Analysis of several biomechanical models for the simulation of lamb liver behaviour using similarity coefficients from medical image

In this study, six biomechanical models for simulating lamb liver behaviour are presented. They are validated using similarity coefficients from Medical Image on reconstructed volumes from computerised tomography images. In particular, the Jaccard and Hausdorff coefficients are used. Loads of 20 and 40 g are applied to the livers and their deformation is simulated by means of the finite element method. The models used are a linear elastic model, a neo-Hookean model, a Mooney–Rivlin model, an Ogden model, a linear viscoelastic model and a viscohyperelastic model. The model that provided a behaviour that is closest to reality was the viscohyperelastic model, where the hyperelastic part was modelled with an Ogden model.     

Coexistence of multiple pathogen strains in stochastic epidemic models with density-dependent mortality

Stochastic differential equations that model an SIS epidemic with multiple pathogen strains are derived from a system of ordinary differential equations. The stochastic model assumes there is demographic variability. The dynamics of the deterministic model are summarized. Then the dynamics of the stochastic model are compared to the deterministic model. In the deterministic model, there can be either disease extinction, competitive exclusion, where only one strain persists, or coexistence, where more than one strain persists. In the stochastic model, all strains are eventually eliminated because the disease-free state is an absorbing state. However, if the population size and the initial number of infected individuals are sufficiently large, it may take a long time until all strains are eliminated. Numerical simulations of the stochastic model show that coexistence cases predicted by the deterministic model are an unlikely occurrence in the stochastic model even for short time periods. In the stochastic model, either disease extinction or competitive exclusion occur. The initial number of infected individuals, the basic reproduction numbers, and other epidemiological parameters are important determinants of the dominant strain in the stochastic epidemic model.     

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。     

A Novel Optimal Load Distribution Algorithm for Divisible Loads

A new model for divisible load problem is introduced. Its characteristics are analyzed. Optimal load distribution algorithms on the new model are presented for the tree-network and linear network. Applications that fit our model are briefly described. We show that our model outperforms the existing model such as Cheng–Robertazzi model. We show that the linear model is equivalent to a single-level tree network if the intermediate processors do not follow the store-and-forward communication model, but they follow the store-and-bypass model. This paper introduces the concept of store-and-bypass for divisible load theory.     

Rheological analysis of soft collagenous tissue. Part II: experimental evaluations and verifications

As a direct continuation of Part I, where the theoretical background for the rheological model was discussed, possible operations on the model are discussed and performed on anterior cruciate ligament preparations from rabbits. Different methods of evaluating model constants are compared and other mathematical expressions than those of the model proposed are tried and discussed. Model parts are verified and numerical values are given for certain constants.     

A Population-Based Morphologically Structured Model for Hyphal Growth and Product Formation in Streptomycin Fermentation

Summary A population model discriminating the hyphae according to the hyphal length and a morphologically structured model considering the specific function of different morphological forms of a hypha are combined together to describe mycelial growth, substrate consumption and secondary metabolite formation in streptomycin fermentation. In the population model, the growth modes of hyphae with different age or length are considered, while in the morphologically structured model, the morphological forms of hyphae and their functions in growth and metabolism are described. The population model and the morphologically structured model are interrelated by a branching function and a differentiation function. In the model, the growth rate of immature apical compartment is distinguished from those of matured ones, branching is proposed to occur only in the subapical region, and the hyphal compartment is assumed to synthesize secondary metabolites. The model is successfully applied to simulate the batch fermentation process of streptomycin production. The growth characteristics of filamentous microorganisms are also discussed using the model predictions.     

Segregating Sites in a Gene Conversion Model with Mutation

Formulae for the expectation and variance of the number of segregating and homogeneous sites in a sample of two chromosomes are found. The model includes gene conversion and infinitely-many-alleles mutation in a coalescent framework. The corresponding infinitely-many-sites model limits are also found. The formulae for the expectation are extended to any sample size. Comparisons are drawn between the pure mutation model and the model where gene conversion has been added.     

Ecological interpretations of the mid-domain effect

The suggestion that spatial gradients in species richness are influenced by geometric constraints resulting in the mid‐domain effect has been investigated by null models. The technical aspects of making such null models are well explored, but the implicit ecological assumptions behind these models are less explored. Four ecological models that all assume that species ranges are constrained by hard boundaries are made: evolutionary model, source‐sink model, dynamic‐environment model, and range‐size model. These models give different predictions that make it possible to separate the models from each other, and from a model that assumes that hard boundaries are not important.     

Reliability Analysis of the Groundwater Conceptual Model

The hydrologic model is the foundation of water resource management and planning. Conceptual model is the essential component of groundwater model. Due to limited understanding of natural hydrogeological conditions, the conceptual model is always constructed incompletely. Therefore, the uncertainty in the model's output is evitable when natural groundwater field is simulated by a single groundwater model. A synthetic groundwater model is built and regarded as the true model, and three alternative conceptual models are constructed by considering incomplete hydrogeological conditions. The outputs (groundwater budget terms from boundary conditions) of these groundwater models are analyzed statistically. The results show that when the conceptual model is closer to the true hydrogeological conditions, the distributions of outputs of the groundwater model are more concentrated on the true outputs. Therefore, the more reliable the structure of the conceptual model is, the more reliable the output of the groundwater model is. Moreover, the uncertainty caused by the conceptual model cannot be compensated by parameter uncertainty.     

周期性静滴给药的稳态动力学研究

对线性一定模型的药物,周期静滴给药的动力学模型为一分段连续函数,本文对其动态动力学特征进行了研究,得到了稳态浓度的ｃ－ｔ方程以及一次给药和多次给药动力学参数之间的关系。为预测稳态浓度提供了依据,在此基础上对给药方案的拟定进行了讨论。     

A Variance Components Model for Analyzing Repeated Measures Designs with Non-Stationary Error Structure

When data are collected in the form of multiple measurements on several subjects, they are often analyzed as repeated measures data with some stationary error structure assumed for the errors. For data with non-stationary error structure, the multivariate model is often used. The multivariate model imposes restrictions that are often not met in practice by data of such type. At the same time, they ignore valuable information in the data that are related to time dependencies and time relations. In this paper, we propose a model that is a reparametrization of the multivariate model and is suitable to analyze general repeated measures designs with non-stationary error structure. The model is shown to be a variance components model whose components are estimated using the method of maximum likelihood. Several other properties of the model are derived and discussed including tests of significance. Finally, an example on neurological data is included to demonstrate its application in biological sciences.     

An Evaluation of Beta-Binomial and Dirichlet-Trinomial Models for the Analysis of Reproductive and Developmental Effects

The common endpoints for the evaluation of reproductive and developmental toxic effects are the number of dead/resorbed fetuses, the number of malformed fetuses, and the number of normal fetuses for each litter. The joint distribution of the three endpoints could be modelled by a Dirichlettrinomial distribution or by a product of two-beta-binomial distributions. A simulation experiment is used to investigate the biases of the maximum likelihood estimate (MLE) for the probability of adverse effects under the Dirichlet-trinomial model and the beta-binomial model. Also, the type I errors and powers of the likelihood ratio test for comparing the difference between treatment and control are evaluated for the two underlying models. In estimation, the two MLE's are comparable, the bias estimates are small. In testing, the likelihood ratio test is generally more powerful under the Dirichlet-trinomial model than the beta-binomial model. The type I error rate is greater than the nominal level using the Dirichlet-trinomial model in some cases, when the data are generated from the two-beta-binomial model, and it is less than the nominal level using the beta-binomial model in other cases, when the data are generated from the Dirichlet-trinomial model.     

A nonparametric mixture model for cure rate estimation

Nonparametric methods have attracted less attention than their parametric counterparts for cure rate analysis. In this paper, we study a general nonparametric mixture model. The proportional hazards assumption is employed in modeling the effect of covariates on the failure time of patients who are not cured. The EM algorithm, the marginal likelihood approach, and multiple imputations are employed to estimate parameters of interest in the model. This model extends models and improves estimation methods proposed by other researchers. It also extends Cox's proportional hazards regression model by allowing a proportion of event-free patients and investigating covariate effects on that proportion. The model and its estimation method are investigated by simulations. An application to breast cancer data, including comparisons with previous analyses using a parametric model and an existing nonparametric model by other researchers, confirms the conclusions from the parametric model but not those from the existing nonparametric model.     

选择性注意的统一模型

根据ＰＥＴ、ＥＲＰ和心理学实验的事实,提出选择性注意的统一模型。在统一模型,把探照灯模型和偏置竞争模型和特点结合起来,既包括竞争机制,又包括增强机制。     

On Zeeman's equations for the nerve impulse

A model for the nerve impulse due to Zeeman (1972) and based on catastrophe theory is compared with alternative models and criticisms of Zeeman's model by Sussmann and Zahler (1977, 1978) are assessed. The criticisms of Zeeman's motivation for his model are found to carry some weight. Sussmann and Zahler (1977, 1978) list numerous features of Zeeman's model which, they state, are not in agreement with experiment. These statements as they stand are largely erroneous, and the model still remains to be tested by a critical series of experiments. However, a detailed analysis reveals defects in Zeeman's model, not among those claimed by Sussmann and Zahler, showing that the explicit equations of the model cannot be correct. The possibility of a modified approach along similar lines and its ultimate adoption remains open.     

A universal bifurcation diagram for seasonally perturbed predator-prey models

The bifurcations of a periodically forced predator-prey model (the chemostat model), with a prey feeding on a limiting nutrient, are numerically detected with a continuation technique. Eight bifurcation diagrams are produced (one for each parameter in the model) and shown to be topologically equivalent. These diagrams are also equivalent to those of the most commonly used predator-prey model (the Rosenzweig-McArthur model). Thus, all basic modes of behavior of the two main predator-prey models can be explained by means of a single bifurcation diagram.     

Modeling of nitrogen in river water using a detailed and a simplified model

To model catchment surface water quantity and quality, different model types are available. They vary from detailed physically based models to simplified conceptual and empirical models. The most appropriate model type for a certain application depends on the project objectives and the data availability. The detailed models are very useful for short-term simulations of representative events. They cannot be used for long-term statistical information or as a management tool. For those purposes, more simplified (conceptual or meta-) models must be used. In this study, nitrogen dynamics are modeled in a river in Flanders. Nitrogen sources from agricultural leaching and domestic point sources are considered. Based on this input, concentrations of ammonium (NH4-N) and nitrate (NO3-N) in the river water are modeled in MIKE 11 by taking into consideration advection and dispersion and the most important biological and chemical processes. Model calibration was done on the basis of available measured water quality data. To this detailed model, a more simplified model was calibrated with the objective to more easily yield long-term simulation results that can be used in a statistical analysis. The results show that the conceptual simplified model is 1800 times faster than the MIKE 11 model. Moreover the two models have almost the same accuracy. The detailed models are recommended for short-term simulations unless there are enough data for model input and model parameters. The conceptual simplified model is recommended for long-term simulations.     

A Pseudo-Markov Model for Series of Neuronal Spike Events

Spike trains of spontaneous neuronal activity in the rabbit brain are submitted to statistical analyses based on the following pseudo-Markov model. The nerve cell is supposed to alternate between a bursting and a resting state. The numbers of consecutive spikes within each state are assumed to be independent integer-valued random variables with discrete probability distributions. Given the state, the interspike intervals are independent real-valued random variables. The two state semi-Markov model is obtained as a special case when the discrete distributions are geometrical. Statistical second-order properties of recorded spike trains are compared with those predicted by the model on the basis of known first-order properties. For that purpose, serial correlation coefficients and intensity functions for spike trains produced by the model are computed. A comparison between observed and predicted results for the spontaneous activity of 17 brain cells yields a good fit in eight cells and discloses some salient features of the statistical structure in the activity of six other cells. By making it feasible to compute theoretical correlograms, the model may advance the understanding of empirical correlograms. The possibilities for integrating this statistical model of spike trains with a model of the mechanism of spike train production are discussed.     

Identifying Optimal Models to Represent Biochemical Systems

Biochemical systems involving a high number of components with intricate interactions often lead to complex models containing a large number of parameters. Although a large model could describe in detail the mechanisms that underlie the system, its very large size may hinder us in understanding the key elements of the system. Also in terms of parameter identification, large models are often problematic. Therefore, a reduced model may be preferred to represent the system. Yet, in order to efficaciously replace the large model, the reduced model should have the same ability as the large model to produce reliable predictions for a broad set of testable experimental conditions. We present a novel method to extract an “optimal” reduced model from a large model to represent biochemical systems by combining a reduction method and a model discrimination method. The former assures that the reduced model contains only those components that are important to produce the dynamics observed in given experiments, whereas the latter ensures that the reduced model gives a good prediction for any feasible experimental conditions that are relevant to answer questions at hand. These two techniques are applied iteratively. The method reveals the biological core of a model mathematically, indicating the processes that are likely to be responsible for certain behavior. We demonstrate the algorithm on two realistic model examples. We show that in both cases the core is substantially smaller than the full model.