Statistical discrimination of fractal and Markov models of single-channel gating.

A statistical comparison is presented of Markov and fractal models of ion channel gating. The analysis is based on single-channel data from two types of ion channels: open times from a 90 pS Ca-activated K channel from GH3 pituitary cells, and closed times from a nonselective channel from rabbit corneal endothelium (Liebovitch et al., 1987a). Maximum likelihood methods were used to fit the data. For both data sets the best Markov model had three exponential components. The best Markov model had a higher likelihood than the fractal model, and the Asymptotic Information Criterion favored the Markov model for each data set. A more detailed analysis, using the Monte Carlo methods described in Horn (1987), showed that the Markov model was not significantly better than the fractal model for the corneal endothelium channels. The inability to discriminate the models definitively in this case was shown to be due in part to the small size of the data set.     

养分循环研究数据管理概念模型的构建

近年来,农田生态系统NPK养分循环研究已经积累了大量的数据,迫切需要建立可长期保存数据、并为养分循环研究全局决策服务的数据管理系统,本文描述了用于养分循环数据管理的概念模型的设计,并说明构建模型的过程,最后给出依据该模型建立数据库系统应用实例。结果表明,依据模型建立的数据库系统可提供按照时间、地点、专题查询的功能,能够管理野外观测数据、专题图和研究报告等多种类型数据,并快速提取和分析数据。     

A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data

We describe a general likelihood-based 'mixture model' for inferring phylogenetic trees from gene-sequence or other character-state data. The model accommodates cases in which different sites in the alignment evolve in qualitatively distinct ways, but does not require prior knowledge of these patterns or partitioning of the data. We call this qualitative variability in the pattern of evolution across sites "pattern-heterogeneity" to distinguish it from both a homogenous process of evolution and from one characterized principally by differences in rates of evolution. We present studies to show that the model correctly retrieves the signals of pattern-heterogeneity from simulated gene-sequence data, and we apply the method to protein-coding genes and to a ribosomal 12S data set. The mixture model outperforms conventional partitioning in both these data sets. We implement the mixture model such that it can simultaneously detect rate- and pattern-heterogeneity. The model simplifies to a homogeneous model or a rate-variability model as special cases, and therefore always performs at least as well as these two approaches, and often considerably improves upon them. We make the model available within a Bayesian Markov-chain Monte Carlo framework for phylogenetic inference, as an easy-to-use computer program.     

On the structures of filamentous bacteriophage Ff (fd,f1, M13)

The filamentous bacteriophage (Inovirus) strain Ff (fd, f1, M13) is widely used in molecular biophysics as a simple model system. A low resolution molecular model of the fd protein coat has been reported, derived from iterative helical real space reconstruction of cryo-electron micrographs (cryoEM). This model is significantly different from the model previously derived from X-ray fibre diffraction and solid-state NMR. We show that the cryoEM model agrees neither with solid-state NMR data nor with X-ray fibre diffraction data of fd, and has some puzzling structural features, for instance nanometre holes through the protein coat. We refine the cryoEM model against the X-ray data, and find that the model after refinement closely approximates the model derived directly from X-ray fibre diffraction and solid-state NMR data. We suggest possible reasons for the differences between the models derived from cryoEM and X-ray diffraction.     

Successive learning in hetero-associative memory using chaotic neural networks

In this paper, we propose a successive learning method in hetero-associative memories, such as Bidirectional Associative Memories and Multidirectional Associative Memories, using chaotic neural networks. It can distinguish unknown data from the stored known data and can learn the unknown data successively. The proposed model makes use of the difference in the response to the input data in order to distinguish unknown data from the stored known data. When input data is regarded as unknown data, it is memorized. Furthermore, the proposed model can estimate and learn correct data from noisy unknown data or incomplete unknown data by considering the temporal summation of the continuous data input. In addition, similarity to the physiological facts in the olfactory bulb of a rabbit found by Freeman are observed in the behavior of the proposed model. A series of computer simulations shows the effectiveness of the proposed model.     

Phylogenetic mixtures on a single tree can mimic a tree of another topology

Phylogenetic mixtures model the inhomogeneous molecular evolution commonly observed in data. The performance of phylogenetic reconstruction methods where the underlying data are generated by a mixture model has stimulated considerable recent debate. Much of the controversy stems from simulations of mixture model data on a given tree topology for which reconstruction algorithms output a tree of a different topology; these findings were held up to show the shortcomings of particular tree reconstruction methods. In so doing, the underlying assumption was that mixture model data on one topology can be distinguished from data evolved on an unmixed tree of another topology given enough data and the "correct" method. Here we show that this assumption can be false. For biologists, our results imply that, for example, the combined data from two genes whose phylogenetic trees differ only in terms of branch lengths can perfectly fit a tree of a different topology.     

Analysis of Rates Using a Generalized Poisson Regression Model

In this paper a generalization of the Poisson regression model indexed by a shape parameter is proposed for the analysis of life table and follow-up data with concomitant variables. The model is suitable for analysis of extra-Poisson variation data. The model is used to fit the survival data given in Holford (1980). The model parameters, the hazard and survival functions are estimated by the method of maximum likelihood. The results obtained from this study seem to be comparable to those obtained by Chen (1988). Approximate tests of the dispersion and goodness-of-fit of the data to the model are also discussed.     

An improved model for determining degree-day values from daily temperature data

Although using hourly weather data offers the greatest accuracy for estimating growing degree-day values, daily maximum and minimum temperature data are often used to estimate these values by approximating the diurnal temperature trends. This paper presents a new empirical model for estimating the hourly mean temperature. The model describes the diurnal variation using a sine function from the minimum temperature at sunrise until the maximum temperature is reached, another sine function from the maximum temperature until sunset, and a square-root function from then until sunrise the next morning. The model was developed and calibrated using several years of hourly data obtained from five automated weather stations located in California and representing a wide range of climate conditions. The model was tested against an additional data-set at each location. The temperature model gave good results, the root-mean-square error being less than 2.0 °C for most years and locations. The comparison with published models from the literature showed that the model was superior to the other methods. Hourly temperatures from the model were used to calculate degree-day values. A comparison between degree-day estimates determined from the model and those obtained other selected methods is presented. The results showed that the model had the best accuracy in general regardless of the season. Received: 25 October 2000 / Revised: 2 July 2001 / Accepted: 2 July 2001     

基于GreenLab原理构建油松成年树的结构-功能模型

 林木的结构-功能模型(functional-structural tree modeling, FSTMs)是基于器官级组件构建的将植物结构和功能结合起来的一类模型, 在应用于成年树时需要解决拓扑结构复杂性和年轮分配模式普适性的问题。该文以18年生和41年生的油松 (Pinus tabulaeformis)成年树为研究对象, 将GreenLab模型应用到成年树的模拟中。采用破坏性取样, 实测了2株油松成年树的形态结构, 利用子结构模型解决成年树拓扑结构复杂性的问题, 引入年轮影响系数λ, 将全局分配模式和Pressler模式结合起 来, 解决年轮分配模式在不同年龄和环境条件下不同的问题。模型的直接参数通过实测数据获得, 隐含参数利用非线性最小二乘法拟合反求获得。通过实测数据与模拟数据的对比、模拟数据与经验模型模拟数据的对比, 对模型的模拟效果进行了评估, 发现节间总重、针叶总重、树高、树干节间重观测值和模型模拟值建立的回归方程的决定系数为0.84–0.98, 结构-功能模型与经验模型对总生物量模拟的决定系数为0.95, 表明该模型能较真实地反映油松的结构和生长过程。     

A new method of sample selections for optimizing phenology model based remote sensing data

 植被物候模型是生态系统模型的重要组成部分, 其精度对准确地模拟陆面和大气之间的能量和物质交换具有重要意义。利用遥感获取空间物候信息并与气候数据进行耦合分析是在中亚干旱区等地面物候观测数据缺乏的地区构建物候模型的重要方法。为减小混合植被像元和气候数据资料的内在误差及二者在空间尺度的不匹配对物候模型构建产生的影响, 该研究提出一种在气象站点周围选取满足规定规则集的“代表植被类型像元”作为样本点的选择方法, 以代表植被类型像元的遥感物候数据和气象站点数据为基础, 结合经典物候模型和改进物候模型, 在粒子群优化算法支持下, 分别以独立的拟合与评价样本数据, 完成了荒漠草原植被与落叶阔叶林的模型拟合与评价。研究发现中亚干旱区荒漠草原植被的最优模型为温度-降水修正模型, 落叶阔叶林的最优模型为替代模型。通过此方法模型总体精度在8–10 d左右。结果表明此方法在气候数据和植物物候空间匹配方面有改进, 有助于提高物候模型精度。     

Bayesian Modeling of ChIP‐chip Data Through a High‐Order Ising Model

Summary ChIP‐chip experiments are procedures that combine chromatin immunoprecipitation (ChIP) and DNA microarray (chip) technology to study a variety of biological problems, including protein–DNA interaction, histone modification, and DNA methylation. The most important feature of ChIP‐chip data is that the intensity measurements of probes are spatially correlated because the DNA fragments are hybridized to neighboring probes in the experiments. We propose a simple, but powerful Bayesian hierarchical approach to ChIP‐chip data through an Ising model with high‐order interactions. The proposed method naturally takes into account the intrinsic spatial structure of the data and can be used to analyze data from multiple platforms with different genomic resolutions. The model parameters are estimated using the Gibbs sampler. The proposed method is illustrated using two publicly available data sets from Affymetrix and Agilent platforms, and compared with three alternative Bayesian methods, namely, Bayesian hierarchical model, hierarchical gamma mixture model, and Tilemap hidden Markov model. The numerical results indicate that the proposed method performs as well as the other three methods for the data from Affymetrix tiling arrays, but significantly outperforms the other three methods for the data from Agilent promoter arrays. In addition, we find that the proposed method has better operating characteristics in terms of sensitivities and false discovery rates under various scenarios.     

Protein conformation from electron spin relaxation data.

Electron spin relaxation data from five ferric proteins are analyzed in terms of the fractal model of protein structures. Details of this model are presented. The results lead to a characterization of protein structures by a single parameter, the fractal dimension, d. This structural parameter is shown to determine the temperature dependence of the Raman electron spin relaxation rate, which varies as T3 + 2d. Computations of d are made using x-ray data for 17 proteins. The results range from d = 1.76 for lysozyme to d = 1.34 for ferredoxin. These values are compared with values of d obtained from the present electron spin relaxation data on five ferric proteins. Typical results are d = 1.34 +/- 0.06 from relaxation data and 1.34 +/- 0.05 from x-ray data for ferredoxin; d = 1.67 +/- 0.03 from relaxation data and 1.66 +/- 0.05 from x-ray data for ferricytochrome c. The data thus support the theoretical model. Applications of this spin resonance technique to the study of changes in protein conformation are discussed.     

A Test of Neutral Molecular Evolution Based on Nucleotide Data

The neutral theory of molecular evolution predicts that regions of the genome that evolve at high rates, as revealed by interspecific DNA sequence comparisons, will also exhibit high levels of polymorphism within species. We present here a conservative statistical test of this prediction based on a constant-rate neutral model. The test requires data from an interspecific comparison of at least two regions of the genome and data on levels of intraspecific polymorphism in the same regions from at least one species. The model is rejected for data from the region encompassing the Adh locus and the 5' flanking sequence of Drosophila melanogaster and Drosophila sechellia. The data depart from the model in a direction that is consistent with the presence of balanced polymorphism in the coding region.     

Using model‐data fusion to interpret past trends,and quantify uncertainties in future projections,of terrestrial ecosystem carbon cycling

Uncertainties in model projections of carbon cycling in terrestrial ecosystems stem from inaccurate parameterization of incorporated processes (endogenous uncertainties) and processes or drivers that are not accounted for by the model (exogenous uncertainties). Here, we assess endogenous and exogenous uncertainties using a model‐data fusion framework benchmarked with an artificial neural network (ANN). We used 18 years of eddy‐covariance carbon flux data from the Harvard forest, where ecosystem carbon uptake has doubled over the measurement period, along with 15 ancillary ecological data sets relative to the carbon cycle. We test the ability of combinations of diverse data to constrain projections of a process‐based carbon cycle model, both against the measured decadal trend and under future long‐term climate change. The use of high‐frequency eddy‐covariance data alone is shown to be insufficient to constrain model projections at the annual or longer time step. Future projections of carbon cycling under climate change in particular are shown to be highly dependent on the data used to constrain the model. Endogenous uncertainties in long‐term model projections of future carbon stocks and fluxes were greatly reduced by the use of aggregated flux budgets in conjunction with ancillary data sets. The data‐informed model, however, poorly reproduced interannual variability in net ecosystem carbon exchange and biomass increments and did not reproduce the long‐term trend. Furthermore, we use the model‐data fusion framework, and the ANN, to show that the long‐term doubling of the rate of carbon uptake at Harvard forest cannot be explained by meteorological drivers, and is driven by changes during the growing season. By integrating all available data with the model‐data fusion framework, we show that the observed trend can only be reproduced with temporal changes in model parameters. Together, the results show that exogenous uncertainty dominates uncertainty in future projections from a data‐informed process‐based model.     

Robustness of neuroprosthetic decoding algorithms

We assessed the ability of two algorithms to predict hand kinematics from neural activity as a function of the amount of data used to determine the algorithm parameters. Using chronically implanted intracortical arrays, single- and multineuron discharge was recorded during trained step tracking and slow continuous tracking tasks in macaque monkeys. The effect of increasing the amount of data used to build a neural decoding model on the ability of that model to predict hand kinematics accurately was examined. We evaluated how well a maximum-likelihood model classified discrete reaching directions and how well a linear filter model reconstructed continuous hand positions over time within and across days. For each of these two models we asked two questions: (1) How does classification performance change as the amount of data the model is built upon increases? (2) How does varying the time interval between the data used to build the model and the data used to test the model affect reconstruction? Less than 1 min of data for the discrete task (8 to 13 neurons) and less than 3 min (8 to 18 neurons) for the continuous task were required to build optimal models. Optimal performance was defined by a cost function we derived that reflects both the ability of the model to predict kinematics accurately and the cost of taking more time to build such models. For both the maximum-likelihood classifier and the linear filter model, increasing the duration between the time of building and testing the model within a day did not cause any significant trend of degradation or improvement in performance. Linear filters built on one day and tested on neural data on a subsequent day generated error-measure distributions that were not significantly different from those generated when the linear filters were tested on neural data from the initial day (p<0.05, Kolmogorov-Smirnov test). These data show that only a small amount of data from a limited number of cortical neurons appears to be necessary to construct robust models to predict kinematic parameters for the subsequent hours. Motor-control signals derived from neurons in motor cortex can be reliably acquired for use in neural prosthetic devices. Adequate decoding models can be built rapidly from small numbers of cells and maintained with daily calibration sessions.     

Computer modeling of mitochondrial tricarboxylic acid cycle, oxidative phosphorylation, metabolite transport, and electrophysiology

A computational model of mitochondrial metabolism and electrophysiology is introduced and applied to analysis of data from isolated cardiac mitochondria and data on phosphate metabolites in striated muscle in vivo. This model is constructed based on detailed kinetics and thermodynamically balanced reaction mechanisms and a strict accounting of rapidly equilibrating biochemical species. Since building such a model requires introducing a large number of adjustable kinetic parameters, a correspondingly large amount of independent data from isolated mitochondria respiring on different substrates and subject to a variety of protocols is used to parameterize the model and ensure that it is challenged by a wide range of data corresponding to diverse conditions. The developed model is further validated by both in vitro data on isolated cardiac mitochondria and in vivo experimental measurements on human skeletal muscle. The validated model is used to predict the roles of NAD and ADP in regulating the tricarboxylic acid cycle dehydrogenase fluxes, demonstrating that NAD is the more important regulator. Further model predictions reveal that a decrease of cytosolic pH value results in decreases in mitochondrial membrane potential and a corresponding drop in the ability of the mitochondria to synthesize ATP at the hydrolysis potential required for cellular function.     

样本量与研究范围变化对MaxEnt模型准确度的影响——以黑白仰鼻猴为例

MaxEnt模型是过去几年最为流行的物种分布预测模型之一。针对一些濒危物种、入侵种和模拟数据的研究表明,MaxEnt模型均能在小样本的分布数据下得到较准确的预测结果。此外,研究范围的变化也会影响MaxEnt模型的构建。 然而,基于动物的实际分布数据来评估MaxEnt模型的研究甚少。 我们以黑白仰鼻猴 (Rhinopithecus bieti)为例,以11个猴群的分布数据为训练数据（样本量从1到10个猴群）,在不同研究范围内构建MaxEnt模型,通过其它5个的猴群分布数据验证,分析样本量和研究范围变化对模型准确度产生的影响。 结果表明,随样本量和研究范围增大,MaxEnt模型准确度及稳定性都有增加。 此外,研究范围变化对模型准确度有一定影响。 应用Maxent进行物种分布预测时,训练数据应尽可能涵盖该物种可能出现的全部环境梯度。构建模型所需的背景数据点选择,应与建模使用的物种出现点形成有效对照。