Mechanics of cellular adhesion to artificial artery templates

We are using polymer templates to grow artificial artery grafts in vivo for the replacement of diseased blood vessels. We have previously shown that adhesion of macrophages to the template starts the graft formation. We present a study of the mechanics of macrophage adhesion to these templates on a single cell and single bond level with optical tweezers. For whole cells, in vitro cell adhesion densities decreased significantly from polymer templates polyethylene to silicone to Tygon (167, 135, and 65 cells/mm(2)). These cell densities were correlated with the graft formation success rate (50%, 25%, and 0%). Single-bond rupture forces at a loading rate of 450 pN/s were quantified by adhesion of trapped 2-mm spheres to macrophages. Rupture force distributions were dominated by nonspecific adhesion (forces, < 40 pN). On polystyrene, preadsorption of fibronectin or presence of serum proteins in the cell medium significantly enhanced adhesion strength from a mean rupture force of 20 pN to 28 pN or 33 pN, respectively. The enhancement of adhesion by fibronectin and serum is additive (mean rupture force of 43 pN). The fraction of specific binding forces in the presence of serum was similar for polystyrene and polymethyl-methacrylate, but specific binding forces were not observed for silica. Again, we found correlation to in vivo experiments, where the density of adherent cells is higher on polystyrene than on silica templates, and can be further enhanced by fibronectin adsorption. These findings show that in vitro adhesion testing can be used for template optimization and to substitute for in-vivo experiments.

Effect of the average soft-segment length on the morphology and properties of segmented polyurethane nanocomposites

Two organically modified layered silicates (with small and large diameters) were incorporated into three segmented polyurethanes with various degrees of microphase separation. Microphase separation increased with the molecular weight of the poly(hexamethylene oxide) soft segment. The molecular weight of the soft segment did not influence the amount of polyurethane intercalating the interlayer spacing. Small-angle neutron scattering and differential scanning calorimetry data indicated that the layered silicates did not affect the microphase morphology of any host polymer, regardless of the particle diameter. The stiffness enhancement on filler addition increased as the microphase separation of the polyurethane decreased, presumably because a greater number of urethane linkages were available to interact with the filler. For comparison, the small nanofiller was introduced into a polyurethane with a poly(tetramethylene oxide) soft segment, and a significant increase in the tensile strength and a sharper upturn in the stress-strain curve resulted. No such improvement occurred in the host polymers with poly(hexamethylene oxide) soft segments. It is proposed that the nanocomposite containing the more hydrophilic and mobile poly(tetramethylene oxide) soft segment is capable of greater secondary bonding between the polyurethane chains and the organosilicate surface, resulting in improved stress transfer to the filler and reduced molecular slippage. (c) 2006 Wiley Periodicals, Inc.

Effect of fluoromica platelet size on the properties of segmented polyurethane nanocomposites

Segmented polyurethane nanocomposites containing three different size fractions of SomasifTM ME100 (synthetic fluoromica) have been prepared via solvent casting. The platelet size was adjusted via a proprietary milling process, and average diameters of approximately 500 nm, 100 nm and 30 nm were measured via TEM. To the best of our knowledge this is the first time the effect of aspect ratio has been studied with the same t-o-t structured mineral. The mechanical properties of these nanocomposites have been found to be highly dependent upon the platelet size. Depending on the aspect ratio and surface treatment selected, significant improvements in tensile strength can be achieved with a minimal reduction in resilience: a problem encountered with elastomeric layered silicate nanocomposites.

Modelling the dynamics of genetic algorithms using statistical mechanics

A formalism for modelling the dynamics of Genetic Algorithms (GAs) using methods from statistical mechanics, originally due to Prugel-Bennett and Shapiro, is reviewed, generalized and improved upon. This formalism can be used to predict the averaged trajectory of macroscopic statistics describing the GA's population. These macroscopics are chosen to average well between runs, so that fluctuations from mean behaviour can often be neglected. Where necessary, non-trivial terms are determined by assuming maximum entropy with constraints on known macroscopics. Problems of realistic size are described in compact form and finite population effects are included, often proving to be of fundamental importance. The macroscopics used here are cumulants of an appropriate quantity within the population and the mean correlation (Hamming distance) within the population. Including the correlation as an explicit macroscopic provides a significant improvement over the original formulation. The formalism is applied to a number of simple optimization problems in order to determine its predictive power and to gain insight into GA dynamics. Problems which are most amenable to analysis come from the class where alleles within the genotype contribute additively to the phenotype. This class can be treated with some generality, including problems with inhomogeneous contributions from each site, non-linear or noisy fitness measures, simple diploid representations and temporally varying fitness. The results can also be applied to a simple learning problem, generalization in a binary perceptron, and a limit is identified for which the optimal training batch size can be determined for this problem. The theory is compared to averaged results from a real GA in each case, showing excellent agreement if the maximum entropy principle holds. Some situations where this approximation brakes down are identified. In order to fully test the formalism, an attempt is made on the strong sc np-hard problem of storing random patterns in a binary perceptron. Here, the relationship between the genotype and phenotype (training error) is strongly non-linear. Mutation is modelled under the assumption that perceptron configurations are typical of perceptrons with a given training error. Unfortunately, this assumption does not provide a good approximation in general. It is conjectured that perceptron configurations would have to be constrained by other statistics in order to accurately model mutation for this problem. Issues arising from this study are discussed in conclusion and some possible areas of further research are outlined.

Using Bayesian neural networks to classify segmented images

We present results that compare the performance of neural networks trained with two Bayesian methods, (i) the Evidence Framework of MacKay (1992) and (ii) a Markov Chain Monte Carlo method due to Neal (1996) on a task of classifying segmented outdoor images. We also investigate the use of the Automatic Relevance Determination method for input feature selection.

Statistical mechanics of error-correcting codes

We investigate the performance of error-correcting codes, where the code word comprises products of K bits selected from the original message and decoding is carried out utilizing a connectivity tensor with C connections per index. Shannon's bound for the channel capacity is recovered for large K and zero temperature when the code rate K/C is finite. Close to optimal error-correcting capability is obtained for finite K and C. We examine the finite-temperature case to assess the use of simulated annealing for decoding and extend the analysis to accommodate other types of noisy channels.