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At least 37 records · Page 2Linked to original sources

Parameter identification of the human lower limb under dynamic, transient torsional loading.

The response of the lower limb to dynamic, transient torsional loading applied at the foot has been measured for a male test subject. The dynamic loading was provided by a computer controlled pneumatic system which applied single haversine (i.e. half cycle of a sine wave) axial moment pulses of variable amplitude (0-100 Nm) and duration (50-600 ms). Potentiometers measured the absolute rotations of the three leg segments. Test variables included rotation direction, weight bearing and joint flexion. Two approaches were explored for specifying parameters (i.e. inertia, damping, stiffness) of a three degree-of-freedom dynamic system model which best duplicated the measured response. One approach involved identification of linear parameters by means of optimization while the other approach entailed estimation. Parameter estimates, which included non-linear, asymmetric stiffness functions, were derived from the literature. The optimization was undertaken so as to identify parameter dependence on test variables. Results indicate that parameter values are influenced by test variables. Results also indicate that the non-linear, estimated model better approximates the experimental data than the linear, identified model. In addition to identifying parameters of a three degree-of-freedom model, parameters were also identified for a single degree-of-freedom model where the motion variable was intended to indicate the rotation of the in vivo knee. It is concluded that the simpler model offers good accuracy in predicting both magnitude and time of occurrence of peak knee axial rotations. Model motion fails to track the measured knee rotation subsequent to the peak, however.

Ankle Joint↗

Mode isolation: a new algorithm for modal parameter identification.

Multiple degree of freedom (MDOF) algorithms are the dominant methods for extracting modal parameters from measured data. These methods are founded on the notion that because the response of a linear dynamic system is the sum of many modal contributions, the extraction technique must deal with all of the modal parameters in a simultaneous fashion. The Mode Isolation Algorithm (MIA) described here is a frequency domain formulation that takes an alternative viewpoint. It extracts the modal parameters of each mode in an iterative search, and then refines the estimation of each mode by isolating its effect from the other modal contributions. The first iteration estimates modes in a hierarchy of their dominance. As each mode is estimated, its contribution is subtracted from the data set, until all that remains is noise. The second and subsequent iterations subtract the current estimates for all other modes to identify the properties of the mode under consideration. The various operations are described in detail, and then illustrated using data from a four-degree-of-freedom system that was previously used to assess the Eigensystem Realization Algorithm (ERA) and Enhanced ERA. Eigenvalues and mode shapes are compared for each algorithm. Another example analyzes simulated data for a cantilever beam with three suspended one-degree-of-freedom subsystems, in which the parameters are adjusted to bring two natural frequencies into close proximity. The results suggest that MIA is more accurate, and more robust in the treatment of noisy data, than either ERA version, and that it is able to identify modes whose bandwidth is comparable to the difference of adjacent natural frequencies.

Journal Article↗

Identifiability analysis and parameter identification of an in vivo ligand-receptor model from PET data.

Identifiability problem is a very important topic in the framework of model justification and not accounting for it during the modeling procedure can lead to meaningless results. While studying the receptor-ligand model parameter estimation from dynamic positron emission tomography data, each of the three possible conclusions to the identifiability problem (i.e., unidentifiable model, multiple solutions, or unique solution) are reached depending on the experimental protocol used. The identification of the model parameters from data obtained with a single tracer injection leads to disappointing numerical results since most of the parameters have to be considered as unidentifiable. A protocol including two injections, a first injection of the labeled ligand and a second injection of the cold ligand (displacement experiment) leads to two very different numerical solutions, which is surprising since such multiplicity of solutions was not indicated by a preliminary theoretical identifiability study. We show that a three-injections protocol, including both a displacement and coinjection experiment, allows to determine which of these two solutions is biologically valid.

Animals↗

Parameter identification of thermophilic anaerobic degradation of valerate.

The considered mathematical model of the decomposition of valerate presents three unknown kinetic parameters, two unknown stoichiometric coefficients, and three unknown initial concentrations for biomass. Applying a structural identifiability study, we concluded that it is necessary to perform simultaneous batch experiments with different initial conditions for estimating these parameters. Four simultaneous batch experiments were conducted at 55 degrees C, characterized by four different initial acetate concentrations. Product inhibition of valerate degradation by acetate was considered. Practical identification was done optimizing the sum of the multiple determination coefficients for all measured state variables and for all experiments simultaneously. The estimated values of kinetic parameters and stoichiometric coefficients were characterized by the parameter correlation matrix, the confidence interval, and the student's t-test at 5% significance level with positive results except for the saturation constant, for which more experiments for improving its identifiability should be conducted. In this article, we discuss kinetic parameter estimation methods.

Algorithms↗

[Parameters identification and uncertainty analysis for environmental model].

This paper examined a case study of hydrological model for identifying parameter uncertainty by using three sensitivity analysis methods: HSY algorithm, linear regressional method and coupling analysis method. The results showed that optimal algorithms cannot give a sound explanation for complexity of model structure and identifying model parameters via uncertainty analysis methods presented an effective alternative to understand model system.

Algorithms↗

The parameter identification problem for the somatic shunt model.

The somatic shunt model, a generalized version of the Rall equivalent cylinder model, is used commonly to describe the passive electrotonic properties of neurons. Procedures for determining the parameters of the somatic shunt model that best describe a given neuron typically rely on the response of the cell to a small step of hyperpolarizing current injected by an intrasomatic recording electrode. In this study it is shown that the problem of estimating model parameters for the somatic shunt model using physiological data is ill-posed, in that very small errors in measured data can lead to large and unpredictable errors in parameter estimates. If the somatic shunt is assumed to be a real property of the intact neuron, the effects of these errors are not severe when predicting EPSP waveshapes resulting from synaptic input at a given location. However, if the somatic shunt is assumed to be a consequence of a leakage pathway around the recording electrode, and a correction for the shunt is applied, then the instability of the inverse problem can introduce large errors in estimates of EPSP waveshape as a function of synaptic location in the intact cell. Morphological constraints can be used to improve the accuracy of the inversion procedure in terms of both parameter estimates and predicted EPSP responses.

Mathematics↗

Parameter identification in dynamical models of anaerobic waste water treatment.

Biochemical reactions can often be formulated mathematically as ordinary differential equations. In the process of modeling, the main questions that arise are concerned with structural identifiability, parameter estimation and practical identifiability. To clarify these questions and the methods how to solve them, we analyze two different second order models for anaerobic waste water treatment processes using two data sets obtained from different experimental setups. In both experiments only biogas production rate was measured which complicates the analysis considerably. We show that proving structural identifiability of the mathematical models with currently used methods fails. Therefore, we introduce a new, general method based on the asymptotic behavior of the maximum likelihood estimator to show local structural identifiability. For parameter estimation we use the multiple shooting approach which is described. Additionally we show that the Hessian matrix approach to compute confidence intervals fails in our examples while a method based on Monte Carlo Simulation works well.

Anaerobiosis↗

Mathematical model of antiviral immune response. II. Parameters identification for acute viral hepatitis B.

Considering the mathematical model of antiviral immune response, we describe a method of fitting the model to the data characterizing acute viral hepatitis B. The corresponding procedure employs an idea of sequential parameter estimation to make the problem of fitting manageable. The underlying mechanisms responsible for the quantitative manifestations of the four basic phases of acute hepatitis B are used to select the model parameters. The identified model of acute hepatitis B is then tested with regard to the following situations: the effect of HBsAg-specific antibodies on HBV challenge; the vaccination and the resistance to challenge using live hepatitis B virus; the dose of viruses--the incubation time relationships. The sensitivity of the model with respect to parameters variations is then analysed. The developed model allows us to quantitatively simulate the basic features of the antiviral immune response during acute hepatitis B and some closely related phenomena.

Acute Disease↗

Online parameter identification of second-order systemic circulation model using the delta operator.

To develop effective medical care with the artificial heart, we proposed a new method that can calculate the time varying and unmeasured hemodynamics of the human body from measured physiological data: aortic pressure, aortic flow, and pump flow in real time. This method comprises first, the second order of systemic circulation model, which consists of aortic compliance (Ca), aortic resistance (Ra), aortic inertia (L), and total peripheral resistance (Rp); and second, system identification using the delta operator. In the computer simulation, we confirmed the effectiveness of this method. During the animal experiment with the left ventricular assist system, the physiological parameters could be identified online: mean Ra = 0.04 mm Hg s/ml, mean Ca = 0.65 mm Hg/ml, mean L = 0.004 mm Hg s(2)/ml, and mean Rp = 0.3 mm Hg s/ml. This new method efficiently identified the physiological parameters, which are important not only to support the medical care but also to develop the control method adapted to the physiological behavior.

Animals↗

Application of a neural observer to phenols ozonation in water: simulation and kinetic parameters identification.

Presented in this study, a dynamic neural network (DNN) is employed to estimate the states dynamics of the phenols-ozone-water system. A new technique based on the dynamic neural network observer (DNNO) with relay (signum) term is applied to estimate the decomposition dynamics of phenols and to identify their kinetic parameters without any mathematical model usage. The decomposition of phenols (phenol (PH), 4-chlorophenol (4-CPH) and 2,4-dichlorophenol (2,4-DCPH)) and their mixture by ozone, realized in a semi-batch reactor, is considered as a process with uncertain model ("black-box"). Only one parameter monitoring, namely, the ozone concentration in gas phase in the reactor outlet, is measured during ozonation. The variation of this variable is used to obtain the summary characteristic curve for the phenols ozonation. Then, using the experimental decomposition dynamics of phenols and of their mixture, obtained by HPLC method, the proposed DNNO is applied to estimate the ozonation constants of phenols at the different pH 2-12. A good correspondence between the decomposition dynamics and the estimated ones by DNNO is obtained.

Computer Simulation↗

A distributed parameter identification problem in neuronal cable theory models.

Dendritic and axonal processes of nerve cells, along with the soma itself, have membranes with spatially distributed densities of ionic channels of various kinds. These ionic channels play a major role in characterizing the types of excitable responses expected of the cell type. These densities are usually represented as constant parameters in neural models because of the difficulty in experimentally estimating them. However, through microelectrode measurements and selective ion staining techniques, it is known that ion channels are non-uniformly spatially distributed. This paper presents a non-optimization approach to recovering a single spatially non-uniform ion density through use of temporal data that can be gotten from recording microelectrode measurements at the ends of a neural fiber segment of interest. The numerical approach is first applied to a linear cable model and a transformed version of the linear model that has closed-form solutions. Then the numerical method is shown to be applicable to non-linear nerve models by showing it can recover the potassium conductance in the Morris-Lecar model for barnacle muscle, and recover the spine density in a continuous dendritic spine model by Baer and Rinzel.

Action Potentials↗

Parameter identification of a neurological control model for the pathological head movements of cerebellar patients.

The objective of this research is to explore the role of the cerebellum in the human motor control system. The present study quantitatively compares the neurological control signals effecting fast, horizontal head rotations in normal subjects to those in patients with a cerebellar lesion. The method involves the use of a computer simulation model for one degree-of-freedom movements. A method for unconstrained global optimization, first proposed by Hans Bremermann (1970), is used to identify the timing and magnitudes of the input neurological control signals to the model, which are compared to recorded electromyograms (EMGs). Experimentally recorded kinematics from cerebellar patients and from normal subjects were used to drive the parameter search. These simulations found that cerebellar patients' neurological control signals were altered with respect to those of normal subjects, and suggest that the electromyographic activity of cerebellar patients may comprise at least five bursts of activity whereas normal subjects typically exhibit only three. The results are discussed with respect to the hypothesis that the cerebellum may be involved in both the timing and magnitudes of the neurological control signals effecting voluntary movement.

Algorithms↗

A dual optimization method for the material parameter identification of a biphasic poroviscoelastic hydrogel: Potential application to hypercompliant soft tissues.

A dual-indentation creep and stress relaxation methodology was developed and validated for the material characterization of very soft biological tissue within the framework of the biphasic poroviscoelastic (BPVE) constitutive model. Agarose hydrogel, a generic porous medium with mobile fluid, served as a mechanical tissue analogue for validation of the experimental procedure. Indentation creep and stress relaxation tests with a solid plane-ended cylindrical indenter were performed at identical sites on a gel sample with dimensions large enough with respect to indenter size in order to satisfy an infinite layer assumption. A finite element (FE) formulation coupled to a global optimization algorithm was utilized to simultaneously curve-fit the creep and stress relaxation data and extract the BPVE model parameters for the agarose gel. A numerical analysis with artificial data was conducted to validate the uniqueness of the computational procedure. The BPVE model was able to successfully cross-predict both creep and stress relaxation behavior for each pair of experiments with a single unique set of material parameters. Optimized elastic moduli were consistent with those reported in the literature for agarose gel. With the incorporation of appropriately-sized indenters to satisfy more stringent geometric constraints, this simple yet powerful indentation methodology can provide a straightforward means by which to obtain the BPVE model parameters of biological soft tissues that are difficult to manipulate (such as brain and adipose) while maintaining a realistic in situ loading environment.

Connective Tissue↗

A new method to model change in cutaneous blood flow due to mechanical skin irritation part II: parameter identification procedure.

Mechanical skin irritation, for example a light scratch with a needle, induces histamine and neuropeptide release on the line of stroke and in the surrounding tissue. Both histamine and neuropeptides are vasodilators. They cause vasodilation by changing the contraction state of the vascular smooth muscles and hence vessel compliance. Smooth muscle contraction state is very difficult to measure in vivo. For that reason we propose in this article an identification procedure to establish an irritation law. The law gives change in vessel compliance as a function of space, time and the intensity of the stroke. We have showed that vessel compliance increases immediately after the stroke not only on the line of stroke, but also in the surrounding tissue. Then, after a short delay, vessel compliance starts decreasing in the surrounding tissue, whereas vessel compliance on the line of stroke keeps increasing. Hence, blood is transported from the surrounding tissue to the line of stroke. In this way, higher blood volume on the line of stroke can be obtained than by only changing vessel compliance locally.

Humans↗

Metabolism of rifabutin in human enterocyte and liver microsomes: kinetic parameters, identification of enzyme systems, and drug interactions with macrolides and antifungal agents.

Biotransformation of rifabutin, an antibiotic used for treatment of tuberculosis in patients infected with the human immunodeficiency virus (HIV), and its interactions with some macrolide and antifungal agents were studied in human intestinal and liver microsomes. Both liver and enterocyte microsomes metabolized rifabutin to 25-O-deacetylrifabutin, 27-O-demethylrifabutin, and 20-, 31-, and 32-hydroxyrifabutin. The same products (except 25-O-deacetylrifabutin) were formed by microsomes from lymphoblastoid cells that contained expressed CYP3A4. The apparent Michaelis-Menten constant (Km); approximately 10 to 12 mumol/L) and maximal velocity (Vmax; approximately 100 pmol/min/mg of protein) values for CYP-mediated metabolism were similar in liver and enterocyte microsomes. Deacetylation of rifabutin (Km approximately 16 to 20 mumol/L and Vmax approximately 50 to 100 pmol/min/mg of protein) was catalyzed by microsomal cholinesterase. Clarithromycin, ketoconazole, and fluconazole inhibited CYP-mediated metabolism of rifabutin in enterocyte microsomes equally or more potently than in liver microsomes but had no effect on cholinesterase activity. Azithromycin did not inhibit in vitro metabolism of rifabutin. This study provides evidence that CYP3A4 and cholinesterase are major enzymes that biotransform rifabutin in humans and that intestinal CYP3A4 contributes significantly to rifabutin presystemic first-pass metabolism and drug interactions with macrolide and antifungal agents.

Acetylation↗