}1.31$, that for contact with friends was $3.18{\pm}0.98$, and that for social participation was $0.68{\pm}0.82$. In the multiple regression analysis of factors affecting depression and quality of life, the explanatory power of physical health status and quality of life was 45.5% and 21.1%, respectively. The explanatory power of depression based on social support and quality of life was 46.7% and 27.5%, respectively. This study indicates that physical health status and social support affect depression and quality of life. Therefore, programs should be developed to increase the physical health status and social support and thus improve the quality of life of the elderly in the community.

• Journal of The Korean Association For Science Education
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• v.29 no.8
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• pp.990-1010
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• 2009

To derive brain-based evolutionary educational principles, this study examined the studies on the structural and functional characteristics of human brain, the biological evolution occurring between- and within-organism, and the evolutionary attributes embedded in science itself and individual scientist's scientific activities. On the basis of the core characteristics of human brain and the framework of universal Darwinism or universal selectionism consisted of generation-test-retention (g-t-r) processes, a Model of Brain-based Evolutionary Scientific Teaching for Learning (BEST-L) was developed. The model consists of three components, three steps, and assessment part. The three components are the affective (A), behavioral (B), and cognitive (C) components. Each component consists of three steps of Diversifying $\rightarrow$ Emulating (Executing, Estimating, Evaluating) $\rightarrow$ Furthering (ABC-DEF). The model is 'brain-based' in the aspect of consecutive incorporation of the affective component which is based on limbic system of human brain associated with emotions, the behavioral component which is associated with the occipital lobes performing visual processing, temporal lobes performing functions of language generation and understanding, and parietal lobes, which receive and process sensory information and execute motor activities of the body, and the cognitive component which is based on the prefrontal lobes involved in thinking, planning, judging, and problem solving. On the other hand, the model is 'evolutionary' in the aspect of proceeding according to the processes of the diversifying step to generate variants in each component, the emulating step to test and select useful or valuable things among the variants, and the furthering step to extend or apply the selected things. For three components of ABC, to reflect the importance of emotional factors as a starting point in scientific activity as well as the dominant role of limbic system relative to cortex of brain, the model emphasizes the DARWIN (Driving Affective Realm for Whole Intellectual Network) approach.

• Journal of Broadcast Engineering
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• v.10 no.4 s.29
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• pp.557-575
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• 2005

Much research in core technologies has been done to make it possible the ubiquitous video services over various kinds of user information terminals anytime anywhere in the way the users want to consume. In this paper, we design plototypesystem architecture for the ubiquitous TV program content consumption based on user preference via various kinds of intelligent information terminals in digital home environment, and present an implementation and testing results for the prototype system. For the system design, we utilize the TV Anytime specification fur the consumption of TV program contents based on user preference in TV programs, and also use the MPEG-21 DIA (Digital Item Adaptation) tools which are the representation schema formats in order to describe the context information for user environments, user terminal characteristics, user characteristics for universal access and consumption of the preferred TV program contents. The proposed ubiquitous content mobility prototype system is designed to make it possible to seamlessly consume contents by a single user or multiple users via various kinds of user terminals for the TV program contents they watch together. The proposed ubiquitous content mobility prototype system in digital home environment consists of a home server, a display TV terminal, and an intelligent information terminal. We use 42 TV programs contents in eight different genres from four different TV channels in order to test our prototype system.