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J B Bassingthwaighte

Publications and source records attributed to J B Bassingthwaighte.

At least 145 records · Page 8Linked to original sources
PubMedDate not supplied

Modeling of palmitate transport in the heart.

Transport of palmitate from the albumin-palmitate complex in the plasma to inside mitochondria where it undergoes beta-oxidation is a multistep process. Albumin's large size prevents permeation via interendothelial clefts. Palmitate dissociation from albumin in solution is too slow to provide an adequate supply of the unbound palmitate. The discovery that the dissociation occurs upon albumin binding to an endothelial surface receptor resolves the conundrum. Palmitate transport across the luminal surface membrane may be either carrier-mediated or passive. Fatty-acid binding protein inside endothelial and cardiac muscle cells facilitates diffusion through cytosol while maintaining the unbound palmitate concentration at a very low level. Within the interstitium, albumin is again the palmitate carrier. Still controversial is whether or not there is a saturable sarcolemmal transporter or simply passive exchange. Inside the myocyte palmitate is again bound to the fatty acid binding protein which buffers the free palmitate concentration, facilitates diffusion, and may facilitate further intracellular reactions.

Animals↗
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A constraint on possible stoichiometries of myocardial sodium-calcium exchange.

We have examined the sodium-calcium exchange stoichiometry in Langendorff-perfused rabbit hearts using gamma-emitting tracers under conditions of sodium pump inhibition. Following a 60-min perfusion with 10(-5) acetylstrophanthidin, and extracellular concentrations [Na]o = 70 mM and [Ca]o = 300 microM, intracellular sodium rose to 59.2 mM. At this point an increase in extracellular calcium [Ca]o = 1.52 mM) caused a net efflux of sodium, but an increase in sodium [Na]o = 105 mM) caused no measurable change. When sodium and calcium were simultaneously increased according to the ratio [Na]o)n/[Ca]o = [Na]'o)n/[Ca]'o, a sodium efflux is observed when n = 4, but not when n = 3. These results are consistent with an exchange stoichiometry of 3 Na+ for each Ca2+ ion, but not values of 4 or more.

Animals↗
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Evaluating rescaled ranged analysis for time series.

Rescaled range analysis is a means of characterizing a time series or a one-dimensional (1-D) spatial signal that provides simultaneously a measure of variance and of the long-term correlation or "memory," The trend-corrected method is based on the statistical self-similarity in the signal: in the standard approach one measures the ratio R/S on the range R of the sum of the deviations from the local mean divided by the standard deviation S from the mean. For fractal signals R/S is a power law function of the length tau of each segment of the set of segments into which the data set has been divided. Over a wide range of tau's the relationship is: R/S = a tau H, where kappa is a scalar and the H is the Hurst exponent. (For a 1-D signal f(t), the exponent H = 2 - D, with D being the fractal dimension.) The method has been tested extensively on fractional Brownian signals of known H to determine its accuracy, bias, and limitations. R/S tends to give biased estimates of H, too low for H > 0.72, and too high for H < 0.72. Hurst analysis without trend correction differs by finding the range R of accumulation of differences from the global mean over the total period of data accumulation, rather than from the mean over each tau. The trend-corrected method gives better estimates of H on Brownian fractal signals of known H when H > or = 0.5, that is, for signals with positive correlations between neighboring elements. Rescaled range analysis has poor convergence properties, requiring about 2,000 points for 5% accuracy and 200 for 10% accuracy. Empirical corrections to the estimates of H can be made by graphical interpolation to remove bias in the estimates. Hurst's 1951 conclusion that many natural phenomena exhibit not random but correlated time series is strongly affirmed.

Algorithms↗
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SENSOP: a derivative-free solver for nonlinear least squares with sensitivity scaling.

Nonlinear least squares optimization is used most often in fitting a complex model to a set of data. An ordinary nonlinear least squares optimizer assumes a constant variance for all the data points. This paper presents SENSOP, a weighted nonlinear least squares optimizer, which is designed for fitting a model to a set of data where the variance may or may not be constant. It uses a variant of the Levenberg-Marquardt method to calculate the direction and the length of the step change in the parameter vector. The method for estimating appropriate weighting functions applies generally to 1-dimensional signals and can be used for higher dimensional signals. Sets of multiple tracer outflow dilution curves present special problems because the data encompass three to four orders of magnitude; a fractional power function provides appropriate weighting giving success in parameter estimation despite the wide range.

Capillaries↗
PubMedDate not supplied

Evaluation of the dispersional analysis method for fractal time series.

Fractal signals can be characterized by their fractal dimension plus some measure of their variance at a given level of resolution. The Hurst exponent, H, is < 0.5 for rough anticorrelated series, > 0.5 for positively correlated series, and = 0.5 for random, white noise series. Several methods are available: dispersional analysis, Hurst rescaled range analysis, autocorrelation measures, and power special analysis. Short data sets are notoriously difficult to characterize; research to define the limitations of the various methods is incomplete. This numerical study of fractional Brownian noise focuses on determining the limitations of the dispersional analysis method, in particular, assessing the effects of signal length and of added noise on the estimate of the Hurst coefficient, H, (which ranges from 0 to 1 and is 2 - D, where D is the fractal dimension). There are three general conclusions: (i) pure fractal signals of length greater than 256 points give estimates of H that are biased but have standard deviations less than 0.1; (ii) the estimates of H tend to be biased toward H = 0.5 at both high H (> 0.8) and low H (< 0.5), and biases are greater for short time series than for long; and (iii) the addition of Gaussian noise (H = 0.5) degrades the signals: for those with negative correlation (H < 0.5) the degradation is great, the noise has only mild degrading effects on signals with H > 0.6, and the method is particularly robust for signals with high H and long series, where even 100% noise added has only a few percent effect on the estimate of H. Dispersional analysis can be regarded as a strong method for characterizing biological or natural time series, which generally show long-range positive correlation.

Bias↗
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Modeling blood flow heterogeneity.

It has been known for some time that regional blood flows within an organ are not uniform. Useful measures of heterogeneity of regional blood flows are the standard deviation and coefficient of variation or relative dispersion of the probability density function (PDF) of regional flows obtained from the regional concentrations of tracers that are deposited in proportion to blood flow. When a mathematical model is used to analyze dilution curves after tracer solute administration, for many solutes it is important to account for flow heterogeneity and the wide range of transit times through multiple pathways in parallel. Failure to do so leads to bias in the estimates of volumes of distribution and membrane conductances. Since in practice the number of paths used should be relatively small, the analysis is sensitive to the choice of the individual elements used to approximate the distribution of flows or transit times. Presented here is a method for modeling heterogeneous flow through an organ using a scheme that covers both the high flow and long transit time extremes of the flow distribution. With this method, numerical experiments are performed to determine the errors made in estimating parameters when flow heterogeneity is ignored, in both the absence and presence of noise. The magnitude of the errors in the estimates depends upon the system parameters, the amount of flow heterogeneity present, and whether the shape of the input function is known. In some cases, some parameters may be estimated to within 10% when heterogeneity is ignored (homogeneous model), but errors of 15-20% may result, even when the level of heterogeneity is modest. In repeated trials in the presence of 5% noise, the mean of the estimates was always closer to the true value with the heterogeneous model than when heterogeneity was ignored, but the distributions of the estimates from the homogeneous and heterogeneous models overlapped for some parameters when outflow dilution curves were analyzed. The separation between the distributions was further reduced when tissue content curves were analyzed. It is concluded that multipath models accounting for flow heterogeneity are a vehicle for assessing the effects of flow heterogeneity under the conditions applicable to specific laboratory protocols, that efforts should be made to assess the actual level of flow heterogeneity in the organ being studied, and that the errors in parameter estimates are generally smaller when the input function is known rather than estimated by deconvolution.

Algorithms↗
PubMedDate not supplied

Efficient numerical methods for nonlinear-facilitated transport and exchange in a blood-tissue exchange unit.

The analysis of experimental data obtained by the multiple-indicator method requires complex mathematical models for which capillary blood-tissue exchange (BTEX) units are the building blocks. This study presents a new, nonlinear, two-region, axially distributed, single capillary, BTEX model. A facilitated transporter model is used to describe mass transfer between plasma and intracellular spaces. To provide fast and accurate solutions, numerical techniques suited to nonlinear convection-dominated problems are implemented. These techniques are the random choice method, an explicit Euler-Lagrange scheme, and the MacCormack method with and without flux correction. The accuracy of the numerical techniques is demonstrated, and their efficiencies are compared. The random choice, Euler-Lagrange and plain MacCormack method are the best numerical techniques for BTEX modeling. However, the random choice and Euler-Lagrange methods are preferred over the MacCormack method because they allow for the derivation of a heuristic criterion that makes the numerical methods stable without degrading their efficiency. Numerical solutions are also used to illustrate some nonlinear behaviors of the model and to show how the new BTEX model can be used to estimate parameters from experimental data.

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