空间直观景观模型的验证方法

空间直观景观模型已是当前景观生态学研究的一大热点。空间景观模型模拟空间格局变化。其模拟结果包含非空间数据和空间数据。空间直观景观模型的验证除进行非空间数据的验证外,还需要进行空间数据的验证。本文回顾了空间直观模型发展历程,总结现有的空间直观模型验证方法。包括主观评价、图形比较、偏差分析、回归分析、假设检验、多尺度拟合度分析和景观指数分析,同时提出今后空间直观景观模型验证方法研究的重点方向。     

樟子松人工林树冠表面积及体积预估模型的研究

基于樟子松(Pinus sylvestris var. mongolica)人工林6块固定标准地30株枝解析数据,在分析树冠表面积和树冠体积与林分变量和林木变量的基础上,利用幂函数建立了树冠表面积（CSA）和树冠体积(CV)的预估模型,同时还对林木材积生长量与CSA和CV进行了相关分析。研究结果表明：樟子松人工林树冠表面积和树冠体积随着林木胸径、树高和冠长的增大而增大,林木材积生长量与树冠表面积和树冠体积均明显呈线性关系。不同林分条件的樟子松人工林CSA和CV随林分年龄和胸径的增大而增大,CSA随林分密度的增大而减小,而CV与林分密度相关不紧密。林分树冠表面积和树冠体积预估模型的检验结果表明,两个模型的平均相对误差都在±8%之内,预估精度均大于91%,说明所建模型可以很好地预估樟子松人工林不同林分条件下的林木树冠表面积和树冠体积。     

H-MEDV描述法对蒙椴树叶挥发性组分的气相色谱保留时间的预测

应用按氢分类的分子电距矢量(H-MEDV)对蒙椴树叶挥发油的45 种组分的气相色谱保留时间(tR)进行了定量结构-色谱保留关系(QSRR)的研究.通过多元线性回归得到的模型(M1)相关系数R为0.953.用逐步回归的方法建立6 变量模型(M2)和7 变量模型(M3),相关系数R分别为0.947 和0.950.再用留一法(Leave-one-out,LOO)交互检验对三模型进行评价,得到的相关系数RCV分别为0.889、0.914 和0.916.结果表明所建模型具有良好的稳定性和预测能力.     

基于带宽优选地理加权回归模型的深圳市植被碳储量反演

植被碳储量估测是自然资源监测的重要内容,遥感技术结合地面样地进行反演可以获得区域范围内植被碳储量的空间连续分布,弥补了传统人工抽样调查估测的不足。然而,现有的参数和非参数遥感估测模型大多忽略了样地数据的变异与空间自相关关系。研究以Landsat 8 OLI影像为数据源提取遥感变量,结合植被碳储量实测调查数据,利用最小信息准则(AICc)、最大空间自相关距离(MSAD)和交叉验证(CV)分别确定最优带宽,组合Gaussian、Bi-square和Exponential核函数构建地理加权回归(GWR)模型估算深圳市植被碳储量,并与多元线性回归(MLR)进行比较,选择最优模型绘制深圳市植被碳储量空间分布图。研究结果表明,GWR模型整体精度优于MLR模型,GWR模型的决定系数(R~2)均高于MLR模型,且均方根误差(RMSE)和平均绝对误差(MAE)显著降低。带宽和核函数的选择对GWR模型估测结果产生了显著影响。以CV确定带宽、Exponential为核函数组合构建的GWR模型效果最佳,其R~2为0.697,RMSE为10.437 Mg C/hm~2,相比其它模型精度上升了13.87%—32....     

结合薄层CT技术建立下颌第一前磨牙三维有限元模型

目的结合薄层CT技术建立下颌第一前磨牙三维有限元模型。方法对正常人下颌第一前磨牙进行薄层CT扫描及图像处理,通过Matlab和ANSYS软件建立三维有限元模型,并加载验证模型力学分析的可行性。结果建立了包含髓腔的下颌第一前磨牙的三维有限元模型,得到101564个单元,144053个节点。载荷后的应力分布主要集中在颊尖部位和根尖部位,牙颈部受力较小。结论薄层CT技术与Matlab和ANSYS软件相结合,建立包含髓腔的下颌第一前磨牙的三维有限元模型,精度高、速度快,使用灵活,为后期的楔缺模型建立和分析奠定了基础。     

基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘

提出一种基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘的算法。该算法在Chan—Vese（CV）模型基础上,定义了一个局部调整项,采用基于水平集的动态轮廓模型提取超声图像乳腺肿瘤边缘。将该算法应用于89例临床超声图像乳腺肿瘤的边缘提取实验,结果表明：该算法比CV模型更适用于具有区域非同质性的超声图像的分割,可有效实现超声图像乳腺肿瘤边缘的提取。     

近红外光谱技术快速预测大豆氨基酸

为探索近红外光谱技术在大豆氨基酸测试中的应用,寻找一种快速的检测方法,以167份大豆[Glycine max(L.)Merr.]种子为材料,采用傅里叶变换近红外光谱技术(FT-NIRS)对经高效液相色谱法(HPLC)分析的18种氨基酸含量进行模拟。结果显示:天冬氨酸(R2CV=0.85)、谷氨酸(R2CV=0.86)、丝氨酸(R2CV=0.82)、甘氨酸(R2CV=0.89)、酪氨酸(R2CV=0.83)、苯丙氨酸(R2CV=0.78)、异亮氨酸(R2CV=0.86)和色氨酸(R2CV=0.81)及15种氨基酸总和(R2CV=0.82)可利用FT-NIRS准确预测;苏氨酸、精氨酸、丙氨酸、缬氨酸、亮氨酸和胱氨酸检测模型有一定的参考价值,可用来进行相对含量的估测;而对组氨酸、赖氨酸、脯氨酸和蛋氨酸的预测不准确。本研究进一步证明,利用FT-NIRS技术预测大豆主要氨基酸组分是稳定可行的。     

农业政策环境扩展模型在我国农业面源污染研究中的应用

我国的面源污染问题逐渐受到政府和科学界的重视,然而面源污染是一个复杂的系统过程,涉及多种因素和多个过程。面源(NPS)污染模型作为解决面源污染相关问题的研究和管理工具,在进行面源污染总量估算和严重程度评价、污染物流失路径和影响因素分析、治理策略制定等方面都有重要的作用。在我国,虽然针对面源污染模型进行了大量相关研究,既包含对基于国外模型的应用与验证,也包含基于观测数据自主研发的模型,但仍然存在模型应用和验证案例不足、已有的模型应用同中国面源污染特征结合不足、模型发展同面源污染机理研究结合不足等问题,而农业政策环境扩展(APEX)模型在应对这些问题上具有一定的优势。结合我国面源污染模型相关研究存在的问题、APEX模型模块和研究进展进行了介绍,对APEX模型在我国面源污染相关问题的研究中涉及的畜禽养殖、复杂耕作系统、特定BMP和水稻田的模拟等相关问题的应用前景进行了探讨,以期能够促进我国农业面源污染模型的发展。     

全缘叶绿绒蒿挥发性组分定量结构-色谱保留关系研究

提出顶点及顶点相互作用矢量的概念,并将该矢量用于复杂样本的分子结构表征。采用逐步回归结合统计检测对变量进行筛选后,再用多元线性回归建立了定量结构-色谱保留(QSRR)关系的7变量模型,模型的建模计算值复相关系数(R)为0.990,标准偏差(SD)为1.325;留一法(LOO)交互检验复相关系数(RCV)为0.983,标准偏差(SDCV)为1.696。结果表明该矢量具有较强的分子结构表达能力,模型具有良好的估计能力与稳定性。     

ELISA检测外用人纤维蛋白原中纤溶酶原残留量的验证及应用

目的 ELISA检测外用人纤维蛋白原中纤溶酶原残留量,并对其进行方法学验证及应用。方法按照方法学验证的要求,对专属性、线性与定量范围、样品稀释线性、准确性、精密度、耐用性、样品稳定性进行验证分析。应用该方法检测外用人纤维蛋白原主要工艺步骤样品及成品中纤溶酶原残留量,并进行分析和控制。结果专属性验证结果表明,制剂缓冲液对检测结果无干扰,准确性均在(100±10)%以内;线性和定量范围验证结果表明,在0.137～100 ng/mL定量范围内,线性相关系数(R~2)>0.99,对照品回收率均在(100±20)%内,变异系数(coefficient of variation,CV)<5%;样品稀释线性验证结果表明,将样品稀释至0.332～83.050 ng/mL范围内,准确性均在(100±20)%内,CV均<15%;准确性验证结果表明,回收率均在(100±20)%以内;精密度验证结果表明,重复性和中间精密度CV均<10%;耐用性验证结果表明,样品孵育时间缩短至2.0 h,回收率为(89.5±1.4)%,CV为1.5%;样品稳定性验证结果表明,样品室温放置6 h以内,冻融3次以内,准确性均在(100±20)%以内。用此方法检测外用人纤维蛋白原主要工艺步骤样品及成品中的纤溶酶原残留量,结果表明层析步骤去除了95.1%纤溶酶原,是控制纤溶酶原的关键步骤;3批样品纤溶酶原残留量检测结果批间CV<20%,生产工艺稳定。结论该方法线性与定量范围、样品稀释线性均达到可接受标准,专属性、准确性、精密度、耐用性、样品稳定性均良好。应用此方法检测外用人纤维蛋白原中的纤溶酶原残留量,为去除痕量纤溶酶原时提供检测分析手段,有利于人纤维蛋白粘合剂产品质量的控制。     

The Effect of Branch Length Variation on the Selection of Models of Molecular Evolution

Models of sequence evolution play an important role in molecular evolutionary studies. The use of inappropriate models of evolution may bias the results of the analysis and lead to erroneous conclusions. Several procedures for selecting the best-fit model of evolution for the data at hand have been proposed, like the likelihood ratio test (LRT) and the Akaike (AIC) and Bayesian (BIC) information criteria. The relative performance of these model-selecting algorithms has not yet been studied under a range of different model trees. In this study, the influence of branch length variation upon model selection is characterized. This is done by simulating sequence alignments under a known model of nucleotide substitution, and recording how often this true model is recovered by different model-fitting strategies. Results of this study agree with previous simulations and suggest that model selection is reasonably accurate. However, different model selection methods showed distinct levels of accuracy. Some LRT approaches showed better performance than the AIC or BIC information criteria. Within the LRTs, model selection is affected by the complexity of the initial model selected for the comparisons, and only slightly by the order in which different parameters are added to the model. A specific hierarchy of LRTs, which starts from a simple model of evolution, performed overall better than other possible LRT hierarchies, or than the AIC or BIC. Received: 2 October 2000 / Accepted: 4 January 2001     

Accounting for uncertainty in the tree topology has little effect on the decision-theoretic approach to model selection in phylogeny estimation

Currently available methods for model selection used in phylogenetic analysis are based on an initial fixed-tree topology. Once a model is picked based on this topology, a rigorous search of the tree space is run under that model to find the maximum-likelihood estimate of the tree (topology and branch lengths) and the maximum-likelihood estimates of the model parameters. In this paper, we propose two extensions to the decision-theoretic (DT) approach that relax the fixed-topology restriction. We also relax the fixed-topology restriction for the Bayesian information criterion (BIC) and the Akaike information criterion (AIC) methods. We compare the performance of the different methods (the relaxed, restricted, and the likelihood-ratio test [LRT]) using simulated data. This comparison is done by evaluating the relative complexity of the models resulting from each method and by comparing the performance of the chosen models in estimating the true tree. We also compare the methods relative to one another by measuring the closeness of the estimated trees corresponding to the different chosen models under these methods. We show that varying the topology does not have a major impact on model choice. We also show that the outcome of the two proposed extensions is identical and is comparable to that of the BIC, Extended-BIC, and DT. Hence, using the simpler methods in choosing a model for analyzing the data is more computationally feasible, with results comparable to the more computationally intensive methods. Another outcome of this study is that earlier conclusions about the DT approach are reinforced. That is, LRT, Extended-AIC, and AIC result in more complicated models that do not contribute to the performance of the phylogenetic inference, yet cause a significant increase in the time required for data analysis.     

Model selection in non-nested hidden Markov models for ion channel gating

An important task in the application of Markov models to the analysis of ion channel data is the determination of the correct gating scheme of the ion channel under investigation. Some prior knowledge from other experiments can reduce significantly the number of possible models. If these models are standard statistical procedures nested like likelihood ratio testing, provide reliable selection methods. In the case of non-nested models, information criteria like AIC, BIC, etc., are used. However, it is not known if any of these criteria provide a reliable selection method and which is the best one in the context of ion channel gating. We provide an alternative approach to model selection in the case of non-nested models with an equal number of open and closed states. The models to choose from are embedded in a properly defined general model. Therefore, we circumvent the problems of model selection in the non-nested case and can apply model selection procedures for nested models.     

Variable selection with incomplete covariate data

SUMMARY: Application of classical model selection methods such as Akaike's information criterion (AIC) becomes problematic when observations are missing. In this article we propose some variations on the AIC, which are applicable to missing covariate problems. The method is directly based on the expectation maximization (EM) algorithm and is readily available for EM-based estimation methods, without much additional computational efforts. The missing data AIC criteria are formally derived and shown to work in a simulation study and by application to data on diabetic retinopathy.     

A modified Bayes information criterion with applications to the analysis of comparative genomic hybridization data

In the analysis of data generated by change-point processes, one critical challenge is to determine the number of change-points. The classic Bayes information criterion (BIC) statistic does not work well here because of irregularities in the likelihood function. By asymptotic approximation of the Bayes factor, we derive a modified BIC for the model of Brownian motion with changing drift. The modified BIC is similar to the classic BIC in the sense that the first term consists of the log likelihood, but it differs in the terms that penalize for model dimension. As an example of application, this new statistic is used to analyze array-based comparative genomic hybridization (array-CGH) data. Array-CGH measures the number of chromosome copies at each genome location of a cell sample, and is useful for finding the regions of genome deletion and amplification in tumor cells. The modified BIC performs well compared to existing methods in accurately choosing the number of regions of changed copy number. Unlike existing methods, it does not rely on tuning parameters or intensive computing. Thus it is impartial and easier to understand and to use.     

Bayesian information criterion for censored survival models

We investigate the Bayesian Information Criterion (BIC) for variable selection in models for censored survival data. Kass and Wasserman (1995, Journal of the American Statistical Association 90, 928-934) showed that BIC provides a close approximation to the Bayes factor when a unit-information prior on the parameter space is used. We propose a revision of the penalty term in BIC so that it is defined in terms of the number of uncensored events instead of the number of observations. For a simple censored data model, this revision results in a better approximation to the exact Bayes factor based on a conjugate unit-information prior. In the Cox proportional hazards regression model, we propose defining BIC in terms of the maximized partial likelihood. Using the number of deaths rather than the number of individuals in the BIC penalty term corresponds to a more realistic prior on the parameter space and is shown to improve predictive performance for assessing stroke risk in the Cardiovascular Health Study.     

应用多模型推论估算北部湾多齿蛇鲻的生长参数

生长参数是渔业资源评估和管理策略中的关键参数,因而对目标鱼种选择合适的生长模型至关重要.本文以北部湾多齿蛇鲻为例,采用2006年12月至2009年7逐月采集的体长与年龄鉴定数据(n=2046),运用5个候选生长模型,利用最大似然法在加性误差条件下估算生长参数,并通过模型近似解释率(R²_adj)、根平均方差(RMSE)、赤井信息准则(AIC)和贝叶斯信息准则(BIC)检验模型拟合度.结果表明: 在当前大样本的情况下,4种统计方法在模型拟合度排序上表现一致;多模型推论检验结果表明,Generalized VBGF获得足够的模型支持,并占到AIC权重的95.9%,可以独立描述多齿蛇鲻的体长与年龄的生长关系,生长方程为:L_t=578.49^{\[1-e-0.051(t-0.14)\]0.361.}     

Nonparametric mixed effects models for unequally sampled noisy curves

We propose a method of analyzing collections of related curves in which the individual curves are modeled as spline functions with random coefficients. The method is applicable when the individual curves are sampled at variable and irregularly spaced points. This produces a low-rank, low-frequency approximation to the covariance structure, which can be estimated naturally by the EM algorithm. Smooth curves for individual trajectories are constructed as best linear unbiased predictor (BLUP) estimates, combining data from that individual and the entire collection. This framework leads naturally to methods for examining the effects of covariates on the shapes of the curves. We use model selection techniques--Akaike information criterion (AIC), Bayesian information criterion (BIC), and cross-validation--to select the number of breakpoints for the spline approximation. We believe that the methodology we propose provides a simple, flexible, and computationally efficient means of functional data analysis.