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PubMed2000

Additive genetic parameter estimates for heifer pregnancy and subsequent reproduction in Angus females.

A primary objective of this study was to determine whether the binary traits heifer pregnancy (HP) and subsequent rebreeding (SR) were heritable in an experimental population of Angus cattle. A second objective was to determine the nature of the additive genetic relationships among HP, SR, and stayability (S(5/1)) in the same population. Heifer pregnancy was defined as the observation of a heifer conceiving and remaining pregnant to palpation at 120 d, given exposure during the breeding season. Subsequent rebreeding was defined as the observation of a 2-yr-old conceiving and remaining pregnant to palpation at 105 d, given pregnancy as a yearling and exposure during the breeding season. Stayability was defined as the probability of a female having at least five calves, given she becomes a dam as a 2 yr old. Data were analyzed using a maximum a posteriori probit threshold model to predict breeding values on the liability scale and Method R procedures to estimate variance components in the determination of heritability (h2). Additive genetic groups were used in determining the additive genetic relationships among these fertility traits. Additive genetic groups were formed on one trait's breeding values and used in the prediction of another trait's breeding values. Analyses yielded h2 estimates that were out of the parameter space 8.5 and 46.3% for HP and SR, respectively, and 5.9% for the reestimation of S(5/1). The majority of point estimates outside the parameter space for SR converged toward 0, whereas those for HP and S(5/1) primarily converged toward 1. From the subsamples producing h2 estimates within the parameter space, average h2 for HP, SR, and S(5/1) were .21, .19, and .15, with standard deviations of .12, .14, and .08, respectively. The estimates of h2 indicate that HP and S(5/1) were heritable and should respond favorably to selection; however, SR did not appear heritable due to the large number of subsamples producing h2 estimates out of the parameter space. Fixed effect estimates for age of dam were significant for HP. From the analyses using additive genetic groups, the relationship among HP and S(5/1) appeared to be nonlinear. This potential nonlinear relationship seen between HP and S(5/1) indicates that selection for improved female fertility would be most effective by having predictions on both traits.

Animals↗

PubMed2006

Pattern statistics on Markov chains and sensitivity to parameter estimation.

BACKGROUND: In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,...). RESULTS: In the particular case where pattern statistics (overlap counting only) computed through binomial approximations we use the delta-method to give an explicit expression of sigma, the standard deviation of a pattern statistic. This result is validated using simulations and a simple pattern study is also considered. CONCLUSION: We establish that the use of high order Markov model could easily lead to major mistakes due to the high sensitivity of pattern statistics to parameter estimation.

Journal Article↗

PubMed1998

Variability of some SEMG parameter estimates with electrode location.

Muscular action potential conduction velocity (CV) and mean power frequency (MPF) are commonly used parameters to describe the surface electromyographic signal (SEMG). The discrepancies concerning the behavior and interpretation of these main parameters in the literature have motivated this work. Our objective was to evaluate within- and between-individual reproducibility, sensitivity and variation of CV and MPF depending on the electrode location with respect to various contraction modalities. The results present evidence for significant influence of electrode location on CV and MPF, not only in their initial values but also in their changes during fatiguing efforts. This influence appears to be subject-dependent. This variability seems to be essentially due to the relative displacements of myotendinous and neuromuscular junctions with respect to the electrode set. Moreover, this study shows that MPF can be seen as force-dependent under certain conditions and that the CV-MPF relationship is strongly influenced by methodological factors. In conclusion, it seems irrelevant to derive reliable SEMG parameter estimates without considering electrode location. There is a strong need for proper standardization based on anatomical and methological aspects before attempting any individual characterization. Finally, we suggest a procedure for assessment of measurement quality.

Action Potentials↗

PubMed1989

The influence of assay variability on pharmacokinetic parameter estimation.

The impact of assay variability on pharmacokinetic modeling was investigated. Simulated replications (150) of three "individuals" resulted in 450 data sets. A one-compartment model with first-order absorption was simulated. Random assay errors of 10, 20, or 30% were introduced and the ratio of absorption rate (Ka) to elimination rate (Ke) constants was 2, 10, or 20. The analyst was blinded as to the rate constants chosen for the simulations. Parameter estimates from the sequential method (Ke estimated with log-linear regression followed by estimation of Ka) and nonlinear regression with various weighting schemes were compared. NONMEM was run on the 9 data sets as well. Assay error caused a sizable number of curves to have apparent multicompartmental distribution or complex absorption kinetic characteristics. Routinely tabulated parameters (maximum concentration, area under the curve, and, to a lesser extent, mean residence time) were consistently overestimated as assay error increased. When Ka/Ke = 2, all methods except NONMEM underestimated Ke, overestimated Ka, and overestimated apparent volume of distribution. These significant biases increased with the magnitude of assay error. With improper weighting, nonlinear regression significantly overestimated Ke when Ka/Ke = 20. In general, however, the sequential approach was most biased and least precise. Although no interindividual variability was included in the simulations, estimation error caused large standard deviations to be associated with derived parameters, which would be interpreted as interindividual error in a nonsimulation environment. NONMEM, however, acceptably estimated all parameters and variabilities. Routinely applied pharmacokinetic estimation methods do not consistently provide unbiased answers. In the specific case of extended-release drug formulations, there is clearly a possibility that certain estimation methods yield Ka and relative bioavailability estimates that would be imprecise and biased.

Biometry↗

PubMed1991

In vivo NMR spectral parameter estimation: a comparison between time and frequency domain methods.

We have compared various methods of in vivo NMR spectral parameter estimation, namely a nonlinear fit of the free induction decay signal in the time domain (NLTD), a nonlinear fit of the fast Fourier transform of the FID data in the frequency domain using either a continuous Lorentzian model (NLLM) or a Fourier-sampled model (NLFM), and a time-domain linear prediction method using singular value decomposition (LPSVD). Monte Carlo simulations of 31P and 13C in vivo experiments were used to assess the bias and statistical uncertainties of spectral parameters obtained with each method. In the 31P case, all methods appear to be equivalent except the LPSVD method that led to significantly biased peak amplitudes (up to 28%). In the 13C case, the only methods able to recover the glycogen peak were the NLTD method and its equivalent in the frequency domain (NLFM). In both the 31P and the 13C cases simulations demonstrated that 256 data points were sufficient. These results demonstrate the feasibility and the robustness of a nonlinear fit of the FID data in the time domain, and we illustrate this on 31P and 13C data sets obtained in humans.

Algorithms↗

PubMed2000

On-line parameter estimation of industrial fermentation process with an alkaliphilic cellulase-producing bacterium by exhaust gas analysis.

A method for on-line estimation of the fermentation parameters (substrate, product, and cells) of an alkaliphilic Bacillus sp. by utilizing data on the O2 and CO2 tensions in the exhaust gas and the pH of the fermentation broth was investigated. The estimation was based on the stoichiometric relations concerning the weight of CO2 formed, O2 consumed, product formed, and substrate consumed by the bacterium and on the ratio of the mass of the product to that of the cell biomass. During the fermentation, in which the pH range was 7.8-8.8, the parameters were successfully estimated by considering the reactions of CO2 to produce HCO3- and/or CO3(2-) in the liquid-phase as well as the desorption of CO2 to the aeration gas. It was verified that the proposed estimation method is applicable to industrial-scale fermentation using an alkaliphilic cellulase-producing Bacillus sp. The correlation coefficients between the estimated and observed fermentation parameters exceeded 0.990, indicating that the method is capable of evaluating fermentation parameters with an accuracy satisfactory for industrial use.

Journal Article↗

PubMed1987

A note on confidence intervals with extended least squares parameter estimates.

It has previously been shown that the extended least squares (ELS) method for fitting pharmacokinetic models behaves better than other methods when there is possible heteroscedasticity (unequal error variance) in the data. Confidence intervals for pharmacokinetic parameters, at the target confidence level of 95%, computed in simulations with several pharmacokinetic and error variance models, using a theoretically reasonable approximation to the asymptotic covariance matrix of the ELS parameter estimator, are found to include the true parameter values considerably less than 95% of the time. Intervals with the ordinary least squares method perform better. Two adjustments to the ELS confidence intervals, taken together, result in better performance. These are: (i) apply a bias correction to the ELS estimate of variance, which results in wider confidence intervals, and (ii) use confidence intervals with a target level of 99% to obtain confidence intervals with actual level closer to 95%. Kineticists wishing to use the ELS method may wish to use these adjustments.

Kinetics↗

PubMed1984

Parameter estimation using the quasi-linear viscoelastic model proposed by Fung.

Using the quasi-linear viscoelastic model proposed by Fung for the description of the viscoelastic properties of soft biological tissues, the parameters governing their time-dependent behavior are commonly estimated from relaxation experiments. Exact quantification is possible from the response to a step change in the strain. Since it is physically impossible to realize a true step change in the strain, in practice the response to a steplike strain change is used. In the present study the discrepancies between the exact and the estimated parameter values are investigated using a hypothetical quasi-linear viscoelastic material. The parameter tau 1, governing the fast viscous phenomena, is found to be subject to the largest errors. Methods for obtaining better estimates of tau 1 are outlined in a number of special cases.

Biomedical Engineering↗

PubMed1998

Spectral parameter estimation by an iterative quadratic maximum likelihood method.

An iterative quadratic maximum likelihood (IQML) method is applied to spectral parameter estimation of 1D NMR data. A careful comparison of the linear prediction (LP) method based on the singular value decomposition, the total least squares (TLS) method, and IQML has clearly demonstrated that IQML is superior to both the LP and TLS methods in terms of the accuracy and bias of the estimation. The superiority of the IQML method lies in the fact that constraints on the NMR signal can easily be incorporated into the iterative process. The iterative quadratic maximum likelihood method can be used to analyze NMR data directly or to provide a starting point for further data refinement.

Algorithms↗

PubMed2005

[Non-parameter estimation algorithm to determine stellar effective temperature].

The effective temperature of a star is one of the most important parameters, which determine the continuum and spectral lines in the stellar spectrum. A non-parameter estimation algorithm is proposed to estimate the stellar effective temperature in the present paper. Firstly, the spectrum data is processed by principal component analysis(PCA), then, an estimating model based on a Gaussian kernel function is set up using the PCA data and their temperatures. Experiments were carried out to verify the efficiency, and numerical robustness of the algorithm is also tested.

English Abstract↗

PubMed1991

Investigation of parameter estimator and adaptive controller for assist pump by computer simulation.

The multi-output adaptive controller of a left ventricular assist device (LVAD) was studied by computer simulation. The controller regulated two outputs--mean aortic pressure (mAoP) and mean atrial pressure (mLAP)--by regulating vacuum pressure (input). The autoregressive models were used to describe the circulatory system. The parameters of the models were estimated by the recursive least squares method. Based on the autoregressive models, the vacuum pressure minimizing a performance index was searched. The index used was the weighted summation of the square errors. Responses of the adaptive controller were simulated when the contractility of the left ventricle was decreased at various rates and the peripheral resistance was changed. Both the mAoP and mLAP were controlled to their predicted values in the steady state. The steady-state errors of the mAoP were less than a few mm Hg, and those of the mLAP were lower than 1 mm Hg. Consequently, the estimated parameters can be regarded as true parameters, and the adaptive controller has the potential to control more than two outputs. The multioutput adaptive controller studied is useful in controlling the LVAD according to the change in circulatory condition.

Aorta, Thoracic↗

PubMed1994

Regurgitant volume in aortic regurgitation from a parameter estimation procedure.

The regurgitant volume and regurgitant orifice area as well as total peripheral resistance and arterial compliance were estimated in a cardiovascular hydromechanical simulator and in 10 patients with aortic regurgitation. A parameter estimation procedure based on a simple model of the cardiovascular system, Doppler measurements of the regurgitant jet, aortic systolic flow, and systolic and diastolic blood pressures was used. In the cardiovascular simulator the estimated regurgitant orifice area was compared with the size of a hole in the disk of a mechanical aortic valve. In the patients the regurgitant fraction was compared with semiquantitative grading from echocardiography routinely performed in our laboratory. In the hydromechanical simulator, the estimated regurgitant orifice area of 26.5 +/- 3.5 (SD) mm2 (n = 9) was not different from the true value of 24 mm2. In the patients there was a fair relationship between the estimated regurgitant fraction and the semiquantitative grading. The estimated regurgitant orifice areas varied between 1.6 and 31.2 mm2. The estimated mean values of total peripheral resistance and arterial compliance were 1.67 +/- 0.55 mmHg.s.ml-1 and 1.30 +/- 0.42 ml/mmHg, respectively.

Adolescent↗

PubMed1999

Using nonlinear regression to estimate parameters of dark adaptation.

An objective technique for estimating the kinetics of dark adaptation is presented, with which one can evaluate models with multiple parameters, evaluate several models of dark adaptation simultaneously, and rapidly analyze large data sets. Another advantage is the ability to simultaneously estimate transition times and rates of sensitivity recovery. Finally, this nonlinear regression technique does not require that the distributional properties of the data be transformed, and thus, parameter estimates are in meaningful units and reflect the actual rate of recovery of sensitivity.

Adult↗

PubMed2001

Auto-SOM: recursive parameter estimation for guidance of self-organizing feature maps.

An important technique for exploratory data analysis is to form a mapping from the high-dimensional data space to a low-dimensional representation space such that neighborhoods are preserved. A popular method for achieving this is Kohonen's self-organizing map (SOM) algorithm. However, in its original form, this requires the user to choose the values of several parameters heuristically to achieve good performance. Here we present the Auto-SOM, an algorithm that estimates the learning parameters during the training of SOMs automatically. The application of Auto-SOM provides the facility to avoid neighborhood violations up to a user-defined degree in either mapping direction. Auto-SOM consists of a Kalman filter implementation of the SOM coupled with a recursive parameter estimation method. The Kalman filter trains the neurons' weights with estimated learning coefficients so as to minimize the variance of the estimation error. The recursive parameter estimation method estimates the width of the neighborhood function by minimizing the prediction error variance of the Kalman filter. In addition, the "topographic function" is incorporated to measure neighborhood violations and prevent the map's converging to configurations with neighborhood violations. It is demonstrated that neighborhoods can be preserved in both mapping directions as desired for dimension-reducing applications. The development of neighborhood-preserving maps and their convergence behavior is demonstrated by three examples accounting for the basic applications of self-organizing feature maps.

Algorithms↗

PubMed2006

Parameter estimation for scoring protein-ligand interactions using negative training data.

Surflex-Dock employs an empirically derived scoring function to rank putative protein-ligand interactions by flexible docking of small molecules to proteins of known structure. The scoring function employed by Surflex was developed purely on the basis of positive data, comprising noncovalent protein-ligand complexes with known binding affinities. Consequently, scoring function terms for improper interactions received little weight in parameter estimation, and an ad hoc scheme for avoiding protein-ligand interpenetration was adopted. We present a generalized method for incorporating synthetically generated negative training data, which allows for rigorous estimation of all scoring function parameters. Geometric docking accuracy remained excellent under the new parametrization. In addition, a test of screening utility covering a diverse set of 29 proteins and corresponding ligand sets showed improved performance. Maximal enrichment of true ligands over nonligands exceeded 20-fold in over 80% of cases, with enrichment of greater than 100-fold in over 50% of cases.

Binding Sites↗

PubMed2005

Characterization of viscoelastic soft tissue properties from in vivo animal experiments and inverse FE parameter estimation.

Soft tissue characterization and modeling based on living tissues has been investigated in order to provide a more realistic behavior in a virtual reality based surgical simulation. In this paper, we characterize the nonlinear viscoelastic properties of intra-abdominal organs using the data from in vivo animal experiments and inverse FE parameter estimation algorithm. In the assumptions of quasi-linear-viscoelastic theory, we estimated the viscoelastic and hyerelastic material parameters to provide a physically based simulation of tissue deformations. To calibrate the parameters to the experimental results, we developed a three dimensional FE model to simulate the forces at the indenter and an optimization program that updates new parameters and runs the simulation iteratively. We can successfully reduce the time and computation resources by decoupling the viscoelastic part and nonlinear elastic part in a tissue model. The comparison between simulation and experimental behavior of pig intra abdominal soft tissue are presented to provide a validness of the tissue model using our approach.

Animals↗

PubMed2004

"Population" approach improves parameter estimation of kinetic models from dynamic PET data.

Kinetic modeling is used to indirectly measure physiological parameters from dynamic positron emission tomography (PET) data. Usually, the unknown parameters of the model are estimated, in any given region of interest (ROI), by least squares (LS). However, when the signal-to-noise ratio (SNR) of PET data is too low, LS does not allow reliable parameter estimation. To overcome this problem, we study in this paper the applicability of approaches originally developed in the pharmacokinetic/pharmacodynamic literature and referred to as "population approaches." In particular, we consider the iterative two stage (ITS) method, which, given a set of M ROIs drawn on PET images of a given individual, estimates the unknown model parameters of each ROI by exploiting the information contained in all the M ROIs. After having revised the theory behind ITS, we assess its performance versus LS by using Monte Carlo simulations which allow us to evaluate the bias of the two methods in a variety of situations. Then, we compare the performance of LS and ITS in two case studies on [18F]FDG kinetics in human skeletal muscle. Both simulated and real case studies results show that a population approach is of potential in modeling PET images since it allows to reliably estimate model parameters also in those ROIs where either a bad SNR or a poor sampling (e.g., infrequent scanning and/or short experiment duration) make the use of LS unsuccessful.

Algorithms↗

PubMed2004

Assessing the informational value of parameter estimates in cognitive models.

Mathematical models of cognition often contain unknown parameters whose values are estimated from the data. A question that generally receives little attention is how informative such estimates are. In a maximum likelihood framework, standard errors provide a measure of informativeness. Here, a standard error is interpreted as the standard deviation of the distribution of parameter estimates over multiple samples. A drawback to this interpretation is that the assumptions that are required for the maximum likelihood framework are very difficult to test and are not always met. However, at least in the cognitive science community, it appears to be not well known that standard error calculation also yields interpretable intervals outside the typical maximum likelihood framework. We describe and motivate this procedure and, in combination with graphical methods, apply it to two recent models of categorization: ALCOVE (Kruschke, 1992) and the exemplar-based random walk model (Nosofsky & Palmeri, 1997). The applications reveal aspects of these models that were not hitherto known and bring a mix of bad and good news concerning estimation of these models.

Algorithms↗