Long-term effects of hydrogel properties on human chondrocyte behavior

Hydrogels provide a 3-dimensional network for embedded cells and offer promise for cartilage tissue engineering applications. Nature-derived hydrogels, including alginate, have been shown to enhance the chondrocyte phenotype but are variable and not entirely controllable. Synthetic hydrogels, including polyethylene glycol (PEG)-based matrices, have the advantage of repeatability and modularity; mechanical stiffness, cell adhesion, and degradability can be altered independently. In this study, we compared the long-term in vitro effects of different hydrogels (alginate and Factor XIIIa-cross-linked MMP-sensitive PEG at two stiffness levels) on the behavior of expanded human chondrocytes and the development of construct properties. Monolayer-expanded human chondrocytes remained viable throughout culture, but morphology varied greatly in different hydrogels. Chondrocytes were characteristically round in alginate but mostly spread in PEG gels at both concentrations. Chondrogenic gene (COL2A1, aggrecan) expression increased in all hydrogels, but alginate constructs had much higher expression levels of these genes (up to 90-fold for COL2A1), as well as proteoglycan 4, a functional marker of the superficial zone. Also, chondrocytes expressed COL1A1 and COL10A1, indicative of de-differentiation and hypertrophy. After 12 weeks, constructs with lower polymer content were stiffer than similar constructs with higher polymer content, with the highest compressive modulus measured in 2.5% PEG gels. Different materials and polymer concentrations have markedly different potency to affect chondrocyte behavior. While synthetic hydrogels offer many advantages over natural materials such as alginate, they must be further optimized to elicit desired chondrocyte responses for use as cartilage models and for development of functional tissue-engineered articular cartilage.

The ratio of VEGF/PEDF expression in bone marrow mesenchymal stem cells regulates neovascularization

Angiogenesis, or neovascularization, is a finely balanced process controlled by pro- and anti-angiogenic factors. Vascular endothelial growth factor (VEGF) is a major pro-angiogenic factor, whereas pigment epithelial-derived factor (PEDF) is the most potent natural angiogenesis inhibitor. In this study, the regulatory role of bone marrow stromal cells (BMSCs) during angiogenesis was assessed by the endothelial differentiation potential, VEGF/PEDF production and responses to pro-angiogenic and hypoxic conditions. The in vivo regulation of blood vessel formation by BMSCs was also explored in a SCID mouse model. Results showed that PEDF was expressed more prominently in BMSCs compared to VEGF. This contrasted with human umbilical vein endothelial cells (HUVECs) where the expression of VEGF was higher than that of PEDF. The ratio of VEGF/PEDF gene expression in BMSCs increased when VEGF concentration reached 40 ng/ml in the culture medium, but decreased at 80 ng/ml. Under CoCl2- induced hypoxic conditions, the VEGF/PEDF ratio of BMSCs increased significantly in both normal and angiogenic culture media. There was no expression of endothelial cell markers in BMSCs cultured in either pro-angiogenic or hypoxia culture conditions when compared with HUVECs. The in vivo study showed that VEGF/PEDF expression closely correlated with the degree of neovascularization, and that hypoxia significantly induced pro-angiogenic activity in BMSCs. These results indicate that, rather than being progenitors of endothelial cells, BMSCs play an important role in regulating the neovascularization process, and that the ratio of VEGF and PEDF may, in effect, be an indicator of the pro- or antiangiogenic activities of BMSCs.

Porphyromonas gingivalis lipopolysaccharide alters atherosclerotic-related gene expression in oxidized low-density-lipoprotein-induced macrophages and foam cells

The molecular mechanism between atherosclerosis formation and periodontal pathogens is not clear although positive correlation between periodontal infections and cardiovascular diseases has been reported. Objective: To determine if atherosclerosis related genes were affected in foam cells during and after its formation by P. gingivalis lipopolysaccharide (LPS) stimulation. Methods: Macrophages from human THP-1 monocytes were treated with oxidized low density lipoprotein (oxLDL) to induce the formation of foam cells. P. gingivalis LPS was added to cultures of either oxLDL-induced macrophages or foam cells. The expression of atherosclerosis related genes was assayed by quantitative real time PCR and the protein production of granulocyte-macrophage colony-stimulating factor（GM-CSF）, monocyte chemotactic protein-1 (MCP-1), IL-1β, IL-10 and IL-12 was determined by ELISA. Nuclear translocation of NF-κB P65 was detected by immunocytochemistry and western blot was used to evaluate IKB-α degradation to confirm the NF-κB pathway activation. Results: P. gingivalis LPS stimulated atherosclerosis related gene expression in foam cells and increased oxLDL induced expression of chemokines, adhesion molecules, growth factors, apoptotic genes, and nuclear receptors in macrophages. Transcription of the pro-inflammatory cytokines IL-1β and IL-12 was elevated in response to LPS in both macrophages and foam cells, whereas the anti-inflammatory cytokine IL-10 was not affected. Increased NF-κB pathway activation was also observed in LPS and oxLDL co-stimulated macrophages. Conclusion: P. gingivalis LPS appears to be an important factor in the development of atherosclerosis by stimulation of atherosclerosis related gene expression in both macrophages and foam cells via activation of the NF-κB pathway.

Robust suspicious behaviour detection for smart surveillance systems

Video surveillance technology, based on Closed Circuit Television (CCTV) cameras, is one of the fastest growing markets in the field of security technologies. However, the existing video surveillance systems are still not at a stage where they can be used for crime prevention. The systems rely heavily on human observers and are therefore limited by factors such as fatigue and monitoring capabilities over long periods of time. To overcome this limitation, it is necessary to have “intelligent” processes which are able to highlight the salient data and filter out normal conditions that do not pose a threat to security. In order to create such intelligent systems, an understanding of human behaviour, specifically, suspicious behaviour is required. One of the challenges in achieving this is that human behaviour can only be understood correctly in the context in which it appears. Although context has been exploited in the general computer vision domain, it has not been widely used in the automatic suspicious behaviour detection domain. So, it is essential that context has to be formulated, stored and used by the system in order to understand human behaviour. Finally, since surveillance systems could be modeled as largescale data stream systems, it is difficult to have a complete knowledge base. In this case, the systems need to not only continuously update their knowledge but also be able to retrieve the extracted information which is related to the given context. To address these issues, a context-based approach for detecting suspicious behaviour is proposed. In this approach, contextual information is exploited in order to make a better detection. The proposed approach utilises a data stream clustering algorithm in order to discover the behaviour classes and their frequency of occurrences from the incoming behaviour instances. Contextual information is then used in addition to the above information to detect suspicious behaviour. The proposed approach is able to detect observed, unobserved and contextual suspicious behaviour. Two case studies using video feeds taken from CAVIAR dataset and Z-block building, Queensland University of Technology are presented in order to test the proposed approach. From these experiments, it is shown that by using information about context, the proposed system is able to make a more accurate detection, especially those behaviours which are only suspicious in some contexts while being normal in the others. Moreover, this information give critical feedback to the system designers to refine the system. Finally, the proposed modified Clustream algorithm enables the system to both continuously update the system’s knowledge and to effectively retrieve the information learned in a given context. The outcomes from this research are: (a) A context-based framework for automatic detecting suspicious behaviour which can be used by an intelligent video surveillance in making decisions; (b) A modified Clustream data stream clustering algorithm which continuously updates the system knowledge and is able to retrieve contextually related information effectively; and (c) An update-describe approach which extends the capability of the existing human local motion features called interest points based features to the data stream environment.

Building a software architecture for a human-robot team using the ORCA framework

This paper considers the problem of building a software architecture for a human-robot team. The objective of the team is to build a multi-attribute map of the world by performing information fusion. A decentralized approach to information fusion is adopted to achieve the system properties of scalability and survivability. Decentralization imposes constraints on the design of the architecture and its implementation. We show how a Component-Based Software Engineering approach can address these constraints. The architecture is implemented using Orca – a component-based software framework for robotic systems. Experimental results from a deployed system comprised of an unmanned air vehicle, a ground vehicle, and two human operators are presented. A section on the lessons learned is included which may be applicable to other distributed systems with complex algorithms. We also compare Orca to the Player software framework in the context of distributed systems.

Scaling acoustic data analysis through collaboration and automation

Monitoring and assessing environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods of time. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data effectively and efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; collaboration, manual, automatic and human-in-the loop analysis.

Characteristics of utility cyclists in Queensland, Australia : an examination of the associations between individual, social and environmental factors and utility cycling

Background: Initiatives to promote utility cycling in countries like Australia and the US, which have low rates of utility cycling, may be more effective if they first target recreational cyclists. This study aimed to describe patterns of utility cycling and examine its correlates, among cyclists in Queensland, Australia. Methods: An online survey was administered to adult members of a state-based cycling community and advocacy group (n=1813). The survey asked about demographic characteristics and cycling behavior, motivators and constraints. Utility cycling patterns were described, and logistic regression modeling was used to examine associations between utility cycling and other variables. Results: Forty-seven percent of respondents reported utility cycling: most did so to commute (86%). Most journeys (83%) were >5 km. Being male, younger, employed full-time, or university-educated increased the likelihood of utility cycling (p<0.05). Perceiving cycling to be a cheap or a convenient form of transport were associated with utility cycling (p<0.05). Conclusions: The moderate rate of utility cycling among recreational cyclists highlights a potential to promote utility cycling among this group. To increase utility cycling, strategies should target female and older recreational cyclists and focus on making cycling a cheap and convenient mode of transport.

Myocyte enhancer factor 2C : an osteoblast transcription factor identified by DMSO enhanced mineralization

Rapid mineralization of cultured osteoblasts could be a useful characteristic in stem-cell mediated therapies for fracture and other orthopaedic problems. Dimethyl sulfoxide (DMSO) is a small amphipathic solvent molecule capable of simulating cell differentiation. We report that, in primary human osteoblasts, DMSO dose-dependently enhanced the expression of osteoblast differentiation markers alkaline phosphatase (ALP) activity and extracellular matrix mineralization. Furthermore, similar DMSO mediated mineralization enhancement was observed in primary osteoblast-like cells differentiated from mouse mesenchymal cells derived from fat, a promising source of starter cells for cell-based therapy. Using a convenient mouse pre-osteoblast model cell line MC3T3-E1 we further investigated this phenomenon showing that numerous osteoblast-expressed genes were elevated in response to DMSO treatment and correlated with enhanced mineralization. Myocyte enhancer factor 2c (Mef2c) was identified as the transcription factor most induced by DMSO, among numerous DMSO-induced genes, suggesting a role for Mef2c in osteoblast gene regulation. Immunohistochemistry confirmed expression of Mef2c in osteoblast-like cells in mouse mandible, cortical and trabecular bone. shRNAi-mediated Mef2c gene silencing resulted in defective osteoblast differentiation, decreased ALP activity and matrix mineralization and knockdown of osteoblast specific gene expression, including osteocalcin and bone sialoprotein. Flow on knockdown of bone specific transcription factors, Runx2 and osterix by shRNAi knockdown of Mef2c suggests that Mef2c lies upstream of these two important factors in the cascade of gene expression in osteoblasts.

Semantic modeling method for configurable enterprise information systems

Business practices vary from one company to another and business practices often need to be changed due to changes of business environments. To satisfy different business practices, enterprise systems need to be customized. To keep up with ongoing business practice changes, enterprise systems need to be adapted. Because of rigidity and complexity, the customization and adaption of enterprise systems often takes excessive time with potential failures and budget shortfall. Moreover, enterprise systems often drag business behind because they cannot be rapidly adapted to support business practice changes. Extensive literature has addressed this issue by identifying success or failure factors, implementation approaches, and project management strategies. Those efforts were aimed at learning lessons from post implementation experiences to help future projects. This research looks into this issue from a different angle. It attempts to address this issue by delivering a systematic method for developing flexible enterprise systems which can be easily tailored for different business practices or rapidly adapted when business practices change. First, this research examines the role of system models in the context of enterprise system development; and the relationship of system models with software programs in the contexts of computer aided software engineering (CASE), model driven architecture (MDA) and workflow management system (WfMS). Then, by applying the analogical reasoning method, this research initiates a concept of model driven enterprise systems. The novelty of model driven enterprise systems is that it extracts system models from software programs and makes system models able to stay independent of software programs. In the paradigm of model driven enterprise systems, system models act as instructors to guide and control the behavior of software programs. Software programs function by interpreting instructions in system models. This mechanism exposes the opportunity to tailor such a system by changing system models. To make this true, system models should be represented in a language which can be easily understood by human beings and can also be effectively interpreted by computers. In this research, various semantic representations are investigated to support model driven enterprise systems. The significance of this research is 1) the transplantation of the successful structure for flexibility in modern machines and WfMS to enterprise systems; and 2) the advancement of MDA by extending the role of system models from guiding system development to controlling system behaviors. This research contributes to the area relevant to enterprise systems from three perspectives: 1) a new paradigm of enterprise systems, in which enterprise systems consist of two essential elements: system models and software programs. These two elements are loosely coupled and can exist independently; 2) semantic representations, which can effectively represent business entities, entity relationships, business logic and information processing logic in a semantic manner. Semantic representations are the key enabling techniques of model driven enterprise systems; and 3) a brand new role of system models; traditionally the role of system models is to guide developers to write system source code. This research promotes the role of system models to control the behaviors of enterprise.