• Korean Journal of Heritage: History & Science
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• v.49 no.1
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• pp.4-21
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• 2016

The purpose of this study is to investigate changes of Jinju Sword Dance, characteristics of the changes, and the current condition of its preservation and succession after the designation as the important intangible cultural property no. 12 in January 16th, 1967. In other words, this study understands the situation which has established the present state of after changes over generations. As of now. the year of 2015, the 3 generation holders have been approved since 1967. In 1967, 8 members of $1^{st}$ generation holders were selected from gisaengs of Gwonbeon. However, the succession training was incomplete due to conflicts among the holders, the deaths of some holders, and economic activities of the individuals. As the need of a pivot for succession training and activities was rising, Seong, Gye-Ok was additionally approved as the $2^{nd}$ generation holder on June $21^{st}$, 1978. Seong, Gye-Ok who had never been a gisaeng had dramatically changed with a lot of new attempts. After the death of Seong, Gye-Ok in 2009, Kim, Tae-Yeon and Yu, Yeong-Hee were approved as the $3^{rd}$ generation holders in February, 2010. Based on the resources including the "Cultural Research Reports of Important Intangible Cultural Properties" in 1966 and videos up to 2014, the changes of the dance and surroundings are as follow. 1. The formation of musical accompaniment has been changed during the 3 generations. In the video of the $1^{st}$ generation(in 1970), the performance lasted about 15 minutes, whereas the performance lasted 25 minutes in the video of the $2^{nd}$ generation. Yumbuldoduri rhythm was considered as Ginyumbul(Sangryeongsan) and played more slowly. The original dance requiring only 15 rhythms was extended to 39 rhythms to provide longer performance time. In the $3^{rd}$ generation, the dance recovered 15 rhythms using the term Ginyumbul. The facts that Yumbul was played for 3 minutes in the $1^{st}$ generation but for 5 minutes in the 3rd generation shows that there was tendency pursuing the slowness from the $2^{nd}$ generation. 2. For the composition of the Dance, the performance included additional 20 rhythms of Ginyumbul and Ah(亞)-shaped formation from the $2^{nd}$ generation. From the $3^{rd}$ generation, the performance excluded the formation which had no traditional base. For the movement of the Dance, the bridge poses of Ggakjittegi and Bangsukdoli have been visibly inflexible. Also, the extention of time value in 1 beat led the Dance less vibrant. 3. At the designation as an important intangible cultural property (in 1967), the swords with rotatable necks were used, whereas the dancers had been using the swords with non-rotatable necks since late 1970s when the $2^{nd}$ generation holder began to used them. The swords in the "Research Reports" (in 1966) was pointy and semilunar, whereas the straight swords are being used currently. The use of the straight swords can be confirmed from the videos after 1970. 4. There is no change in wearing Jeonlib, Jeonbok, and Hansam, whereas the arrangement of Saekdong of Hansam was different from the arrangement shown in the "Research Reports". Also, dancers were considered to begin wearing the navy skirts when the swords with non-rotatable necks began to be used. Those results showed that has been actively changed for 50 years after the designation. The $2^{nd}$ generation holder, Seong, Gye-Ok, was the pivot of the changes. However, , which was already designated as an important intangible cultural property, is considered to be only a victim of the change experiment from the project to restore Gyobang culture in Jinju, and it is a priority to conduct studies with historical legitimacy. First of all, the slowing beat should be emphasized as the main fact to reduce both the liveliness and dynamic beauty of the Dance.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.2
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• pp.110-118
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• 2021

This study was conducted to investigate the effects of species and varieties of summer forage crops on growth characteristics and productivity in Sihwa reclaimed land. The summer forage crops used in the trial were silage corn, sorghum×sudangrass hybrid(SSH), and proso millet. For each forage species, Gwangpyeongok(GPO), P15453, P1952 and P2088 were used for silage corn, and 877F, Green star, Honey chew, and Turbo gold cultivars were used for SSH. For proso millet, Ibaekchal, Geumsilchal and Manhongchal developed by the National Institute of Crop Science were used. Silage corn and SSH were sown on May 21, 2019 and proso millet on June 4, and harvested on September 2. There was no significant difference in plant and ear height of silage corn among varieties. P1543 was the highest and P2088 was the lowest in yield of silage corn, but there was no significant difference among treatments. Among the SSH, the plant height of 877F was the highest and Turbo gold variety had the smallest (p<0.05). As for the dry matter(DM) yields, 877F had the highest at 3,862 kg/ha and Green star had the lowest at 2,669 kg/ha (p<0.05). The fresh matter yield of proso millet was 15,778 kg/ha, which was higher than that of corn or SSH, The average dry matter yield was 4,780 kg/ha, and Ibaekchal variety had the highest DM yield compared to other varieties (p<0.05). P2088 had the highest TDN content and GPO was the lowest (p<0.05). As for the SSH, the TDN content of Green star and Honey chew varieties was significantly higher, and the RFV value was the lowest in Turbo gold. The average crude protein content of proso millet was 7.03%, and the highest TDN and RFV values were 64.36% and 106 in Geumsilchal. In the experiment of the germination rate of summer forage crops according to salt concentration, silage corn showed a germination rate of 83.1% even at 0.4% salinity. In particular, P2088 and P1921 varieties had more than 80% germination rate even at 0.6% salt concentration. As for the SSH, the germination rate of 877F was 93.3% even at 0.8% salinity, and 88.3% with Honey chew, indicating higher resistance to salt concentration compared to other varieties. Proso millet showed a high germination rate of 84.0 to 88.7% even at a salt concentration of 0.6%. Considering the above results, proso millet was recommended as the most suitable forage crop species in the Sihwa reclaimed land with high salt concentration, and the Ibaekchal variety is recommended as a suitable forage crop due to its high yield.

• The Journal of Korean Society for Radiation Therapy
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• v.33
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• pp.145-153
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• 2021

Purpose: The purpose of this study is to evaluate the ovarian dose during radiation therapy for breast cancer in women of childbearing age through an experiment. The ovarian dose is evaluated by comparing and analyzing between the calculated dose in the treatment planning system according to the treatment technique and the measured dose using a thermoluminescence dosimeter (TLD). The clinical usefulness of lead (Pb) apron is investigated through dose analysis according to whether or not it is used. Materials and Methods: Rando humanoid phantom was used for measurement, and wedge filter radiation therapy, 3D conformal radiation therapy, and intensity modulated radiation therapy were used as treatment techniques. A treatment plan was established so that 95% of the prescribed dose could be delivered to the right breast of the Rando humanoid phantom 3D image obtained using the CT simulator. TLD was inserted into the surface and depth of the virtual ovary of the Rando hunmanoid phantom and irradiated with radiation. The measurement location was the center of treatment and the point moved 2 cm to the opposite breast from the center of the Rando hunmanoid phantom, 5cm, 10cm, 12.5cm, 15cm, 17.5cm, 20cm from the boundary of the right breast to the center of treatment and downward, and the surface and depth of the right ovary. Measurements were made at a total of 9 central points. In the dose comparison of treatment planning systems, two wedge filter treatment techniques, three-dimensional conformal radiotherapy, and intensity-modulated radiation therapy were established and compared. Treatments were compared, and dose measurements according to the use of lead apron were compared and analyzed in intensity-modulated radiation therapy. The measured value was calculated by averaging three TLD values for each point and converting using the TLD calibration value, which was calculated as the point dose mean value. In order to compare the treatment plan value with the actual measured value, the absolute dose value was measured and compared at each point (%Diff). Results: At Point A, the center of treatment, a maximum of 201.7cGy was obtained in the treatment planning system, and a maximum of 200.6cGy was obtained in the TLD. In all treatment planning systems, 0cGy was calculated from Point G, which is a point 17.5cm downward from the breast interface. As a result of TLD, a maximum of 2.6cGy was obtained at Point G, and a maximum of 0.9cGy was obtained at Point J, which is the ovarian dose, and the absolute dose was 0.3%~1.3%. The difference in dose according to the use of lead aprons was from a maximum of 2.1cGy to a minimum of 0.1cGy, and the %Diff value was 0.1%~1.1%. Conclusion: In the treatment planning system, the difference in dose according to the three treatment plans did not show a significant difference from 0.85% to 2.45%. In the ovary, the difference between the Rando humanoid phantom's treatment planning system and the actual measured dose was within 0.9%, and the actual measured dose was slightly higher. This did not accurately reflect the effect of scattered radiation in the treatment planning system, and it is thought that the dose of scattered radiation and the dose taken by CBCT with TLD inserted were reflected in the actual measurement. In dosimetry according to the with or without a lead apron, when a lead apron was used, the closer the distance from the treatment range, the more effective the shielding was. Although it is not clinically appropriate for pregnancy or artificial insemination during radiotherapy, the dose irradiated to the ovaries during treatment is not expected to significantly affect the reproductive function of women of childbearing age after radiotherapy. However, since women of childbearing age have constant anxiety, it is thought that psychological stability can be promoted by presenting the data from this study.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.26 no.1
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• pp.56-68
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• 1981

A maize line was selected in 1979 among 1000 Korean local maize lines collected in 1977. The selected maize line was characterized by having three to four tillers and eight to 10 ears on each individual plant. The line was assumed to have a great potential as a silage crop. The investigation was conducted as one of the serial studies on the Korean maize collected lines to provide basic information on the genetic variabilities of the multi-eared and tillered (MET) line and on other agronomic characters, prior to use the line as material for future breeding works for silage crop. The MET line and Suwon #19, single cross hybrid, as check variety were planted on May 1, 15 and 30, in three different levels of plant populations. The results obtained were summarized as follows: 1. The genetic variabilities of multi-ear and tillering habits were greater than environmental variabilities. 2. Total dry leaf weight of individual plant of MET line was also significantly higher than that of Suwon #19. 3. The mean number of tillers and ears bearing on the individual plant of MET line varied greatly with plant densities. The number of tillers and ears was on the average 2.9 and 7.0, respectively, when planted in 60cm. by 60cm. 4. The total dry matter and dried stem weight of the individual plant on MET line were comparable to those of Suwon #19. 5. The kernel weight from the individual plant of MET line was 5 to 40% less than that of Suwon #19, depending upon the plant densities. 6. The Kernel to stover ratio was higher for Suwon #19 than for the MET line. (41% to 35%). 7. The MET line had shown first tiller two weeks after planted on May 1. The second and third tillers appeared three to five days after the appearance of the first tiller. 8. The MET line was very specific in tillering habits. All the tillers were borne on the first few nodes of main stem below the soil surface. 9. The tillering habits of MET line were vigorous in the early part of the growing season, but less vigorous in the later part of the growing season. The number of efficient tillers bearing useable ears, was around two to three, when planted in 60cm. by 60cm. 10. The difference of plant height between main stem and first few tillers was around 10cm. 11. The ear size of MET line was around one-third of the major corn belt hybrids. The shape of ear of MET line was conical, with different diameter. 12. The kernel of the MET line was flinty with small soft starch patches on the endosperm part. 13. The 100 kernel weight was around 15gr., which is about one half of the major high yielding hybrids. 14. The ear height of MET line was comparatively higher than that of Suwon #19. 15. Significantly high and positive phenotypic correlation coefficients were obtained among major plant characters. 16. The growth rate of MET line was slower than that of Suwon #19. 17. MET line and Suwon #19 were both heavily infected with black streaked mosaic virus.

• Korean Journal of Soil Science and Fertilizer
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• v.1 no.1
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• pp.43-60
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• 1968

In order to find the effects of nitrogen type (ammonium sulfate and urea fertilizer) and fertilized depth, (0~10cm, 0cm, 5~10cm, 10~15cm, 15~20cm, and 20cm below) on leaching and absorption of nitrogen in paddy soil, and growth and yields of rice, the pot culture experiment was carried out, using the variety Jaekun, one of the Korean leading variety. Experimental results were Summarized as follows: 1. No variations of the pH of percolating water were induced by the differences of nitrogen types and their fertilized depth (Table. 2). 2. The leaching of nitrogen was less in ammonium sulfate and top soil fertilizing plots than in urea and subsoil fertilizing plot, and the growth of rice in early stage was more promoted in ammonium sulfate and topsoil fertilizing plots (Table. 1, 7 and 8). 3. Leachng of nitrogen through the percolating water almost came to an end at the most numerous tiller stage (Table 1). 4. The absorption of nitrogen of each part of the rice plant in the harvesting stage correlated closely with the yields of each part (Table 5, 6, 9 and 10) and the leaching of nitrogen in the early stage was inversely proportion to the absorption of nitrogen of rice plant in the harvesting time (Table 1, 5, 6, 9 and 10). 5. The number of spikes was more numerous in ammonium sulfate plots than in urea plots on an average, so that the yields were higher in the ammonium sulfate plots than in urea plots although no differences in the grain number per spike were found in above two plots. The number of spikes was more numerous in topsoil fertilizing plots than in subsoil fertilizing plots, but the grain number per spike was less in former than in latter, so that no difference in yields was found. The absorption of nitrogen correlated closely with the yields in complete paddy grains (Table 5, 9, and 10). 6. At the ammonium sulfate fertilizing plots, the number of spikes was more numerous in topsoil fertilizing plots than in subsoil fertilizing plots, (among the each of the topsoil plots, 0~10cm and 5~10cm fertilizing plots kept more spikes than the 0cm fertilizing plots), but the grain number per spike was less in former than in latter (among the each of topsoil plots, no differences were found), so that no significant difference in yields was showed between the topsoil and subsoil fertilizing plots, but the results showed the tendency that the yields were highest in 0~10cm plots and the lowest in 20cm below plots. At the urea fertilizing plots, the number of spikes decreased in proportion to the increasing of fertilized depth, but no variations were found in the grain number per spike, so that the yields decreased in proportion to the increasing of fertilized depth. The absorption of nitrogen correlated closely with the yields in complete paddy grains (Table 5, 6, 9, and 10). 7. When fertilized in topsoil, the number of spikes was more numerous in ammonium sulfate plot than in urea plot, but the grain number per spike variated reversely, so that no differences were found in the yields between the ammonium sulfate and the urea plots, when fertilized in subsoil, both the number of spikes and the grain number per spike were larger in ammonium sulfate than in urea plot, so that the yields were also higher in ammonium sulfate plots (Table 5, 6, 9 and 10). 8. The weight of straw and its nitrogen absorption were higher in ammonium sulfate plot than in urea plot and decreased in proportion to the increasing of fertilized depth. Among the each of topsoil fertilizing plots, the 0~10cm and the 5~10cm fertilizing plots excelled the 0cm plot (Table 5, 6, 9 and 10). 9. No significant variations in the fertilizer treatments were found in the characters of heading date, maturing date, length of culm, length of spike, weight of empty grain, 1,000 grain weight, and one liter weight.

• Tuberculosis and Respiratory Diseases
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• v.49 no.6
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• pp.691-702
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• 2000

Background : Acute lung injury (ALI) is a commonly encountered respiratory disease and its prognosis is poor when the treatment is not provided promptly and properly. However no specific pharmacologic treatment is currently available for ALI, although recently several supportive drugs have been under scrutiny. We studied anti-inflammatory effects of pentoxifylline (PF), a methylated xanthine, and ONO-5046, a synthetic neutrophil elastase inhibitor on lipopolysaccharide (LPS)-induced ALI in vitro. Methods : To establish an in vitro model of LPS-induced ALI, primary rat alveolar macrophages and peripheral neutrophils in various ratios (1:0, 5:1, 1:1, 1:5, 0:1) were co-cultured with transformed rat alveolar epithelial cells (L2 cell line) or vascular endothelial cells (IP2-E4 cell line) under LPS stimulation. Each experiment was divided into five groups-control, LPS, LPS+PF, LPS+ONO, and LPS+PF+ONO. We compared LPS-induced superoxide anion productions from primary rat alveolar macrophages and peripheral neutrophils in various ratios, and the resultant cytotoxicity on L2 cells or IP2-E4 cells between groups. In addition we also compared the productions of tumor necrosis factor (TNF)-$\alpha$ interleukin (IL)-$1{\beta}$, monocyte chemotactic protein(MCP)-1, IL-6, and IL-10 as well as mRNA expressions of TNF-$\alpha$ inducible nitric oxide synthetase(iNOS), and MCP-1 from LPS-stimulated primary rat alveolar macrophages between groups. Results : (1) PF and ONO-5046 in each or both showed a trend to suppress LPS-induced superoxide anion productions from primary rat alveolar macrophages and peripheral neutrophils regardless of their ratio, except for the LPS+PF+ONO group with the 1:5 ratio, although statistical significance was limited to a few selected experimental conditions. (2) PF and ONO-5046 in each or both showed a trend to prevent IP2-E4 cells from LPS-induced cytotoxicity by primary rat alveolar macrophages and peripheral neutrophils regardless their ratio, although statistical significance was limited to a few selected experimental conditions. the effects of PF and/or ONO-5046 on LPS-induced L2 cell cytotoxicity varied according to experimental conditions. (3) PF showed a trend to inhibit LPS-induced productions of INF-$\alpha$ MCP-1, and IL-10 from primary rat alveolar macrophages. ONO-5046 alone didnot affect the LPS-induced productions of proinflammatory cytokines from primary rat alveolar macrophages but the combination of PF and ONO-5046 showed a trend to suppress LPS-induced productions of INF-$\alpha$ and IL-10 PF and ONO-5046 in each or both showed a trend to increase LPS-induced IL-$\beta$ and IL-6 productions from primary rat alveolar macrophages. (4) PF and ONO-5046 in each or both showed a trend to attenuate LPS-induced mRNA expressions of TNF-$\alpha$ and MCP-1 from primary rat alveolar macrophages but at the same time showed a trend increase iNOS mRNA expression. Conclusion : These results suggest that PF and ONO-5046 may play a role in attenuating inflammation in LPS-induced ALI and that further study is needed to use these drugs as a new supportive therapeutic strategy for ALI.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.3
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• pp.89-98
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• 1965

The experimental studies were intended to clarify the effects of selection, and also aimed at estimating the heritabilities, the genotypic correlations among some agronomic characters, and at calculating the selection index on some selective characters for the selection of desirable lines, under different climatic conditions. Finally practical implications of these studies, especially on the selection index, were discussed. Twenty-two varieties, determinate growing habit type, were selected at random from the 138 soybean varieties cultivated the year before, were grown in a randomized block design with three replicates at Chinju, Korea, under May and June sowing conditions. The method of estimating heritabilities for the eleven agronomic characters-flowering date, maturity date, stem length, branch numbers per plant, stem diameter, plant weight, pod numbers per plant, grain numbers per plant and 100 grain weight, shown in Table 3, was the variance components procedures in a replicated trial for the varieties. The analysis of covariance was used to obtain the genotypic correlations and phenotypic correlations among the eight characters, and the selection indexes for some agronomic characters were calculated by Robinson's method. The results are summarized as follows: Heritabilities : The experiment on the genotype-environment interaction revealed that in almost all of the characters investigated the interaction was too large to be neglected and materially affected the estimates of various genotypic parameters. The variation in heritability due to the change of environments was larger in the characters of low heritability than in those of high heritability. Heritability values of flowering date, fruiting period (days from flowering to maturity), stem length and 100 grain weight were the highest in both environments, those of yield(grain weight) and other characters were showed the lower values(Table 3). These heritability values showed a decreasing trend with the delayed sowing in the experiments. Further, all calculated heritability values were higher than anticipated. This was expected since these values, which were the broad sense heritability, contain the variance due to dominance and epistasisf in addition to the additive genetic variance. Genotypic correlations : Genotypic correlations were slightly higher than the corresponding phenotypic correlations in both environments, but the variation in values due to the change of environment appeared between grain weight and some other characters, especially an increase between grain weight and flowering date, and the total growing period(Table 6). Genotypic correlations between grain weight and other characters indicated that high seed yield was genetically correlated with late flowering, late maturity, and the other five characters namely branch numbers per plant, stem diameter, plant weight, pod numbers per plant and grain numbers per plant, but not with 100 grain weight of soybeans. Pod numbers and grain numbers per plant were more closely correlated with seed yields than with other characters. Selection index : For the comparison and the use of selection indexes in the selection, two kinds of selection indexes were calculated, the former was called selection index A and the later selection index B as shown in Table 7. Selection index A was calculated by the values of grain weight per plant as the character of yield(character Y), but the other, selection index B, was calculated by the values of pod numbers per plant, instead of grain weight per plant, as the character of yield'(character Y'). These results suggest that selection index technique is useful in soybean breeding. In reality, however, as the selection index varies with population and environment, it must be calculated in each population to which selection is applied and in each environment in which the population is located. In spite of the expected usefulness of selection index technique in soybean breeding, unsolved problems such as the expense, time and labor involved in calculating the selection index remain. For these reasons and from these experimental studies, it was recognized that in the breeding of self-fertilized soybean plants the selection for yield should be based on a more simple selection index such as selection index B of these experiments rather than on the complex selection index such as selection index A. Furthermore, it was realized that the selection index for the selection should be calculated on the basis of the data of some 3-4 agronomic characters-maturity date(X$_1$), branch numbers per plant(X$_2$), stem diameter(X$_3$) and pod numbers per plant etc. It must be noted that it should be successful in selection to select for maturity date(X$_1$) which has high heritability, and the selection index should be calculated easily on the basis of the data of branch numbers per plant(X$_2$), stem diameter(X$_3$) and pod numbers per plant, directly after the harvest before drying and threshing. These characters should be very useful agronomic characters in the selection of Korean soybeans, determinate growing habit type, as they could be measured or counted easily thus saving time and expense in the duration from harvest to drying and threshing, and are affected more in soybean yields than the other agronomic characters.

• THE JOURNAL OF KOREAN ORIENTAL ONCOLOGY
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• v.4 no.1
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• pp.37-53
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• 1998

The sprig of Jinryungtang Gagambang has been used for curing as a traditional medicine without any experimental evidence to support the rational basis for their clinical use. This experiment was carried out to evaluate the possible therapeutic or antitumoral effects of Jinryungtang Gagambang extract against cancer, and to study some mechanisms responsible for its effect. The cytotoxic and antitumor effects were evaluated on human cell liens (A549, hep3B, Caki-1, Sarcoma 180) after exposure to Jinryungtang Gagambang extract using in ILS, colony forming efficency and SRB assay which were regarded as a valuable method for cytotoxic and antitumor effects of unknown compound on tumor cell lines. The results obtained in this studies were as follows. 1. As a result of exposure to Jinryungtang Gagambang extract, the proliferation of A549, hep3B, Caki-1, good correlations were shown from the results of SRB assay and those of clogenetic assay. 2. The oral administration of Jinryungtang Gagambang extract showed significant effects of increase of MST(mean survival time) and ILS(increased life span) depending on the increasing concentration. 3. Against squamous cell carcinoma induced by MCA, Jinryungtang Gagambang decreased not only the frequency of tumor production but also the number and weight of tumors per tumor bearing mice(TBM). Jinryungtang Gagambang also significantly suppressed the development of 3LL cell-implanted tumors by frequency and their size, and some developed tumors were regressed by the continuous treatment of Jinryungtang Gagambang extract into TBM. 4. Jinryungtang Gagambang extract also increased NK cell activities. According to the above results, it could be suggested that Jinryungtang Gagambang extract has prominent antiutmor effect.