• KOREAN JOURNAL OF CROP SCIENCE
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• v.17
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• pp.45-78
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• 1974

To provide useful information for developing new high yielding soybean varieties and for improving cultural practices, an investigation was made on variation of dry matter production and on relationship among several agronomic characters of soybean plants grown under different planting times and densities as well as under different fertilizer levels, using Kwang-kyo, Dong puk-tae, and Suke ＃ 51 as determinate types and Shelby, SRF-300 and Harosoy as indeterminate types at the Crop Experiment Station during the period of 1972 and 1973. The results obtained were summarized as follow: 1. The dry weight, CGR and LAI at the initial flowering stage were high in the high plant population irrespective of varieties, planting times, and fertilizer levels. However, those characters of the indeterminate type were lower than those of the determinate types. The same characters of the indererminate type at the terminal leaf stage were either same or higher than those of the determinate types. 2. The dry weight of the determinate type at the initial flowering stage was similar to the indeterminate, type, when planting times were May 21 or June 15. The dry weights of both types of varieties were low when planted on July 10. When fertilizer levels were increased, the CGR, dry weight and LAI at the initial flowering stages were also increased. 3. Even though significant differences of LAI were obtained among the varieties within the same plant type, the indeterminate type was in general lower than that of the determinate type regardless of planting time and densities, or fertilizer levels, while the yield of the indeterminate type was comparable to the yield of the determinate type. 4. The high degree of leaf- and petiole-fall at the greenbean stage was highly associated with early planting and high levels of fertilizers. However, less amount of leaf- or petiole-fall was found when planted on July 10 or under low plant population. 5. The percent of stem weight was high under higher plant population, while the percent of leaf weight was high under lower plant population. When planting time was late, the percent of stem and petiole weight were reduced, while the leaf weight was increased. 6. The percent of pod weight of the determinate type at the terminal leaf stage was about 2% when planted on May 21, about 8% when planted on June 15, and about 9% when planted on July 10. The percent of pod weight of the indeterminate type at the terminal leaf stage were about 6 % when planted on May 21, 14% when planted on June 15 and 21% when planted on July 10. 7. Kwang kyo showed less degree of leaf-fall even when lodged due to high levels of fertilizer applied, while SRF-300 showed great damage due to lodging. 8. High yields were obtained when planted on May 21, but there were little yield differences between yields from May 21 and June 15 plantings. The reduction of yield due to late planting of July 10 was less apparent in the determinate type of varieties, while it was high in the indeterminate type. 9. The optimum plant population per are for high yield was 1, 250 to 2, 500 plants when planted on May 21, 2, 500 plants when planted on June 15, and 3, 333 plants when planted on July 10. 10. High correlation coefficients were obtained between dry matter weight and LAI at the terminal leaf stages, and between the dry matter weight and yield at the greenbean stages. The optimum dry weight for high yield in the determinate type was expected to be 25 kg. per are at the initial flowering stage and 50 kg. per are at the terminal leaf stage. In the indeterminate type the LAI and dry weight at the greenbean stage were 4 to 5 and 80 kg. per are, respectively. 11. Under the high plant population plant height was increased, while the stem diameter and the number of nodes and branches were reduced. Consequently, the percent of mainstem to main stem plus branches were increased, and the length of internode was also elongated. The ratios of stem weight, number of nodes and pods, and yield of main stem were increased when high plant population was associated with the early planting. The percent of main stem to branches for the indeterminate type was higher than that of the determinate type. 12. Under the high plant densities and late planting, the percent of the pod number and yields of main stem were increased, indicating that varieties with no or less branches were better adaptable under such conditions. 13. High degree of simple correlation coefficients was obtained between the LAI at the initial flowering stage and terminal leaf stage, and the total node number, dry matter and dry stem weight of both determinate and indeterminate types. Even though no significant correlation was found between the LAI at the initial flowering stage of the determinate type and the stem length and pod number per are, highly significant correlation coefficients were obtained between such characters in the indeterminate type of varieties. 14. The dry matter was positively correlated with the LAI, CGR, stem length, and pod number, node number and dry stem weight per are, while no significant correlation was found between the dry matter and stem diameter. 15. The correlation coefficients between lodging index and the LAI, dry weight, stem length and dry stem weight were highly significant. Negative correlation was obtained for the indeterminate type between the stem diameter and lodging index. The correlation coefficient between the stem diameter and lodging index was non-significant for the determinate type, while positive correlation was obtained between the yield and lodging index in the determinate type. The lodging index was also positively correlated with average length of internode of main stem. 16. The 100 seed weight appeared to be lowered under the high plant population and no fertilizer condition, and when planted late. Apparent differences of 100 seed weight were found between main stem and branches, being higher for the main stem than for the branches. 17. No variation of protein content was found due to different cultural practices. However, the oil content was apparently reduced when planted late.

• Journal of Acupuncture Research
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• v.22 no.3
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• pp.155-167
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• 2005

Objectives : The purpose of this study was to investigate the anti-inflammatory effect of Cobrotoxin on binding affinity of cobrotoxin with P50, $IKK{\alpa}$ and $IKK{\beta}$, activities of NF-${\kappa}B$, Cell viability of astrocyte, expressions of protein molecules of NF-${\kappa}B$ such as P50, P-$1{kappa}B$, $1{\kappa}B$ and iflammation related genes such as Cox-2, iNOS, cPLA2 in the SNP or LPS induced Inflammatory pathway of Rats' astrocytes. Methods : In this study, The expression of cytosolic phospholipase A2, Nitric oxcide, Cyclooxygenase-2 and inducible nitrogen oxide synthase was determined by western blotting with corresponding antibodies, and the generation of NF-${\kappa}B$ was assayed by EMSA method in astrocytes of rats. The Cell viability of astrocytes was determined by MTT assay, and Binding affinity of Cobrotoxin with P50, $IKK{\alpha}$ and $IKK{\beta}$ was assayed by Surface plasmon resonance analysis, and NF-${\kappa}B$ dependent luciferase activity was determined by luciferase analysis, and Uptake of cobrotoxin in astrocytes was identified by Confocal laser scanning microscope Results : 1. Compared with control, LPS-induced NF-${\kappa}B$ DNA binding activity was decreased significantly by 0.1, $0.5{\mu}g/m{\ell}$ of Cobrotoxin in Astrocyte. 2. Compared with control, LPS-induced NF-kB dependent luciferase expression was decreased significantly by 0.1, 0.5 and $1{\mu}g/m{\ell}$ of Cobrotoxin in Astrocyte. 3. Compared with control, SNP induced P50, $I{\kappa}B$ expressions in astrocyte were decreased significantly by 0.1, 0.5 and $1{\mu}g/m{\ell}$ of Cobrotoxin and P-$1{\kappa}B$ expression was decreased significantly by 0.5 and $1{\mu}g/m{\ell}$ of Cobrotoxin. 4. Compared with control, LPS induced P50, $1{\kappa}B$ expressions in astrocyte were decreased significantly by 0.5 and $1{\mu}g/m{\ell}$ of Cobrotoxin. 5. Compared with control, SNP induced Cox-2, iNOS, CPLA2 expressions in astrocyte were decreased significantly by $1{\mu}g/m{\ell}$ of Cobrotoxin. 6. Compared with control, LPS induced Cox-2, cPLA2 expressions in astrocyte were decreased significantly by 0.1, 0.5, $1{\mu}g/m{\ell}$ of Cobrotoxin and iNOS expression was decreased significantly by 0.5, $1{\mu}g/m{\ell}$ of Cobrotoxin. 7. Compared with $0.5{\mu}g/m{\ell}$ of Cobrotoxin, SNP-induced NF-${\kappa}B$ DNA bindins activity in astrocyte was increased significantly by Cobrotoxin $0.5{\mu}g/m{\ell}$ with DTT 1mM and Cobrotoxin $0.5{\mu}g/m{\ell}$ with DTT 5mM. 8. Compared with $0.5{\mu}g/m{\ell}$ of Cobrotoxin, LPS-induced NF-${\kappa}B$ DNA binding activity in astrocyte was increased significantly by Cobrotoxin $0.5{\mu}g/m{\ell}$ with DTT 1mM, Cobrotoxin $0.5{\mu}g/m{\ell}$ with DTT 5mM, Cobrotoxin $0.5{\mu}g/m{\ell}$with GSH 1mM and Cobrotoxin $0.5{\mu}g/m{\ell}$ with GSH 5mM 9. Compared with $0.1{\mu}g/m{\ell}$ of cobrotoxin, SNP induced P50 expressions in astrocyte were increased significantly by Cobrotoxin $0.5{\mu}g/m{\ell}$ with DTT 1mM, Cobrotoxin $0.5{\mu}g/m{\ell}$ with DTT 5mM Cobrotoxin $0.5{\mu}g/m{\ell}$ with GSH 1mM and Cobrotoxin $0.5{\mu}g/m{\ell}$ with GSH 5mM. 10. The uptake of the labeled cobrotoxin into the cells was shown under a confocal laser scanning microscope. cobrotoxin was uptaken into the membrane and nucleus of astrocytes. Conclusions : In summary, the present results demonstrate that cobrotoxin directly binds to sulfhydryl group of p50 and IKKS resulting In the reduction of translocation of p50 and IkB release, thereby inhibits activation of NF-${\kappa}B$, and suggest that pico to nanomolar range of cobrotoxin could inhibit the expression of genes in the NF-${\kappa}B$ signal pathway.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.