温室作物生物量积累率二阶差分模型

为了研究温室作物生物量积累的变化过程,本文构建了一类二阶差分模型,该模型具有有界性、单调性与全局渐近稳定性.对实验数据的模拟表明,该模型能很好解释温室黄瓜生物量轨迹运动趋势与干重积累率增长特性,比原有的生长模型拟合效果好.     

生态对策影响种群增长的数学模型研究

本文根据不同生态对策的种群的增长特点和Alllee规律,组建了一个能反映种群自我调节机制以及这种机制与环境负荷协同作用对种群施加影响的种群增长模型。模型在变化其参数时,可以转变为几个具有实用价值的模型。因此,该模型具有一定的应用意义。文章最后还讨论了该模型对生态管理的指导意义。     

动物标志重捕法模拟实验设计与应用

以高中生物学"种群和群落"中介绍的动物标志重捕法探究种群密度为知识背景,以培养学生探究性学习为目标,以模拟实验要求为设计思路,用塑料瓶模拟动物生活区域,塑料球模拟该区域动物,带孔塑料瓶盖模拟捕获动物的器具,保鲜袋模拟捕获动物暂时的活动区。通过对该模型方案的模拟应用,效果显著。该模型有制作简单、材料易得、操作安全、结果可靠等优点。该模型无需教师协助,学生可以在家中或学校独立完成实验,该模型是一个实用性极强的方案。     

MAXENT最大熵模型在预测物种潜在分布范围方面的应用

MAXENT最大熵模型是以最大熵理论为基础的物种地理尺度空间分布模型。该模型自提出之后在国内外迅速得到广泛应用,被越来越多地应用于入侵生物学、保护生物学、全球气候变化对物种分布影响,以及进化生物学等领域的研究。主要从MAXENT模型在入侵生物潜在分布区预测、濒危物种及有经济价值物种的适生区预测,以及全球气候变化对物种分布的影响预测等3个方面,对其应用现状进行综述,分析应用该模型时应该注意的一些问题。     

基于移动序列模式分析人基因组调控短序列

基因表达过程主要包括转录、剪接和翻译,多种调控元件参与其中,是个高度调控的过程。建模识别分析这些调控元件,对理解基因表达具有重要意义。本研究提出了一个基于移动序列模式的短序列建模模型,并对转录启动子和剪接调控元件进行了建模分析。启动子是基因转录的核心调控元件,剪接调控元件参与调控剪接位点的识别。分类实验结果表明,该模型可有效识别转录启动子序列和剪接调控元件序列。并进一步利用该模型,建模分析已为生物实验验证的、会导致剪接影响的基因组变异,实验结果表明,该模型可有效预测基因组变异的剪接影响,进一步验证了该模型的有效性。     

产耐热蛋白酶苏云金芽胞杆菌的发酵动力学

对苏云金芽胞杆菌FZ62耐热蛋白酶的代谢过程进行初步研究,酶生成与菌体生长为耦联过程。以Logistic方程和Luedeking—Piret方程为基础,建立该菌发酵过程中菌体生长、产物生成和基质消耗的动力学模型,求得模型参数,该动力学模型与试验结果拟和程度较好。     

落叶松播种苗木双目标营养施肥优化模型

本文对落叶松播种苗木的双目标营养施肥问题进行了分析,运用数学规划理论建立了该苗木双目标营养施肥的优化模型,给出了该模型的具体计算方法．应用实例对所建模型方法进行检验,取得了良好效果．     

银耳红外辐射干燥特性研究及动力学模型构建

利用红外辐射干燥技术,对银耳红外辐射干燥特性及其动力学模型进行研究,探讨不同干燥温度(60~140℃)、辐射强度(1~5 W/cm2)、空气流速(0.5~1.5 m/s)等因素对银耳失水特性的影响。结果发现,干燥温度和辐射强度对银耳失水特性影响显著,而空气流速对其影响较小。根据试验数据建立银耳红外辐射干燥的水分比与干燥时间关系的动力学模型,并对模型进行拟合检验,结果发现Midilli模型能准确的描述银耳红外辐射干燥过程,该模型预测值与实测值拟合良好。该模型可以准确预测银耳在红外辐射干燥过程中的失水特性。     

酶模型

结合各相关专著、文献中对酶模型的介绍,简要回顾了酶模型的发展背景,较系统综述了其概念、理论基础、主要类型及该领域的研究进展。     

格氏栲种群生态学研究 Ⅲ. 格氏栲种群优势度增长动态规律研究

采用有限空间种群增长的逻辑斯谛模型探讨格氏栲种群基面积增长规律。提出自适应通用模型ds/dt=rs(1-s^θ/k^φ).该模型包括Logistic模型、Smith模型、Gompertz模型、崔Lawson模型和Z-Logistic模型;运用改进单纯形对自适应通用模型进行优化,拟合结果比Logistic模型更符合格氏栲种群实际增长趋势,增长速度最大是在147年。     

Context-Dependent Evolutionary Models for Non-Coding Sequences: An Overview of Several Decades of Research and an Analysis of Laurasiatheria and Primate Evolution

The past decade has seen a growing interest in evolutionary models that relax the assumption of site-independent evolution for non-coding sequences. While phylogenetic inference using such so-called context-dependent models is currently computationally prohibitive, these models have been shown to yield significant increases in model fit compared to site-independent evolutionary models, which remain the most widely used evolutionary models to study substitution patterns and perform phylogenetic inference. Context-dependent models have been shown to be suited to study the spontaneous deamination of cytosine in mammalian sequences. In this paper, I discuss various approaches presented in recent years to model context-dependent evolution. I start with discussing the empirical research and results that have led to the development of these models. To accurately estimate the context-dependent substitution patterns that arise from these models, accurate sampling of substitution histories under such models is required. Further, appropriate model selection techniques to assess model performance has become more important than ever, given the drastic increase in parameters of context-dependent models and the tendency of older model selection techniques to prefer parameter-rich models. I also present new results on two mammalian datasets (Primate and Laurasiatheria data) to shed a light on so-called lineage-dependent context-dependent evolution. I conclude this paper with a discussion on current challenges in the development of context-dependent modeling approaches.     

High-throughput functional curation of cellular electrophysiology models

Effective reuse of a quantitative mathematical model requires not just access to curated versions of the model equations, but also an understanding of the functional capabilities of the model, and the advisable scope of its application. To enable this “functional curation” we have developed a simulation environment that provides high-throughput evaluation of a mathematical model’s functional response to an arbitrary user-defined protocol, and optionally compares the results against experimental data. In this study we demonstrate the efficacy of this simulation environment on 31 cardiac electrophysiology cell models using two test cases. The S1-S2 response is evaluated to characterise the models’ restitution curves, and their L-type calcium channel current-voltage curves are evaluated. The significant variation in the response of these models, even when the models represent the same species and temperature, demonstrates the importance of knowing the functional characteristics of a model prior to its reuse.We also discuss the wider implications for this approach, in improving the selection of models for reuse, enabling the identification of models that exhibit particular experimentally observed phenomena, and making the incremental development of models more robust.     

Cyberkelp: an integrative approach to the modelling of flexible organisms

Biomechanical models come in a variety of forms: conceptual models; physical models; and mathematical models (both of the sort written down on paper and the sort carried out on computers). There are model structures (such as insect flight muscle and the tendons of rats' tails), model organisms (such as the flying insect, Manduca sexta), even model systems of organisms (such as the communities that live on wave-swept rocky shores). These different types of models are typically employed separately, but their value often can be enhanced if their insights are integrated. In this brief report we explore a particular example of such integration among models, as applied to flexible marine algae. A conceptual model serves as a template for the construction of a mathematical model of a model species of giant kelp, and the validity of this numerical model is tested using physical models. The validated mathematical model is then used in conjunction with a computer-controlled tensile testing apparatus to simulate the loading regime placed on algal materials. The resulting information can be used to create a more precise mathematical model.     

Selecting the best-fit model of nucleotide substitution

Despite the relevant role of models of nucleotide substitution in phylogenetics, choosing among different models remains a problem. Several statistical methods for selecting the model that best fits the data at hand have been proposed, but their absolute and relative performance has not yet been characterized. In this study, we compare under various conditions the performance of different hierarchical and dynamic likelihood ratio tests, and of Akaike and Bayesian information methods, for selecting best-fit models of nucleotide substitution. We specifically examine the role of the topology used to estimate the likelihood of the different models and the importance of the order in which hypotheses are tested. We do this by simulating DNA sequences under a known model of nucleotide substitution and recording how often this true model is recovered by the different methods. Our results suggest that model selection is reasonably accurate and indicate that some likelihood ratio test methods perform overall better than the Akaike or Bayesian information criteria. The tree used to estimate the likelihood scores does not influence model selection unless it is a randomly chosen tree. The order in which hypotheses are tested, and the complexity of the initial model in the sequence of tests, influence model selection in some cases. Model fitting in phylogenetics has been suggested for many years, yet many authors still arbitrarily choose their models, often using the default models implemented in standard computer programs for phylogenetic estimation. We show here that a best-fit model can be readily identified. Consequently, given the relevance of models, model fitting should be routine in any phylogenetic analysis that uses models of evolution.     

The relationship between count-location and stationary renewal models for the chiasma process

It is often convenient to define models for the process of chiasma formation at meiosis as stationary renewal models. However, count-location models are also useful, particularly to capture the biological requirement of at least one chiasma per chromosome. The Sturt model and truncated Poisson model are both count-location models with this feature. We show that the truncated Poisson model can also be expressed as a stationary renewal model, while the Sturt model cannot. More generally, we show that there is only one family of count-location models for the chiasma process that can also be expressed as stationary renewal models. The models in this family can exhibit either positive or negative interference.     

Predictive modeling of mixed microbial populations in food products: evaluation of two-species models

Predictive microbiology is an emerging research domain in which biological and mathematical knowledge is combined to develop models for the prediction of microbial proliferation in foods. To provide accurate predictions, models must incorporate essential factors controlling microbial growth. Current models often take into account environmental conditions such as temperature, pH and water activity. One factor which has not been included in many models is the influence of a background microflora, which brings along microbial interactions. The present research explores the potential of autonomous continuous-time/two-species models to describe mixed population growth in foods. A set of four basic requirements, which a model should satisfy to be of use for this particular application, is specified. Further, a number of models originating from research fields outside predictive microbiology, but all dealing with interacting species, are evaluated with respect to the formulated model requirements by means of both graphical and analytical techniques. The analysis reveals that of the investigated models, the classical Lotka-Volterra model for two species in competition and several extensions of this model fulfill three of the four requirements. However, none of the models is in agreement with all requirements. Moreover, from the analytical approach, it is clear that the development of a model satisfying all requirements, within a framework of two autonomous differential equations, is not straightforward. Therefore, a novel prototype model structure, extending the Lotka-Volterra model with two differential equations describing two additional state variables, is proposed to describe mixed microbial populations in foods.     

地表水热通量研究进展

介绍了当前国内外地表水热通景观测研究的进展及3种不同类型的土壤-植被-大气传输模型（SVAT）：单层模型、双层模型和多层模型。遥感手段常用于监测大面积地表水热通量。基于地表能量平衡方程,现已建立了许多遥感模型以估算水热通量（如简化模型、单层模型、附加阻抗模型、作物缺水指数模型和二源阻抗模型等）,并对这些模型复杂程度及应用范围进行了分析。     

Comparative Analysis of Modeling Techniques for Coliform Organisms in Streams

The use of models for predicting changes in water quality parameters is currently considered an integral part of river basin management. The application of modeling techniques to coliform organisms is in its infancy due to the complexities involved and the lack of definitive information on coliform populations in natural environments. The purpose of this study was to make a comparative analysis of the available models for coliform organisms in order to improve on the state of the art of this subject. The available coliform models may be classified into deterministic or statistical types. In this study, six different models, three of each type, were selected for analysis and were applied to coliform data available on the Leaf River. Results of comparing the models indicated that a deterministic model was best suited for total coliform and a statistical model was best suited for fecal coliform. Ultimate selection of a model for coliform organisms is dependent not only on the accuracy of the model but on ease of implementation. Current technology would probably dictate the use of a deterministic model because of the lack of a complete data base on which to base statistical models.     

An Empirical Comparison of Parametric and Semiparametric Cure Models

Parametric and semiparametric cure models have been proposed for cure proportion estimation in cancer clinical research. In this paper, several parametric and semiparametric models are compared, and their estimation methods are discussed within the framework of the EM algorithm. We show that the semiparametric PH cure model can achieve efficiency levels similar to those of parametric cure models, provided that the failure time distribution is well specified and uncured patients have an increasing hazard rate. Therefore the semiparametric model is a viable alternative to parametric cure models. When the hazard rate of uncured patients is rapidly decreasing, the estimates from the semiparametric cure model tend to have large variations and biases. However, all other models also tend to have large variations and biases in this case.     

Model choice for phylogeographic inference using a large set of models

Model‐based analyses are common in phylogeographic inference because they parameterize processes such as population division, gene flow and expansion that are of interest to biologists. Approximate Bayesian computation is a model‐based approach that can be customized to any empirical system and used to calculate the relative posterior probability of several models, provided that suitable models can be identified for comparison. The question of how to identify suitable models is explored using data from Plethodon idahoensis, a salamander that inhabits the North American inland northwest temperate rainforest. First, we conduct an ABC analysis using five models suggested by previous research, calculate the relative posterior probabilities and find that a simple model of population isolation has the best fit to the data (PP = 0.70). In contrast to this subjective choice of models to include in the analysis, we also specify models in a more objective manner by simulating prior distributions for 143 models that included panmixia, population isolation, change in effective population size, migration and range expansion. We then identify a smaller subset of models for comparison by generating an expectation of the highest posterior probability that a false model is likely to achieve due to chance and calculate the relative posterior probabilities of only those models that exceed this expected level. A model that parameterized divergence with population expansion and gene flow in one direction offered the best fit to the P. idahoensis data (in contrast to an isolation‐only model from the first analysis). Our investigation demonstrates that the determination of which models to include in ABC model choice experiments is a vital component of model‐based phylogeographic analysis.