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Studies on the selection in soybean breeding. -II. Additional data on heritability, genotypic correlation and selection index- (대두육종에 있어서의 선발에 관한 실험적연구 -속보 : 유전력ㆍ유전상관, 그리고 선발지수의 재검토-)

  • Kwon-Yawl Chang
    • 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.

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Comparison of Effects of Normothermic and Hypothermic Cardiopulmonary Bypass on Cerebral Metabolism During Cardiac Surgery (체외순환 시 뇌 대사에 대한 정상 체온 체외순환과 저 체온 체외순환의 임상적 영향에 관한 비교연구)

  • 조광현;박경택;김경현;최석철;최국렬;황윤호
    • Journal of Chest Surgery
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    • v.35 no.6
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    • pp.420-429
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    • 2002
  • Moderate hypothermic cardiopulmonary bypass (CPB) has commonly been used in cardiac surgery. Several cardiac centers recently practice normothermic CPB in cardiac surgery, However, the clinical effect and safety of normothermic CPB on cerebral metabolism are not established and not fully understood. This study was prospectively designed to evaluate the clinical influence of normothermic CPB on brain metabolism and to compare it with that of moderate hypothermic CPB. Material and Method: Thirty-six adult patients scheduled for elective cardiac surgery were randomized to receive normothermic (nasopharyngeal temperature >34.5 $^{\circ}C$, n=18) or hypothermic (nasopharyngeal temperature 29~3$0^{\circ}C$, n=18) CPB with nonpulsatile pump. Middle cerebral artery blood flow velocity (VMCA), cerebral arteriovenous oxygen content difference (CAVO$_{2}$), cerebral oxygen extraction (COE), modified cerebral metabolic rate for oxygen (MCMRO$_{2}$), cerebral oxygen transport (TEO$_{2}$), cerebral venous desaturation (oxygen saturation in internal jugular bulb blood$\leq$50 %), and arterial and internal jugular bulb blood gas analysis were measured during six phases of the operation: Pre-CPB (control), CPB-10 min, Rewarm-1 (nasopharyngeal temperature 34 $^{\circ}C$ in the hypothermic group), Rewarm-2 (nasopharyngeal temperature 37 $^{\circ}C$ in the both groups), CPB-off and Post-CPB (skin closure after CPB-off). Postoperaitve neuropsychologic complications were observed in all patients. All variables were compared between the two groups. Result: VMCA at Rewarm-2 was higher in the hypothermic group (153.11$\pm$8.98%) than in the normothermic group (131.18$\pm$6.94%) (p<0.05). CAVO$_{2}$ (3.47$\pm$0.21 vs 4.28$\pm$0.29 mL/dL, p<0.05), COE (0.30$\pm$0.02 vs 0.39$\pm$0.02, p<0.05) and MCMRO$_{2}$ (4.71 $\pm$0.42 vs 5.36$\pm$0.45, p<0.05) at CPB-10 min were lower in the hypothermic group than in the normothermic group. The hypothermic group had higher TEO$_{2}$ than the normothermic group at CPB-10 (1,527.60$\pm$25.84 vs 1,368.74$\pm$20.03, p<0.05), Rewarm-2 (1,757.50$\pm$32.30 vs 1,478.60$\pm$27.41, p<0.05) and Post-CPB (1,734.37$\pm$41.45 vs 1,597.68$\pm$27.50, p<0.05). Internal jugular bulb oxygen tension (40.96$\pm$1.16 vs 34.79$\pm$2.18 mmHg, p<0.05), saturation (72.63$\pm$2.68 vs 64.76$\pm$2.49 %, p<0.05) and content (8.08$\pm$0.34 vs 6.78$\pm$0.43 mL/dL, p<0.05) at CPB-10 were higher in the hypothermic group than in the normothermic group. The hypothermic group had less incidence of postoperative neurologic complication (delirium) than the normothermic group (2 vs 4 patients, p<0.05). Lasting periods of postoperative delirium were shorter in the hypothermic group than in the normothermic group (60 vs 160 hrs, p<0.01). Conclusion: These results indicate that normothermic CPB should not be routinely applied in all cardiac surgery, especially advanced age or the clinical situations that require prolonged operative time. Moderate hypothermic CPB may have beneficial influences relatively on brain metabolism and postoperative neuropsychologic outcomes when compared with normothermic CPB.

Radiation Dose-escalation Trial for Glioblastomas with 3D-conformal Radiotherapy (3차원 입체조형치료에 의한 아교모세포종의 방사선 선량증가 연구)

  • Cho, Jae-Ho;Lee, Chang-Geol;Kim, Kyoung-Ju;Bak, Jin-Ho;Lee, Se-Byeoung;Cho, Sam-Ju;Shim, Su-Jung;Yoon, Dok-Hyun;Chang, Jong-Hee;Kim, Tae-Gon;Kim, Dong-Suk;Suh, Chang-Ok
    • Radiation Oncology Journal
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    • v.22 no.4
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    • pp.237-246
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    • 2004
  • Purpose: To investigate the effects of radiation dose-escalation on the treatment outcome, complications and the other prognostic variables for glioblastoma patients treated with 3D-conformal radiotherapy (3D-CRT). Materials and Methods: Between Jan 1997 and July 2002, a total of 75 patients with histologically proven diagnosis of glioblastoma were analyzed. The patients who had a Karnofsky Performance Score (KPS) of 60 or higher, and received at least 50 Gy of radiation to the tumor bed were eligible. All the patients were divided into two arms; Arm 1, the high-dose group was enrolled prospectively, and Arm 2, the low-dose group served as a retrospective control. Arm 1 patients received $63\~70$ Gy (Median 66 Gy, fraction size $1.8\~2$ Gy) with 3D-conformal radiotherapy, and Arm 2 received 59.4 Gy or less (Median 59.4 Gy, fraction size 1.8 Gy) with 2D-conventional radiotherapy. The Gross Tumor Volume (GTV) was defined by the surgical margin and the residual gross tumor on a contrast enhanced MRI. Surrounding edema was not included in the Clinical Target Volume (CTV) in Arm 1, so as to reduce the risk of late radiation associated complications; whereas as in Arm 2 it was included. The overall survival and progression free survival times were calculated from the date of surgery using the Kaplan-Meier method. The time to progression was measured with serial neurologic examinations and MRI or CT scans after RT completion. Acute and late toxicities were evaluated using the Radiation Therapy Oncology Group neurotoxicity scores. Results: During the relatively short follow up period of 14 months, the median overall survival and progression free survival times were $15{\pm}1.65$ and $11{\pm}0.95$ months, respectively. The was a significantly longer survival time for the Arm 1 patients compared to those in Arm 2 (p=0.028). For Arm 1 patients, the median survival and progression free survival times were $21{\pm}5.03$ and $12{\pm}1.59$ months, respectively, while for Arm 2 patients they were $14{\pm}0.94$ and $10{\pm}1.63$ months, respectively. Especially in terms of the 2-year survival rate, the high-dose group showed a much better survival time than the low-dose group; $44.7\%$ versus $19.2\%$. Upon univariate analyses, age, performance status, location of tumor, extent of surgery, tumor volume and radiation dose group were significant factors for survival. Multivariate analyses confirmed that the impact of radiation dose on survival was independent of age, performance status, extent of surgery and target volume. During the follow-up period, complications related directly with radiation, such as radionecrosis, has not been identified. Conclusion: Using 3D-conformal radiotherapy, which is able to reduce the radiation dose to normal tissues compared to 2D-conventional treatment, up to 70 Gy of radiation could be delivered to the GTV without significant toxicity. As an approach to intensify local treatment, the radiation dose escalation through 3D-CRT can be expected to increase the overall and progression free survival times for patients with glioblastomas.

Development of a complex failure prediction system using Hierarchical Attention Network (Hierarchical Attention Network를 이용한 복합 장애 발생 예측 시스템 개발)

  • Park, Youngchan;An, Sangjun;Kim, Mintae;Kim, Wooju
    • 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.