SEARCH · PubMed Health
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This paper studies the influence of parameter estimation on integrated backscatter using a computer model. The study is specifically aimed at applications to the heart muscle; however, the validity of the conclusions drawn from this work should be confirmed by in vivo tests.
A structural identifiability analysis is performed on a mathematical model for the coupled transmission of two classes of pathogen. The pathogens, classified as major and minor, are aetiological agents of mastitis in dairy cows that interact directly and via the immunological reaction in their hosts. Parameter estimates are available from experimental data for all but four of the parameters in the model. Data from a longitudinal study of infection are used to estimate these unknown parameters. A novel approach and application of structural identifiability analysis is combined in this paper with the estimation of cross-protection parameters using epidemiological data.
The ability of four different mathematical models of the DNA histogram to give accurate estimates for the fractions of cells in G1, S, and G2 + M has been investigated. The models studied differ in the form and number of parameters of the function used to represent cells in S-phase. Results obtained from simulated DNA histograms suggest that the standard deviations of the model parameters increase exponentially with the width of the G1 and G2 + M peaks of the histogram. Error analysis is presented as a method to select a model of optimal complexity in relation to the resolution provided by the data in a given set of DNA histograms. Introduction of additional parameters improves the agreement between model and data but may result in a less well-posed model. A model with an optimal number of parameters can therefore be found that will yield parameter estimates with the smallest possible standard deviations.
We review the derivation of the fast orthogonal search algorithm, first proposed by Korenberg, with emphasis on its application to the problem of estimating coefficient matrices of vector autoregressive models. New aspects of the algorithm not previously considered are examined. One of these is the application of the algorithm to estimate coefficient matrices of a vector autoregressive process with time-varying coefficients when multiple realizations of the said process are available. Computer simulations were also performed to characterize the statistical properties of the estimates. The results show that even for shorter time series the algorithm works well and obtains good estimates of the time-varying parameters. Statistical characterization indicates that the standard deviation of the estimates decreases as 1 square root N (N being the length of the time series), a typical behavior of least-squares estimators. Another key aspect of the approach, which has previously been considered, is its direct extension to the parameter estimation of vector nonlinear autoregressive models. Nonlinear terms can be added to the model and the same algorithm can be applied to effectively estimate their associated parameters. Using chaotic time series generated from the Lorenz equations, the algorithm produces a model that captures the nonlinear structure of the data and exhibits the same chaotic attractor as that of the original system.
Procedures for the estimation of the four parameters of a new mathematical model of survival and mortality kinetics are given. A formulation of the model has been found which had the advantage of maintaining three of four parameters independent of the unit chosen for the age; in addition, two of these parameters have values falling in a narrow range, even when the model is applied to rather different curves. Since, in any problem of this type, the initial estimate of the parameters plays a major role in the achievement of good final estimates, some simple methods of estimation are indicated based upon the characteristics of the function. The initial estimates may enter three different types of procedures; the best one can be chosen according to the precision of the initial estimates. The method is capable of fitting both survivorship and dying functions directly to the empirical data. An interactive approach to the computer facilities has been used as at each step the operator has to decide whether or not to apply a corrective factor. Goodness of fit, usually high, is estimated by chi 2 test.
In biological studies it may be necessary to compare ratios of two separately estimated parameters under test and control conditions. Since it is not always possible technically to obtain paired data for the two parameters, how does one take account of their variances? By extending previously described approximations in a modified t-test we present a BASIC computer program which may be used to solve this problem.
A new algorithm for autoregresive moving average (ARMA) parameter estimation is introduced. The algorithm is based on the group method of data handling (GMDH) first introduced by the Russian cyberneticist, A. G. Ivakhnenko, for solving high-order regression polynomials. The GMDH is heuristic in nature and self-organizes into a model of optimal complexity without any a priori knowledge about the system's inner workings. We modified the GMDH algorithm to solve for ARMA model parameters. Computer simulations have been performed to examine the efficacy of the GMDH and comparison of the GMDH is made to one of the most accurate and one of the most widely used algorithms, the fast orthogonal search (FOS) and the least-squares methods, respectively. The results show that in some cases with noise contamination and incorrect model order assumptions, the GMDH performs better than either the FOS or the least-squares methods in providing only the parameters that are associated with the true model terms.
The 2-pK one-site triple layer model (TLM) is one of the most popular surface complexation models (SCMs). The present paper deals with parameter estimation for this model. It is shown that there is a need for new, reliable estimation techniques for TLM parameters. The existing methods are not convincing. In the present paper this becomes evident from application to theoretical data, which unfortunately never yields the expected parameters, be it with graphical or numerical approaches. A possible alternative method is proposed, which uses systems with differences in points of zero charge to determine the inner layer capacitance and the pristine point of zero charge for theoretical and experimental data. This method is based on a simple equation for the isoelectric point. Application of the approach to three sets of experimental surface charge data showed that it is possible to decide if the TLM is a reasonable model for a certain system. The equation for the point of zero net proton charge could in principle be applied in a similar way, but would involve some assumption(s) about the results of electrokinetic measurements. No knowledge about site density is required in such approaches. An equation for the point of zero surface potential was found to be at present too complex to be used in a similar way since it involves the site density parameter. Reliable methods might have a promising perspective insofar as they would in principle allow the prediction of parameters for a wide range of sorbents. The alternative approach suggested in this paper can in principle be applied to other SCMs (both single site 1-pK and 2-pK), which account for electrolyte adsorption. Copyright 1998 Academic Press.
A set of programs is presented which has been developed for parameter estimation and simulation of models arising from pharmacokinetic applications. The programs can accommodate linear and nonlinear models with multiple inputs and multiple outputs. When the model is defined by differential equations, non-uniform repetitive dosage regimens can be handled. The model may also be entered in integrated form when single dose studies or uniform multiple dose studies are being considered. The programs employ a variable-step, variable-order integration routine to solve the model differential equations, and the Nelder-Mead simplex procedure to determine the parameter values which minimize a weighted least squares criterion. The programs have been written for an interactive time-sharing environment with the experimental data and model equations stored in files for future use.
This paper presents some results on the modelling and the corresponding parameter estimation of the human shoulder. This system consists of the clavicle, the scapula, the humerus and the various joints between these bodies and the trunk through the sternum; it will be represented as a succession of a rotational joint between the sternum and the clavicle and a constant distance joint, representing the scapula between, the clavicle and the humerus head. The parameters of this system are the components of the position vectors of the joint characteristic points (the corresponding centres of the rotations). Experimental results are presented as well as a validation of the proposed model.
In Drosophila simulans, cytoplasmically transmitted Wolbachia microbes cause reduced egg hatch when infected males mate with uninfected females. A Wolbachia infection and an associated mtDNA variant have spread northward through California since 1986. PCR assays show that Wolbachia infection is prevalent throughout the continental US and Central and South America, but some lines from Florida and Ecuador that are PCR-positive for Wolbachia do not cause incompatibility. We estimate from natural populations infection frequencies and the transmission and incompatibility parameter values that affect the spread of the infection. On average, infected females from nature produce 3-4% uninfected ova. Infected females with relatively low fidelity of maternal transmission show partial incompatibility with very young infected laboratory males. Nevertheless, crosses between infected flies in nature produce egg-hatch rates indistinguishable from those produced by crosses between uninfected individuals. Incompatible crosses in nature produce hatch rates 30-70% as high as those from compatible crosses. Wild-caught infected and uninfected females are equally fecund in the laboratory. Incompatibility decreases with male age, and age-specific incompatibility levels suggest that males mating in nature may often be 2 or 3 weeks old. Our parameter estimates accurately predict the frequency of Wolbachia infection in California populations.
The problem of comparing and pooling experimentally independent estimates of a parameter such as a Michaelis constant (K) has been treated as a simple analysis of variance of "within" and "between" set deviations from the fitted variable (v). As applied to assessing the reproducibility of multiple estimates of the same K, this is identical to the procedure of Duggleby (Anal. Biochem. 189, 84-87, 1990). However, the theory developed here shows that applying Duggleby's procedure to the comparison of two experiments (each consisting of multiple data sets) depends critically on the assumption of equal errors within and between the individual sets, i.e., Fvb vw = s2wv/s2bv is close to 1. Application of the method when this is not the case will underestimate the common error (s2rv), overestimate its associated degrees of freedom (vr = vb+vw), and may suggest apparently significant differences where there are none. The theory also shows that this situation is an instance of the Fisher-Behrens problem and shows how Welch's solution can be applied. This gives the between set error s2bv as the corrected estimate of the common error and the corrected degrees of freedom as a simple function of vb, vw, and Fvb vw. When the nine prephenate dehydratase data sets which originally showed three apparently significant differences were reanalyzed in this way, all the variations in K were found to be within the range of the experimental error.
This paper concerns real-time, multi-rate, nonlinear state and parameter estimation in a pilot-scale biochemical reactor in which cultivation of mouse-mouse hybridoma cells takes place. A multi-rate estimator is designed and implemented to estimate specific growth rate and concentrations of viable cells, total cells, glucose, glutamine, and monoclonal antibodies (MAb) in the reactor. These are estimated from frequent measurement (inferred values) of oxygen uptake rate (OUR) and infrequent and delayed measurements of the concentrations of viable cells, total cells, glucose, glutamine, and MAb. The infrequent measurements are available every 2 to 17 h with a time delay of 0.08 to 2.00 h, and OUR is inferred from dissolved oxygen concentration measurements that are available very 0.17 h. For each of the process variables, its infrequent measurement data and the profile of its estimate are presented to show the performance of the multi-rate estimator.
A new BASIC exponential stripping program, ESTRIP, allows the relatively rapid calculation of initial polyexponential parameter estimates, as does the previously published FORTRAN IV program, CSTRIP. The potential advantages of the new program are that it can be run on microcomputers and minicomputers with BASIC capability and a relatively small core and that it can be easily modified by the user.
We present a procedure that optimally adjusts specified parameters of a mathematical model to describe a set of measured data. The technique integrates a dynamic systems-simulation language with a robust algorithm for nonlinear parameter estimation, and it can be implemented on a microcomputer. Sensitivity functions are generated that indicate how the operation of the model is affected by each updated parameter. This procedure offers a greater resolution of optimal parameter values than other, less rigorous methods. To illustrate this technique we have applied it to the model of human smooth pursuit eye movements proposed by D.A. Robinson and colleagues (1986).
In this paper a bivariate, time-variant model able to continuously measure the mutual interactions between heart rate and systolic blood pressure variability signals is presented. A recursive identification of the model parameters makes it possible to estimate, on a beat-to-beat basis, spectral low-frequency (LF) and high-frequency (HF) power, (LF/HF ratio) and cross-spectral (coherence and phase relationships between spectral peaks) indexes during nonstationary events. These indexes can be helpful in: 1) physiological study of autonomic nervous system mechanisms of cardiovascular control and 2) quantification and clinical evaluation of the neural and mechanical links between the two signals. In addition, an estimate of baroreceptive activation (alpha-gain) is continuously extracted. Before applying the model to cardiovascular signals, the reliability of the estimated parameters was tested on simulated signals. Subsequently, the model was applied to investigating vasovagal syncope episodes, aiming at the assessment of autonomic nervous system status and autonomic role in the dynamic phenomena which lead to syncope. The proposed model, which provides noninvasive beat-to-beat evaluation of the autonomic events, may be useful in the description of the syncopal episodes and in the comprehension of the complex physiological mechanisms of syncope.
Working with pig brain striatum in which A1 and A2 adenosine receptor subtypes coexist, we describe an uncomplicated method for unequivocally obtaining the equilibrium parameters (KD and binding capacity) of A1 receptor without interference from ligand binding to A2 receptor. Also, the equilibrium parameter estimation method we propose avoids the experimental determination of nonspecific binding by the inclusion of the corresponding unknown parameter in the function. This not only saves time but also avoids the use of expensive radioligands in saturation experiments. The method is suitable for any system with two different receptor subtypes for the same physiological ligand, and good estimates of the equilibrium parameters corresponding to the subtype displaying the higher affinity for the ligand can be obtained.
This article proposes three stochastic algorithms to optimize a Bayesian design criterion for Bayesian estimation of the parameters of nonlinear regression models; this criterion is the information expected from an experiment. The first algorithm is based on a stochastic version of the simplex with an adaptive sampling procedure. The others are stochastic approximation algorithms: the Kiefer-Wolfowitz and the pseudogradient algorithms. We first present the information criterion and the optimization algorithms. The efficiency of each algorithm for optimizing this Bayesian design criterion is then assessed by a simulation study for a nonlinear model assuming a discrete prior distribution. An application for designing an experiment to estimate the kinetics of radioiodine thyroid uptake is then proposed.