Influence of culture conditions on the molecular signature of mesenchymal stem cells

Cell based therapies require cells capable of self renewal and differentiation, and a prerequisite is the ability to prepare an effective dose of ex vivo expanded cells for autologous transplants. The in vivo identification of a source of physiologically relevant cell types suitable for cell therapies is therefore an integral part of tissue engineering. Bone marrow is the most easily accessible source of mesenchymal stem cells (MSCs), and harbours two distinct populations of adult stem cells; namely hematopoietic stem cells (HSCs) and bone mesenchymal stem cells (BMSCs). Unlike HSCs, there are yet no rigorous criteria for characterizing BMSCs. Changing understanding about the pluripotency of BMSCs in recent studies has expanded their potential application; however, the underlying molecular pathways which impart the features distinctive to BMSCs remain elusive. Furthermore, the sparse in vivo distribution of these cells imposes a clear limitation to their in vitro study. Also, when BMSCs are cultured in vitro there is a loss of the in vivo microenvironment which results in a progressive decline in proliferation potential and multipotentiality. This is further exacerbated with increased passage number, characterized by the onset of senescence related changes. Accordingly, establishing protocols for generating large numbers of BMSCs without affecting their differentiation potential is necessary. The principal aims of this thesis were to identify potential molecular factors for characterizing BMSCs from osteoarthritic patients, and also to attempt to establish culture protocols favourable for generating large number of BMSCs, while at the same time retaining their proliferation and differentiation potential. Previously published studies concerning clonal cells have demonstrated that BMSCs are heterogeneous populations of cells at various stages of growth. Some cells are higher in the hierarchy and represent the progenitors, while other cells occupy a lower position in the hierarchy and are therefore more committed to a particular lineage. This feature of BMSCs was made evident by the work of Mareddy et al., which involved generating clonal populations of BMSCs from bone marrow of osteoarthritic patients, by a single cell clonal culture method. Proliferation potential and differentiation capabilities were used to group cells into fast growing and slow growing clones. The study presented here is a continuation of the work of Mareddy et al. and employed immunological and array based techniques to identify the primary molecular factors involved in regulating phenotypic characteristics exhibited by contrasting clonal populations. The subtractive immunization (SI) was used to generate novel antibodies against favourably expressed proteins in the fast growing clonal cell population. The difference between the clonal populations at the transcriptional level was determined using a Stem Cell RT2 Profiler TM PCR Array which focuses on stem cell pathway gene expression. Monoclonal antibodies (mAb) generated by SI were able to effectively highlight differentially expressed antigenic determinants, as was evident by Western blot analysis and confocal microscopy. Co-immunoprecipitation, followed by mass spectroscopy analysis, identified a favourably expressed protein as the cytoskeletal protein vimentin. The stem cell gene array highlighted genes that were highly upregulated in the fast growing clonal cell population. Based on their functions these genes were grouped into growth factors, cell fate determination and maintenance of embryonic and neural stem cell renewal. Furthermore, on a closer analysis it was established that the cytoskeletal protein vimentin and nine out of ten genes identified by gene array were associated with chondrogenesis or cartilage repair, consistent with the potential role played by BMSCs in defect repair and maintaining tissue homeostasis, by modulating the gene expression pattern to compensate for degenerated cartilage in osteoarthritic tissues. The gene array also presented transcripts for embryonic lineage markers such as FOXA2 and Sox2, both of which were significantly over expressed in fast growing clonal populations. A recent groundbreaking study by Yamanaka et al imparted embryonic stem cell (ESCs) -like characteristic to somatic cells in a process termed nuclear reprogramming, by the ectopic expression of the genes Sox2, cMyc and Oct4. The expression of embryonic lineage markers in adult stem cells may be a mechanism by which the favourable behaviour of fast growing clonal cells is determined and suggests a possible active phenomenon of spontaneous reprogramming in fast growing clonal cells. The expression pattern of these critical molecular markers could be indicative of the competence of BMSCs. For this reason, the expression pattern of Sox2, Oct4 and cMyc, at various passages in heterogeneous BMSCs population and tissue derived cells (osteoblasts and chondrocytes), was investigated by a real-time PCR and immunoflourescence staining. A strong nuclear staining was observed for Sox2, Oct4 and cMyc, which gradually weakened accompanied with cytoplasmic translocation after several passage. The mRNA and protein expression of Sox2, Oct4 and cMyc peaked at the third passage for osteoblasts, chondrocytes and third passage for BMSCs, and declined with each subsequent passage, indicating towards a possible mechanism of spontaneous reprogramming. This study proposes that the progressive decline in proliferation potential and multipotentiality associated with increased passaging of BMSCs in vitro might be a consequence of loss of these propluripotency factors. We therefore hypothesise that the expression of these master genes is not an intrinsic cell function, but rather an outcome of interaction of the cells with their microenvironment; this was evident by the fact that when removed from their in vivo microenvironment, BMSCs undergo a rapid loss of stemness after only a few passages. One of the most interesting aspects of this study was the integration of factors in the culture conditions, which to some extent, mimicked the in vivo microenvironmental niche of the BMSCs. A number of studies have successfully established that the cellular niche is not an inert tissue component but is of prime importance. The total sum of stimuli from the microenvironment underpins the complex interplay of regulatory mechanisms which control multiple functions in stem cells most importantly stem cell renewal. Therefore, well characterised factors which affect BMSCs characteristics, such as fibronectin (FN) coating, and morphogens such as FGF2 and BMP4, were incorporated into the cell culture conditions. The experimental set up was designed to provide insight into the expression pattern of the stem cell related transcription factors Sox2, cMyc and Oct4, in BMSCs with respect to passaging and changes in culture conditions. Induction of these pluripotency markers in somatic cells by retroviral transfection has been shown to confer pluripotency and an ESCs like state. Our study demonstrated that all treatments could transiently induce the expression of Sox2, cMyc and Oct4, and favourably affect the proliferation potential of BMSCs. The combined effect of these treatments was able to induce and retain the endogenous nuclear expression of stem cell transcription factors in BMSCs over an extended number of in vitro passages. Our results therefore suggest that the transient induction and manipulation of endogenous expression of transcription factors critical for stemness can be achieved by modulating the culture conditions; the benefit of which is to circumvent the need for genetic manipulations. In summary, this study has explored the role of BMSCs in the diseased state of osteoarthritis, by employing transcriptional profiling along with SI. In particular this study pioneered the use of primary cells for generating novel antibodies by SI. We established that somatic cells and BMSCs have a basal level of expression of pluripotency markers. Furthermore, our study indicates that intrinsic signalling mechanisms of BMSCs are intimately linked with extrinsic cues from the microenvironment and that these signals appear to be critical for retaining the expression of genes to maintain cell stemness in long term in vitro culture. This project provides a basis for developing an “artificial niche” required for reversion of commitment and maintenance of BMSC in their uncommitted homeostatic state.

Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Managing the project learning paradox : a set-theoretic approach toward project knowledge transfer

Managing project-based learning is becoming an increasingly important part of project management. This article presents a comparative case study of 12 cases of knowledge transfer between temporary inter-organizational projects and permanent parent organizations. Our set-theoretic analysis of these data yields two major findings. First, a high level of absorptive capacity of the project owner is a necessary condition for successful project knowledge transfer, which implies that the responsibility for knowledge transfer seems to in the first place lie with the project parent organization, not with the project manager. Second, none of the factors are sufficient by themselves. This implies that successful project knowledge transfer is a complex process always involving configurations of multiple factors. We link these implications with the view of projects as complex temporary organizational forms in which successful project managers need to cope with complexity by simultaneously paying attention to both relational and organizational processes.

The undirected feedback vertex set problem has a Poly(k) Kernel

Resolving a noted open problem, we show that the Undirected Feedback Vertex Set problem, parameterized by the size of the solution set of vertices, is in the parameterized complexity class Poly(k), that is, polynomial-time pre-processing is sufficient to reduce an initial problem instance (G, k) to a decision-equivalent simplified instance (G', k') where k' � k, and the number of vertices of G' is bounded by a polynomial function of k. Our main result shows an O(k11) kernelization bound.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Classifying Socially-Valued Role Functioning Among Community Residents with Psychiatric Disorders

An increased emphasis on community-based care has not ensured that people recovering from psychiatric disorders return to active and valued roles in their local communities. Although clinical recovery remains a priority for mental health services there is increasing recognition of the need for functional recovery to be attained and demonstrated in roles valued by the wider community. With this need in mind, a method for classifying socially-valued role functioning among people with schizophrenia or schizoaffective disorder was developed and trialed. Participants (n = 104) were recruited via mental health, psychosocial rehabilitation, and other community support services. Socially-valued roles were investigated via participation in five categories: (1) self-care and home duties; (2) caring for others; (3) self-development, voluntary work or rehabilitation; (4) formal education or training; and (5) employment. Activities were classified by primary role type and role status level at baseline, six, and 12 months. Current role status was assessed along with highest and lowest status in the previous year. Preliminary psychometric results were favorable. Research applications are now recommended for monitoring socially-valued role functioning in community settings.

Robust multidisciplinary UAS design optimisation

There are many applications in aeronautical/aerospace engineering where some values of the design parameters states cannot be provided or determined accurately. These values can be related to the geometry(wingspan, length, angles) and or to operational flight conditions that vary due to the presence of uncertainty parameters (Mach, angle of attack, air density and temperature, etc.). These uncertainty design parameters cannot be ignored in engineering design and must be taken into the optimisation task to produce more realistic and reliable solutions. In this paper, a robust/uncertainty design method with statistical constraints is introduced to produce a set of reliable solutions which have high performance and low sensitivity. Robust design concept coupled with Multi Objective Evolutionary Algorithms (MOEAs) is defined by applying two statistical sampling formulas; mean and variance/standard deviation associated with the optimisation fitness/objective functions. The methodology is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. It is implemented for two practical Unmanned Aerial System (UAS) design problems; the flrst case considers robust multi-objective (single disciplinary: aerodynamics) design optimisation and the second considers a robust multidisciplinary (aero structures) design optimisation. Numerical results show that the solutions obtained by the robust design method with statistical constraints have a more reliable performance and sensitivity in both aerodynamics and structures when compared to the baseline design.

Corporate governance mechanisms and earnings management in transitional countries – evidence from Chinese listed firms

The objective of this thesis is to investigate whether the corporate governance practices adopted by Chinese listed firms are associated with the quality of earnings information. Based on a review of agency and institutional theory, this study develops hypotheses that predict the monitoring effectiveness of the board and the audit committee. Using a combination of univariate and multivariate analyses, the association between corporate governance mechanisms and earnings management are tested from 2004 to 2008. Through analysing the empirical results, a number of findings are summarised as below. First, board independence is weakened by the introduction of government officials as independent directors on the boards. Government officials acting as independent directors, claim that they meet the definition of independent director set by the regulation. However, they have some connection with the State, which is the controlling shareholder in listed SOEs affiliated companies. Consequently, the effect of the independent director’s expertise in constraining earnings management is mitigated as demonstrated by an insignificant association between board expertise and earnings management. An alternative explanation for the inefficiency of board independence may point to the pre-selection of independent directors by the powerful CEO. It is argued that a CEO can manipulate the board composition and choose the "desirable" independent directors to monitor themselves. Second, a number of internal mechanisms, such as board size, board activities, and the separation of the roles of the CEO and chair are found to be significantly associated with discretionary accruals. This result suggests that there are advantages in having a large and active board in the Chinese setting. This can offset the disadvantages associated with large boards, such as increased bureaucracy, and hence, increase the constraining effects of a large and resourceful board. Third, factor analysis identifies two factors: CEO power and board power. CEO power is the factor which consists of CEO duality and turnover, and board power is composed of board size and board activity. The results of CEO power show that if a Chinese listed company has CEO duality and turnover at the same time, it is more likely to have a high level of earnings management. The significant and negative relationship between board power and accruals indicate that large boards with frequent meetings can be associated with low level of earnings management. Overall, the factor analysis suggests that certain governance mechanisms complement each other to become more efficient monitors of opportunistic earnings management. A combination of board characteristics can increase the negative association with earnings management. Fourth, the insignificant results between audit committees and earnings management in Chinese listed firms suggests that the Chinese regulator should strengthen the audit committee functions. This thesis calls for listed firms to disclose more information on audit committee composition and activities, which can facilitate future research on the Chinese audit committee’s monitoring role. Fifth, the interactive results between State ownership and board characteristics show that dominant State ownership has a moderating effect on board monitoring power as the State totally controls 42% of the issued shares. The high percentage of State ownership makes it difficult for the non-controlling institutional shareholders to challenge the State’s dominant status. As a result, the association between non-controlling institutional ownership and earnings management is insignificant in most situations. Lastly, firms audited by the international Big4 have lower abnormal accruals than firms audited by domestic Chinese audit firms. In addition, the inverse U-shape relationship between audit tenure and earnings quality demonstrates the changing effects of audit quality after a certain period of appointment. Furthermore, this thesis finds that listing in Hong Kong Stock Exchanges can be an alternative governance mechanism to discipline Chinese firms to follow strict Hong Kong listing requirements. Management of Hong Kong listed companies are exposed to the scrutiny of international investors and Hong Kong regulators. This in turn reduces their chances of conducting self-interested earnings manipulation. This study is designed to fill the gap in governance literature in China that is related to earnings management. Previous research on corporate governance mechanisms and earnings management in China is not conclusive. The current research builds on previous literature and provides some meaningful implications for practitioners, regulators, academic, and international investors who have investment interests in a transitional country. The findings of this study contribute to corporate governance and earnings management literature in the context of the transitional economy of China. The use of alternative measures for earnings management yields similar results compared with the accruals models and produces additional findings.

Beyond MOF constraints : multiple constraint set metamodelling for lifecycle management

The management of models over time in many domains requires different constraints to apply to some parts of the model as it evolves. Using EMF and its meta-language Ecore, the development of model management code and tools usually relies on the meta- model having some constraints, such as attribute and reference cardinalities and changeability, set in the least constrained way that any model user will require. Stronger versions of these constraints can then be enforced in code, or by attaching additional constraint expressions, and their evaluations engines, to the generated model code. We propose a mechanism that allows for variations to the constraining meta-attributes of metamodels, to allow enforcement of different constraints at different lifecycle stages of a model. We then discuss the implementation choices within EMF to support the validation of a state-specific metamodel on model graphs when changing states, as well as the enforcement of state-specific constraints when executing model change operations.

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

Civil applications in the Magistrates Court are often set down for hearing by a judicial registrar, but there is now some uncertainty on the limitation of this practice

The decision of the District Court of Queensland in Mark Treherne & Associates -v- Murray David Hopkins [2010] QDC 36 will have particular relevance for early career lawyers. This decision raises questions about the limits of the jurisdiction of judicial registrars in the Magistrates Court.

Structural damage detection using frequency response functions and neural networks

This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

Structural damage identification with noise polluted Frequency Response Functions (FRFs)

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

Developing a fuzzy multi-criteria decision-making model for selecting design-build operational variations

Many academic researchers have conducted studies on the selection of design-build (DB) delivery method; however, there are few studies on the selection of DB operational variations, which poses challenges to many clients. The selection of DB operational variation is a multi-criteria decision making process that requires clients to objectively evaluate the performance of each DB operational variation with reference to the selection criteria. This evaluation process is often characterized by subjectivity and uncertainty. In order to resolve this deficiency, the current investigation aimed to establish a fuzzy multicriteria decision-making (FMCDM) model for selecting the most suitable DB operational variation. A three-round Delphi questionnaire survey was conducted to identify the selection criteria and their relative importance. A fuzzy set theory approach, namely the modified horizontal approach with the bisector error method, was applied to establish the fuzzy membership functions, which enables clients to perform quantitative calculations on the performance of each DB operational variation. The FMCDM was developed using the weighted mean method to aggregate the overall performance of DB operational variations with regard to the selection criteria. The proposed FMCDM model enables clients to perform quantitative calculations in a fuzzy decision-making environment and provides a useful tool to cope with different project attributes.