Composite electrospun gelatin fiber-alginate gel scaffolds for mechanically robust tissue engineered cornea.

A severe shortage of good quality donor cornea is now an international crisis in public health. Alternatives for donor tissue need to be urgently developed to meet the increasing demand for corneal transplantation. Hydrogels have been widely used as scaffolds for corneal tissue regeneration due to their large water content, similar to that of native tissue. However, these hydrogel scaffolds lack the fibrous structure that functions as a load-bearing component in the native tissue, resulting in poor mechanical performance. This work shows that mechanical properties of compliant hydrogels can be substantially enhanced with electrospun nanofiber reinforcement. Electrospun gelatin nanofibers were infiltrated with alginate hydrogels, yielding transparent fiber-reinforced hydrogels. Without prior crosslinking, electrospun gelatin nanofibers improved the tensile elastic modulus of the hydrogels from 78±19 kPa to 450±100 kPa. Stiffer hydrogels, with elastic modulus of 820±210 kPa, were obtained by crosslinking the gelatin fibers with carbodiimide hydrochloride in ethanol before the infiltration process, but at the expense of transparency. The developed fiber-reinforced hydrogels show great promise as mechanically robust scaffolds for corneal tissue engineering applications.

Composite electrospun gelatin fiber-alginate gel scaffolds for mechanically robust tissue engineered cornea

A severe shortage of good quality donor cornea is now an international crisis in public health. Alternatives for donor tissue need to be urgently developed to meet the increasing demand for corneal transplantation. Hydrogels have been widely used as scaffolds for corneal tissue regeneration due to their large water content, similar to that of native tissue. However, these hydrogel scaffolds lack the fibrous structure that functions as a load-bearing component in the native tissue, resulting in poor mechanical performance. This work shows that mechanical properties of compliant hydrogels can be substantially enhanced with electrospun nanofiber reinforcement. Electrospun gelatin nanofibers were infiltrated with alginate hydrogels, yielding transparent fiber-reinforced hydrogels. Without prior crosslinking, electrospun gelatin nanofibers improved the tensile elastic modulus of the hydrogels from 78±19. kPa to 450±100. kPa. Stiffer hydrogels, with elastic modulus of 820±210. kPa, were obtained by crosslinking the gelatin fibers with carbodiimide hydrochloride in ethanol before the infiltration process, but at the expense of transparency. The developed fiber-reinforced hydrogels show great promise as mechanically robust scaffolds for corneal tissue engineering applications. © 2013 Elsevier Ltd.

N-gram posterior probability confidence measures for statistical machine translation: An empirical study

We report an empirical study of n-gram posterior probability confidence measures for statistical machine translation (SMT). We first describe an efficient and practical algorithm for rapidly computing n-gram posterior probabilities from large translation word lattices. These probabilities are shown to be a good predictor of whether or not the n-gram is found in human reference translations, motivating their use as a confidence measure for SMT. Comprehensive n-gram precision and word coverage measurements are presented for a variety of different language pairs, domains and conditions. We analyze the effect on reference precision of using single or multiple references, and compare the precision of posteriors computed from k-best lists to those computed over the full evidence space of the lattice. We also demonstrate improved confidence by combining multiple lattices in a multi-source translation framework. © 2012 The Author(s).

Time-dependent fracture toughness of cornea.

The fracture and time-dependent properties of cornea are very important for the development of corneal scaffolds and prostheses. However, there has been no systematic study of cornea fracture; time-dependent behavior of cornea has never been investigated in a fracture context. In this work, fracture toughness of cornea was characterized by trouser tear tests, and time-dependent properties of cornea were examined by stress-relaxation and uniaxial tensile tests. Control experiments were performed on a photoelastic rubber sheet. Corneal fracture resistance was found to be strain-rate dependent, with values ranging from 3.39±0.57 to 5.40±0.48kJm(-2) over strain rates from 3 to 300mmmin(-1). Results from stress-relaxation tests confirmed that cornea is a nonlinear viscoelastic material. The cornea behaved closer to a viscous fluid at small strain but became relatively more elastic at larger strain. Although cornea properties are greatly dependent on time, the stress-strain responses of cornea were found to be insensitive to the strain rate when subjected to tensile loading.

Cell shape changes indicate a role for extrinsic tensile forces in Drosophila germ-band extension.

Drosophila germ-band extension (GBE) is an example of the convergence and extension movements that elongate and narrow embryonic tissues. To understand the collective cell behaviours underlying tissue morphogenesis, we have continuously quantified cell intercalation and cell shape change during GBE. We show that the fast, early phase of GBE depends on cell shape change in addition to cell intercalation. In antero-posterior patterning mutants such as those for the gap gene Krüppel, defective polarized cell intercalation is compensated for by an increase in antero-posterior cell elongation, such that the initial rate of extension remains the same. Spatio-temporal patterns of cell behaviours indicate that an antero-posterior tensile force deforms the germ band, causing the cells to change shape passively. The rate of antero-posterior cell elongation is reduced in twist mutant embryos, which lack mesoderm. We propose that cell shape change contributing to germ-band extension is a passive response to mechanical forces caused by the invaginating mesoderm.

Nonparametric Bayesian Density Modeling with Gaussian Processes

We present the Gaussian process density sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a distribution defined by a density that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We describe two such MCMC methods. Both methods also allow inference of the hyperparameters of the Gaussian process.

Models and algorithms for tracking of maneuvering objects using variable rate particle filters

Standard algorithms in tracking and other state-space models assume identical and synchronous sampling rates for the state and measurement processes. However, real trajectories of objects are typically characterized by prolonged smooth sections, with sharp, but infrequent, changes. Thus, a more parsimonious representation of a target trajectory may be obtained by direct modeling of maneuver times in the state process, independently from the observation times. This is achieved by assuming the state arrival times to follow a random process, typically specified as Markovian, so that state points may be allocated along the trajectory according to the degree of variation observed. The resulting variable dimension state inference problem is solved by developing an efficient variable rate particle filtering algorithm to recursively update the posterior distribution of the state sequence as new data becomes available. The methodology is quite general and can be applied across many models where dynamic model uncertainty occurs on-line. Specific models are proposed for the dynamics of a moving object under internal forcing, expressed in terms of the intrinsic dynamics of the object. The performance of the algorithms with these dynamical models is demonstrated on several challenging maneuvering target tracking problems in clutter. © 2006 IEEE.

Bayesian separation and recovery of convolutively mixed autoregressive sources

In this paper we address the problem of the separation and recovery of convolutively mixed autoregressive processes in a Bayesian framework. Solving this problem requires the ability to solve integration and/or optimization problems of complicated posterior distributions. We thus propose efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) methods. We present three algorithms. The first one is a classical Gibbs sampler that generates samples from the posterior distribution. The two other algorithms are stochastic optimization algorithms that allow to optimize either the marginal distribution of the sources, or the marginal distribution of the parameters of the sources and mixing filters, conditional upon the observation. Simulations are presented.

Parameter Estimation for Hidden Markov Models with Intractable Likelihoods

Approximate Bayesian computation (ABC) is a popular technique for analysing data for complex models where the likelihood function is intractable. It involves using simulation from the model to approximate the likelihood, with this approximate likelihood then being used to construct an approximate posterior. In this paper, we consider methods that estimate the parameters by maximizing the approximate likelihood used in ABC. We give a theoretical analysis of the asymptotic properties of the resulting estimator. In particular, we derive results analogous to those of consistency and asymptotic normality for standard maximum likelihood estimation. We also discuss how sequential Monte Carlo methods provide a natural method for implementing our likelihood-based ABC procedures.

Convergence of the auxiliary particle implementation of the PHD filter

Optimal Bayesian multi-target filtering is in general computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency was proposed by Whiteley et. al. Numerical examples were presented for two scenarios, including a challenging nonlinear observation model, to support the claim. This paper studies the theoretical properties of this auxiliary particle implementation. $\mathbb{L}_p$ error bounds are established from which almost sure convergence follows.