三种直径分布拟合模型在长白落叶松林分的实际应用

基于长白落叶松中龄纯林样地调查,采用相对直径法、指数函数法和Weibull分布函数法三种常用方法拟合林分胸径分布规律.结果表明,三种方法均能有效描述林分直径分布规律,特别是指数函数简单易行、拟合精度高、适用性强,可视为长白落叶松中龄林直径分布拟合的最优模型.     

基准剂量软件BMDS中连续模型的研究

对美国环境保护署开发的基准剂量软件BMDS的2.1.1版中连续模型进行了剖析,详细介绍了模型形式;参数初始值的设置、估计方法、标准误、置信上下限;全模型、残差模型和拟合模型的对数似然函数、模型的显著性检验;各剂量下概率的预测、残差;两种风险模式下基准剂量的计算方法,并将计算结果与BMDS软件的计算结果进行比较,结果表明本文介绍的计算方法的正确性,为下一步进行软件设计奠定了理论基础.     

山西翅果油树群落优势种群分布格局研究

 应用扩散系数、聚集指数、平均拥挤度、聚块性指数、Green指数、聚集强度、Poisson分布和负二项分布的X2拟合检验等方法,研究了山西翅果油树群落优势种群的分布格局,并用相关分析比较了6个指数间的关系,结果表明：翅果油树分布格局呈随机型,其余22个优势种的分布格局皆为聚集型,这主要与物种本身的生态和生物学特性有关,以及与物种的竞争排斥作用有密切联系。在判定物种分布格局的8种方法中,以方差／均值比率、Poisson分布和负二项分布的X2拟合检验联合运用效果较好,不仅生态学意义明确,而且结果具有严格的统计学意义。     

食管癌三维适形放疗和常规放疗比较

目的对比研究三维适形放疗（3DCRT）和常规模拟机定位放疗两种不同方法在食管癌放射治疗中的优缺点。方法 20例食管癌患者采用3DCRT方法进行治疗,应用同一治疗计划系统,制定适形放疗和常规模拟机定位放疗方案。结果与常规模拟定位机定位放疗相比,食管癌照射中3DCRT有最好的剂量分布,既可明显提高靶区的剂量,同时能较好地保护正常组织。结论食管癌的适形放疗技术能降低正常组织的放射损伤和并发症,提高放疗治疗的适形度,改善靶区的剂量分布。     

长白山东北红豆杉生境内针阔混交林直径结构分布研究

选择能够拟合长白山自然保护区东北红豆杉生境内针阔混交林的直径结构分布模型,为该区林分经营管理提供参考。以长白山自然保护区龙荒沟林场15块东北红豆杉林分内针阔混交林为研究对象,采用负指数函数和三参数Weibull分布函数2种直径分布模型拟合和χ^2检验,分析了15块样地的直径结构分布规律。15块样地的密度为400~981株·hm^-2,平均胸径变化范围为10.5~19.9 cm,各样地林分的直径分布的偏度均为正值,直径分布函数曲线均往左偏,除了9~11号样地的直径分布用三参数Weibull分布模型效果较好外,其他样地均适合用负指数模型拟合。两种模型均能有效描述直径结构分布规律,三参数Weibull分布模型用来拟合不规则直径分布时效果较好,而负指数分布模型拟合近似“反J”型曲线时效果好。该区东北红豆杉林分内针阔混交林直径结构分布不合理,应加强该林区经营技术措施。     

陆地生态系统中低剂量毒物刺激作用及拟合模型研究进展

低剂量毒物刺激作用(hormesis)是在毒物剂量/效应关系中低剂量毒物可能表现出对生物生长的一种刺激作用.大量的实验数据表明毒物刺激作用发生的剂量低于未观察到毒性效应的剂量(NOAEL),毒物刺激作用的最大刺激效应一般是对照的130%～160%,是一种客观存在的剂量/反应现象.就毒物刺激作用的概念、机理、毒物刺激作用剂量/反应曲线的一些定量特点和模型的拟合等方面进行了综述,并用实例说明毒物刺激作用模型的最新拟合方法的应用,最后提出了目前毒物刺激作用研究中存在的问题及今后的研究方向.     

Bt玉米秸秆Bt蛋白的土壤降解及其拟合模型的比较

研究4种Bt玉米(34B24、NK58-D1、R×601RR/YG和农大61)秸秆分解释放Bt蛋白的土壤降解动态,分别应用一级动力学反应方程、双指数模型和移动对数模型进行了拟合。结果表明,4种Bt玉米Bt蛋白的土壤降解均呈现前期负指数大量快速降解和中后期极少量稳定两个阶段,双指数模型与移动对数模型的拟合结果比一级动力学反应方程更符合实际。判断模型拟合精度时不能只用拟合结果的统计指标值来确定,图示对比实测值与模拟值的差异是避免应用统计学拟合精度指标很高但并不符合实际的模型错误估算DT50的有效办法。在研究转基因作物秸秆中Bt蛋白土壤降解规律时,应该根据其土壤降解前期较快的特点,增加前期取样的次数,以便更好地对模拟模型求得的安全性评价关键参数DT50的准确性进行科学判断。     

亚高寒草甸不同生境植物群落物种多度分布格局的拟合

物种多度分布是群落生态学研究的核心内容。通过对青藏高原东部亚高寒草甸3种不同生境草本植物群落的抽样调查,结合16个物种多度分布模型的两种曲线拟合优度检验得出如下结果：多种不同模型可以拟合同一生境的物种多度分布。相比于其他可拟合模型,几何级数模型在3种生境中两种拟合优度检验方法下的平均拟合效果是最好的,拟合优度值均在最优拟合优度值10左右波动。次优模型鉴于不同生境不同的检验方法表现不一。除了几何级数模型外,Sugihara分数模型在最小二乘法的拟合方法下,也可以拟合3种生境的物种多度分布。研究结果表明,仅用拟合优度检验区分产生不同物种分布格局的模型和机制是不可靠的,需要做进一步的检验性实验研究。     

一维响应变量时最高无毒副作用剂量水平的识别

在毒性试验中,将暴露在某一剂量水平下的处理组与接受相当于零剂量处理的对照组相比,随着药物剂量的增加,感兴趣的变量通常会呈现一种递增的趋势。考虑不会导致风险显著增加的最高剂量,本文使用AIC(Akaike information criterion)方法得到该剂量水平的强相合估计,并且通过两组数据及模拟的结果来说明AIC方法的优良性.     

低能离子束及γ辐照对小菜蛾颗粒体病毒的影响及剂量效应关系的拟合分析

本文比较了Ｎ＋离子注入和６０Ｃｏγ射线对ＰｘＧＶ感染活性的影响,并用多种辐射生物学模型拟合、分析了它们的剂量效应关系。结果表明：在中高剂量区（１０１５－１０１７Ｎ＋／ｃｍ２）,ＰｘＧＶ感染率的剂效关系遵循指数失活规律,而在更宽的剂量段内（１０１２－１０１７Ｎ＋／ｃｍ２）,曲线则呈特有的“马鞍型”。用多种数学模型对实验数据进行计算机拟合,发现ＥＭＣ模型和三次项方程能较好拟合上述曲线。讨论了多种“马鞍型”剂效曲线的凹点剂量Ｄｃ值与实验系统的直径以及与注入离子质量沉积效应之间的可能联系。γ辐照组的剂效曲线基本为指数型关系     

基于秦岭样区的四种时序EVI函数拟合方法对比研究

函数曲线拟合方法是植被指数时间序列重建的一个重要方法,已经广泛应用于森林面积动态变化监测、农作物估产、遥感物候信息提取、生态系统碳循环研究等领域。基于秦岭样区多年MODIS EVI遥感数据及其质量控制数据,探讨并改进了时序EVI重建过程中噪声点优化和对原始高质量数据保真能力的评价方法;在此基础上,比较了常用的非对称性高斯函数拟合法(AG)、双Logistic函数拟合法(DL)和单Logistic函数拟合法(SL)。基于SL方法,调整了模型形式并重新定义d的参数意义,提出了最值优化单Logistic函数拟合法(MSL),并与其他3种方法进行对比。结果表明;在噪声点优化及保留原始高质量数据方面,AG方法和DL方法二者整体差别不大,而在部分像元的处理上AG方法表现出更好的拟合效果;MSL方法和SL方法相比于AG方法和DL方法其效果更为突出;在地形气候复杂,植被指数噪声较多的山区,MSL方法表现出更好的适用性。     

Symmetry-restrained flexible fitting for symmetric EM maps

Many large biological macromolecules have inherent structural symmetry, being composed of a few distinct subunits, repeated in a symmetric array. These complexes are often not amenable to traditional high-resolution structural determination methods, but can be imaged in functionally relevant states using cryo-electron microscopy (cryo-EM). A number of methods for fitting atomic-scale structures into cryo-EM maps have been developed, including the molecular dynamics flexible fitting (MDFF) method. However, quality and resolution of the cryo-EM map are the major determinants of a method's success. In order to incorporate knowledge of structural symmetry into the fitting procedure, we developed the symmetry-restrained MDFF method. The new method adds to the cryo-EM map-derived potential further restraints on the allowed conformations of a complex during fitting, thereby improving the quality of the resultant structure. The benefit of using symmetry-based restraints during fitting, particularly for medium to low-resolution data, is demonstrated for three different systems.     

Invariance of least squared-error estimation under some commonly used neurophysiological data pre-processing methods

The relationship of least squared-error estimation to the commonly used data pre-processing method of stimulus locked signal averaging is discussed. First, a generalized squared-error estimate is derived. Second, two data pre-processing methods are introduced and shown analytically to be equivalent with respect to subsequent least squared-error estimation. The first method consists of fitting known functions directly to unaltered data while the second method fits to the same data after it has been time-averaged. A third method of less utility is also demonstrated to be equivalent. It consists of first fitting to sub-blocks of the unaltered data and then averaging the resulting estimates. Finally, a numerical example is presented. It substantiates the analytical contentions and points out practical considerations which might arise in the course of implementation of the estimation procedure.     

Using a second‐order differential model to fit data without baselines in protein isothermal chemical denaturation

In vitro protein stability studies are commonly conducted via thermal or chemical denaturation/renaturation of protein. Conventional data analyses on the protein unfolding/(re)folding require well‐defined pre‐ and post‐transition baselines to evaluate Gibbs free‐energy change associated with the protein unfolding/(re)folding. This evaluation becomes problematic when there is insufficient data for determining the pre‐ or post‐transition baselines. In this study, fitting on such partial data obtained in protein chemical denaturation is established by introducing second‐order differential (SOD) analysis to overcome the limitations that the conventional fitting method has. By reducing numbers of the baseline‐related fitting parameters, the SOD analysis can successfully fit incomplete chemical denaturation data sets with high agreement to the conventional evaluation on the equivalent completed data, where the conventional fitting fails in analyzing them. This SOD fitting for the abbreviated isothermal chemical denaturation further fulfills data analysis methods on the insufficient data sets conducted in the two prevalent protein stability studies.     

Fast and accurate fitting of relaxation dispersion data using the flexible software package GLOVE

Relaxation dispersion spectroscopy is one of the most widely used techniques for the analysis of protein dynamics. To obtain a detailed understanding of the protein function from the view point of dynamics, it is essential to fit relaxation dispersion data accurately. The grid search method is commonly used for relaxation dispersion curve fits, but it does not always find the global minimum that provides the best-fit parameter set. Also, the fitting quality does not always improve with increase of the grid size although the computational time becomes longer. This is because relaxation dispersion curve fitting suffers from a local minimum problem, which is a general problem in non-linear least squares curve fitting. Therefore, in order to fit relaxation dispersion data rapidly and accurately, we developed a new fitting program called GLOVE that minimizes global and local parameters alternately, and incorporates a Monte-Carlo minimization method that enables fitting parameters to pass through local minima with low computational cost. GLOVE also implements a random search method, which sets up initial parameter values randomly within user-defined ranges. We demonstrate here that the combined use of the three methods can find the global minimum more rapidly and more accurately than grid search alone.     

Determination of molecular parameters by fitting sedimentation data to finite-element solutions of the Lamm equation.

A method for fitting experimental sedimentation velocity data to finite-element solutions of various models based on the Lamm equation is presented. The method provides initial parameter estimates and guides the user in choosing an appropriate model for the analysis by preprocessing the data with the G(s) method by van Holde and Weischet. For a mixture of multiple solutes in a sample, the method returns the concentrations, the sedimentation (s) and diffusion coefficients (D), and thus the molecular weights (MW) for all solutes, provided the partial specific volumes (v) are known. For nonideal samples displaying concentration-dependent solution behavior, concentration dependency parameters for s(sigma) and D(delta) can be determined. The finite-element solution of the Lamm equation used for this study provides a numerical solution to the differential equation, and does not require empirically adjusted correction terms or any assumptions such as infinitely long cells. Consequently, experimental data from samples that neither clear the meniscus nor exhibit clearly defined plateau absorbances, as well as data from approach-to-equilibrium experiments, can be analyzed with this method with enhanced accuracy when compared to other available methods. The nonlinear least-squares fitting process was accomplished by the use of an adapted version of the "Doesn't Use Derivatives" nonlinear least-squares fitting routine. The effectiveness of the approach is illustrated with experimental data obtained from protein and DNA samples. Where applicable, results are compared to methods utilizing analytical solutions of approximated Lamm equations.     

An evaluation of fitting methods for the sum of two hyperbolas: application to uptake studies

Analysis of data in terms of the sum of two rectangular hyperbolas is frequently required in solute uptake studies. Four methods for such analysis have been compared. Three are based on least-squares fitting whereas the fourth (partition method I) is an extension of a single hyperbola fitting procedure based on non-parametric statistics. The four methods were tested using data sets which had been generated with two primary types of random, normal error in the dependent variable: one of constant error variance and the other of constant coefficient of variation. The methods were tested on further data sets which were obtained by incorporating single 10% bias errors at different positions in the original two sets. Partition method I consistently gave good estimates for the four parameters defining the double hyperbola and was highly insensitive to the bias errors. The least-squares procedures performed well under conditions satisfying the least-squares assumptions regarding error distribution, but frequently gave poor estimates when these assumptions did not hold. Our conclusion is that in view of the errors inherent in many solute uptake experiments it would usually be preferable to analyse data by a method such as partition method I rather than to rely on a least-squares procedure.     

Quantitative analysis of electrophoresis data: novel curve fitting methodology and its application to the determination of a protein-DNA binding constant.

A computer program, GelExplorer, which uses a new methodology for obtaining quantitative information about electrophoresis has been developed. It provides a straightforward, easy-to-use graphical interface, and includes a number of features which offer significant advantages over existing methods for quantitative gel analysis. The method uses curve fitting with a nonlinear least-squares optimization to deconvolute overlapping bands. Unlike most curve fitting approaches, the data is treated in two dimensions, fitting all the data across the entire width of the lane. This allows for accurate determination of the intensities of individual, overlapping bands, and in particular allows imperfectly shaped bands to be accurately modeled. Experiments described in this paper demonstrate empirically that the Lorentzian lineshape reproduces the contours of an individual gel band and provides a better model than the Gaussian function for curve fitting of electrophoresis bands. Results from several fitting applications are presented and a discussion of the sources and magnitudes of uncertainties in the results is included. Finally, the method is applied to the quantitative analysis of a hydroxyl radical footprint titration experiment to obtain the free energy of binding of the lambda repressor protein to the OR1 operator DNA sequence.     

A rapid method for determining kinetic parameters of enzymes exhibiting nonlinear thermal inactivation behavior

A rapid method is developed to analyze the kinetics of thermal inactivation of enzymes that exhibit a nonlinear biphasic log(activity)-time relationship. Thermal destruction experiments on alcohol dehydrogenase from baker's yeast demonstrate the applicability of the method. The method is based on physical considerations (as opposed to mathematical curve fitting/regression methods) and also serves as a quick check of results obtained using nonlinear regression. It is superior to fitting nonlinear enzyme inactivation data by first-order kinetics or taking the initial and final slopes of the inactivation data. In fact, the method is of general validity and can be applied to any decay process that can be represented by a sum of exponentials. (c) 1996 John Wiley & Sons, Inc.