set at variance

set at variance的基本解释

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使不和, 离间

与set at variance相关的网络例句 [注：此内容来源于网络，仅供参考]

Thecompare of genetic map between Lowes and ours showed 26 homology marker situ,which occupied 21.1% of the marker situ in the experiment. 81 QTLs were detected for 11 agronomic traits. 4 QTLs were detected for plantheight, which explained 10.3%～28.9% of trait variance; 2 QTLs were detected forNo. of effective 1-st branches, which explained 22.1%～47% of trait variance; 16QTLs were detected for effective branches height, which explained 12.2%～51.8% oftrait variance; 15 QTLs were detected for length of main inflorenscence, whichexplained 7.4%～26.6% of trait variance; 5 QTLs were detected for effective siliquesof main inflorenscence, which explained 11.2%～25% of trait variance; 1 QTLs weredetected for density of main infiorenscence, which explained 17.3% of trait variance;12 QTLs were detected for length of silique, which explained 24%～36.7% of traitvariance; 2 QTLs were detected for seed per sillique, which explained 9.6% and16.9% of trait variance; 2 QTLs were detected for 1000 seed weight, which explained26%～13.7% of trait variance; 11 QTLs were detected for Total effective siliques perplant, which explained 14.8%～47.2% of trait variance; 11 QTLs were detected forplant height, which explained 14.3%～32.8% of trait variance.

其中，株高检测到4个QTLs，解释性状表型变异的10.3％～28.9％；一次有效分枝数检测到2个QTLs，解释性状表型变异的22.1％和47％；有效分枝部位检测到16个QTLs，解释性状表型变异的12.2％～51.8％；主花序长度检测到15个QTLs，解释性状表型变异的7.4％～26.6％；主花序有效角数检测到5个QTLs，解释性状表型变异的11.2％～25％；主花序角密度检测到1个QTLs，解释性状表型变异的17.3％；角果长度检测到12个QTLs，解释性状表型变异的24％～36.7％；每角粒数检测到2个QTLs，解释性状表型变异的9.6％和16.9％；千粒重检测到2个QTLs，解释性状表型变异的26％和13.7％；单株有效角果总数检测到11个QTLs，解释性状表型变异的14.8％～47.2％；单株产量检测到11个QTLs，解释性状表型变异的14.3％～32.8％。

This paper presents the portfolio selection problem of two-attribute money and creates a model of portfolio selection based on two-attribute money, which can both contain the existing portfolio models and overcome the above-mentioned deficiencies. A series of new concepts is put forward, such as, holding wealth, obtainable wealth, short-term utility function, short-term expectation-variance utility function, state-expectation-variance utility function, short-term expectation-variance utility curve, long-term expectation-variance utility curve, margin utility contribution force, additional contribution force, profit-risk exchange rate and optimal portfolio expansion curve; The state-expectation-variance analytical method is developed from the expectation-variance analytical method; A set of systematic theories concerning two-attribute portfolio selection is thus established.

本文提出了两属性货币的证券组合选择问题；创建了既能包含现有证券组合选择模型又能克服上述两点不足的两属性证券组合选择模型；提出了持有财富、可获财富，短期效用函数，短期期望—方差效用函数、状态—期望—方差效用函数，短期期望—方差效用曲线、长期期望—方差效用曲线，边际效用贡献力，附加贡献力，收益—风险替换率，最优证券组合扩展线等一系列新概念；把期望—方差分析方法发展成状态—期望—方差分析方法；建立了两属性证券组合选择模型的一套系统的理论。

The results showed that:(1) we can mapping quantitative trait locus while estimating the variance component of QTL;(2) granddaughter design is better than daughter design when mapping QTL;(3) it is easy to map a QTL for trait with a high heriability and a large QTL variance contribution;(4) we can estimate the variance component of a QTL by TM-BLUP based on ML method whether the QTL has only 2 alleles or QTL has normal distributed alleles effects;(5) the estimation accuracy of variance component contributed by QTL was improved by using of grand daughter design;(6) the higher the heritability and the QTL variance contribution was, the more accurate estimation of QTL variance component.

结果表明：（1）采用随机QTL效应模型和最大似然法，在估计QTL方差组分的同时，能够定位QTL；（2）孙女设计与女儿设计相比，在其它因素相同时，容易检出QTL；（3）遗传力高，QTL方差贡献较大的性状，QTL检出的效果优于遗传力低，QTL方差贡献较小的性状；（4）无论QTL上有2个等位基因，还是QTL上等位基因的效应服从正态分布，都可将其看作随机效应，采用基于TM-BLUP的ML法，估计其方差组分和定位QTL；（5）QTL方差组分估计的准确性，孙女设计高于女儿设计；（6）遗传力高的性状，QTL方差贡献大的QTL，QTL方差组分估计的准确性高。

与set at variance相关的网络解释 [注：此内容来源于网络，仅供参考]

set at variance：使不和, 离间

backplan 底视图 | set at variance 使不和, 离间 | catch thetide 趁机

set at variance：不和

set at naught 蔑视 | set at variance 不和 | set at 设置成