• Animal Bioscience
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• v.37 no.8
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• pp.1345-1354
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• 2024

Objective: The objective of this study was to identify candidate genes that play important roles in skeletal muscle development in ducks. Methods: In this study, we investigated the transcriptional sequencing of embryonic pectoral muscles from two specialized lines: Liancheng white ducks (female) and Cherry valley ducks (male) hybrid Line A (LCA) and Line C (LCC) ducks. In addition, prediction of target genes for the differentially expressed mRNAs was conducted and the enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes signaling pathways were further analyzed. Finally, a protein-to-protein interaction network was analyzed by using the target genes to gain insights into their potential functional association. Results: A total of 1,428 differentially expressed genes (DEGs) with 762 being up-regulated genes and 666 being down-regulated genes in pectoral muscle of LCA and LCC ducks identified by RNA-seq (p<0.05). Meanwhile, 23 GO terms in the down-regulated genes and 75 GO terms in up-regulated genes were significantly enriched (p<0.05). Furthermore, the top 5 most enriched pathways were ECM-receptor interaction, fatty acid degradation, pyruvate degradation, PPAR signaling pathway, and glycolysis/gluconeogenesis. Finally, the candidate genes including integrin b3 (Itgb3), pyruvate kinase M1/2 (Pkm), insulin-like growth factor 1 (Igf1), glucose-6-phosphate isomerase (Gpi), GABA type A receptor-associated protein-like 1 (Gabarapl1), and thyroid hormone receptor beta (Thrb) showed the most expression difference, and then were selected to verification by quantitative real-time polymerase chain reaction (qRT-PCR). The result of qRT-PCR was consistent with that of transcriptome sequencing. Conclusion: This study provided information of molecular mechanisms underlying the developmental differences in skeletal muscles between specialized duck lines.

• The Journal of Korean Academy of Prosthodontics
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• v.62 no.2
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• pp.123-130
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• 2024

There are various methods for restoring the dentition of completely edentulous patients. Removable complete dentures have the advantage of being relatively economical, but they can be uncomfortable to wear. With the introduction of implant prosthodontics, various options such as implant-supported overdentures and hybrid prostheses have become available. If there is inadequate remaining ridge or limited financial resources, an overdenture supported by a few implants with additional attachments may be more suitable. In this case, due to severe peri-implantitis and other complications, the implants were removed. Subsequently, four implants (two on each side) were placed in the maxilla and a milled-bar with attachment was fabricated for each side of the maxilla.

• The Journal of the Korean Society for Microbiology
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• v.21 no.1
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• pp.151-161
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• 1986

In order to develop sensitive and sepcific assay methods for E. coli heat labile enterotoxin(LT) hybridoma cell lines secreting LT specific monoclonal antibody were obtained. LT was purified from cell lysate of E. coli O15H11. The steps included disruption of bacteria by French pressure, DEAE Sephacel ion exchange chromatography, Sephadex G200 gel filtration, and second DEAE Sephacel ion exchange chromatography, successively. Spleen cells from Balb/c mice immunized with the purified LT and $HGPRT^{(-)}$ plasmacytomas, $P3{\times}63Ag8.V653$ were mixed and fused by 50% (w/v) PEG. Hybrid cells were grown in 308 wells out of 360 wells, and 13 wells out of them secreted antibodies reacting to LT. Among these hybridoma cell 1G8-1D1 cell line was selected since it had produced high-titered monoclonal antibody continuously. By using culture supernatant and ascites from 1G8-1D1 cells the monoclonal antibody was characterized, and an assay system for detecting enterotoxigenic E. coli was established by double sandwich enzyme-linked immunosorbent assay (ELISA). The following results were obtained. 1. Antibody titers of culture supernatant and ascites from 1G8-1D1 hybridoma cells were 512, and 102, 400, respectively by GM1-ELISA and its immunoglobulin class was IgM. 2. The maximum absorption ratio of 1G8-1D1 cell culture supernatant to LT was 90% at $300\;{\mu}g/ml$ of LT concentration. LT concentration shown at 50% absorption ratio was $103.45{\mu}g$ and the absorption ratio was decreased with tile reduction of LT concentration. This result suggests that monoclonal antibody from 1G8-1D1 hybridoma cell bound with LT specifically. 3. The reactivities of 1G8-1D1 cell culture supernatant to LT and V. cholerae enterotoxin(CT) were 0.886 and 0.142(O.D. at 492nm) measured by the GM1-ELISA, indicating 1G8-1D1 monoclonal antibody reacted specifically with LT but not with CT. 4. The addition of 0.1ml of ascites to 0.6mg and 0.12mg of LT decreased the vascular permeability factor to 41% and 44% respectively, but it did not completely neutralize LT. 5. By double sandwich ELISA using monoclonal antibody, as little as 75ng of the purified LT per ml could be detected. 6. The results by assay of detecting LT in culture supernatants of 14 wild strains E. coli isolated from diarrhea patients by the double sandwich ELISA were almost the same level as those by reverse passive latex agglutination.

• Proceedings of the Korean Society of Fisheries Technology Conference
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• 2000.05a
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• pp.71-73
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• 2000

Campylobacter jejuni is most frequently identified cause of cause of acute diarrhoeal infections in developeed countries, exceeding rates of illness caused by both salmonella and shigilla(Skirrow, 1990 ; Lior 1994). Previous studies on campylobacter jejuni contamination of commercial broiler carcasses in u.s.(Stern, 1992). Most cases of the disease result from indirect transmission of Campylobactor from animals via milk, water and meat. In addition to Campylobactor jejuni. the closely relates species Campylobactor coli and Campylobactor lari have also been implicated as agents of gastroenteritis in humans. Campylobactor coli represented only approximately 3% of the Campylobactor isolates from patients with Campylobactor enteritis(Griffiths and Park, 1990) whereas Campylobactor coli is mainly isolated from pork(Lmmerding et al., 1988). Campylobactor jejuni has also been isolated from cases of bacteremia, appendicitis and, recently, has been associated with Guillai-Barre syndrome(Allos and Blaser, 1994; von Wulffen et al., 1994; Phillips, 1995). Studies in volunteers indicated that the infectious dose for Campylobactor jejuni is low(about 500 organisms)(Robinson, 1981). The methods traditionally used to detect Campylobactor ssp. in food require at least two days of incubation in an enrichment broth followed by plating and two days of incubation on complex culture media containing many antibiotics(Goossens and Butzler, 1992). Finnaly, several biochemical tests must be done to confirm the indentification at the species level. Therfore, sensitive and specific methods for the detection of small numbers of Campylobactor cells in food are needed. Polymerase chain reaction(PCR) assays targeting specific DNA sequences have been developed for the detection of Campylobactor(Giesendorf and Quint, 1995; Hemandex et al., 1995; Winter and Slavidk, 1995). In most cases, a short enrichment step is needed to enhance the sensitivity of the assay prior to detection by PCR as the number of bacteria in the food products is low in comparison with those found in dinical samples, and because the complex composition of food matrices can hinder the PCR and lower its sensitivity. However, these PCR systems are technically demanding to carry out and cumbersome when processing a large number of samples simutaneously. In this paper, an immunomagnetic method to concentrate Campylobactor cells present in food or clinical samples after an enrichment step is described. To detect specifically the thermophilic Campylobactor. a monoclonal antibody was adsorbed on the surface of the magnetic beads which react against a major porin of 45kDa present on the surface of the cells(Huyer et al., 1986). After this partial purification and concentration step, detection of bound cells was achieved using a simple, inexpensive microtitre plate-based hybridization system. We examined two alternative detection systems, one specific for thermophilic Campylobactor based on the detection of 23S rRNA using an immobilized DNA probe. The second system is less specific but more sensitive because of the high copy number of the rRNA present in bacterial cell($10^3-10^4$). By using specific immunomagnetic beads against thermophilic Campylobactor, it was possible to concentrate these cells from a heterogeneous media and obtain highly specific hybridization reactions with good sensitivity. There are several advantages in using microtitre plates instead of filter membranes or other matrices for hybridization techniques. Microtitre plates are much easier to handle than filter membranes during the adsorption, washing, hybridization and detection steps, and their use faciilitates the simultanuous analysis of multiple sample. Here we report on the use of a very simple detection procedure based on a monoclonal anti-RNA-DNA hybrid antibody(Fliss et al., 1999) for detection of the RNA-DNA hybrids formed in the wells.

• The Journal of Korean Academy of Prosthodontics
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• v.46 no.2
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• pp.105-115
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• 2008

Statement of problem: Proper finishing and polishing enhance both the esthetics and the longevity of restored teeth. Blade finishing technique would be suited for smoothing and finishing. Evaluation of this technique are necessary. Purpose: The purpose of this study was to evaluate the blade finishing and polishing procedures on the surface profile and roughness of ormocer-based composite resin and nanohybrid composite resin. Material and methods: The material included a ormocer-based composite resin ($Admira^{(R)}$ & $Admira^{(R)}$ Flow); a nanohybrid composite resin ($Grandio^{(R)}$ & $Grandio^{(R)}$ Flow). One hundred forty specimens of each group were prepared using a mylar strip and randomly divied into blade finishing and rubber polishing groups (n=10). The average surface roughness (Ra) in micrometers was measured and the surface profile was examined by scanning electron microscopy (SEM) (Magnification ${\times}$ 200). The data were analyzed by Mann-Whitney Test at 0.05 significance level. Conclusion: The results of this study indicated that the mylar strip produced the smoothest surface on all materials and among the finishing-polishing methods was not significanct difference (P>0.05). Ormocer-based flowable composite resin performedthe lowest variability in initial surface roughness among the tested materials.

• Restorative Dentistry and Endodontics
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• v.35 no.2
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• pp.125-133
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• 2010

Objectives: Rapid polymerization of overlying composite resin causes high polymerization shrinkage stress at the adhesive layer. In order to alleviate the shrinkage stress, increasing the light intensity over the first 5 seconds was suggested as an exponential curing mode by an LED light curing unit (Elipar FreeLight2, 3M ESPE). In this study, the effectiveness of the exponential curing mode on reducing stress was evaluated with measuring microtensile bond strength of three adhesives after the overlying composite resin was polymerized with either continuous or exponential curing mode. Methods: Scotchbond Multipurpose Plus (MP, 3M ESPE), Single Bond 2 (SB, 3M ESPE), and Adper Prompt (AP, 3M ESPE) were applied onto the flat occlusal dentin of extracted human molar. The overlying hybrid composite (Denfil, Vericom, Korea) was cured under one of two exposing modes of the curing unit. At 48h from bonding, microtensile bond strength was measured at a crosshead speed of 1.0 mm/min. The fractured surfaces were observed under FE-SEM. Results: There was no statistically significant difference in the microtensile bond strengths of each adhesive between curing methods (Two-way ANOVA, p > 0.05). The microtensile bond strengths of MP and SB were significantly higher than that of AP (p < 0.05). Mixed failures were observed in most of the fractured surfaces, and differences in the failure mode were not observed among groups. Conclusion: The exponential curing method had no beneficial effect on the microtensile dentin bond strengths of three adhesives compared to continuous curing method.

• Journal of Christian Education in Korea
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• v.74
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• pp.107-134
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• 2023

The purpose of this study is to provide a foundation for establishing the correct direction of education that utilizes artificial intelligence, a key technology of the Fourth Industrial Revolution, in the context of Christian education. To achieve this, theoretical and literature research was conducted. First, the research analyzed the historical development of artificial intelligence to understand its characteristics. Second, the research analyzed the use of artificial intelligence in convergence education from an educational perspective and examined the current policy direction in South Korea. Through this analysis, the research examined the direction of Christian education in the era of artificial intelligence. In particular, the research critically examined the perspectives of continuity and change in the context of Christian education in the era of artificial intelligence. The research reflected upon the fundamental educational purposes of Christian education that should remain unchanged despite the changing times. Furthermore, the research deliberated on the educational curriculum and teaching methods that should adapt to the changing dynamics of the era. In conclusion, this research emphasizes that even in the era of artificial intelligence, the fundamental objectives of Christian education should not be compromised. The utilization of artificial intelligence in education should serve as a tool that fulfills the mission permitted by God. Therefore, Christian education should remain centered around God, rooted in the principles of the Bible. Moreover, Christian education should aim to foster creative and convergent Christian nurturing. To achieve this, it is crucial to provide learners with an educational environment that actively utilizes AI-based hybrid learning environments and metaverse educational platforms, combining online and offline learning spaces. Moreover, to enhance learners' engagement and effectiveness in education, it is essential to actively utilize AI-based edutech that reflects the aforementioned educational environments. Lastly, in order to cultivate Christian learners with dynamic knowledge, it is crucial to employ a variety of teaching and learning methods grounded in constructivist theories, which emphasize active learner participation, collaboration, inquiry, and reflection. These approaches seek to align knowledge with life experiences, promoting a holistic convergence of faith and learning.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.73-92
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• 2014

An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the user to a particular item using a similar item subset and a similar user subset composed based on the preference of users to items. For this reason, if the density of the user preference matrix is low, the reliability of the recommendation system will decrease rapidly. Therefore, the difficulty of creating a similar item subset and similar user subset will be increased. In addition, as the scale of service increases, the time needed to create a similar item subset and similar user subset increases geometrically, and the response time of the recommendation system is then increased. To solve these problems, this paper suggests a collaborative filtering technique that adapts a condition actively to the model and adopts the concepts of a context-based filtering technique. This technique consists of four major methodologies. First, items are made, the users are clustered according their feature vectors, and an inter-cluster preference between each item cluster and user cluster is then assumed. According to this method, the run-time for creating a similar item subset or user subset can be economized, the reliability of a recommendation system can be made higher than that using only the user preference information for creating a similar item subset or similar user subset, and the cold start problem can be partially solved. Second, recommendations are made using the prior composed item and user clusters and inter-cluster preference between each item cluster and user cluster. In this phase, a list of items is made for users by examining the item clusters in the order of the size of the inter-cluster preference of the user cluster, in which the user belongs, and selecting and ranking the items according to the predicted or recorded user preference information. Using this method, the creation of a recommendation model phase bears the highest load of the recommendation system, and it minimizes the load of the recommendation system in run-time. Therefore, the scalability problem and large scale recommendation system can be performed with collaborative filtering, which is highly reliable. Third, the missing user preference information is predicted using the item and user clusters. Using this method, the problem caused by the low density of the user preference matrix can be mitigated. Existing studies on this used an item-based prediction or user-based prediction. In this paper, Hao Ji's idea, which uses both an item-based prediction and user-based prediction, was improved. The reliability of the recommendation service can be improved by combining the predictive values of both techniques by applying the condition of the recommendation model. By predicting the user preference based on the item or user clusters, the time required to predict the user preference can be reduced, and missing user preference in run-time can be predicted. Fourth, the item and user feature vector can be made to learn the following input of the user feedback. This phase applied normalized user feedback to the item and user feature vector. This method can mitigate the problems caused by the use of the concepts of context-based filtering, such as the item and user feature vector based on the user profile and item properties. The problems with using the item and user feature vector are due to the limitation of quantifying the qualitative features of the items and users. Therefore, the elements of the user and item feature vectors are made to match one to one, and if user feedback to a particular item is obtained, it will be applied to the feature vector using the opposite one. Verification of this method was accomplished by comparing the performance with existing hybrid filtering techniques. Two methods were used for verification: MAE(Mean Absolute Error) and response time. Using MAE, this technique was confirmed to improve the reliability of the recommendation system. Using the response time, this technique was found to be suitable for a large scaled recommendation system. This paper suggested an Adaptive Clustering-based Collaborative Filtering Technique with high reliability and low time complexity, but it had some limitations. This technique focused on reducing the time complexity. Hence, an improvement in reliability was not expected. The next topic will be to improve this technique by rule-based filtering.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.49-63
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• 2018

Purpose : The purpose of this study is investigating the changes of treatment plan and comparing skin dose with or without the skin flash. To investigate optimal applications of the skin flash, the changes of skin dose of each plans by various thicknesses of skin flash were measured and analyzed also. Methods and Material : Anthropomorphic phantom was scanned by CT for this study. The 2 fields hybrid IMRT and the 6 fields static IMRT were generated from the Eclipse (ver. 13.7.16, Varian, USA) RTP system. Additional plans were generated from each IMRT plans by changing skin flash thickness to 0.5 cm, 1.0 cm, 1.5 cm, 2.0 cm and 2.5 cm. MU and maximum doses were measured also. The treatment equipment was 6MV of VitalBeam (Varian Medical System, USA). Measuring device was a metal oxide semiconductor field-effect transistor(MOSFET). Measuring points of skin doses are upper (1), middle (2) and lower (3) positions from center of the left breast of the phantom. Other points of skin doses, artificially moved to medial and lateral sides by 0.5 cm, were also measured. Results : The reference value of 2F-hIMRT was 206.7 cGy at 1, 186.7 cGy at 2, and 222 cGy at 3, and reference values of 6F-sIMRT were measured at 192 cGy at 1, 213 cGy at 2, and 215 cGy at 3. In comparison with these reference values, the first measurement point in 2F-hIMRT was 261.3 cGy with a skin flash 2.0 cm and 2.5 cm, and the highest dose difference was 26.1 %diff. and 5.6 %diff, respectively. The third measurement point was 245.3 cGy and 10.5 %diff at the skin flash 2.5 cm. In the 6F-sIMRT, the highest dose difference was observed at 216.3 cGy and 12.7 %diff. when applying the skin flash 2.0 cm for the first measurement point and the dose difference was the largest at the application point of 2.0 cm, not the skin flash 2.5 cm for each measurement point. In cases of medial 0.5 cm shift points of 2F-hIMRT and 6F-sIMRT without skin flash, the measured value was -75.2 %diff. and -70.1 %diff. at 2F, At -14.8, -12.5, and -21.0 %diff. at the 1st, 2nd and 3rd measurement points, respectively. Generally, both treatment plans showed an increase in total MU, maximum dose and %diff as skin flash thickness increased, except for some results. The difference of skin dose using 0.5 cm thickness of skin flash was lowest lesser than 20 % in every conditions. Conclusion : Minimizing the thickness of skin flash by 0.5 cm is considered most ideal because it makes it possible to keep down MUs and lowering maximum doses. In addition, It was found that MUs, maximum doses and differences of skin doses did not increase infinitely as skin flash thickness increase by. If the error margin caused by PTV or other factors is lesser than 1.0 cm, It is considered that there will be many advantages in with the skin flash technique comparing without it.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.