• Analytical Science and Technology
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• v.17 no.3
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• pp.199-210
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• 2004

A new polystyrene-divinylbenzene chelating resin containing 4,5-dihydroxy-naphthalene-2,7-disulfonic acid (chromotropic acid : CTA) as functional group has been synthesized and characterized. The sorption and desorption properties of this chelating resin for Cr(III) ion and Cr(VI) ion including nine metal bloodstain. As a results, FOB test kit could be effectively applied to identification of human blood at chelating resin was stable in acidic and alkaline solution. The Cr(VI) ion is selectively separated from Cr (III) ion at pH 2 and the maximum sorption capacity of Cr(VI) ion is 1.2 mmol/g. In the presence of anions such as $F^-$, $SO{_4}^{2-}$, $CN^-$, $CH_3COO^-$, $NO{_3}^-$, the sorption of Cr(VI) ion was reduced but anions such as $PO{_4}^{3-}$ and $Cl^-$ revealed no interference effect. The elution order of metal ions obtained from breakthrough capacity and overall capacity at pH 2 was Cr(VI)>Sn(II)>Fe(III)>Cu(II)>Cd(II)${\simeq}Pb(II){\simeq}Cr(III){\simeq}Mn(II){\simeq}Ni(II){\simeq}Al(III)$. Desorption characteristics for Cr(VI) ion was investigated with desorption agents such as $HNO_3$, HCl, and $H_2SO_4$. It was found that the ion showed high desorption efficiency with 3 M HCl. As the result, the chelating resin, XAD-16-CTA was successfully applied to separation and preconcentration of Cr (VI) ion from several metal ions in metal finishing works.

• Journal of Life Science
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• v.25 no.7
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• pp.801-809
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• 2015

Homotypic cell adhesion (homotypic aggregation) in activated monocytes plays a central role in physiological and pathological processes including inflammatory responses, differentiation and migration. The extract of the Prunus mume Sieb. et Zucc. fruit (Maesil) has potential benefits to human health; such as anti-viral, anti-microbial, and anti-cancer activities. Indeed, Maesil extract may modulate inflammatory responses via interference with homotypic aggregation in monocytes. In the present study, the molecular mechanisms underpinning the therapeutic efficacy of Maesil extract in inflammatory diseases were investigated. It was found that Maesil extract inhibited homotypic aggregation in lipopolysaccharide (LPS)-activated monocytes. This was mediated by reduction of nitric oxide (NO) production, partly via inhibition of inducible nitric oxide synthase (iNOS) expression in LPS-activated THP-1 cells. It was confirmed that NO inhibition is a key mechanism in Maesil induced blockade of monocyte aggregation through identification of reversal of this inhibitory effect by the NO-producing agent S-nitroso-N-acetyl penicillamine (SNAP). In addition, Maesil extract significantly attenuated LPS-induced IκB-α phosphorylation and NF-κB translocation into the nucleus. In conclusion, Maesil extract exerts anti-inflammatory effects via inhibition of homotypic aggregation of LPS-activated monocytes through mechanisms involving the suppression of NO production and NF-κB activity, suggesting Maesil extract as a potential therapeutic candidate for the prevention and treatment of chronic inflammatory diseases.

• Restorative Dentistry and Endodontics
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• v.31 no.6
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• pp.437-444
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• 2006

This study was aimed to elucidate the biological function of OD314 (Apin protein), which is related to ameloblast differentiation and amelogenesis. Apin protein, calcifying epithelial odontogenic (pindborg) tumors (CEOTs)-associated amyloid, were isolated from CEOTs, and has similar nucleotide sequences to OD314. We examined expression of the OD314 mRNA using in-situ hybridization during tooth development in mice. Expression of OD314 and several enamel matrix proteins were examined in the cultured ameloblast cell line up to 28 days by reverse transcription-polymerase chain reaction (RT-PCR) amplification. After inactivation and over-expression of the OD314 gene in ameloblast cell lines using U6 vectordriven RNA interference and CMV-OD314 construct, RT-PCR were performed to evaluate the effect of the OD314 during amelogenesis. The results were as follows: 1. In in-situ hybridization, OD314 mRNAs were more strongly expressed in ameloblast than odontoblast. 2. When ameloblast cells were cultured in the diffcrentiation and mineralization medium for 28 days, the tuftelin mRNA expression was maintained from the beginning to day 14, and then gradually decreased to day 28. The expressions of amelogenin and enamelin were gradually decreased according to the ameloblast differentiation. 3. Inactivation of OD314 by U6-OD314 siRNA construct down-regulated the expression of OD314, MMP-20, and tuftelin, whereas over-expression of OD314 by CMV-OD314 construct up-regulated the expression of OD314 and MMP-20 without change in tuftelin. These results suggest that OD314 is considered as an ameloblast-enriched gene and may play the important roles in ameloblast differentiation and mineralization.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.8
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• pp.2052-2063
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• 1998

Various full-scale radio location systems have been developed since ground-based radio navigation systems appeared during World War II, and more recently global positioning systems (GPS) have been widely used as a representative location system. In addition, radio location systems based on cellular systems are intensively being studied as cellular services become more and more popular. However, these studies have been focused mainly on macrocellular systems of which based stations are mutually synchronized. There has been no study about systems of which based stations are asynchronous. In this paper, we proposed two radio location algorithms in microcellular CDMA systems of which base stations are asychronous. The one is to estimate the position of a personal station at the center of rectangular shaped area which approximates the realistic common area. The other, as a method based on road map, is to first find candidate positions, the centers of roads pseudo-range-distant from the base station which the personal station belongs to and then is to estimate the position by monitoring the pilot signal strengths of neighboring base stations. We compare these two algorithms with three wide-spread algorithms through computer simulations and investigate interference effect on measuring pseudo ranges. The proposed algorithms require no recursive calculations and yield smaller position error than the existing algorithms because of less affection of non-line-of-signt propagation in microcellular environments.

• Tuberculosis and Respiratory Diseases
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• v.63 no.1
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• pp.42-51
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• 2007

Background: TRAIL is a cytokine that selectively induces apoptosis in various cancer cell lines. Gefitinib is new targeted drug applied in lung cancer that selectively inhibits EGFR tyrosine kinase. However, lung cancers have shown an initial or acquired resistance to these drugs. This study examined the effect of IGF-1R and its blockade on enhancing the sensitivity of lung cancer cell lines to TRAIL and gefitinib. Methods: Two lung cancer cell lines were used in this study. NCI H460 is very sensitive to TRAIL and gefitinib. On the other hand, A549 shows moderate resistance to TRAIL and gefitinib. The IGF-1R blockade was performed using adenoviruses expressing the dominant negative IGF-1R and shRNA to IGF-1R and AG1024 (IGF-1R tyrosine kinase inhibitor). Results: The adenovirus expressing dominant negative IGF-1R(950st) induced the increased expression of defective IGF-1R on the lung cancer cell surface, and the adenovirus-shIGF-1R effectively decreased the level of IGF-1R expression on cell surface. The genetic blockade of IGF-1R by the adenovirus-dnIGF-1R and AG1024 increased the sensitivity of A549 cells to TRAIL. The reduction of IGF-1R by transduction with ad-shIGF-1R also increased the sensitivity of the A549 cells to gefitinib. Conclusion: The blockade of IGF-1R through various mechanisms increased the sensitivity of the lung cancer cell line that was resistant to TRAIL and gefitinib. However, further studies using other cell lines showing acquired resistance as well as in vivo animal experiments will be needed.

• Economic and Environmental Geology
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• v.54 no.1
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• pp.77-89
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• 2021

In this study, a method has been proposed to improve the performance of hydraulic property estimation model developed by Jeong et al. (2020). In their study, low-dimensional features of the annual groundwater level (GWL) fluctuation patterns extracted based on a Denoising autoencoder (DAE) was used to develop a regression model for predicting hydraulic properties of an aquifer. However, low-dimensional features of the DAE are highly dependent on the precipitation pattern even if the GWL is monitored at the same location, causing uncertainty in hydraulic property estimation of the regression model. To solve the above problem, a process for generating the GWL fluctuation pattern for conditioning the precipitation is proposed based on a conditional variational autoencoder (CVAE). The CVAE trains a statistical relationship between GWL fluctuation and precipitation pattern. The actual GWL and precipitation data monitored on a total of 71 monitoring stations over 10 years in South Korea was applied to validate the effect of using CVAE. As a result, the trained CVAE model reasonably generated GWL fluctuation pattern with the conditioning of various precipitation patterns for all the monitoring locations. Based on the trained CVAE model, the low-dimensional features of the GWL fluctuation pattern without interference of different precipitation patterns were extracted for all monitoring stations, and they were compared to the features extracted based on the DAE. Consequently, it can be confirmed that the statistical consistency of the features extracted using CVAE is improved compared to DAE. Thus, we conclude that the proposed method may be useful in extracting a more accurate feature of GWL fluctuation pattern affected solely by hydraulic characteristics of the aquifer, which would be followed by the improved performance of the previously developed regression model.

• Korean Journal of Environmental Agriculture
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• v.41 no.2
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• pp.82-94
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• 2022

BACKGROUND: Spiropidion and its metabolite are tetramic acid insecticide and require the establishment of an official analysis method for the safety management because they are newly registered in Korea. Therefore, this study was to determine the analysis method of residual spiropidion and its metabolite for the five representative agricultural products. METHODS AND RESULTS: Three QuEChERS methods (original, AOAC, and EN method) were applied to optimize the extraction method, and the EN method was finally selected by comparing the recovery test and matrix effect results. Various adsorbent agents were applied to establish the clean up method. As a result, the recovery of spiropidion was reduced when using the dispersive-SPE method with MgSO₄, primary secondary amine (PSA), graphitized carbon black (GCB) and octadecyl (C₁₈) in soybean. Color interference was minimized by selecting the case including GCB and C18 in addition to MgSO₄. This method was established as the final analysis method. LC-MS/MS was used for the analysis by considering the selectivity and sensitivity of the target pesticide and the analysis was performed in MRM mode. The results of the recovery test using the established analysis method and inter laboratory validation showed a valid range of 79.4-108.4%, with relative standard deviation and coefficient of variation were less than 7.2% and 14.4%, respectively. CONCLUSION(S): Spiropidion and its metabolite could be analyzed with a modified QuEChERS method, and the established method would be widely available to ensure the safety of residual insecticides in Korea.

• The Korean Society of Law and Medicine
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• v.23 no.3
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• pp.3-44
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• 2022

As a result of a close review focusing on the case of obstruction of epidemiological investigation by a religious group A in Daegu, which was a problem when the pandemic of Covid-19 infection began in Korea around February 2, 2020, when an epidemiological investigator requested a specific group to submit a list, While there have been cases where an act of not responding or submitting an edited omission list was sentenced to the effect that the act did not fall under an epidemiological investigation, in the case of non-submission of the visitor list for the B Center, even though a 'list of visitors' was requested. Regarding the fact of refusal without a justifiable reason, 'providing a list of persons entering the building is a key factual act that forms a link between epidemiological investigations accompanying an epidemiological investigation, and refusing to do so is also an act of refusal and obstruction of an epidemiological investigation. There are cases where it is possible to demand criminal punishment. Regardless of whether the request for submission of the membership list falls under the epidemiological investigation, there are cases in which the someones' actions correspond to the refusal or obstruction of the epidemiological investigation. A lower court ruling that if an epidemiological investigation is rejected or obstructed as a result of interfering with factual acts accompanying an epidemiological investigation, comprehensively considering whether or not the list has been diverted for purposes other than epidemiological investigation, the logic is persuasive. Epidemiological investigations such as surveys and human specimen collection and testing are conducted for each infectious disease patient or contact confirmed as a result of the epidemiological investigation, but epidemiological investigations conducted on individual individuals cannot exist independently of each other, and the This is because the process of identification and tracking is essential to an epidemiological investigation, and if someone intentionally interferes with or rejects the process of confirming this link, it will result in direct, realistic, and widespread interference with the epidemiological investigation. In this article, ① there are differences between an epidemiological investigation and a request for information provision under the Infectious Disease Control and Prevention Act, but there are areas that fall under the epidemiological investigation even in the case of a request for information, ② Considering the medical characteristics of COVID-19 and the continuity of the epidemiological investigation, the epidemiological investigator the fact that the act of requesting a list may fall under the epidemiological investigation, ③ that the offense of obstructing the epidemiological investigation in certain cases may constitute 'obstruction of Performance of Official Duties by Fraudulent Means', and ④ rejecting the request for information provision under the Infectious Disease Control and Prevention Act from September 29, 2020 In this case, it is intended to be helpful in the application of the Infectious Disease control and Prevention Act and the practical operation of epidemiological investigations in the future by pointing out the fact that a new punishment regulation of imprisonment or fine is being implemented.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of Global Scholars of Marketing Science
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• v.18 no.3
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• pp.89-114
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• 2008

Most of the extant studies on communication effects have been devoted to the typical issue, "what types of communication activities are more effective for brand awareness or brand attitudes?" However, little research has addressed another question on communication decisions, "what makes communication activities less effective?" Our study focuses on factors negatively influenced on the efficiency of communication activities, especially of Advertising. Some studies have introduced concepts closely related to our topic such as consumer confusion, brand confusion, or belief confusion. Studies on product belief confusion have found some factors misleading consumers to misunderstand the physical features of products. Studies on brand confusion have uncovered factors making consumers confused on brand names. Studies on advertising confusion have tested the effects of ad models' employed by many other firms for different products on communication efficiency. We address a new concept, Ad noises, which are any factors interfering with consumers exposed to a particular advertisement in understanding messages provided by advertisements. The objective of this study is to understand the effects of ad noises caused by ad models on brand awareness and brand attitude. There are many different types of AD noises. Particularly, we study the effects of AD noises generated from ad model selection decision. Many companies want to employ celebrities as AD models while the number of celebrities who command a high degree of public and media attention are limited. Inevitably, several firms have been adopting the same celebrities as their AD models for different products. If the same AD model is adopted for TV commercials for different products, consumers exposed to those TV commercials are likely to fail to be aware of the target brand due to interference of TV commercials, for other products, employing the same AD model. This is an ad noise caused by employing ad models who have been exposed to consumers in other advertisements, which is the first type of ad noises studied in this research. Another type of AD noises is related to the decision of AD model replacement for the same product advertising. Firms sometimes launch another TV commercial for the same products. Some firms employ the same AD model for the new TV commercial for the same product and other firms employ new AD models for the new TV commercials for the same product. The typical problem with the replacement of AD models is the possibility of interfering with consumers in understanding messages of the TV commercial due to the dissimilarity of the old and new AD models. We studied the effects of these two types of ad noises, which are the typical factors influencing on the effect of communication: (1) ad noises caused by employing ad models who have been exposed to consumers in other advertisements and (2) ad noises caused by changing ad models with different images for same products. First, we measure the negative influence of AD noises on brand awareness and attitudes, in order to provide the importance of studying AD noises. Furthermore, our study unveiled the mediating conditions(variables) which can increase or decrease the effects of ad noises on brand awareness and attitudes. We study the effects of three mediating variables for ad noises caused by employing ad models who have been exposed to consumers in other advertisements: (1) the fit between product image and AD model image, (2) similarity between AD model images in multiple TV commercials employing the same AD model, and (3) similarity between products of which TV commercial employed the same AD model. We analyze the effects of another three mediating variables for ad noises caused by changing ad models with different images for same products: (1) the fit of old and new AD models for the same product, (2) similarity between AD model images in old and new TV commercials for the same product, and (3) concept similarity between old and new TV commercials for the same product. We summarized the empirical results from a field survey as follows. The employment of ad models who have been used in advertisements for other products has negative effects on both brand awareness and attitudes. our empirical study shows that it is possible to reduce the negative effects of ad models used for other products by choosing ad models whose images are relevant to the images of target products for the advertisement, by requiring ad models of images which are different from those of ad models in other advertisements, or by choosing ad models who have been shown in advertisements for other products which are not similar to the target product. The change of ad models for the same product advertisement can positively influence on brand awareness but positively on brand attitudes. Furthermore, the effects of ad model change can be weakened or strengthened depending on the relevancy of new ad models, the similarity of previous and current ad models, and the consistency of the previous and current ad messages.