In vitro cultivation of adult human chondrocytes : importance of culture system, oxygen and zonal differences

Articular cartilage exhibits limited intrinsic regenerative capacity and focal tissue defects can lead to the development of osteoarthritis (OA), a painful and debilitating loss of cartilage tissue. In Australia, 1.4 million people are affected by OA and its prevalence is increasing in line with current demographics. As treatment options are limited, new therapeutic approaches are being investigated including biological resurfacing of joints with tissue-engineered cartilage. Despite some progress in the field, major challenges remain to be addressed for large scale clinical success. For example, large numbers of chondrogenic cells are required for cartilage formation, but chondrocytes lose their chondrogenic phenotype (dedifferentiate) during in vitro propagation. Additionally, the zonal organization of articular cartilage is critical for normal cartilage function, but development of zonal structure has been largely neglected in cartilage repair strategies. Therefore, we hypothesised that culture conditions for freshly isolated human articular chondrocytes from non-OA and OA sources can be improved by employing microcarrier cultures and a reduced oxygen environment and that oxygen is a critical factor in the maintenance of the zonal chondrocyte phenotype. Microcarriers have successfully been used to cultivate bovine chondrocytes, and offer a potential alternative for clinical expansion of human chondrocytes. We hypothesised that improved yields can be achieved by propagating human chondrocytes on microcarriers. We found that cells on microcarriers acquired a flattened, polygonal morphology and initially proliferated faster than monolayercultivated cells. However, microcarrier cultivation over four weeks did not improve growth rates or the chondrogenic potential of non-OA and OA human articular chondrocytes over conventional monolayer cultivation. Based on these observations, we aimed to optimise culture conditions by modifying oxygen tension, to more closely reflect the in vivo environment. We found that propagation at 5% oxygen tension (moderate hypoxia) did not improve proliferation or redifferentiation capacity of human osteoarthritic chondrocytes. Moderate hypoxia increased the expression of chondrogenic markers during redifferentiation. However, osteoarthritic chondrocytes cultivated on microcarriers exhibited lower expression levels of chondrogenic surface marker proteins and had at best equivalent redifferentiation capacities compared to monolayer-cultured cells. This suggests that monolayer culture with multiple passaging potentially selects for a subpopulation of cells with higher differentiation capacity, which are otherwise rare in osteoarthritic, aged cartilage. However, fibroblastic proteins were found to be highly expressed in all cultures of human osteoarthritic chondrocytes indicating the presence of a high proportion of dedifferentiated, senescent cells with a chondrocytic phenotype that was not rescued by moderate hypoxia. The different zones of cartilage support chondrocyte subpopulations, which exhibit characteristic protein expression and experience varying oxygen tensions. We, therefore, hypothesised that oxygen tension affects the zonal marker expression of human articular chondrocytes isolated from the different cartilage layers. We found that zonal chondrocytes maintained these phenotypic differences during in vitro cultivation. Low oxygen environments favoured the expression of the zonal marker proteoglycan 4 in superficial cells, most likely through the promotion of chondrogenesis. The putative zonal markers clusterin and cartilage intermediate layer protein were found to be expressed by all subpopulations of human osteoarthritic chondrocytes ex vivo and, thus, may not be reliable predictors of in vitro stratification using these clinically relevant cells. The findings in this thesis underline the importance of considering low oxygen conditions and zonal stratification when creating native-like cartilaginous constructs. We have not yet found the right cues to successfully cultivate clinically-relevant human osteoarthritic chondrocytes in vitro. A more thorough understanding of chondrocyte biology and the processes of chondrogenesis are required to ensure the clinical success of cartilage tissue engineering.

Classification with a reject option using a hinge loss

We consider the problem of binary classification where the classifier can, for a particular cost, choose not to classify an observation. Just as in the conventional classification problem, minimization of the sample average of the cost is a difficult optimization problem. As an alternative, we propose the optimization of a certain convex loss function φ, analogous to the hinge loss used in support vector machines (SVMs). Its convexity ensures that the sample average of this surrogate loss can be efficiently minimized. We study its statistical properties. We show that minimizing the expected surrogate loss—the φ-risk—also minimizes the risk. We also study the rate at which the φ-risk approaches its minimum value. We show that fast rates are possible when the conditional probability P(Y=1|X) is unlikely to be close to certain critical values.

Evaluating multivariate volatility forecasts : how effective are statistical and economic loss functions?

Multivariate volatility forecasts are an important input in many financial applications, in particular portfolio optimisation problems. Given the number of models available and the range of loss functions to discriminate between them, it is obvious that selecting the optimal forecasting model is challenging. The aim of this thesis is to thoroughly investigate how effective many commonly used statistical (MSE and QLIKE) and economic (portfolio variance and portfolio utility) loss functions are at discriminating between competing multivariate volatility forecasts. An analytical investigation of the loss functions is performed to determine whether they identify the correct forecast as the best forecast. This is followed by an extensive simulation study examines the ability of the loss functions to consistently rank forecasts, and their statistical power within tests of predictive ability. For the tests of predictive ability, the model confidence set (MCS) approach of Hansen, Lunde and Nason (2003, 2011) is employed. As well, an empirical study investigates whether simulation findings hold in a realistic setting. In light of these earlier studies, a major empirical study seeks to identify the set of superior multivariate volatility forecasting models from 43 models that use either daily squared returns or realised volatility to generate forecasts. This study also assesses how the choice of volatility proxy affects the ability of the statistical loss functions to discriminate between forecasts. Analysis of the loss functions shows that QLIKE, MSE and portfolio variance can discriminate between multivariate volatility forecasts, while portfolio utility cannot. An examination of the effective loss functions shows that they all can identify the correct forecast at a point in time, however, their ability to discriminate between competing forecasts does vary. That is, QLIKE is identified as the most effective loss function, followed by portfolio variance which is then followed by MSE. The major empirical analysis reports that the optimal set of multivariate volatility forecasting models includes forecasts generated from daily squared returns and realised volatility. Furthermore, it finds that the volatility proxy affects the statistical loss functions’ ability to discriminate between forecasts in tests of predictive ability. These findings deepen our understanding of how to choose between competing multivariate volatility forecasts.

Selecting volatility forecasting models for portfolio allocation purposes

Techniques for evaluating and selecting multivariate volatility forecasts are not yet understood as well as their univariate counterparts. This paper considers the ability of different loss functions to discriminate between a set of competing forecasting models which are subsequently applied in a portfolio allocation context. It is found that a likelihood-based loss function outperforms its competitors, including those based on the given portfolio application. This result indicates that considering the particular application of forecasts is not necessarily the most effective basis on which to select models.

Epithelial-mesenchymal interconversions in normal ovarian surface epithelium and ovarian carcinomas : an exception to the norm

Cancer that arises from the ovarian surface epithelium (OSE) accounts for approximately 90% of human ovarian cancer, and is the fourth leading cause of cancer-related deaths among women in developed countries. The pathophysiology of epithelial ovarian cancer is still unclear because of the poor understanding of the complex nature of its development and the unusual mechanism(s) of disease progression. Recent studies have reported epithelial-mesenchymal transition (EMT) in cultured OSE and ovarian cancer cell lines in response to various stimuli, but our understanding of the importance of these observations for normal ovarian physiology and cancer progression is not well established. This review highlights the current literature on EMT-associated events in normal OSE and ovarian cancer cell lines, and discusses its implication for normal ovarian function as well as acquisition of neoplastic phenotypes. The pathological changes in OSE in response to EMT during neoplastic transformation and the contribution of hormones, growth factors, and cytokines that initiate and drive EMT to sustain normal ovarian function, as well as cancer development and progression are also discussed. Finally, emphasis is placed on the clinical implications of EMT and potential therapeutic opportunities that may arise from these observations have been proposed.

Tissue engineering of articular cartilage with biomimetic zones

Articular cartilage damage is a persistent and increasing problem with the aging population, and treatments to achieve biological repair or restoration remain a challenge. Cartilage tissue engineering approaches have been investigated for over 20 years, but have yet to achieve the consistency and effectiveness for widespread clinical use. One of the potential reasons for this is that the engineered tissues do not have or establish the normal zonal organization of cells and extracellular matrix that appears critical for normal tissue function. A number of approaches are being taken currently to engineer tissue that more closely mimics the organization of native articular cartilage. This review focuses on the zonal organization of native articular cartilage, strategies being used to develop such organization, the reorganization that occurs after culture or implantation, and future prospects for the tissue engineering of articular cartilage with biomimetic zones.

Convexity, Classification, and Risk Bounds

Many of the classification algorithms developed in the machine learning literature, including the support vector machine and boosting, can be viewed as minimum contrast methods that minimize a convex surrogate of the 0–1 loss function. The convexity makes these algorithms computationally efficient. The use of a surrogate, however, has statistical consequences that must be balanced against the computational virtues of convexity. To study these issues, we provide a general quantitative relationship between the risk as assessed using the 0–1 loss and the risk as assessed using any nonnegative surrogate loss function. We show that this relationship gives nontrivial upper bounds on excess risk under the weakest possible condition on the loss function—that it satisfies a pointwise form of Fisher consistency for classification. The relationship is based on a simple variational transformation of the loss function that is easy to compute in many applications. We also present a refined version of this result in the case of low noise, and show that in this case, strictly convex loss functions lead to faster rates of convergence of the risk than would be implied by standard uniform convergence arguments. Finally, we present applications of our results to the estimation of convergence rates in function classes that are scaled convex hulls of a finite-dimensional base class, with a variety of commonly used loss functions.

Fast rates for estimation error and oracle inequalities for model section

We consider complexity penalization methods for model selection. These methods aim to choose a model to optimally trade off estimation and approximation errors by minimizing the sum of an empirical risk term and a complexity penalty. It is well known that if we use a bound on the maximal deviation between empirical and true risks as a complexity penalty, then the risk of our choice is no more than the approximation error plus twice the complexity penalty. There are many cases, however, where complexity penalties like this give loose upper bounds on the estimation error. In particular, if we choose a function from a suitably simple convex function class with a strictly convex loss function, then the estimation error (the difference between the risk of the empirical risk minimizer and the minimal risk in the class) approaches zero at a faster rate than the maximal deviation between empirical and true risks. In this paper, we address the question of whether it is possible to design a complexity penalized model selection method for these situations. We show that, provided the sequence of models is ordered by inclusion, in these cases we can use tight upper bounds on estimation error as a complexity penalty. Surprisingly, this is the case even in situations when the difference between the empirical risk and true risk (and indeed the error of any estimate of the approximation error) decreases much more slowly than the complexity penalty. We give an oracle inequality showing that the resulting model selection method chooses a function with risk no more than the approximation error plus a constant times the complexity penalty.

Optimal strategies and minimax lower bounds for online convex games

A number of learning problems can be cast as an Online Convex Game: on each round, a learner makes a prediction x from a convex set, the environment plays a loss function f, and the learner’s long-term goal is to minimize regret. Algorithms have been proposed by Zinkevich, when f is assumed to be convex, and Hazan et al., when f is assumed to be strongly convex, that have provably low regret. We consider these two settings and analyze such games from a minimax perspective, proving minimax strategies and lower bounds in each case. These results prove that the existing algorithms are essentially optimal.

Evolutionary design of bone scaffolds with reference to material selection

The favourable scaffold for bone tissue engineering should have desired characteristic features, such as adequate mechanical strength and three-dimensional open porosity, which guarantee a suitable environment for tissue regeneration. In fact, the design of such complex structures like bone scaffolds is a challenge for investigators. One of the aims is to achieve the best possible mechanical strength-degradation rate ratio. In this paper we attempt to use numerical modelling to evaluate material properties for designing bone tissue engineering scaffold fabricated via the fused deposition modelling technique. For our studies the standard genetic algorithm was used, which is an efficient method of discrete optimization. For the fused deposition modelling scaffold, each individual strut is scrutinized for its role in the architecture and structural support it provides for the scaffold, and its contribution to the overall scaffold was studied. The goal of the study was to create a numerical tool that could help to acquire the desired behaviour of tissue engineered scaffolds and our results showed that this could be achieved efficiently by using different materials for individual struts. To represent a great number of ways in which scaffold mechanical function loss could proceed, the exemplary set of different desirable scaffold stiffness loss function was chosen. © 2012 John Wiley & Sons, Ltd.

On the efficacy of techniques for evaluating multivariate volatility forecasts

The performance of techniques for evaluating multivariate volatility forecasts are not yet as well understood as their univariate counterparts. This paper aims to evaluate the efficacy of a range of traditional statistical-based methods for multivariate forecast evaluation together with methods based on underlying considerations of economic theory. It is found that a statistical-based method based on likelihood theory and an economic loss function based on portfolio variance are the most effective means of identifying optimal forecasts of conditional covariance matrices.

Signaling of receptor tyrosine kinases in the nucleus

Since the discovery of the first receptor tyrosine kinase (RTK) proteins in the late 1970s and early 1980s, many scientists have explored the functions of these important cell signaling molecules. The finding that these proteins are often deregulated or mutated in diseases such as cancers and diabetes, together with their potential as clinical therapeutic targets, has further highlighted the necessity for understanding the signaling functions of these important proteins. The mechanisms of RTK regulation and function have been recently reviewed by Lemmon & Schlessinger (2010) but in this review we instead focus on the results of several recent studies that show receptor tyrosine kinases can function from subcellular localisations, including in particular the nucleus, in addition to their classical plasma membrane location. Nuclear localisation of receptor tyrosine kinases has been demonstrated to be important for normal cell function but is also believed to contribute to the pathogenesis of several human diseases.

Genetic analysis of GRIA2 and GRIA4 genes in migraine

Background Migraine is a brain disorder affecting ∼12% of the Caucasian population. Genes involved in neurological, vascular, and hormonal pathways have all been implicated in predisposing individuals to developing migraine. The migraineur presents with disabling head pain and varying symptoms of nausea, emesis, photophobia, phonophobia, and occasionally visual sensory disturbances. Biochemical and genetic studies have demonstrated dysfunction of neurotransmitters: serotonin, dopamine, and glutamate in migraine susceptibility. Glutamate mediates the transmission of excitatory signals in the mammalian central nervous system that affect normal brain function including cognition, memory and learning. The aim of this study was to investigate polymorphisms in the GRIA2 and GRIA4 genes, which encode subunits of the ionotropic AMPA receptor for association in an Australian Caucasian population. Methods Genotypes for each polymorphism were determined using high resolution melt analysis and the RFLP method. Results Statistical analysis showed no association between migraine and the GRIA2 and GRIA4 polymorphisms investigated. Conclusions Although the results of this study showed no significant association between the tested GRIA gene variants and migraine in our Australian Caucasian population further investigation of other components of the glutamatergic system may help to elucidate if there is a relationship between glutamatergic dysfunction and migraine.

Risk-management of UAS robust autonomy for its integration into civil aviation safety frameworks

This paper discusses a model of the civil aviation reg- ulation framework and shows how the current assess- ment of reliability and risk for piloted aircraft has limited applicability for Unmanned Aircraft Systems (UAS) with high levels of autonomous decision mak- ing. Then, a new framework for risk management of robust autonomy is proposed, which arises from combining quantified measures of risk with normative decision making. The term Robust Autonomy de- scribes the ability of an autonomous system to either continue or abort its operation whilst not breaching a minimum level of acceptable safety in the presence of anomalous conditions. The decision making associ- ated with risk management requires quantifying prob- abilities associated with the measures of risk and also consequences of outcomes related to the behaviour of autonomy. The probabilities are computed from an assessment under both nominal and anomalous sce- narios described by faults, which can be associated with the aircraft’s actuators, sensors, communication link, changes in dynamics, and the presence of other aircraft in the operational space. The consequences of outcomes are characterised by a loss function which rewards the certification decision

Effect of doping with Co and/or Cu on electronic structure and optical properties of ZnO

This paper reports on ab initio numerical simulations of the effect of Co and Cu dopings on the electronic structure and optical properties of ZnO, pursued to develop diluted magnetic semiconductors vitally needed for spintronic applications. The simulations are based upon the Perdew-Burke-Enzerh generalized gradient approximation on the density functional theory. It is revealed that the electrons with energies close to the Fermi level effectively transfer only between Cu and Co ions which substitute Zn atoms, and are located in the neighbor sites connected by an O ion. The simulation results are consistent with the experimental observations that addition of Cu helps achieve stable ferromagnetism of Co-doped ZnO. It is shown that simultaneous insertion of Co and Cu atoms leads to smaller energy band gap, redshift of the optical absorption edge, as well as significant changes in the reflectivity, dielectric function, refractive index, and electron energy loss function of ZnO as compared to the doping with either Co or Cu atoms. These highly unusual optical properties are explained in terms of the computed electronic structure and are promising for the development of the next-generation room-temperature ferromagnetic semiconductors for future spintronic devices on the existing semiconductor micromanufacturing platform.