• Journal of Periodontal and Implant Science
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• v.23 no.3
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• pp.535-563
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• 1993

New techniques for regenerating the destructed periodontal tissue have been studied for many years. Current acceptable methods of promoting periodontal regeneration alre basis of removal of diseased soft tissue, root treatment, guided tissue regeneration, graft materials, biological mediators. Platelet-derived growth factor (PDGF) is one of polypeptide growth factor. PDGF have been reported as a biological mediator which regulate activities of wound healing progress including cell proliferation, migration, and metabolism. The purposes of this study is to evaluate the possibility of using the PDGF as a regeneration promoting agent for furcation involvement defect. Eight adult mongrel dogs were used in this experiment. The dogs were anesthetized with Pentobarbital Sodium (25-30 mg/kg of body weight, Tokyo chemical Co., Japan) and conventional periodontal prophylaxis were performed with ultrasonic scaler. With intrasulcular and crestal incision, mucoperiosteal flap was elevated. Following decortication with 1/2 high speed round bur, degree III furcation defect was made on mandibular second(P2) and fourth(P4) premolar. For the basic treatment of root surface, fully saturated citric acid was applied on the exposed root surface for 3 minutes. On the right P4 20ug of human recombinant PDGF-BB dissolved in acetic acid was applied with polypropylene autopipette. On the left P2 and right P2 PDGF-BB was applied after insertion of ${\beta}-Tricalcium$ phosphate(TCP) and collagen (Collatape) respectively. Left mandibular P4 was used as control. Systemic antibiotics (Penicillin-G benzathine and penicillin-G procaine, 1 ml per 10-25 1bs body weight) were administrated intramuscular for 2 weeks after surgery. Irrigation with 0.1% Chlorhexidine Gluconate around operated sites was performed during the whole experimental period except one day immediate after surgery. Soft diets were fed through the whole experiment period. After 2, 4, 8, 12 weeks, the animals were sacrificed by perfusion technique. Tissue block was excised including the tooth and prepared for light microscope with H-E staining. At 2 weeks after surgery, therer were rapid osteogenesis phenomenon on the defected area of the PDGF only treated group and early trabeculation pattern was made with new osteoid tissue produced by activated osteoblast. Bone formation was almost completed to the fornix of furcation by 8 weeks after surgery. New cementum fromation was observed from 2 weeks after surgery, and the thickness was increased until 8 weeks with typical Sharpey’s fibers reembedded into new bone and cementum. In both PDGF-BB with TCP group and PDGF-BB with Collagen group, regeneration process including new bone and new cementum formation and the group especially in the early weeks. It might be thought that the migration of actively proliferating cells was prohibited by the graft materials. In conclusion, platelet-derived growth factor can promote rapid osteogenesis during early stage of periodontal tissue regeneration.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.147-157
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• 2013

With the advent of the digital environment that can be accessed anytime, anywhere with the introduction of high-speed network, the free distribution and use of digital content were made possible. Ironically this environment is raising a variety of copyright infringement, and product images used in the online shopping mall are pirated frequently. There are many controversial issues whether shopping mall images are creative works or not. According to Supreme Court's decision in 2001, to ad pictures taken with ham products is simply a clone of the appearance of objects to deliver nothing but the decision was not only creative expression. But for the photographer's losses recognized in the advertising photo shoot takes the typical cost was estimated damages. According to Seoul District Court precedents in 2003, if there are the photographer's personality and creativity in the selection of the subject, the composition of the set, the direction and amount of light control, set the angle of the camera, shutter speed, shutter chance, other shooting methods for capturing, developing and printing process, the works should be protected by copyright law by the Court's sentence. In order to receive copyright protection of the shopping mall images by the law, it is simply not to convey the status of the product, the photographer's personality and creativity can be recognized that it requires effort. Accordingly, the cost of making the mall image increases, and the necessity for copyright protection becomes higher. The product images of the online shopping mall have a very unique configuration unlike the general pictures such as portraits and landscape photos and, therefore, the general image watermarking technique can not satisfy the requirements of the image watermarking. Because background of product images commonly used in shopping malls is white or black, or gray scale (gradient) color, it is difficult to utilize the space to embed a watermark and the area is very sensitive even a slight change. In this paper, the characteristics of images used in shopping malls are analyzed and a watermarking technology which is suitable to the shopping mall images is proposed. The proposed image watermarking technology divide a product image into smaller blocks, and the corresponding blocks are transformed by DCT (Discrete Cosine Transform), and then the watermark information was inserted into images using quantization of DCT coefficients. Because uniform treatment of the DCT coefficients for quantization cause visual blocking artifacts, the proposed algorithm used weighted mask which quantizes finely the coefficients located block boundaries and coarsely the coefficients located center area of the block. This mask improves subjective visual quality as well as the objective quality of the images. In addition, in order to improve the safety of the algorithm, the blocks which is embedded the watermark are randomly selected and the turbo code is used to reduce the BER when extracting the watermark. The PSNR(Peak Signal to Noise Ratio) of the shopping mall image watermarked by the proposed algorithm is 40.7~48.5[dB] and BER(Bit Error Rate) after JPEG with QF = 70 is 0. This means the watermarked image is high quality and the algorithm is robust to JPEG compression that is used generally at the online shopping malls. Also, for 40% change in size and 40 degrees of rotation, the BER is 0. In general, the shopping malls are used compressed images with QF which is higher than 90. Because the pirated image is used to replicate from original image, the proposed algorithm can identify the copyright infringement in the most cases. As shown the experimental results, the proposed algorithm is suitable to the shopping mall images with simple background. However, the future study should be carried out to enhance the robustness of the proposed algorithm because the robustness loss is occurred after mask process.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.

• Journal of the East Asian Society of Dietary Life
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• v.12 no.6
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• pp.554-565
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• 2002

The purpose of this study is to investigate the effect of raw Cassia tora L. powder added-diets on reducing cadmium accumulation in rats. The experimental animals were Sprague-Dawley family(♂, 4 weeks) which was classified into normal group CN, compared group CS, Cd-added group Cl and groups C2, C3, C4 in which 0.5, 1.0 and 1.5% of the Cassia tora L. powder are added, respectively. The growth rate and food efficiency ratio, and the amounts of accumulated cadmium in rats for S weeks were measured and analyzed. The results are as follows; 1. The rates of weight gain decreased in the order of C3>C2>C4>Cn>Cs>Cl groups, and Cl group to which only cadmium water had been fed was the lowest among them. The correlation between groups Cl and C3 was significantly different at the 1% level. 2. Food efficiency ratio(FER) decreased in the order of C3>C2>Cs>Cn>C4>Cl, and the FERs of C3, C2, CS, CN and C4 are greater than that of Cl by 22.87, 19.59, 18.54, 14.20 and 13.17%, respectively. 3. As for the Cassia tora L. powder-added groups, the amounts of cadmium accumulated in organs and tissues, that is, the brain, heart, spleen, liver, lungs, testicles. kidney, femoral muscle and leg bones were 0.45 $\pm$ 0.04 to 0.83$\pm$0.04, 1.68$\pm$0.02 to 2.16$\pm$0.02, 3.26$\pm$0.05 to 4.62$\pm$0.27, 37.52$\pm$0.09 to 47.71$\pm$0.73, 1.07$\pm$0.10 to 1.66$\pm$0.04, 1.04$\pm$0.06 to 1.24$\pm$0.08, 36.79$\pm$0.20 to 39.61 $\pm$0.53, 0.87$\pm$0.02 to 1.00$\pm$0.02 and 0.65$\pm$0.17 to 1.27 $\pm$ 0.06 $\mu\textrm{g}$/g, respectively. 4. The accumulated Cd content for C4 was the lowest among Cassia tora L. powder-added groups. When the results for C4 are compared with those for Cl, it is observed that each cadmium content accumulated in the brain, heart spleen, liver, lungs, testicles, kidney. femoral muscle and leg bones is dropped by 49.03, 22.56, 36.02, 35.75, 41.75, 36.20, 37.00, 22.77 and 56.67 %, respectively. On the other hand. the accumulated Cd content increased in the order of brain

• Clinical and Experimental Reproductive Medicine
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• v.35 no.2
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• pp.99-110
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• 2008

Objectives: Preimplantation genetic diagnosis (PGD) has become an assisted reproductive technique for couples carrying genetic conditions that may affect their offspring. Osteogenesis imperfecta (OI) is an autosomal dominant disorder of connective tissue characterized by bone fragility and low bone mass. At least 95% of cases are caused by dominant mutations in the COL1A1 or COL1A2. In this study, we report on our experience clinical outcomes with 5 PGD cycles for OI in two couples. Methods: Before clinical PGD, we assessed the amplification rate and allele drop-out (ADO) rate of alkaline lysis and nested PCR protocol using heterozygous patient's single lymphocytes in the pre-clinical diagnostic tests for OI. We performed 5 cycles of PGD for OI by nested PCR for the causative mutation loci, COL1A1 c.2452G>A and c.3226G>A, in case 1 and case 2, respectively. The PCR products were analyzed by agarose gel electrophoresis, restriction fragment length polymorphism (RFLP) analysis with HaeIII restriction enzyme in the case 1 and direct DNA sequencing. Results: We confirmed the causative mutation loci, COL1A1 c.2452G>A in case 1 and c.3226G>A in case 2. In the pre-clinical tests, the amplification rate was 94.2% and ADO rate was 22.5% in case 1, while 98.1% and 1.9% in case 2, respectively. In case 1, a total of 34 embryos were analyzed and 31 embryos (91.2%) were successfully diagnosed in 3 PGD cycles. Eight out of 19 embryos diagnosed as unaffected embryos were transferred in all 3 cycles, and in the third cycle, pregnancy was achieved and a healthy baby was delivered without any complications in July, 2005. In case 2, all 19 embryos (100.0%) were successfully diagnosed and 4 out of 11 unaffected embryos were transferred in 2 cycles. Pregnancy was achieved in the second cycle and the healthy baby was delivered in March, 2008. The causative locus was confirmed as a normal by amniocentesis and postnatal diagnosis. Conclusions: To our knowledge, these two cases are the first successful PGD for OI in Korea. Our experience provides a further demonstration that PGD is a reliable and effective clinical techniques and a useful option for many couples with a high risk of transmitting a genetic disease.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Animal Environmental Science
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• v.16 no.1
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• pp.81-92
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• 2010

This study was conducted to evaluate the effects of fermented diets including liquid by-products on nutrient digestibility and nitrogen balance in growing pigs. Treatments were 1) CON (basal diet), 2) F (fermented diet with basal diet), 3) KF (fermented diet with basal diet including 30% kale pomace), 4) AF (fermented diet with basal diet including 30% angelica keiskei pomace), 5) CF (fermented diet with basal diet including 30% carrot pomace) and 6) OF (fermented diet with basal diet including 30% grape pomace). A total of 24 pigs (41.74kg average initial body weight, Landrace $\times$ Yorkshire $\times$ Duroc), were assigned to 6 treatments, 4 replicates and 1 pig per metabolic cage in a randomized complete block (RCB) design. Pigs were housed in $0.5\times1.3m$ metabolic cage in a 17d digestibility trial. During the entire experimental period, Digestibility of dry matter (p<0.05) of treatment CON, F and CF were higher than other treatments. In crude protein digestibility, treatment F was higher than treatment AF and GF (p<0.05). Treatment GF showed the lowest digestibility of crude fiber among all treatments (p<0.05). In ether extract digestibility, treatment AF and CF showed higher than other treatments (p<0.05) except KF treatment. CF treatment showed the best digestibility of ash among all treatments (p<0.05). Whereas, For Ca and P digestibility, CF and OF treatments were improved than other treatments (p<0.05). Energy digestibility (p<0.05) of CON, F and CF treatments were higher than KF, AF and GF treatments. In total essential amino acid digestibility, F treatment was improved than AF, CF and GF treatments (p<0.05). In total non-essential amino acid digestibility, F treatment was higher than CON, AF and GF treatments (p<0.05). In total amino acid digestibility, F treatment was higher than AF and CF treatments (p<0.05) and GF treatment showed the lowest digestibility (p<0.05). In fecal nitrogen excretion ratio, GF treatment was greatest among all treatments (p<0.05) and F treatment was decreased than other treatments (p<0.05). In urinary nitrogen excretion ratio, CON and GF treatments showed the lowest among all treatments (p<0.05). In nitrogen retention ratio, CON treatment showed the high and KF treatment showed the lost among all treatments (p<0.05). Therefore, this experiment suggested that fermented diet could improve nutrient and amino acid digestibilities of growing pigs.

• Clinical and Experimental Reproductive Medicine
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• v.34 no.1
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• pp.41-48
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• 2007

Objective: To determine whether the presence of Y-chromosome microdeletion affects the outcome of in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) program. Methods: Fourteen couples with microdeletion in azoospermic factor (AZF)c region who attempted IVF/ICSI or cryopreserved and thawed embryo transfer cycles were enrolled. All of the men showed severe oligoasthenoteratoazoospermia (OATS) or azoospermia. As a control, 12 couples with OATS or azoospermia and having normal Y-chromosome were included. Both groups were divided into two subgroups by sperm source used in ICSI such as those who underwent testicular sperm extraction (TESE) and those used ejaculate sperm. We retrospectively analyzed our database in respect to the IVF outcomes. The outcome measures were mean number of good quality embryos, fertilization rates, implantation rates, $\beta$-hCG positive rates, early pregnancy loss and live birth rates. Results: Mean number of good quality embryos, implantation rates, $\beta$-hCG positive rates, early pregnancy loss rates and live birth rates were not significantly different between Y-chromosome microdeletion and control groups. But, fertilization rates in the Y-chromosome microdeletion group (61.1%) was significantly lower than that of control group (79.8%, p=0.003). Also, the subgroup underwent TESE and having AZFc microdeletion showed significantly lower fertilization rates (52.9%) than the subgroup underwent TESE and having normal Y-chromosome (79.5%, p=0.008). Otherwise, in the subgroups used ejaculate sperm, fertilization rates were showed tendency toward lower in couples having Y-chromosome microdeletion than couples with normal Y-chromosome. (65.5% versus 79.9%, p=0.082). But, there was no significance statistically. Conclusions: In IVF/ICSI cycles using TESE sperm, presence of V-chromosome microdeletion may adversely affect to fertilization ability of injected sperm. But, in cases of ejaculate sperm available for ICSI, IVF outcome was not affected by presence of Y-chromosome AZFc microdeletion. However, more larger scaled prospective study was needed to support our results.