单向分类随机效应模型的局部影响判断

利用局部影响方法对线性模型中的单向分类随机效应模型进行了讨论.这种方法的优点在于避免了线性模型中有些参数估计不便于进行单点删除的诊断分析.     

三种线性模型在杉木与马尾松地位指数相关关系研究中的比较

地位指数法是当前立地质量评价中广泛采用的一种评定方法。在同一立地上,实现不同树种地位指数之间的转换,建立地位指数互导模型有助于立地质量的评定.譬如,在某一立地上现时生长着马尾松,欲知在马尾松被采伐后营造杉木林的生长潜力,这时就可利用上层木树种间地位指数的关系进行立地质量评定。以雪峰山杉木与马尾松地位指数配对样地数据为基础,建立了杉木与马尾松地位指数的常规线性模型、对偶回归模型和度量误差线性模型,从建模精度和模型适用性检验两方面,对3种模型进行了比较分析。结果表明3种模型的精度均比较高,模型效果差异不明显,其中,常规线性模型、度量误差模型和对偶回归模型的相对误差分别为5.39%、5.39%和5.54%。由于杉木地位指数和马尾松地位指数均存在度量误差,因此,线性度量误差模型和对偶回归模型比常规线性模型更适宜,这是因为,前两种模型的自变量和因变量是可以存在度量误差的,而后者的因变量是没有度量误差的。此外,线性度量误差模型的相对误差比对偶回归模型的要小,所以,3种线性模型中,线性度量误差模型最优。研究结果实现了相同立地条件下杉木地位指数和马尾松地位指数的互导,为不同树种间的立地质量评价提供了可行的方法。     

ARIMA与SVM组合模型在害虫预测中的应用

害虫发生是一种复杂、 动态时间序列数据, 单一预测模型都是基于线性或非线性数据, 不能同时捕捉害虫发生的线性和非线性规律, 很难达到理想的预测精度。本研究首先采用差分自回归移动平均模型对昆虫发生时间序列进行线性建模, 然后采用支持向量机对非线性部分进行建模, 最后得到两种模型的组合预测结果。将组合模型应用到松毛虫Dendrolimus punctatus发生面积的预测, 实验结果表明组合模型的预测精度明显优于单一模型, 发挥了两种模型各自的优势。组合模型是一种切实可行的害虫预测预报方法。     

家畜抗性等级性状的QTL定位方法

在广义线性模型的框架内模拟研究了家畜抗性等级性状的QTL定位方法,QTL参数的估计采用最大似然方法,比较了阈模型方法与一般线性方法的QTL定位效率,并对影响等级性状QTL定位效率的主要因素（QTL效应、性状的遗传力）进行了模拟研究,实验设计为多个家系的女儿设计,资源群体大小为500头。研究结果表明：在QTL位置参数估计及检验功效方面,阈模型方法具有一定的优势,对抗性等级性状QTL定位的功效也高于线性方法。另外,性状遗传力和QTL效应的大小对QTL定位的准确度也有直接的影响,随着性状遗传力QTL效应的     

广义岭回归在家禽育种值估计中的应用

讨论了岭回归方法应用于混合线性模型方程组中估计家禽育种值的方法,其实质是将传统的混合线性模型方程组理解为一种广义岭回归估计,为确定遗传参数的估计提供了一种途径;同时,以番鸭为例,考虑了一个性状和两个固定效应,采用广义岭回归法对公番鸭育种值进行了估计,并与最佳线性无偏预测法(BLUP 法)进行了比较,结果表明,广义岭回归方法和BLUP 法估计的育种值及其排序非常接近,其相关系数和秩相关系数分别达到了0.998~(**)和0.986~(**),且采用广义岭回归法预测的误差率低(在±10%以内);表明在混合线性模型方程组中使用广义岭回归估计动物育种值的方法具有可行性,并可省去估计遗传参数的过程,使BLUP 法在动物选育中的应用更具实用性.     

利用稳定氢氧同位素定量区分白刺水分来源的方法比较

水是影响植物分布的重要生态因子之一,对植物水源的研究有助于在全球变化背景下了解植物的时空分布格局.根据同位素质量守恒,利用稳定氢氧同位素可以确定植物水分来源,相关的方法也不断改进.利用三源线性混合模型、多源线性混合模型、吸水深度模型以及动态模型分别对格尔木白刺(Nitraria Tangutorum)的水分来源进行了对比研究,发现格尔木白刺主要吸收利用50-100 cm处的土壤水及地下水.在研究方法上,各模型都有自己的应用范围和局限:三源线性混合模型一般只能在植物吸收的水分来源不超过3个的情况下运行;多源线性混合模型弥补了三源线性混合模型的不足,可以同时比较多种来源水各自对白刺的贡献率及贡献范围;吸水深度模型弥补了混合模型中不能计算白刺对土壤水的平均吸水深度的缺陷;动态模型则会为未来降水格局变化对植物的时空分布的影响研究起很大作用.针对不同的适用范围,模型的选择及综合应用会更广泛.但是,该技术还存在一些不足,需要结合测定土水势,富氘水的示踪等方法来弥补.     

利用线性混合效应模型模拟杉木人工林枝条生物量

基于福建省将乐林场45株人工杉木解析木的572组枝条生物量数据,采用线性混合效应模型方法,建立杉木人工林枝条总生物量和枝、叶生物量的预测模型,并利用独立样本数据对模型进行检验.结果表明： 线性混合效应模型比传统多元线性回归模型的拟合精度高.不同随机效应参数的组合,其混合模型的精度不同.考虑异方差结构的混合模型能够消除数据间的异方差性,其精度更高,其中,对于枝条总生物量和叶生物量模型,以指数函数作为异方差结构时的模型精度最高;对于枝生物量模型,以常数加幂函数作为异方差结构时的模型精度最高.模型检验结果表明：对于杉木人工林枝条生物量预测模型,考虑随机效应和异方差结构的线性混合模型的检验精度比传统多元线性回归模型的精度有明显提高.     

豚鼠脑干听觉诱发反应的分析及建模初探

本文运用系统建模的方法,在时域和频域中分析了听觉脑干诱发反应(ABR)和听觉频率跟随反应(FFR)的实验数据,讨论了此两种反应之间即相联系又相独立的关系,及两种反应对刺激信号的线性和非线性关系。用单纯型最优化方法,对其参数进行辨识,建立一个线性传递函数和非线性函数及脉冲函数组合的模型。 整个模型在计算机上进行模拟,模拟的结果与FFR和ABR记录的数据相符。     

一类生态系统的脉冲控制

利用脉冲系统的线性近似判定的方法对一个具有脉冲控制的生物模型进行研究,得到了使其渐近稳定到原先不稳定的平衡点的充要条件并给出了生态解释。     

三种预测林木生长量方法的比较

本研究选择了三种方法灰色GM(1,1)模型法、回归法、灰色线性回归组合模型法,经过数据的加载,灰色GM(1,1)为Ⅱ级合格模型,发展系数|a|＜0.3可用于中长期预测;在回归法中选用5个模型进行拟合,以Richards方程的复相关系高,残差平方和最小,被选中;灰色线性回组合模型达到Ⅲ级勉强可用模型.用三个模型分别对32、34龄阶的生长量进行预测,其平均相对精度分别为94.64%、80.68%、92.41%.     

QTL mapping in outbred half-sib families using Bayesian model selection

In this article, we propose a model selection method, the Bayesian composite model space approach, to map quantitative trait loci (QTL) in a half-sib population for continuous and binary traits. In our method, the identity-by-descent-based variance component model is used. To demonstrate the performance of this model, the method was applied to map QTL underlying production traits on BTA6 in a Chinese half-sib dairy cattle population. A total of four QTLs were detected, whereas only one QTL was identified using the traditional least square (LS) method. We also conducted two simulation experiments to validate the efficiency of our method. The results suggest that the proposed method based on a multiple-QTL model is efficient in mapping multiple QTL for an outbred half-sib population and is more powerful than the LS method based on a single-QTL model.     

杨扇舟蛾卵和幼虫的空间分布型及抽样技术

研究了杨扇舟蛾Clostera anachoreta(Fabricius)卵和幼虫的空间分布型及抽样技术,结果表明杨扇舟蛾卵和幼虫在杨树林的分布型均为聚集型中的负二项分布。几种抽样方法中,卵以棋盘式最好,幼虫以平行线法最好。同时建立了杨扇舟蛾卵的下层抽样模型为y=2.4481x 0.4243;幼虫的中、下层抽样模型为y=2.4605x 3.9126。     

Statistical methods for estimating doubling time in in vitro cell growth

Summary Doubling time has been widely used to represent the growth pattern of cells. A traditional method for finding the doubling time is to apply gray-scaled cells, where the logarithmic transformed scale is used. As an alternative statistical method, the log-linear model was recently proposed, for which actual cell numbers are used instead of the transformed gray-scaled cells. In this paper, I extend the log-linear model and propose the extended log-linear model. This model is designed for extra-Poisson variation, where the log-linear model produces the less appropriate estimate of the doubling time. Moreover, I compare statistical properties of the gray-scaled method, the log-linear model, and the extended log-linear model. For this purpose, I perform a Monte Carlo simulation study with three data-generating models: the additive error model, the multiplicative error model, and the overdispersed Poisson model. From the simulation study, I found that the gray-scaled method highly depends on the normality assumption of the gray-scaled cells; hence, this method is appropriate when the error model is multiplicative with the log-normally distributed errors. However, it is less efficient for other types of error distributions, especially when the error model is additive or the errors follow the Poisson distribution. The estimated standard error for the doubling time is not accurate in this case. The log-linear model was found to be efficient when the errors follow the Poisson distribution or nearly Poisson distribution. The efficiency of the log-linear model was decreased accordingly as the overdispersion increased, compared to the extended log-linear model. When the error model is additive or multiplicative with Gamma-distributed errors, the log-linear model is more efficient than the gray-scaled method. The extended log-linear model performs well overall for all three data-generating models. The loss of efficiency of the extended log-linear model is observed only when the error model is multiplicative with log-normally distributed errors, where the gray-scaled method is appropriate. However, the extended log-linear model is more efficient than the log-linear model in this case.     

基于分布式水文模型的水源涵养不同功能评估方法——以渭河涵养区为例

为了实现水源涵养量计算和不同功能的综合评估,基于分布式水文模型(WEP-L)提出了一种新方法,即利用WEP-L模型计算次降水过程中降水量和地表产流、冠层截留量的差值作为水源涵养量,并分别评估削洪(地表径流量)、补枯(地下径流量)、维持植被生态系统用水(植被蒸腾量)等不同水源涵养功能的评估方法。为了验证该方法的合理性,以渭河流域咸阳站以上区域为例,对比了该方法和InVEST模型方法的计算结果,由于两种方法在评估内容和使用模型上都存在差异,为了保证计算结果的可比性,先对比了基于相同评价内容的WEP-L模型法I和InVEST模型方法,再对比了基于不同评价内容的WEP-L模型法I和WEP-L模型法II,结论如下:基于相同评价内容的WEP模型法I和InVEST模型法计算结果数值接近,研究区2000-2018年水源涵养量年均值分别为12.43 mm(5.76亿m³)和12.08 mm(5.6亿m³),两种方法所得结果空间分布特征相似,稍有差异之处与InVEST模型的参数没有经过本地化处理有关;基于不同评价内容的WEP-L模型法Ⅰ和WEP-L模型法II计算结果数值相差较大,研究区2000-2018年水源涵养量均值分别为12.43 mm和432.57 mm,空间分布特征上有差异的地方分布于研究区的北部、东部及东北部,主要与两种方法评价时是否考虑蒸散发有关。WEP-L模型法II评估结果中削洪、补枯、维持植被生态系统用水等功能多年变化趋势分别为:2006-2010年期间增加、2012年以后下降以及增加。2012年后补枯功能和维持植被生态系统用水功能之间可能存在权衡关系。通过不同方法计算结果差异原因分析,证明了基于WEP-L模型的不同涵养功能评估方法的合理性,其结果也可为渭河涵养区水资源和生态保护策略的制定提供更多依据。     

一种新型大鼠心脏移植模型的制作体会

目的采用腹部套管法建立大鼠心脏移植模型,并分析该方法优劣,为器官移植研究提供合适的动物模型。方法SD大鼠60只,参照Baxter的报道进行同种腹部异位心脏移植,成功制作了该模型并总结了经验。结果共实施手术30例,成功27例,成功率90%;移植心存活30 d以上。结论新型的大鼠心脏移植模型简单易行,成功率高,适合用于器官移植研究,值得推广应用。     

农药残留预测模型可靠性的检验与改良

对农药残留预测模型可靠性问题进行了分析。根据矩阵的条件数理论,提供了检验预测模型可靠性的方法,基于岭回归和广义岭回归估计理论,建立了对不可靠预测模型改良的方法。最后通过氰戊菊酯在甘篮上的残留动态预测,对所建改良方法进行了检验,结果表明,预测模型的精度得到了大幅度提高.     

静电纺丝纳米纤维体外释放与蛋白活性的研究

目的:通过选择不同的模型蛋白,探讨准确的研究静电纺丝纳米纤维支架的体外释放和快速的测定蛋白活性的方法.方法:通过O/W乳液法静电纺丝制备纳米纤维,并用扫描电镜对纳米纤维表面进行了表征.以GM-CSF为模型蛋白,采用ELISA双抗体夹心法考察纤维的体外释放行为;以BSA为模型蛋白,用SEC-H-PLC比较纤维制备前后蛋白的聚集情况;以β-半乳糖苷酶为模型蛋白,用ONPG法比较纤维制备前后酶的催化活性.结果:纤维表面平滑,直径均一,呈现互相连通的三维网状结构.纤维在5天内释放90％以上;纤维中回收的BSA单体比例为66.53％;β-半乳糖苷酶在纤维中的催化活性保持原活性的3.37％.结论:通过选择不同的模型蛋白,能够准确的测定静电纺丝纤维的体外释放,快速的考察纤维中的蛋白活性,对于更好的研究蛋白药物纳米纤维支架具有重要的参考价值.     

Assessing the Performance of Neural Encoding Models in the Presence of Noise

An analytical method is introduced for evaluating the performance of neural encoding models. The method addresses a critical question that arises during the course of the development and validation of encoding models: is a given model near optimal in terms of its accuracy in predicting the stimulus-elicited responses of a neural system, or can the predictive accuracy be improved significantly by further model development? The evaluation method is based on a derivation of the minimum mean-square error between actual responses and modeled responses. It is formulated as a comparison between the mean-square error of the candidate model and the theoretical minimum mean-square error attainable through an optimal model for the system. However, no a priori information about the nature of the optimal model is required. The theoretically minimum error is determined solely from the coherence function between pairs of system responses to repeated presentations of the same dynamic stimulus. Thus, the performance of the candidate model is judged against the performance of an optimal model rather than against that of an arbitrarily assumed model. Using this method, we evaluated a linear model for neural encoding by mechanosensory cells in the cricket cercal system. At low stimulus intensities, the best-fit linear model of encoding by single cells was found to be nearly optimal, even though the coherence between stimulus-response pairs (a commonly used measure of system linearity) was low. In this low-stimulus-intensity regime, the mean square error of the linear model was on the order of the power of the cell responses. In contrast, at higher stimulus intensities the linear model was not an accurate representation of neural encoding, even though the stimulus-response coherence was substantially higher than in the low-intensity regime.     

An improved model for the analysis of combined antimicrobials: a replacement for the Chou–Talalay combination index method

Aims

To rationalize confusion in the literature concerning the analysis of combined antimicrobials, specifically to see if the combination index (CI) method of analysis was as rigorous as claimed.

Methods and Results

Data from previous studies of the inhibition of Staphylococcus aureus by mixed antimicrobials were re‐analysed using the CI method and a model which takes account of differences in the concentration exponents of individual antimicrobials.

Conclusions

The Chou–Talalay combination index method for the analysis of combined antimicrobials was found to be valid only in the specific cases where concentration exponents were equal. In these cases, the CI method was found to be a function of the residuals of fitting the additive model to the observed data. Where concentration exponents were not equal, the CI method was invalid, whereas the additive model took these differences into account.

Significance and Impact of Study

The CI method can be replaced wholly by the additive model described. The model allows simple regression to be used to analyse whole data sets and provides simple graphical output.