• Pediatric Infection and Vaccine
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• v.29 no.3
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• pp.131-140
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• 2022

Purpose: Since the coronavirus disease 2019 (COVID-19) pandemic began, new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged, and distinct epidemic waves of COVID-19 have occurred for an extended period. This study aimed to analyze the clinical and epidemiological characteristics of children with COVID-19 from the third wave to the middle of the fourth epidemic wave in Korea. Methods: We retrospectively reviewed the medical records of hospitalized patients aged ≤18 years with laboratory-confirmed COVID-19. The study periods were divided into the third wave (from November 13, 2020 to July 6, 2021) and the fourth wave (from July 7 to October 31, 2021). Results: Ninety-three patients were included in the analysis (33 in the third and 60 in the fourth waves). Compared with the third wave, the median age of patients was significantly older during the fourth wave (6.7 vs. 2.8 years, P=0.014). Household contacts was reported in 60.2% of total patients, similar in both periods (69.7 vs. 55.0%, P=0.190). Eighty-one (87.1%) had symptomatic SARS-CoV-2 infection. Among these, 10 (12.3%) had no respiratory symptoms. Anosmia or ageusia were more commonly observed in the fourth epidemic wave (10.7 vs. 34.0%, P=0.032). Most respiratory illness were upper respiratory tract infections (94.4%, 67/71), 4 had pneumonia. The median cycle threshold values (detection threshold, 40) for RNA-dependent RNA polymerase (RdRp) and envelope (E) genes of SARS-CoV-2 were 21.3 and 19.3, respectively. There was no significant difference in viral load during 2 epidemic waves. Conclusions: There were different characteristics during the two epidemic waves of COVID-19.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.4
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• pp.253-264
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• 2022

In this study, we investigated the occurrence of viral diseases in the early growth stage of soybean to establish management practices. We collected 83 soybean samples showing abnormal symptoms, approximately 3-4 weeks after seeding in the breeding field of the National Institute of Crop Science. Viruses were detected in the collected samples using reverse transcription polymerase chain reaction (RT-PCR) and metatranscriptome analysis of all those samples. The incidence of viral diseases in the field was less than 1% overall and up to 50% in certain cultivars and lines. RT-PCR and metatranscriptome analysis detected Soybean yellow mottle mosaic virus (SYMMV), Soybean mosaic virus (SMV), Soybean yellow common mosaic virus, Peanut stunt virus, and soybean geminivirus A (SGVA). Among these detected viruses, SYMMV and SMV were identified as major viruses causing infection in the early growth stage of soybean, with detection rates of 53.7% and 42.6%, respectively. Soybeans infected with SYMMV showed typical mosaic symptoms, whereas those infected with SMV showed a variety of symptoms such as mosaic, mottle, stunt, and chlorotic spots. Transmission characteristics of these viruses are variable, such that SMV is primarily transmitted by seeds, whereas SYMMV could be transmitted by insects, soil, and seeds. In this study, SGVA was detected in the early growth stage of soybean, and research on the current status and its effects on soybean after the early growth stage should be conducted.

• Research in Plant Disease
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• v.29 no.3
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• pp.276-285
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• 2023

This work investigated the viral infection in jujube plants in Korea. A total of 61 samples with the symptoms of putative viral infection were collected from experimental fields and orchards. Thereafter, the samples were subjected to metatranscriptome analysis, Reverse transcription polymerase chain reaction analysis, and nucleotide sequence analysis. These analyses identified the presence of two DNA viruses, jujube-associated badnavirus (JuBV), jujube mosaic-associated virus (JuMaV), and one RNA virus, jujube yellow mottle-associated virus (JYMaV). All samples collected were confirmed to be infected by at least one of the three viruses, with most showed multiple infections. The detection rates of JuBV, JYMaV, and JuMaV were 100%, 90.2%, and 8.2%, respectively. Only three combinations of viral infections were found: 9.8% of samples showed single infection of JuBV, 82.0% showed double infection of JuBV+JYMaV, and 8.2% showed triple infection of JuBV+JYMaV+JuMaV. Sequence analysis of the three viruses showed very high homology with respective virus isolates reported in China. This study is predicted to provide fundamental data to produce virus-free jujube seedlings and represents the first report of JuBV and JuMaV infection in Korea.

• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.171-187
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• 2022

Recently, the demand and development for non-face-to-face services are rapidly progressing due to the pandemic caused by the COVID-19, and attention is focused on the metaverse at the center. Entering the era of the 4th industrial revolution, Metaverse, which means a world beyond virtual and reality, combines various sensing technologies and 3D reconstruction technologies to provide various information and services to users easily and quickly. In particular, due to the miniaturization and economic increase of convergence sensors such as unmanned aerial vehicle(UAV) capable of high-resolution imaging and high-precision LiDAR(Light Detection and Ranging) sensors, research on digital-Twin is actively underway to create and simulate real-life twins. In addition, Game engines in the field of computer graphics are developing into metaverse engines by expanding strong 3D graphics reconstuction and simulation based on dynamic operations. This study constructed a mirror-world type metaverse that reflects real-world coordinate-based reality using Unreal Engine 5, a recently announced metaverse engine, with accurate 3D spatial information data of convergence sensors based on unmanned aerial system(UAS) and LiDAR. and then, spatial information contents and simulations for users were produced based on various public data to verify the accuracy of reconstruction, and through this, it was possible to confirm the construction of a more realistic and highly utilizable metaverse. In addition, when constructing a metaverse that users can intuitively and easily access through the unreal engine, various contents utilization and effectiveness could be confirmed through coordinate-based 3D spatial information with high reproducibility.

• Korean Journal of Psychosomatic Medicine
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• v.32 no.1
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• pp.43-51
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• 2024

Objectives : This study aims to explore the prevalence of depressive symptoms among elderly residents in the relatively stable rural areas of Jeju and to examine the relationships between levels of depression, sociodemographic factors, and health habits. Methods : The study site was within rural Jeju, where elderly individuals aged 65 and older were randomly selected from the 'Agricultural Cohort' registered at the Centers for Farmers' Safety and Health Center. Trained interviewers conducted surveys using the Short Form Geriatric Depression Scale (sGDS-K), defining those with scores of 6 or above as experiencing depressive symptoms for the analysis. Other variables such as sex, age, educational level, marital status, annual income, subjective health status, underlying disease, perceived stress levels, smoking, and drinking status were also recorded Results : Out of 533 subjects, the prevalence of depressive symptoms was 35.3%, with 28.5% in male and 45.6% in female (p<0.001). Factors significantly associated with the prevalence of depressive symptoms included marital status (p=0.014), educational level (p<0.001), annual income (p=0.034), subjective health status (p<0.001), perceived stress level (p<0.001), feeling of despair (p<0.001) and suicidal ideas (p<0.001). Multivariate logistic regression analysis revealed that subjective health status, perceived stress level, and feelings of despair were associated with the prevalence of depressive symptoms. Conclusions : The high prevalence of depressive symptoms among the rural elderly in Jeju highlights the need for targeted mental health interventions. Addressing sociocultural factors and improving early detection and intervention strategies can help reduce the socioeconomic impact of depression in this population.

• Tuberculosis and Respiratory Diseases
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• v.41 no.4
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• pp.339-353
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• 1994

Background : The p53 gene codes for a DNA-binding nuclear phosphoprotein that appears to inhibit the progression of cells from the G1 to the S phase of the cell cycle. Mutations of the p53 gene are common in a wide variety of human cancers, including lung cancer. In lung cancers, point mutations of the p53 gene have been found in all histological types including approximately 45% of resected NSCLC and even more frequently in SCLC specimens. Mutant forms of the p53 protein have transforming activity and interfere with the cell-cycle regulatory function of the wild-type protein. The majority of p53 gene mutations produce proteins with altered conformation and prolonged half life; these mutant proteins accumulate in the cell nucleus and can be detected by immunohistochemical staining. But protein overexpression has been reported in the absence of mutation. p53 protein overexpression or gene mutation is reported poor prognostic factor in breast cancer, but in lung cancer, its prognostic significance is controversial. Method : We investigated the p53 abnormalities by nucleotide sequencing, polymerase chain reaction-single strand conformation polymorphism(PCR-SSCP), and immunohistochemical staining. We correlated these results with each other and survival in 75 patients with NSCLC resected with curative intent. Overexpression of the p53 protein was studied immunohistochemically in archival paraffin- embedded tumor samples using the D07(Novocastra, U.K.) antibody. Overexpression of p53 protein was defined by the nuclear staining of greater than 25% immunopositive cells in tumors. Detection of p53 gene mutation was done by PCR-SSCP and nucleotide sequencing from the exon 5-9 of p53 gene. Result: 1) Of the 75 patients, 36%(27/75) showed p53 overexpression by immunohistochemical stain. There was no survival difference between positive and negative p53 immunostaining(overall median survival of 26 months, disease free median survival of 13 months in both groups). 2) By PCR-SSCP, 27.6%(16/58) of the patients showed mobility shift. There was no significant difference in survival according to mobility shift(overall median survival of 27 in patients without mobility shift vs 20 months in patients with mobility shift, disease free median survival of 8 months vs 10 months respectively). 3) Nucleotide sequence was analysed from 29 patients, and 34.5%(10/29) had mutant p53 sequence. Patients with the presence of gene mutations showed tendency to shortened survival compared with the patients with no mutation(overall median survival of 22 vs 27 months, disease free median survival of 10 vs 20 months), but there was no statistical significance. 4) The sensitivity and specificity of immunostain based on PCR-SSCP was 67.0%, 74.0%, and that of the PCR-SSCP based on the nucleotide sequencing was 91.8%, 96.2% respectively. The concordance rate between the immunostain and PCR-SSCP was 62.5%, and the rate between the PCR-SSCP and nucleotide sequencing was 95.3%. Conclusion : In terms of detection of p53 gene mutation, PCR-SSCP was superior to immunostaining. p53 gene abnormalities either overexpression or mutation were not a significant prognostic factor in NSCLC patients resected with curative intent. However, patients with the mutated p53 gene showed the trends of early relapse.

• Tuberculosis and Respiratory Diseases
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• v.56 no.5
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• pp.465-484
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• 2004

Background : Insulin-like growth factor (IGF)-binding protein-3 (IGFBP-3) inhibits the proliferation of non-small cell lung cancer (NSCLC) cells by inducing apoptosis. Methods : In this study, we investigated whether hypermethylation of IGFBP-3 promoter play an important role in the loss of IGFBP-3 expression in NSCLC. We also studied the mechanisms that mediate the silencing of IGFBP-3 expression in the cell lines which have hypermethylated IGFBP-3 promoter. Results : The IGFBP-3 promoter has hypermethylation in 7 of 15 (46.7%) NSCLC cell lines and 16 (69.7%) of 23, 7 (77.8%) of 9, 4 (80%) of 5, 4 (66.7 %) of 6, and 6 (100%) of 6 tumor specimens from patients with stage I, II, IIIA, IIIB, and IV NSCLC, respectively. The methylation status correlated with the level of protein and mRNA in NSCLC cell lines. Expression of IGFBP-3 was restored by the demethylating agent 5'-aza-2'-deoxycytidine (5'-aza-dC) in a subset of NSCLC cell lines. The Sp-1/ Sp-3 binding element in the IGFBP-3 promoter, important for promoter activity, was methylated in the NSCLC cell lines which have reduced IGFBP-3 expression and the methylation of this element suppressed the binding of the Sp-1 transcription factor. A ChIP assay showed that the methylation status of the IGFBP-3 promoter influenced the binding of Sp-1, methyl-CpG binding protein-2 (MeCP2), and histone deacetylase (HDAC) to Sp-1/Sp-3 binding element, which were reversed by by 5'-aza-dC. In vitro methylation of the IGFBP-3 promoter containing the Sp-1/Sp-3 binding element significantly reduced promoter activity, which was further suppressed by the overexpression of MeCP2. This reduction in activity was rescued by 5'-aza-dC. Conclusion : These findings indicate that hypermethylation of the IGFBP-3 promoter is one mechanism by which IGFBP-3 expression is silenced and MeCP2, with recruitment of HDAC, may play a role in silencing of IGFBP-3 expression. The frequency of this abnormality is also associated with advanced stages among the patients with NSCLC, suggesting that IGFBP-3 plays an important role in lung carcinogenesis/progression and that the promoter methylation status of IGFBP-3 may be a marker for early molecular detection and/or for monitoring chemoprevention efforts.

• Journal of Preventive Medicine and Public Health
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• v.33 no.3
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• pp.285-298
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• 2000

Objectives : To investigate the positive rate of Helicobacter pylori in patients with dyspepsia; medical compliance and related factors; the eradication rate a year after screening and related factors; the relationship between the eradication of Helicobacter pylori and the improvement of symptoms; and the estimated cost of three alternative approaches to treat Helicobacter pylori in the community. Methods : A total of 510 subjects with dyspeptic symptoms were selected and given the serological test in March 1998. The subjects were all adults over 30 years of age residing in Kyongju city. Results : Of the 510 selected subjects, 375 (73.5%) subjects proved positive for Helicobacter pylori on serological testing. Of these 304 (81.1%) who consented to an endoscopic examination, underwent a Campylobacter-like organism (CLO) test. Of these 304 subjects, 204 (67.1%), who had positive CLO test results, were given the triple therapy - tripotassium dicitrato bismuthate, amoxicillin, and metronidazole. To determine the eradication rate of Helicobacter pylori, 181 (88.1%) out of the 204 subjects who were given the triple therapy completed a follow-up urea breath test one year later. Of these, the Helicobacter pylori of 87(48.1%) subjects was eradicated. Among the 122 subjects who were medication compliant, the Helicobacter pylori eradication rate was 57.4% (70 subjects), while the eradication rates was only 28.8% (17subjects) in the non-compliant group. The Helicobacter pylori eradication was significantly related to compliance (p<0.01), but not to other characteristics and habits. The symptom improvement rate tended to be higher 62.1%), in the Helicobacter pylori eradicated group than in the non-eradicated group (59.6%). Conclusions : When the advantages and disadvantages of each alternative treatment were considered in the light of cost, antibiotic tolerance and the number of patients to be treated, alternative II was favorable in terms of cost. Alternative III was favorable in terms of the number of patients to be treated, antibiotic tolerance and early detection of gastric cancer. Further long-term research analyzing the cost-benefit and cost-effectiveness of each treatment will be needed as supporting material in creating new policies.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.