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PubMed2006

Tropical--parameter estimation and simulation of reaction-diffusion models based on spatio-temporal microscopy images.

UNLABELLED: Tropical is a software for simulation and parameter estimation of reaction-diffusion models. Based on spatio-temporal microscopy images, Tropical estimates reaction and diffusion coefficients for user-defined models. Tropical allows the investigation of systems with an inhomogeneous distribution of molecules, making it well suited for quantitative analyses of microscopy experiments such as fluorescence recovery after photobleaching (FRAP). AVAILABILITY: Tropical is available free of charge for academic use at http://www.dkfz.de/tbi/projects/modellingAndSimulationOfCelluarSystems/tropical.jsp after signing a material transfer agreement.

Algorithms↗

PubMed2006

[Influence of maternal genetic effect on genetic parameter estimates of production traits of cashmere goat].

The derivative-free restricted maximum likelihood (DFREML) method was used to compare the differences of genetic parameter estimates of Inner Mongolian Cashmere Goats under two models, which differ in whether maternal genetic effect is taken into account. The differences between the two models were, tested by likelihood ratio test. The results show that maternal genetic effect highly affects live body weight and cashmere thickness while has no significant effect on raw cashmere weight, staple length, fibre diameter and fibre length.

Analysis of Variance↗

PubMed1993

A simple computational approach to model parameter estimation.

The desire to describe biological data using mathematical models has led to the rapid development of various analytical techniques for model identification and parameter estimation. The procedures used may be non-linear and complex, and require long calculation periods. Thus, the aid of a personal computer renders efficient the application of these rather complicated procedures. In this study we developed a simple identification programme for heparan sulfate pharmacodynamics which can be easily and rapidly implemented on a personal computer. The programme is based on an iterative algorithm performing a non-linear regression analysis by the least-square method. This programme was applied to a clinical measured variables with which it was possible to quantify the pharmacodynamic effect of heparan sulfate.

Aged↗

PubMed2004

Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation.

It is well known in the pattern recognition community that the accuracy of classifications obtained by combining decisions made by independent classifiers can be substantially higher than the accuracy of the individual classifiers. We have previously shown this to be true for atlas-based segmentation of biomedical images. The conventional method for combining individual classifiers weights each classifier equally (vote or sum rule fusion). In this paper, we propose two methods that estimate the performances of the individual classifiers and combine the individual classifiers by weighting them according to their estimated performance. The two methods are multiclass extensions of an expectation-maximization (EM) algorithm for ground truth estimation of binary classification based on decisions of multiple experts (Warfield et al., 2004). The first method performs parameter estimation independently for each class with a subsequent integration step. The second method considers all classes simultaneously. We demonstrate the efficacy of these performance-based fusion methods by applying them to atlas-based segmentations of three-dimensional confocal microscopy images of bee brains. In atlas-based image segmentation, multiple classifiers arise naturally by applying different registration methods to the same atlas, or the same registration method to different atlases, or both. We perform a validation study designed to quantify the success of classifier combination methods in atlas-based segmentation. By applying random deformations, a given ground truth atlas is transformed into multiple segmentations that could result from imperfect registrations of an image to multiple atlas images. In a second evaluation study, multiple actual atlas-based segmentations are combined and their accuracies computed by comparing them to a manual segmentation. We demonstrate in both evaluation studies that segmentations produced by combining multiple individual registration-based segmentations are more accurate for the two classifier fusion methods we propose, which weight the individual classifiers according to their EM-based performance estimates, than for simple sum rule fusion, which weights each classifier equally.

Algorithms↗

PubMed2004

Microwave image reconstruction from 3-D fields coupled to 2-D parameter estimation.

An efficient Gauss-Newton iterative imaging technique utilizing a three-dimensional (3-D) field solution coupled to a two-dimensional (2-D) parameter estimation scheme (3-D/2-D) is presented for microwave tomographic imaging in medical applications. While electromagnetic wave propagation is described fully by a 3-D vector field, a 3-D scalar model has been applied to improve the efficiency of the iterative reconstruction process with apparently limited reduction in accuracy. In addition, the image recovery has been restricted to 2-D but is generalizable to three dimensions. Image artifacts related primarily to 3-D effects are reduced when compared with results from an entirely two-dimensional inversion (2-D/2-D). Important advances in terms of improving algorithmic efficiency include use of a block solver for computing the field solutions and application of the dual mesh scheme and adjoint approach for Jacobian construction. Methods which enhance the image quality such as the log-magnitude/unwrapped phase minimization were also applied. Results obtained from synthetic measurement data show that the new 3-D/2-D algorithm consistently outperforms its 2-D/2-D counterpart in terms of reducing the effective imaging slice thickness in both permittivity and conductivity images over a range of inclusion sizes and background medium contrasts.

Algorithms↗

PubMed2006

Sample size planning for the standardized mean difference: accuracy in parameter estimation via narrow confidence intervals.

Methods for planning sample size (SS) for the standardized mean difference so that a narrow confidence interval (CI) can be obtained via the accuracy in parameter estimation (AIPE) approach are developed. One method plans SS so that the expected width of the CI is sufficiently narrow. A modification adjusts the SS so that the obtained CI is no wider than desired with some specified degree of certainty (e.g., 99% certain the 95% CI will be no wider than omega). The rationale of the AIPE approach to SS planning is given, as is a discussion of the analytic approach to CI formation for the population standardized mean difference. Tables with values of necessary SS are provided. The freely available Methods for the Behavioral, Educational, and Social Sciences (K. Kelley, 2006a) R (R Development Core Team, 2006) software package easily implements the methods discussed.

Confidence Intervals↗

PubMed1993

Reconstruction of experimental hyperthermia temperature distributions: application of state and parameter estimation.

Subsets of data from spatially sampled temperatures measured in each of nine experimental heatings of normal canine thighs were used to test the feasibility of using a state and parameter estimation (SPE) technique to predict the complete measured data set in each heating. Temperature measurements were made at between seventy-two and ninety-six stationary thermocouple locations within the thigh, and measurements from as few as thirteen of these locations were used as inputs to the estimation algorithm. The remaining (non "input") measurements were compared to the predicted temperatures for the corresponding "unmeasured" locations to judge the ability of the estimation algorithm to accurately reconstruct the complete experimental data set. The results show that the predictions of the "unmeasured" steady-state temperatures are quite accurate in general (average errors usually < 0.5 degrees C; and small variances about those averages) and that this reconstruction procedure can yield improved descriptors of the steady-state temperature distribution. The accuracy of the reconstructed temperature distribution was not strongly affected by either the number of perfusion zones or by the number of input sensors used by the algorithm. One situation extensively considered in this study modeled the thigh with twenty-seven independent regions of perfusion. For this situation, measurements from ninety-six to thirteen sensors were used as input to the estimation algorithm. The average error for all of these cases ranged from -0.55 degrees C to +0.75 degrees C, respectively, and was not strongly related to the number of sensors used as input to the estimation algorithm.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗

PubMed1982

Parameter estimation of transpulmonary mechanics by a nonlinear inertive model.

Transpulmonary mechanics of anesthetized intubated dogs were studied during control breathing and hemorrhage-induced hyperventilation by least-mean-squares parameter estimation using several model versions. The classical elastance-resistance model was modified to include nonlinear elastic and viscous pressure terms with and without a linear inertive pressure component. Inclusion of the nonlinear terms decreased the root-mean-square error of fitting (q) of the classical model on the average to 67% in the control period and to 58% during hyperventilation. An additional decrease due to inertance was 4% (control) and 22% (hyperventilation) and was associated with acceptable estimates of inertance [0.056 +/- 0.02 (SD) and 0.063 +/- 0.008 cmH2O . l-1 . s2, respectively]. When inertance alone was added to the classical model, negligible improvement in q and unrealistic values of inertance were obtained. Conventional measures (Edyn and midvolume resistance) were close to the corresponding least-mean-squares estimates (E and R) of all model versions, except that in hyperventilation neglecting the inertance caused Edyn to markedly overestimate E of nonlinear inertive model.

Animals↗

PubMed2001

Parameter estimation procedure for complex non-linear systems: calibration of ASM No. 1 for N-removal in a full-scale oxidation ditch.

When applied to large simulation models, the process of parameter estimation is also called calibration. Calibration of complex non-linear systems, such as activated sludge plants, is often not an easy task. On the one hand, manual calibration of such complex systems is usually time-consuming, and its results are often not reproducible. On the other hand, conventional automatic calibration methods are not always straightforward and often hampered by local minima problems. In this paper a new straightforward and automatic procedure, which is based on the response surface method (RSM) for selecting the best identifiable parameters, is proposed. In RSM, the process response (output) is related to the levels of the input variables in terms of a first- or second-order regression model. Usually, RSM is used to relate measured process output quantities to process conditions. However, in this paper RSM is used for selecting the dominant parameters, by evaluating parameters sensitivity in a predefined region. Good results obtained in calibration of ASM No. 1 for N-removal in a full-scale oxidation ditch proved that the proposed procedure is successful and reliable.

Automation↗

PubMed1976

CSTRIP, a fortran IV computer program for obtaining initial polyexponential parameter estimates.

A new exponential stripping program, CSTRIP, has been developed. This program overcomes the problems associated with the use of previously published techniques and enables the rapid economical calculation of initial polyexponential parameter estimates. Values for the coefficients and exponents of the exponential terms are calculated as well as estimates of lag times. An exhaustive search procedure ensures that the results are comparable to, or better than, those obtained by manual residual methods.

Computers↗

PubMedDate not supplied

Detection and parameter estimation for quantitative trait loci using regression models and multiple markers.

A strategy of multi-step minimal conditional regression analysis has been developed to determine the existence of statistical testing and parameter estimation for a quantitative trait locus (QTL) that are unaffected by linked QTLs. The estimation of marker-QTL recombination frequency needs to consider only three cases: 1) the chromosome has only one QTL, 2) one side of the target QTL has one or more QTLs, and 3) either side of the target QTL has one or more QTLs. Analytical formula was derived to estimate marker-QTL recombination frequency for each of the three cases. The formula involves two flanking markers for case 1), two flanking markers plus a conditional marker for case 2), and two flanking markers plus two conditional markers for case 3). Each QTL variance and effect, and the total QTL variance were also estimated using analytical formulae. Simulation data show that the formulae for estimating marker-QTL recombination frequency could be a useful statistical tool for fine QTL mapping. With 1,000 observations, a QTL could be mapped to a narrow chromosome region of 1.5 cM if no linked QTL is present, and to a 2.8 cM chromosome region if either side of the target QTL has at least one linked QTL.

Journal Article↗

PubMed2006

Parameter estimation in stochastic mammogram model by heuristic optimization techniques.

The appearance of disproportionately large amounts of high-density breast parenchyma in mammograms has been found to be a strong indicator of the risk of developing breast cancer. Hence, the breast density model is popular for risk estimation or for monitoring breast density change in prevention or intervention programs. However, the efficiency of such a stochastic model depends on the accuracy of estimation of the model's parameter set. We propose a new approach-heuristic optimization-to estimate more accurately the model parameter set as compared to the conventional and popular expectation-maximization (EM) algorithm. After initial segmentation of a given mammogram, the finite generalized Gaussian mixture (FGGM) model is constructed by computing the statistics associated with different image regions. The model parameter set thus obtained is estimated by particle swarm optimization (PSO) and evolutionary programming (EP) techniques, where the objective function to be minimized is the relative entropy between the image histogram and the estimated density distributions. When our heuristic approach was applied to different categories of mammograms from the Mini-MIAS database, it yielded lower floor of estimation error in 109 out of 112 cases (97.3 %), and 101 out of 102 cases (99.0%), for the number of image regions being five and eight, respectively, with the added advantage of faster convergence rate, when compared to the EM approach. Besides, the estimated density model preserves the number of regions specified by the information-theoretic criteria in all the test cases, and the assessment of the segmentation results by radiologists is promising.

Algorithms↗

PubMed2004

Determining the slow crack growth parameter and Weibull two-parameter estimates of bilaminate disks by constant displacement-rate flexural testing.

OBJECTIVES: This study examined the influence of displacement-rate and relative layer heights (RLH) on the slow crack growth exponent and Weibull two-parameter estimates of bilayered ceramic composite disks composed of In-Ceram Alumina and Vitadur Alpha porcelain. METHODS: Equibiaxial disks were fabricated with RLH of 1:2, 1:1 and 2:1, for In-Ceram Alumina and Vitadur Alpha porcelain, respectively. Ninety specimens each (30 1:2, 30 1:1, and 30 2:1) were tested in an equibiaxial ring-on-ring testing apparatus at displacement-rates of 0.127, 1.27 and 12.7 mm min(-1). RESULTS: Weibull parameters were statistically significantly affected by changes in RLH at a constant displacement-rate and the slow crack growth parameters were significantly affected by RLH. Many specimens exhibited nonbrittle failure modes. Nonbrittle failures usually exhibited a fall, followed by a rise in load prior to catastrophic failure, and most occurred in specimens with thicker cores at low displacement-rates. SIGNIFICANCE: Geometries of layered materials may affect their reliability and longevity.

Aluminum Oxide↗

PubMed1994

Genetic and phenotypic parameter estimates of pelvic measurements and birth weight in beef heifers.

Data on 1210 spring-born (1983 to 1988) yearling heifers were analyzed by paternal half-sib procedures to obtain genetic and phenotypic parameter estimates involving birth weight and pelvic measurements. Data included records on 629 Angus, 325 Simmental, and 256 Salers representing 93, 49, and 22 paternal half-sib sire groups, respectively. Heritabilities for birth weight (BW), pelvic height (PH), pelvic width (PW), and pelvic area (PA) for Angus were 0.30, 0.61, 0.28, and 0.43, respectively. Corresponding values for Simmental and Salers heifers were 0.14, 0.34, 0.44, 0.37, and 0.18, 0.02, 0.29, 0.15, respectively. Genetic correlations among pelvic measurements (PH-PW, PH-PA, PW-PA) were positive (0.25 to 1.03) except for the estimate of -0.07 for PH-PW in Simmentals. Genetic correlations between BW and the 3 pelvic measurements (BW-PH, BW-PW, BW-PA) were negative (-0.18 to -0.36) except for the estimates of 0.53 (BW-PW) and 0.26 (BW-PA) in Simmentals and 2.84 (BW-PH) and 0.39 (BW-PA) in Salers. Phenotypic correlations among pelvic measurements ranged from 0.16 to 0.80. Phenotypic correlations between birth weight and the 3 pelvic measurements were consistently lower (-0.02 to 0.09) than the genetic correlations.

Journal Article↗

PubMed1987

Genetic and phenotypic parameter estimates for scrotal circumference and semen traits in young beef bulls.

Estimated in this study were heritabilities and genetic and phenotypic correlations involving scrotal circumference (SC), percent live sperm, sperm number, sperm concentration, sperm motility, and an overall measure of a bull's potential breeding efficiency. Potential breeding efficiency is a composite trait based on a consideration of sperm concentration, sperm motility, sperm morphology and scrotal circumference. Data used were from three sources. Records on 863 Angus, 753 Polled Hereford, and 302 Simmental bulls were made available through the Missouri Performance-Tested Bull Sale and records on 1169 Polled Hereford bulls came from the American Polled Hereford Association. Information from these first two data sets were used to estimate heritability of scrotal circumference. The third data set was provided by Nichols Farms of Bridgewater, Iowa, and included information from the records of 465 yearling Polled Hereford and 264 yearling Simmental bulls. This latter data set was used to estimate all of the above mentioned parameters. Each data set was kept separately for the purpose of statistical analysis. Parameters were estimated using components from paternal half-sib analysis of variance and covariance. Pooled estimates of heritability for SC, sperm concentration, sperm motility, percent live sperm, sperm number and potential breeding efficiency were 0.51 +/- 0.09, 0.20 +/- 0.13, 0.11 +/- 0.12, 0.00, 0.19 +/- 0.14 and 0.13 +/- 0.12, respectively. Phenotypic correlations involving the six traits were very consistent for the two breeds. Combined across breeds their values ranged from 0.47 for SC and percent live sperm to 0.96 for sperm concentration and potential breeding efficiency. Corresponding genetic correlations were generally positive and high and ranged from 0.65 for SC and sperm motility to 1.14 for sperm number and potential breeding efficiency.

Journal Article↗

PubMed1984

PHARM--an interactive graphic program for individual and population pharmacokinetic parameter estimation.

This paper describes a new computer program PHARM to estimate individual or population pharmacokinetic parameters in nonlinear models. PHARM is an interactive program which uses graphic facilities to display data and results. The structural model can be defined using differential or integrated equations. The user can also define an error model associated with experimental data. The nonlinear mixed effect model is used to estimate the mean population parameters and their interindividual variability. The maximum likelihood and Bayesian criteria are used to estimate simultaneously the error and structural model parameters.

Computers↗

PubMed1999

Accurate quantification of (1)H spectra: from finite impulse response filter design for solvent suppression to parameter estimation.

A scheme for accurate quantification of (1)H spectra is presented. The method uses maximum-phase finite impulse response (FIR) filters for solvent suppression and an iterative nonlinear least-squares (NLLS) algorithm for parameter estimation. The estimation algorithm takes the filter influence on the metabolites of interest into account and can thereby correctly incorporate a large variety of prior knowledge into the estimation phase. The FIR filter is designed in such a way that no distortion of the important initial samples is introduced. The FIR filter method is compared numerically with the HSVD method for water signal removal in a number of examples. The results show that the FIR method, using an automatic filter design scheme, slightly outperforms the HSVD method in most cases. The good performance and ease of use of the FIR filter method combined with its low computational complexity motivate the use of the proposed method.

Algorithms↗

PubMed1992

Model parameters estimation when the evoked potential recordings are affected by a random scale factor.

In many situations an important source of the average evoked potentials (EPs) variability is a random scale factor affecting each recording. As a result, the outcome of any EP detection method may be greatly affected. However, using an appropriate probabilistic model these scale factor can be estimated, and the performance of any available detection index improved by data rescaling. In this paper the Maximum Likelihood Estimators of the waveform of the response and the scale factor affecting both background noise and this waveform are obtained. Also, an iterative algorithm for model parameters estimation is presented and its convergence is examined in a simulation study. The Linear Discriminant function is computed using simulated test data in both situations, before and after rescaling of recordings. The performance of these statistics is evaluated by mean of ROC curves.

Algorithms↗