Strategy in developing cell based therapy for large bone

Regeneration of osseous defects by tissue-engineering approach provides a novel means of treatment utilizing cell biology, materials science, and molecular biology. The concept of in vitro cultured osteoblasts having an ability to induce new bone formation has been demonstrated in the critical size defects using small animal models. The bone derived cells can be incorporated into bioengineered scaffolds and synthesize bone matrix, which on implantation can induce new bone formation. In search of optimal cell delivery materials, the extracellular matrix as cell carriers for the repair and regeneration of tissues is receiving increased attention. We have investigated extracellular matrix formed by osteoblasts in vitro as a scaffold for osteoblasts transplantation and found a mineralized matrix, formed by human osteoblasts in vitro, can initiate bone formation by activating endogenous mesenchymal cells. To repair the large bone defects, osteogenic or stem cells need to be prefabricated in a large three dimensional scaffold usually made of synthetic biomaterials, which have inadequate interaction with cells and lead to in vivo foreign body reactions. The interstitial extracellular matrix has been applied to modify biomaterials surface and identified vitronectin, which binds the heparin domain and RGD (Arg-Gly-Asp) sequence can modulate cell spreading, migration and matrix formation on biomaterials. We also synthesized a tri-block copolymer, methoxy-terminated poly(ethylene glycol)(MPEG)-polyL-lactide(PLLA)-polylysine(PLL) for human osteoblasts delivery. We identified osteogenic activity can be regulated by the molecular weight and composition of the triblock copolymers. Due to the sequential loss of lineage differentiation potential during the culture of bone marrow stromal cells that hinderers their potential clinical application, we have developed a clonal culture system and established several stem cell clones with fast growing and multi-differentiation properties. Using proteomics and subtractive immunization, several differential proteins have been identified and verified their potential application in stem cell characterization and tissue regeneration

Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime. The partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when asset health inspections are imperfect. However, existing applications of the POMDP to maintenance decision-making largely adopt the discrete time and state assumptions. The discrete-time assumption requires the health state transitions and maintenance activities only happen at discrete epochs, which cannot model the failure time accurately and is not cost-effective. The discrete health state assumption, on the other hand, may not be elaborate enough to improve the effectiveness of maintenance. To address these limitations, this paper proposes a continuous state partially observable semi-Markov decision process (POSMDP). An algorithm that combines the Monte Carlo-based density projection method and the policy iteration is developed to solve the POSMDP. Different types of maintenance activities (i.e., inspections, replacement, and imperfect maintenance) are considered in this paper. The next maintenance action and the corresponding waiting durations are optimized jointly to minimize the long-run expected cost per unit time and availability. The result of simulation studies shows that the proposed maintenance optimization approach is more cost-effective than maintenance strategies derived by another two approximate methods, when regular inspection intervals are adopted. The simulation study also shows that the maintenance cost can be further reduced by developing maintenance strategies with state-dependent maintenance intervals using the POSMDP. In addition, during the simulation studies the proposed POSMDP shows the ability to adopt a cost-effective strategy structure when multiple types of maintenance activities are involved.

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

Transferring a successful strategy for supporting accelerated nursing students from a small to a large cohort

In 2009 and 2010, withdrawal rates from a Pharmacology unit of accelerated QUT nursing students in the first year of their degree, were higher than for continuing students. The cohort of 216 accelerated students in 2011 had university or non-university qualification or equivalent experience and included domestic and international students. A previously tested intervention was introduced in 2011 to improve retention rates and support all Pharmacology students in their first year of nursing. The intervention involved a community website, on-line tutors and an “O week” workshop comprising information about library resources, effective learning strategies and learning tips from a previous student as well as review anatomy, physiology and microbiology lectures. Withdrawal rates for accelerated students in the Pharmacology unit improved and all students found the workshop and review lectures to be informative and valuable. The intervention was therefore successfully transferred to a large, diverse cohort of accelerated nursing students.

Condition-based maintenance optimisation without a predetermined strategy structure for a two-component series system

Most existing research on maintenance optimisation for multi-component systems only considers the lifetime distribution of the components. When the condition-based maintenance (CBM) strategy is adopted for multi-component systems, the strategy structure becomes complex due to the large number of component states and their combinations. Consequently, some predetermined maintenance strategy structures are often assumed before the maintenance optimisation of a multi-component system in a CBM context. Developing these predetermined strategy structure needs expert experience and the optimality of these strategies is often not proofed. This paper proposed a maintenance optimisation method that does not require any predetermined strategy structure for a two-component series system. The proposed method is developed based on the semi-Markov decision process (SMDP). A simulation study shows that the proposed method can identify the optimal maintenance strategy adaptively for different maintenance costs and parameters of degradation processes. The optimal maintenance strategy structure is also investigated in the simulation study, which provides reference for further research in maintenance optimisation of multi-component systems.

Linear programming for large-scale Markov decision problems

We consider the problem of controlling a Markov decision process (MDP) with a large state space, so as to minimize average cost. Since it is intractable to compete with the optimal policy for large scale problems, we pursue the more modest goal of competing with a low-dimensional family of policies. We use the dual linear programming formulation of the MDP average cost problem, in which the variable is a stationary distribution over state-action pairs, and we consider a neighborhood of a low-dimensional subset of the set of stationary distributions (defined in terms of state-action features) as the comparison class. We propose a technique based on stochastic convex optimization and give bounds that show that the performance of our algorithm approaches the best achievable by any policy in the comparison class. Most importantly, this result depends on the size of the comparison class, but not on the size of the state space. Preliminary experiments show the effectiveness of the proposed algorithm in a queuing application.

A Training-Based Iterative Detection/Channel Estimation Scheme for Large Non-Orthogonal STBC MIMO Systems

In this paper, we propose a training-based channel estimation scheme for large non-orthogonal space-time block coded (STBC) MIMO systems.The proposed scheme employs a block transmission strategy where an N-t x N-t pilot matrix is sent (for training purposes) followed by several N-t x N-t square data STBC matrices, where Nt is the number of transmit antennas. At the receiver, we iterate between channel estimation (using an MMSE estimator) and detection (using a low-complexity likelihood ascent search (LAS) detector) till convergence or for a fixed number of iterations. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed scheme at low complexities. The fact that we could show such good results for large STBCs (e.g., 16 x 16 STBC from cyclic division algebras) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot-based channel estimation and turbo coding) establishes the effectiveness of the proposed scheme.

Social dynamics for sustainable food systems : Actors' orientations towards sustainability in primary production and public consumption

The modern food system and sustainable development form a conceptual combination that suggests sustainability deficits in environmental impacts and nutritional status of western populations. This study explores actors orientations towards sustainability by probing into social dynamics for sustainability within primary production and public consumption. If actors within these two worlds were to express converging orientations for sustainability, the system dynamics of the market would enable more sustainable growth in terms of production dictated by consumption. The study is based on a constructivist research approach with qualitative text analyses. The findings were validated by internal and external food system actors and are suggested to represent current social dynamics within Finnish food system. The key findings included primary producers social skilfulness, which enabled networking with other actors in very different paths of life, learning in order to promote one s trade, and trusting reflectively in partners in order to expand business. These activities extended the supply chain in a spiral fashion by horizontal and vertical forward integration, until large retailers were met for negotiations on a more equal basis. This mode of chain level coordination, typically building around the core of social and partnership relations, was coined as a socially overlaid network, and seen as sustainable coordination mode for endogenous growth. The caterers exhibited more or less committed professional identity for sustainability within their reach. The facilitating approaches for professional identities dealt successfully with local and organic food in addition to domestic food, and also imported food. The co-operation with supply chains created innovative solutions and savings for the business parties to be shared. There were also more complicated identities as juggling, critical and delimited approaches for sustainability, with less productive efforts due to restrictions such as absence of organisational sustainability strategy, weak presence of local and organic suppliers, limited understanding about sustainability and no organisational resources for informed choices for sustainability. The convergence between producers and caterers existed to an extent allowing suggestion that increased clarity about sustainable consumption and production by actors could be constructed using advanced tools. The study looks for introduction of more profound environmental and socio-economic knowledge through participatory research with supply chain actors. Learning in the workplace about food system reality in terms of supply chain co-operation may prove to be a change engine that leads to advanced network operations and a more sustainable food system.

Social dynamics for sustainable food systems. Actors’ orientations towards sustainability in primary production and public consumption

Abstract The modern food system and sustainable development form a conceptual combination that suggests sustainability deficits in the ways we deal with food consumption and production - in terms of economic relations, environmental impacts and nutritional status of western population. This study explores actors’ orientations towards sustainability by taking into account actors’ embedded positions within structures of the food system, actors’ economic relations and views about sustainability as well as their possibilities for progressive activities. The study looks particularly at social dynamics for sustainability within primary production and public consumption. If actors within these two worlds were to express converging orientations for sustainability, the system dynamics of the market would enable more sustainable growth in terms of production dictated by consumption. The study is based on a constructivist research approach with qualitative text analyses. The data consisted of three text corpora, the ‘local food corpus’, the ‘catering corpus’ and the ‘mixed corpus’. The local food actors were interviewed about their economic exchange relations. The caterers’ interviews dealt with their professional identity for sustainability. Finally, the mixed corpus assembled a dialogue as a participatory research approach, which was applied in order to enable researcher and caterer learning about the use of organic milk in public catering. The data were analysed for theoretically conceptualised relations, expressing behavioural patterns in actors’ everyday work as interpreted by the researcher. The findings were corroborated by the internal and external communities of food system actors. The interpretations have some validity, although they only present abstractions of everyday life and its rich, even opaque, fabric of meanings and aims. The key findings included primary producers’ social skilfulness, which enabled networking with other actors in very different paths of life, learning in order to promote one’s trade, and trusting reflectively in partners in order to extend business. These activities expanded the supply chain in a spiral fashion by horizontal and vertical forward integration, until large retailers were met for negotiations on a more equal or ‘other regarding’ basis. This kind of chain level coordination, typically building around the core of social and partnership relations, was coined as a socially overlaid network. It supported market access of local farmers, rooted in their farms, who were able to draw on local capital and labour in promotion of competitive business; the growth was endogenous. These kinds of chains – one conventional and one organic – were different from the strategic chain, which was more profit based and while highly competitive, presented exogenous growth as it depended on imported capital and local employees. However, the strategic chain offered learning opportunities and support for the local economy. The caterers exhibited more or less committed professional identity for sustainability within their reach. The facilitating and balanced approaches for professional identities dealt successfully with local and organic food in addition to domestic food, and also imported food. The co-operation with supply chains created innovative solutions and savings for the business parties to be shared. The rule-abiding approach for sustainability only made choices among organic supply chains without extending into co-operation with actors. There were also more complicated and troubled identities as juggling, critical and delimited approaches for sustainability, with less productive efforts due to restrictions such as absence of organisational sustainability strategy, weak presence of local and organic suppliers, limited understanding about sustainability and no organisational resources to develop changes towards a sustainable food system. Learning in the workplace about food system reality in terms of supply chain co-operation may prove to be a change engine that leads to advanced network operations and a more sustainable food system. The convergence between primary producers and caterers existed to an extent allowing suggestion that increased clarity about sustainable consumption and production by actors could be approached using advanced tools. The study looks for introduction of more profound environmental and socio-economic knowledge through participatory research with supply chain actors in order to promote more sustainable food systems. Summary of original publications and the authors’ contribution I Mikkola, M. & Seppänen, L. 2006. Farmers’ new participation in food chains: making horizontal and vertical progress by networking. In: Langeveld, H. & Röling N. (Eds.). Changing European farming systems for a better future. New visions for rural areas. Wageningen, The Netherlands. Wageningen Academic Publishers: 267–271. II Mikkola, M. 2008. Coordinative structures and development of food supply chains. British Food Journal 110 (2): 189–205. III Mikkola, M. 2009. Shaping professional identity for sustainability. Evidence in Finnish public catering. Appetite 53 (1): 56–65. IV Mikkola, M. 2009. Catering for sustainability: building a dialogue on organic milk. Agronomy Research 7 (Special issue 2): 668–676. Minna Mikkola has been responsible for developing the generic research frame, particular research questions, the planning and collection of the data, their qualitative analysis and writing the articles I, II, III and IV. Dr Laura Seppänen has contributed to the development of the generic research frame and article I by introducing the author to the basic concepts of economic sociology and by supporting the writing of article II with her critical comments. Articles are printed with permission from the publishers.

A novel MCMC algorithm for near-optimal detection in large-scale uplink mulituser MIMO systems

In this paper, we propose a low-complexity algorithm based on Markov chain Monte Carlo (MCMC) technique for signal detection on the uplink in large scale multiuser multiple input multiple output (MIMO) systems with tens to hundreds of antennas at the base station (BS) and similar number of uplink users. The algorithm employs a randomized sampling method (which makes a probabilistic choice between Gibbs sampling and random sampling in each iteration) for detection. The proposed algorithm alleviates the stalling problem encountered at high SNRs in conventional MCMC algorithm and achieves near-optimal performance in large systems with M-QAM. A novel ingredient in the algorithm that is responsible for achieving near-optimal performance at low complexities is the joint use of a randomized MCMC (R-MCMC) strategy coupled with a multiple restart strategy with an efficient restart criterion. Near-optimal detection performance is demonstrated for large number of BS antennas and users (e.g., 64, 128, 256 BS antennas/users).

Adaptive Mesh Refinement for Fast Convergence of EFIE-Based 3-D Extraction

3-D full-wave method of moments (MoM) based electromagnetic analysis is a popular means toward accurate solution of Maxwell's equations. The time and memory bottlenecks associated with such a solution have been addressed over the last two decades by linear complexity fast solver algorithms. However, the accurate solution of 3-D full-wave MoM on an arbitrary mesh of a package-board structure does not guarantee accuracy, since the discretization may not be fine enough to capture spatial changes in the solution variable. At the same time, uniform over-meshing on the entire structure generates a large number of solution variables and therefore requires an unnecessarily large matrix solution. In this paper, different refinement criteria are studied in an adaptive mesh refinement platform. Consequently, the most suitable conductor mesh refinement criterion for MoM-based electromagnetic package-board extraction is identified and the advantages of this adaptive strategy are demonstrated from both accuracy and speed perspectives. The results are also compared with those of the recently reported integral equation-based h-refinement strategy. Finally, a new methodology to expedite each adaptive refinement pass is proposed.