• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.5 no.1
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• pp.11-18
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• 2002

Purpose: It is known that lactoferrin serves as a source of iron for H. pylori in gastric mucosa. This study was undertaken to investigate the relationship between lactoferrin and H. pylori infection coexistent with iron-deficiency anemia by determining the lactoferrin levels in gastric biopsy specimens, and by locating the major sites of lactoferrin expression, according to the presence or absence of iron-deficiency anemia. Methods: Fifty-five adolescents that underwent gastroduodenoscopy were divided into three groups: NL (n=19) for normal controls, HP (n=15) for patients with H. pylori, and IDA (n=21) for patients with H. pylori gastritis and coexisting iron-deficiency anemia. Histopathologic features were graded from to marked on the basis of the Updated Sydney System. The gastric mucosal levels of lactoferrin were measured by immunoassay. Immunohistochemical technique was used to allow identification of the location and quantification of the lactoferrin expression. Results: Lactoferrin levels in the antrum increased significantly, in proportion to, H. pylori density, polymorphonuclear cell infiltration, and chronic inflammation in the histologic specimens. Patients in the HP and IDA groups showed significantly increased mucosal levels of lactoferrin compared with that observed in the normal group (p=0.0001). The lactoferrin level in IDA group tended to be higher than that in the HP group (p=0.2614). The major sites of lactoferrin expression by immunohistochemistry were in glands and neutrophils within epithelium. Lactoferrin was stained weakly in NL, and strongly in HP and IDA. Conclusion: The lactoferrin sequestration in the gastric mucosa of IDA was remarkable, and this finding seems to give a clue that leads to the clarification of the mechanism by which H. pylori infection contributes to iron-deficiency anemia.

• Korean Journal of Organic Agriculture
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• v.22 no.4
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• pp.743-760
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• 2014

This study aimed to isolate and identify freshwater algae from the organic agricultural ecosystems and investigate its biological characteristics to study the possibility of utilizing a biomass freshwater algae in organic farming. In the survey area, average water temperature was $12.4{\sim}28.2^{\circ}C$ and the pH ranges were from 6.1 to 8.5. The solid culture method is more suitable than liquid culture method for isolation of freshwater algae with lower contamination level and higher isolation frequency. A total of 115 strains were isolated from six freshwater algae habitats in nine regions in Korea. BGMM (BG11 Modified Medium) amended with NaNO3 and $KNO_3$ as a nitrogen, and $Na_2CO_3$ as carbon source was designed to isolate and culture freshwater algae. Absorbance of freshwater algae culture has increased dramatically to four days and decreased after eight days after inoculation. CHK008 of the seven isolates showed the highest absorbance in seven days after culturing in BGMM. The optimal pH of BGMM for culturing freshwater algae was pH 6-7. As light intensity increased, growth of freshwater algae increased. Among the five kinds of carbon sources, glucose and galactose promoted good growth of freshwater algae in BGMM. The colony color of purified 16 green algae isolates showed a separation of green, dark and light green, and of them, eleven algae strains showed a strong fluorescent light under fluorescence microscopy. Cell size of the green algae showed a wide range of variation depending on the species. General morphology of the green algae strains was spherical. Chlamydomonas sp. was elliptical, and Chlorella sorokiniana was ellipsoidal and cylindrical. All strains of the green algae except for Chlamydomonas sp. did not have flagella. One isolate of Chlamydomonas sp. and five isolates of C. sorokiniana secreted mucus. Sixteen isolates of 16 green algae were identified as two family and six species, Chlorella vulgalis, C. sorokiniana, C. pyrenoidosa, C. kessleri, C. emersonii, and Chlamydomonas sp. based on their morphological characteristics.

• Journal of Food Hygiene and Safety
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• v.27 no.4
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• pp.366-374
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• 2012

Recently pathogenic E. coli is one of the main foodborne pathogens resulting in many patients in Korea. To understand the characteristics of pathogenic E. coli outbreaks in Korea, the epidemiological investigation reports of pathogenic E. coli outbreak in 2009 (41 reports) and in 2010 (27 reports) were collected in the web site of the Korea Centers for Disease Control and Prevention, reviewed and analysed in this study. The main places of the pathogenic E. coli outbreaks were food catering service area (64.8%) and restaurants (25.0%). The main type of the pathogens were EPEC (44.7%) and ETEC (34.2%). EAEC and EHEC was responsible for 10.5 and 9.2%, respectively. Eight of 68 outbreak cases were caused by more than 2 types of pathogenic E. coli which implicates the complicated contamination pathways of pathogenic E. coli. The incidence rate of pathogenic E. coli was $33.6{\pm}30.5%$ and the main symptoms were diarrhea, stomach ache, nausea, vomiting, and fever etc. The two identified food sources were identified as frozen hamburger pattie and squid-vegetable mixture. To improve the food source identification by epidemiological investigation, food poisoning notification to the agency should not be delayed, whole food items attributed the outbreak should be collected and detection method of the various pathogenic E. coli in food has to be improved. In conclusion, the characteristics between the EHEC outbreaks in the western countries and the EPEC or ETEC outbreaks in Korea needs to be distinguished to prepare food safety management plan. In addition, the development of the trace back system to find the contamination pathway with the improved detection method in food and systemic and cooperative support by the related agencies are necessary.

• Korean Journal of Ecology and Environment
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• v.52 no.3
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• pp.179-191
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• 2019

Heavy metals in stream water and sediments around industrial complex were studied in order to assess the contamination and to identify the potential source of metals. High variability has been observed for both dissolved and particulate phases in stream water with coefficient of variation (CV) ranging from 1.3 to 2.8. The highest metal concentrations in both phases were observed in Gunja for Ni and Cu, in Jungwang for Zn and Pb and in Shiheung for Cd, respectively. These results indicate that the different metal sources could be existing. The concentrations of the heavy metals in sediments decreased in the order of Cu>Zn>Pb>Cr>Ni>As>Cd>Hg, with mean of 2,549, 1,742, 808, 539, 163, 17.1, 5.8, $0.07mg\;kg^{-1}$, respectively. Mean of metal concentrations(except for As) in sediments showed the highest values at Shiheung stream comparing with other streams. In sediments, the percent exceedance of class II grade that metal may potentially harmful impact on benthic organism for Cr, Ni, Cu, Zn, Cd, Pb was about 57%, 62%, 84%, 60%, 68%, 81% for all stream sediments, respectively. Sediments were classified as heavily to extremely polluted for Cu and Cd, heavily polluted for Zn and Pb, based on the calculation of Igeo value. About 59% and 35% of sediments were in the categories of "poor" and "very poor" pollution status for heavy metals. Given the high metal concentrations, industrial wastes and effluents, having high concentrations of most metals originated from the manufacture and use of metal products in this region, might be discharged into the stream through sewer outlet. The streams receive significant amounts of industrial waste from the industrial facilities which is characterized by light industrial complexes of approximately 17,000 facilities. Thus, the transport of metal loads through streams is an important pathway for metal pollution in Shihwa Lake.

• Journal of Life Science
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• v.31 no.5
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• pp.453-463
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• 2021

Hearing loss is a group of clinically and genetically heterogeneous disorders characterized by congenital- to adult-onset deafness with frequent additional symptoms such as myopathy, nephropathy, and optic disorders. It is commonly divided into two types: syndromic, with no other symptoms, and nonsyndromic, with other symptoms. Autosomal recessive hearing loss is relatively frequent in Pakistan, which may be due in part to frequent consanguineous marriages. This study was performed by whole exome sequencing to determine the genetic causes in two Pakistani consanguineous families with autosomal recessive hearing loss. We identified a pathogenic homozygous variant (p.Leu326Gln in MYO7A) in a family with prelingual-onset hearing loss and two variants of uncertain significance (p.Val3094Ile in GPR98 and p.Asp56Gly in PLA2G6) in a family with early-onset hearing loss concurrent with muscular atrophy. The missense mutations in MYO7A and PLA2G6 were located in the highly conserved sites, and in silico analyses predicted pathogenicity, while the GPR98 mutation was located in the less conserved site, and most in silico analysis programs predicted its nonpathogenic effect. Homozygosity mapping showed that both alleles of the homozygous mutations identified in each family originated from a single founder; spread from this single source might be due to consanguineous marriages. This study will help provide exact molecular diagnosis and treatment for autosomal recessive hearing loss patients in Pakistan.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.3
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• pp.134-146
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• 2023

Phytophthora root rot (PRR) is a major soybean disease caused by an oomycete, Phytophthora sojae. PRR can be severe in poorly drained fields or wet soils. The disease management primarily relies on resistance genes called Rps (resistance to P. sojae). This study aimed to identify resistance loci associated with resistance to P. sojae isolate 40468 in Daepung × CheonAl recombinant inbred line (RIL) population. CheonAl is resistant to the isolate, while Daepung is generally susceptible. We genotyped the parents and RIL population via high-throughput single nucleotide polymorphism genotyping and constructed a set of genetic maps. The presence or absence of resistance to P. sojae was evaluated via hypocotyl inoculation technique, and phenotypic distribution fit to a ratio of 1:1 (R:S) (χ² = 0.57, p = 0.75), indicating single gene mediated inheritance. Single-marker association and the linkage analysis identified a highly significant genomic region of 55.9~56.4 megabase pairs on chromosome 18 that explained ~98% of phenotypic variance. Many previous studies have reported several Rps genes in this region, and also it contains nine genes that are annotated to code leucine-rich repeat or serine/threonine kinase within the approximate 500 kilobase pairs interval based on the reference genome database. CheonAl is the first domestic soybean genotype characterized for resistance against P. sojae isolate 40468. Therefore, CheonAl could be a valuable genetic source for breeding resistance to P. sojae.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.