Expansion and chondrogenic potential of human articular chondrocytes on macroporous microcarriers

Regenerative medicine techniques are currently being investigated to replace damaged cartilage. Critical to the success of these techniques is the ability to expand the initial population of cells while minimising de-differentiation to allow for hyaline cartilage to form. Three-dimensional culture systems have been shown to enhance the differentiation of chondrocytes in comparison to two-dimensional culture systems. Additionally, bioreactor expansion on microcarriers can provide mechanical stimulation and reduce the amount of cellular manipulation during expansion. The aim of this study was to characterise the expansion of human chondrocytes on microcarriers and to determine their potential to form cartilaginous tissue in vitro. High-grade human articular cartilage was obtained from leg amputations with ethics approval. Chondrocytes were isolated by collagenase digestion and expanded in either monolayers (104 cells/cm2) or on CultiSpher-G microcarriers (104 cells/mg) for three weeks. Following expansion, monolayer cells were passaged and cells on microcarriers were either left intact or the cells were released with trypsin/EDTA. Pellets from these three groups were formed and cultured for three weeks to establish the chondrogenic differentiation potential of monolayer-expanded and microcarrier-expanded chondrocytes. Cell viability, proliferation, glycosaminoglycan (GAG) accumulation, and collagen synthesis were assessed. Histology and immunohistochemistry were also performed. Human chondrocytes remained viable and expanded on microcarriers 10.2±2.6 fold in three weeks. GAG content significantly increased with time, with the majority of GAG found in the medium. Collagen production per nanogram DNA increased marginally during expansion. Histology revealed that chondrocytes were randomly distributed on microcarrier surfaces yet most pores remained cell free. Critically, human chondrocytes expanded on microcarriers maintained their ability to redifferentiate in pellet culture, as demonstrated by Safranin-O and collagen II staining. These data confirm the feasibility of microcarriers for passage-free cultivation of human articular chondrocytes. However, cell expansion needs to be improved, perhaps through growth factor supplementation, for clinical utility. Recent data indicate that cell-laden microcarriers can be used to seed fresh microcarriers, thereby increasing the expansion factor while minimising enzymatic passage.

Adult human articular chondrocytes in a microcarrier-based culture system : expansion and redifferentiation

xpanding human chondrocytes in vitro while maintaining their ability to form cartilage remains a key challenge in cartilage tissue engineering. One promising approach to address this is to use microcarriers as substrates for chondrocyte expansion. While microcarriers have shown beneficial effects for expansion of animal and ectopic human chondrocytes, their utility has not been determined for freshly isolated adult human articular chondrocytes. Thus, we investigated the proliferation and subsequent chondrogenic differentiation of these clinically relevant cells on porous gelatin microcarriers and compared them to those expanded using traditional monolayers. Chondrocytes attached to microcarriers within 2 days and remained viable over 4 weeks of culture in spinner flasks. Cells on microcarriers exhibited a spread morphology and initially proliferated faster than cells in monolayer culture, however, with prolonged expansion they were less proliferative. Cells expanded for 1 month and enzymatically released from microcarriers formed cartilaginous tissue in micromass pellet cultures, which was similar to tissue formed by monolayer-expanded cells. Cells left attached to microcarriers did not exhibit chondrogenic capacity. Culture conditions, such as microcarrier material, oxygen tension, and mechanical stimulation require further investigation to facilitate the efficient expansion of clinically relevant human articular chondrocytes that maintain chondrogenic potential for cartilage regeneration applications.

Model-order selection in Zernike polynomial expansion of corneal surfaces using the efficient detection criterion

Corneal-height data are typically measured with videokeratoscopes and modeled using a set of orthogonal Zernike polynomials. We address the estimation of the number of Zernike polynomials, which is formalized as a model-order selection problem in linear regression. Classical information-theoretic criteria tend to overestimate the corneal surface due to the weakness of their penalty functions, while bootstrap-based techniques tend to underestimate the surface or require extensive processing. In this paper, we propose to use the efficient detection criterion (EDC), which has the same general form of information-theoretic-based criteria, as an alternative to estimating the optimal number of Zernike polynomials. We first show, via simulations, that the EDC outperforms a large number of information-theoretic criteria and resampling-based techniques. We then illustrate that using the EDC for real corneas results in models that are in closer agreement with clinical expectations and provides means for distinguishing normal corneal surfaces from astigmatic and keratoconic surfaces.

A stochastic model for natural feature representation

This paper presents a robust stochastic model for the incorporation of natural features within data fusion algorithms. The representation combines Isomap, a non-linear manifold learning algorithm, with Expectation Maximization, a statistical learning scheme. The representation is computed offline and results in a non-linear, non-Gaussian likelihood model relating visual observations such as color and texture to the underlying visual states. The likelihood model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The likelihoods are expressed as a Gaussian Mixture Model so as to permit convenient integration within existing nonlinear filtering algorithms. The resulting compactness of the representation is especially suitable to decentralized sensor networks. Real visual data consisting of natural imagery acquired from an Unmanned Aerial Vehicle is used to demonstrate the versatility of the feature representation.

Discovering characteristics of stochastic collections of process models

Process models in organizational collections are typically modeled by the same team and using the same conventions. As such, these models share many characteristic features like size range, type and frequency of errors. In most cases merely small samples of these collections are available due to e.g. the sensitive information they contain. Because of their sizes, these samples may not provide an accurate representation of the characteristics of the originating collection. This paper deals with the problem of constructing collections of process models, in the form of Petri nets, from small samples of a collection for accurate estimations of the characteristics of this collection. Given a small sample of process models drawn from a real-life collection, we mine a set of generation parameters that we use to generate arbitrary-large collections that feature the same characteristics of the original collection. In this way we can estimate the characteristics of the original collection on the generated collections.We extensively evaluate the quality of our technique on various sample datasets drawn from both research and industry.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

Shrinking neighborhood evolution : a novel stochastic algorithm for numerical optimization

Focusing on the conditions that an optimization problem may comply with, the so-called convergence conditions have been proposed and sequentially a stochastic optimization algorithm named as DSZ algorithm is presented in order to deal with both unconstrained and constrained optimizations. The principle is discussed in the theoretical model of DSZ algorithm, from which we present the practical model of DSZ algorithm. Practical model efficiency is demonstrated by the comparison with the similar algorithms such as Enhanced simulated annealing (ESA), Monte Carlo simulated annealing (MCS), Sniffer Global Optimization (SGO), Directed Tabu Search (DTS), and Genetic Algorithm (GA), using a set of well-known unconstrained and constrained optimization test cases. Meanwhile, further attention goes to the strategies how to optimize the high-dimensional unconstrained problem using DSZ algorithm.

A Stochastic View of Optimal Regret through Minimax Duality

We study the regret of optimal strategies for online convex optimization games. Using von Neumann's minimax theorem, we show that the optimal regret in this adversarial setting is closely related to the behavior of the empirical minimization algorithm in a stochastic process setting: it is equal to the maximum, over joint distributions of the adversary's action sequence, of the difference between a sum of minimal expected losses and the minimal empirical loss. We show that the optimal regret has a natural geometric interpretation, since it can be viewed as the gap in Jensen's inequality for a concave functional--the minimizer over the player's actions of expected loss--defined on a set of probability distributions. We use this expression to obtain upper and lower bounds on the regret of an optimal strategy for a variety of online learning problems. Our method provides upper bounds without the need to construct a learning algorithm; the lower bounds provide explicit optimal strategies for the adversary. Peter L. Bartlett, Alexander Rakhlin

Seeing the Wood for the Trees: A Critical Evaluation of Methods to Estimate the Parameters of Stochastic Differential Equations

Maximum-likelihood estimates of the parameters of stochastic differential equations are consistent and asymptotically efficient, but unfortunately difficult to obtain if a closed-form expression for the transitional probability density function of the process is not available. As a result, a large number of competing estimation procedures have been proposed. This article provides a critical evaluation of the various estimation techniques. Special attention is given to the ease of implementation and comparative performance of the procedures when estimating the parameters of the Cox–Ingersoll–Ross and Ornstein–Uhlenbeck equations respectively.