• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.496-507
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• 2023

This study investigated species identification and labeling compliance of 48 shrimp products sold in the Korean online markets. Species identification was conducted using the standard DNA barcoding method, using the cytochrome c oxidase subunit I gene. The obtained sequences were compared with those deposited in the NCBI GenBank and BOLD Systems databases. Additionally, phylogenetic analysis was performed to further verify the identified shrimp species. Consequently, 16 shrimp species were identified, including Penaeus vannamei, Pandalus borealis, Palaemon gravieri, Leptochela gracilis, Penaeus monodon, Pleoticus muelleri, Metapenaeopsis dalei, Euphausia pacifica, Lebbeus groenlandicus, Trachypenaeus curvirostris, Argis lar, Metanephrops thomsoni, Metapenaeopsis barbata, Alpheus japonicus, Penaeus chinensis, and Mierspenaeopsis hardwickii. The most prevalent species was Penaeus vannamei, found in 45.8% of the analyzed products. A significant mislabeling rate of 72.9% was found; however, upon excluding generic names such as shrimp, the mislabeling rate reduced to 10.4%. The mislabeling rate was higher in highly-processed products (89.3%) compared with that in minimally-processed products (50%). No correlation was found between the country of origin and mislabeling rate. The results of this study provide crucial data for future monitoring of shrimp products and improving the labeling of shrimp species in Korea.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.169-189
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• 2005

In order to derive a better classifier with a limited number of training examples, active teaming alternately repeats the querying stage fur category labeling and the subsequent learning stage fur rebuilding the calssifier with the newly expanded training set. To relieve the user from the burden of labeling, especially in an on-line environment, it is important to minimize the number of querying steps as well as the total number of query examples. We can derive a good classifier in a small number of querying steps by using only a small number of examples if we can select multiple of diverse, representative, and ambiguous examples to present to the user at each querying step. In this paper, we propose a cluster-based batch query selection method which can select diverse, representative, and highly ambiguous examples for efficient active learning. Experiments with various text data sets have shown that our method can derive a better classifier than other methods which only take into account the ambiguity as the criterion to select multiple query examples.

• Journal of Korean Neurosurgical Society
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• v.49 no.1
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• pp.26-30
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• 2011

Objective: The primary objective of this study was to perform a retrospective evaluation of the radiological and pathological features influencing the formation of peritumoral brain edema (PTBE) in meningiomas. Methods: The magnetic resonance imaging (MRI) and pathology data for 86 patients with meningiomas, who underwent surgery at our institution between September 2003 and March 2009, were examined. We evaluated predictive factors related to peritumoral edema including gender, tumor volume, shape of tumor margin, presence of arachnoid plane, the signal intensity (SI) of the tumor in T2-weighted image (T2WI), the WHO histological classification (GI, GII/GIII) and the Ki-67 antigen labeling index (LI). The edema-tumor volume ratio was calculated as the edema index (EI) and was used to evaluate peritumoral edema. Results: Gender (p=0.809) and pathological finding (p=0.084) were not statistically significantly associated with peritumoral edema by univariate analysis. Tumor volume was not correlated with the volume of peritumoral edema. By univariate analysis, three radiological features, and one pathological finding, were associated with PTBE of statistical significance: shape of tumor margin (p=0.001), presence of arachnoid plane (p=0.001), high SI of tumor in T2WI (p=0.001), and Ki-67 antigen LI (p=0.049). These results suggest that irregular tumor margins, hyperintensity in T2WI, absence of arachnoid plane on the MRI, and high Ki-67 LI can be important predictive factors that influence the formation of peritumoral edema in meningiomas. By multivariate analysis, only SI of the tumor in T2WI was statistically significantly associated with peritumoral edema. Conclusion: Results of this study indicate that irregular tumor margin, hyperintensity in T2WI, absence of arachnoid plane on the MRI, and high Ki-67 LI may be important predictive factors influencing the formation of peritumoral edema in meningiomas.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.11
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• pp.634-639
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• 2002

A Shortest Path Algorithm is the method to find the most efficient route among many routes from a start node to an end node. It is based on Labeling methods. In Labeling methods, there are Label-Setting method and Label-Correcting method. Label-Setting method is known as the fastest one among One-to-One shortest path algorithms. But Benjamin[1,2] shows Label-Correcting method is faster than Label-Setting method by the experiments using large road data. Since Graph Growth algorithm which is based on Label-Correcting method is made to find One-to-All shortest path, it is not suitable to find One-to-One shortest path. In this paper, we propose a new One-to-One shortest path algorithm. We show that our algorithm is faster than Graph Growth algorithm by extensive experiments.

• Journal of Oriental Neuropsychiatry
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• v.20 no.2
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• pp.1-17
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• 2009

Objectives : Alzheimer's disease(AD) is the most common form of dementia, which is characterized by progressive deterioration of memory and higher cortical functions that ultimately results in total degradation of intellectual and mental activities. Nokyongdaebo-tang(Lurongdabutang) has been usually used for the treatment for the deficiency syndrome dementia and amnesia. This experiment was designed to investigate the effect of the Nokyongdaebo-tang(Lurongdabutang) hot water extract on pathological AD model. Methods : The effects of the Nokyongdaebo-tang(Lurongdabutang) hot water extract on cultured spinal cord cells induced by ${\beta}$-amyloid were investigated. The effects of the Nokyongdaebo-tan(Lurongdabutang) hot water extract on the memory deficit mice induced by scopolamine were investigated. Results : 1. ${\beta}$-amyloid treatment on cultured spinal cord cells increased both GFAP-staining intensity of astrocytes and caspase 3 immunoreactivity on cultured cells. Then, Nokyongdaebo-tang(Lurongdabutang) treatment reduced the labeling intensity for both GFAP and caspase 3 proteins in culture cells. 2. Scopolamine treatment into mice increased levels of GFAP-positive astrocytes and caspase 3-labeled cells of the hippocampal subfields dentate hilar region, CA3 and CA1 area. In vivo administration of Nokyongdaebo-tang(Lurongdabutang) attenuated labeling intensity for those two proteins in the same hippocampal areas. Similar effects were observed by the treatment of galanthamine, an inhibitor of acetylcholinesterase. Conclusions : This experiment shows that the Nokyongdaebo-tang(Lurongdabutang) may play a protective role in damaged neural tissues. Since neuronal damage seen in degenerative brains such as AD are largely unknown, the current data may provide possible insight into therapeutic strategies for AD treatments. Nokyongdaebo-tang(Lurongdabutang) might be effective for the prevention and treatment of AD.

• Safety and Health at Work
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• v.11 no.2
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• pp.152-158
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• 2020

Background: The Globally Harmonized System of Classification and Labeling of Chemicals (GHS) was developed to enhance chemical classification and hazard communication systems worldwide. However, some of the elements such as building blocks and data sources have the potential to cause "disharmony" to the GHS, particularly in its classification results. It is known that some countries have developed their own lists of classified chemicals in accordance with the GHS to "standardize" the classification results within their respective countries. However, the lists of classified chemicals may not be consistent among these countries. Method: In this study, the lists of classified chemicals developed by the European Union, Japan, Malaysia, and New Zealand were selected for comparison of classification results for carcinogenicity, germ cell mutagenicity, and reproductive toxicity. Results: The findings show that only 54%, 66%, and 37% of the classification results for each Carcinogen, Mutagen and Reproductive toxicants hazard classes, respectively are the same among the selected countries. This indicates a "moderate" level of consistency among the classified chemicals lists. Conclusion: By using classification results for the carcinogenicity, germ cell mutagenicity, and reproductive toxicity hazard classes, this study demonstrates the "disharmony" in the classification results among the selected countries. We believe that the findings of this study deserve the attention of the relevant international bodies.

• Korean Journal of Community Nutrition
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• v.26 no.6
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• pp.482-494
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• 2021

Objectives: This study evaluated dietary behavior and nutritional status according to the metabolic syndrome status in Korean menopausal women. Methods: The subjects were 1,392 menopausal women aged 50 to 64 who took part in the Korea National Health and Nutrition Examination Survey of 2016 and 2017. Subjects were classified into normal (NOR) group, pre-metabolic syndrome (Pre-MetS) group, and metabolic syndrome (MetS) groups according to the number of metabolic syndrome risk factors present. Results: The overall prevalence of metabolic syndrome was 33.7%. Using the NOR group as a reference, the odds of belonging to the MetS group in Model 1 adjusted for age were higher at 53% (OR = 1.53, 95% CI:1.011-2.307) for 'not used' subjects compared to 'used' subjects of the nutrition labeling system. Using the NOR group as a reference, every 1g increase in the intake of monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA) decreased the odds of belonging to the MetS group in Model 1 adjusted for age by 3% (MUFA, OR = 0.97, 95% CI:0.946-0.991; PUFA, OR = 0.97, 95% CI:0.942-0.993). Conclusions: These results suggest that to reduce the number of risk factors of metabolic syndrome in menopausal women, nutritional education should emphasize the adequate intake of riboflavin, unsaturated fatty acids, protein, and calcium, and also encourage the recognition and use of nutritional labeling. Results of this study are expected to be utilized as basic data for the health management of menopausal women.

• Korean Journal of Community Nutrition
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• v.28 no.4
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• pp.269-281
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• 2023

Objectives: This study aimed to examine the frequency of convenience food consumption at convenience stores (CVS) and the CVS usage patterns of middle and high school students as well as to understand students' attitude toward sodium and sugar reduction. Methods: We used an online questionnaire for data collection. The questionnaire comprised five distinct categories: general characteristics, CVS usage, frequency of consumption according to convenience food menus at CVS, attitude toward sodium and sugar reduction, and adherence to dietary guidelines. Results: A total of 75 students from Seoul (14 middle school students and 61 high school students) participated in the study. Most respondents visit CVS 3-5 times a week. CVS are predominantly used during weekdays, mostly during lunch, and dinner. The students mostly checked the caloric content and expiration date as food labeling information. The participants were aware of the need to reduce their sugar and sodium intake. Among frequent CVS convenience food consumers, there was an increased consideration of the need to reduce their sugar and sodium consumption, despite their actual selection of foods with high sugar and sodium content. Additionally, they did not check the sugar and sodium levels indicated in food labeling. Further, the dietary action guide from the Ministry of Health and Welfare were poorly followed by most students. Conclusions: There is a need for nutrition education specifically addressing the sugar and sodium content of the convenience foods predominantly consumed by students. Additionally, educating students with frequent convenience food consumption to actively check the sugar and sodium information on food labels could help promote healthier food choices.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.421-437
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• 2015

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on information fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the performance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.

• MALSORI
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• no.58
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• pp.67-81
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• 2006

For a large corpus of time-aligned data, HMM based approaches are most widely used for automatic segmentation, providing a consistent and accurate phone labeling scheme. There are two methods for training in HMM. Flat starting method has a property that human interference is minimized but it has low accuracy. Bootstrap method has a high accuracy, but it has a defect that manual segmentation is required In this paper, a new algorithm is proposed to minimize manual work and to improve the performance of automatic segmentation. At first phase, voiced, unvoiced and silence classification is performed for each speech data frame. At second phase, the phoneme sequence is aligned dynamically to the voiced/unvoiced/silence sequence according to the acoustic phonetic rules. Finally, using these segmented speech data as a bootstrap, phoneme model parameters based on HMM are trained. For the performance test, hand labeled ETRI speech DB was used. The experiment results showed that our algorithm achieved 10% improvement of segmentation accuracy within 20 ms tolerable error range. Especially for the unvoiced consonants, it showed 30% improvement.