Mixed-Effects Estimation in Dynamic Models of Plant Growth for the Assessment of Inter-individual Variability

Modeling inter-individual variability in plant populations is a key issue to understand crop heterogeneity and its variations in response to the environment. Being able to describe the interactions among plants and explain the variability observed in the population could provide useful information on how to control it and improve global plant growth. We propose here a method to model plant variability within a field, by extending the so-called GreenLab functional-structural plant model from the individual to the population scale via nonlinear mixed-effects modeling. Parameter estimation of the population model is achieved using the stochastic approximation expectation maximization algorithm, implemented in the platform for plant growth modeling and analysis PyGMAlion. The method is first applied on a set of simulated data and then on a real dataset from a population of 34 winter oilseed rape plants at the rosette stage. Results show that our method allows for a good characterization of the variability in the population with only a limited number of parameters, which is a key point for plant models. Results on simulated data show that parameters associated with a low sensitivity index are inaccurately estimated by the algorithm when considered as random effects, but a good stability of the results can be obtained by considering them as fixed effects. These results open new ways for the analysis of inter-plant variability within a population and the study of plant–plant competition.Supplementary materials accompanying this paper appear online.     

A Nonlinear Mixed-Effects Model for Multivariate Longitudinal Data with Dropout with Application to HIV Disease Dynamics

The main challenge in biomedical and clinical studies which involve collection of longitudinal data is the premature withdrawal of the subjects from the study resulting in incomplete data. Standard statistical analysis approaches usually give biased estimates of the model parameters if the mechanisms that led to dropout are ignored. In this discussion, we consider nonlinear mixed-effects models for multivariate longitudinal data in the presence of subject dropout. We present techniques for estimation of model parameters. These procedures are applied to estimate the parameters in the HIV dynamic system using routine observational data from an HIV clinic.     

Ecological Prediction With Nonlinear Multivariate Time-Frequency Functional Data Models

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.     

Nonlinear Varying-Coefficient Models with Applications to a Photosynthesis Study

Motivated by a study on factors affecting the level of photosynthetic activity in a natural ecosystem, we propose nonlinear varying-coefficient models, in which the relationship between the predictors and the response variable is allowed to be nonlinear. One-step local linear estimators are developed for the nonlinear varying-coefficient models and their asymptotic normality is established leading to point-wise asymptotic confidence bands for the coefficient functions. Two-step local linear estimators are also proposed for cases where the varying-coefficient functions admit different degrees of smoothness; bootstrap confidence intervals are utilized for inference based on the two-step estimators. We further propose a generalized F-test to study whether the coefficient functions vary over a covariate. We illustrate the proposed methodology via an application to an ecology data set and study the finite sample performance by Monte Carlo simulation studies.     

Hierarchical Nonlinear Spatio-temporal Agent-Based Models for Collective Animal Movement

Modeling complex collective animal movement presents distinct challenges. In particular, modeling the interactions between animals and the nonlinear behaviors associated with these interactions, while accounting for uncertainty in data, model, and parameters, requires a flexible modeling framework. To address these challenges, we propose a general hierarchical framework for modeling collective movement behavior with multiple stages. Each of these stages can be thought of as processes that are flexible enough to model a variety of complex behaviors. For example, self-propelled particle (SPP) models (e.g., Vicsek et al. in Phys Rev Lett 75:1226–1229, 1995) represent collective behavior and are often applied in the physics and biology literature. To date, the study and application of these models has almost exclusively focused on simulation studies, with less attention given to rigorously quantifying the uncertainty. Here, we demonstrate our general framework with a hierarchical version of the SPP model applied to collective animal movement. This structure allows us to make inference on potential covariates (e.g., habitat) that describe the behavior of agents and rigorously quantify uncertainty. Further, this framework allows for the discrete time prediction of animal locations in the presence of missing observations. Due to the computational challenges associated with the proposed model, we develop an approximate Bayesian computation algorithm for estimation. We illustrate the hierarchical SPP methodology with a simulation study and by modeling the movement of guppies.Supplementary materials accompanying this paper appear online.     

基于非线性参数优化的水稻花前生育期参数估计

水稻生育期模型为复杂的非线性模型,其参数的合理标定是模型应用的重要环节。本文采用两种不同温度响应函数的花前生育期模型（MBETA和MBILN）,利用基于GML（Gauss-Marquardt-Levenberg）算法的模型独立参数优化程序PEST（model-independent parameter estimation）对模型参数进行优化,并在优化中引入参数先验信息和参数初始值扰动方法,以提高参数优化结果的可靠性。结果显示,参数先验信息有效降低了待优化参数的不确定性。最优参数值的95%置信区间较初始值域显著缩小。在优化得到的参数相关系数矩阵中也未显现出高度相关的参数。从目标函数值（?）序列看,MBETA和MBILN的?值最终收敛至相当接近的最小值,分别为11.71和11.82。但该最小值下两个模型的温度、光周期效应等参数值存在一定差异。这种差异平衡了不同温度响应方程与模型其它方程对水稻生育期模拟误差的贡献。在最优参数值组合下,两个模型验证结果表现一致。其中,抽穗开花期模拟值与实测值的相关性均通过了0.01水平的显著性检验。模拟误差主要来自幼穗分化期,与缺少对水稻光周期敏感始期的观测有关。本文优化方法降低了待优化参数收敛于局部小值的几率,对稳定参数优化和提高优化结果的可靠性具有重要作用。     

Assessing First-Order Emulator Inference for Physical Parameters in Nonlinear Mechanistic Models

We present an approach for estimating physical parameters in nonlinear models that relies on an approximation to the mechanistic model itself for computational efficiency. The proposed methodology is validated and applied in two different modeling scenarios: (a) Simulation and (b) lower trophic level ocean ecosystem model. The approach we develop relies on the ability to predict right singular vectors (resulting from a decomposition of computer model experimental output) based on the computer model input and an experimental set of parameters. Critically, we model the right singular vectors in terms of the model parameters via a nonlinear statistical model. Specifically, we focus our attention on first-order models of these right singular vectors rather than the second-order (covariance) structure.     

利用线性和非线性耦合方式建立温室温湿度预测模型

基于蔬菜种植试验温室内温度、相对湿度和光照强度的实测数据,根据ARIMA模型和RBF神经网络对线性和非线性问题的预测能力差异,构建ARIMA-RBF神经网络权重组合的温湿度预测模型,对温室内温度和湿度的动态变化进行预测,并比较各模型预测精度。结果表明：温室内温湿度分别具有更明显的线性和非线性变化特征,对应预测性能较好的单一模型分别为ARIMA模型和RBF模型。相较单一模型,ARIMA-RBF神经网络权重组合模型的预测精度更高、稳定性更好。最佳温度组合模型的MAE、MAPE和RMSE分别为1.04℃、2.95%和1.21℃;最佳湿度组合模型的MAE、MAPE和RMSE分别为0.35个百分点、0.36%和0.55个百分点。权重组合模型通过适当的加权策略充分发挥了单一模型对数据不同特征的处理能力,能较好地评估温室内温湿度状态,可为建立更具普适性的温室环境因子模型提供参考。     

Models for strawberry inflorescence data

The flowers of strawberry plants grow on very variable branched structures called inflorescences, in which each branch gives rise to 0, 1, or 2 offspring branches. We extend previous modeling of the number of strawberry flowers at each individual level in the inflorescence structure conditional on the number of strawberry flowers at the previous level. We consider a range of logistic regression models, including models that incorporate inflorescence effects and random effects. The models can be used to summarize the overall structure of any particular variety and to indicate the main differences between varieties. For the data of the article, we show that models based on convolutions of correlated Bernoulli random variables outperform binomial regression models.     

Models for microbiological colony counts

Concentrations of microorganisms can be estimated from colony counts at different dilutions. However, complications can occur because of colony overlap or inhibition of colony growth. We develop a model of inhibition in which colonies fail to develop if spores are close to spores of other inhibitory species. The model has three parameters, but a limiting case of the model with only two parameters is shown to be more useful in practice. This latter model, which is a generalized linear model, is fitted to colony counts of the fungus Verticillium dahliae and contrasted with a model suggested in an earlier article.     

Expression of the Truncated vip3A Gene at the 3'-terminus from Bacillus thuringiensis in Escherichia coli

将苏云金芽孢杆菌(Bacillus thuringiensis)缺失C端154个氨基酸编码区的vip3A基因(vip3T)插入原核表达载体pQE30，构建了重组表达载体pQEvip3T，并转化大肠杆菌(Escherichia coli ) M15进行IPTG诱导表达，比较了完整的Vip3A蛋白和C端缺失的蛋白Vip3T的可溶性和杀虫活性。与Vip3A不同，融合蛋白Vip3T以不可溶的包含体形式存在，诱导表达的菌液中没有检测到可溶性Vip3T蛋白。生物测定结果表明，M15(pOTP)诱导表达的Vip3A蛋白对初孵斜纹夜蛾(Spodoptera litua)和甜菜夜蛾(S. exigua)幼虫具有较高的杀虫活性，其提纯的包含体无毒，但包含体的碱性裂解液却又恢复了对夜蛾科害虫的活性；M15(pQEvip3T)菌液、包含体及其碱性裂解液对这两种昆虫幼虫则完全无毒，说明在大肠杆菌中，Vip3A蛋白C端氨基酸可能对Vip3A蛋白的可溶性和杀虫活性具有重要的影响。     

赤羽病病毒囊膜糖蛋白G1基因的截短表达、纯化及间接ELISA诊断方法的建立

为获得赤羽病病毒（Akabane Virus, AKAV）囊膜糖蛋白重组抗原作诊断应用研究,通过软件分析AKAV OBE-1株的囊膜糖蛋白G1的氨基酸序列,筛选出抗原性较好的基因片段G1-2作为目的片段,经RT-PCR扩增后,插入pMD18-T载体。经测序鉴定正确后,将该片段定向亚克隆于pET-28a (+)表达载体中,转化至BL21 (DE3)感受态细胞中进行诱导表达。经SDS-PAGE和Western-blot分析,诱导表达产物以包涵体的形式存在,大小约为40ku,且具有免疫学活性。亲和纯化后的融合蛋白浓度为2mg/ml,纯度为92.6%。以该融合蛋白作为诊断抗原,建立了间接ELISA诊断方法。确定的抗原包被浓度为20g/ml,血清最佳稀释度为1:200。交叉试验表明该方法对牛常见的6种疾病阳性血清无交叉反应。应用该方法对病毒微量中和试验检测过的云南（77份）和内蒙（70份）牛血清样品进行了检测。以中和试验为参照,通过统计学处理,得出两地临界值分别为0.493和0.488,本方法的特异性为73%和86.9%,二者的符和率分别为80%和85.9%。     

强非线性水平混沌振动台

该文研究了一种水平混沌振动台,机构的运动微分方程是强非线性的,数值仿真和实验结果表明,该振动台的动态响应是混沌的,且敏感依赖于初始条件。     

Nonlinear optimization modeling of coliform bacteria

An optimization model for use in the planning stage of the water quality management process was developed. The model determined the optimal removal of coliform bacteria in accordance with an objective function which reflects the uncertainty aspects of the problem. Several management options are available through model use thereby providing a decision-maker with considerable latitude to evaluate the relative importance of the conflicting objectives of the problem.     

Age Specificity in Conditional Ring-Recovery Models

We consider the case of age-specific ring-recovery data obtained only from recovered individual birds and modelled by conditioning a multinomial distribution on the recovery. These models may be appealing when the information about the numbers of marked individuals is missing, but they have previously been analyzed by ignoring a large set of nuisance parameters, the recovery probabilities. We investigate the consequences of this conditioning by relating the age-time specific structure of recovery probabilities to the estimation of survival.     

Comparison of Models for Olfactometer Data

Olfactometer experiments are used to study the responses of arthropods to potential attractants, for purposes such as understanding natural defenses of plants against their herbivores. Such experiments typically lead to multivariate data consisting of small correlated counts, which are overdispersed relative to standard models. In this paper models that account for the overdispersion under different hypotheses on insect behavior are described and illustrated with an example, and a graphical approach to discriminating among them is briefly discussed.     

国外土壤侵蚀模型发展概述

 土壤侵蚀模型是预报水土流失、指导水土保持措施配置、优化水土资源利用的有效工具。根据土壤侵蚀模型发展过程的特征,从时间上划分了土壤侵蚀模型发展的3个阶段,即通用土壤流失方程(USLE)建立以前的时期、USLE的建立及发展的时期和土壤侵蚀模型百家争鸣时期。介绍了世界上一些基本的土壤侵蚀模型,从而概述国外土壤侵蚀模型的发展过程。     

Identification of some Benin Republic's Guinea yam (Dioscorea cayenensis/Dioscorea rotundata complex) cultivars using Randomly Amplified Polymorphic DNA

DNA from twenty-three late maturing cultivars of Guinea yams (D. cayenensis/D. rotundata complex) from the Benin Republic that could not be separated using isozyme markers, were examined using randomly amplified polymorphic DNA (RAPD) markers with decamer primers of arbitrary sequence. All the twelve primers tested were informative and yielded 63 amplified DNA bands from which 47 (75%) were polymorphic. Although no single primer produced polymorphic bands in all cultivars, the great majority of the cultivars were separated with the combinations of polymorphic bands generated by various primers. Putative duplicates and cultivar misclassifications were identified. Many morphologically distinct cultivars were close. The dwarf cultivar Tam-Sam considered as derived from Tabane, appeared more distant from the latter than was believed. RAPD analysis was found as a practical tool for the identification of duplicates toward establishment of an accurate core collection of Guinea yams in Benin Republic and in the other countries of the African yam belt.