Strontium-containing mesoporous bioactive glass scaffolds with improved osteogenic/cementogenic differentiation of periodontal ligament cells for periodontal tissue engineering

To achieve the ultimate goal of periodontal tissue engineering, it is of great importance to develop bioactive scaffolds which could stimulate the osteogenic/cementogenic differentiation of periodontal ligament cells (PDLCs) for the favorable regeneration of alveolar bone, root cementum, and periodontal ligament. Strontium (Sr) and Sr-containing biomaterials have been found to induce osteoblast activity. However, there is no systematic report about the interaction between Sr or Sr-containing biomaterials and PDLCs for periodontal tissue engineering. The aims of this study were to prepare Sr-containing mesoporous bioactive glass (Sr-MBG) scaffolds and investigate whether the addition of Sr could stimulate the osteogenic/cementogenic differentiation of PDLCs in tissue engineering scaffold system. The composition, microstructure and mesopore properties (specific surface area, nano-pore volume and nano-pore distribution) of Sr-MBG scaffolds were characterized. The proliferation, alkaline phosphatase (ALP) activity and osteogenesis/cementogenesis-related gene expression (ALP, Runx2, Col I, OPN and CEMP1) of PDLCs on different kinds of Sr-MBG scaffolds were systematically investigated. The results show that Sr plays an important role in influencing the mesoporous structure of MBG scaffolds in which high contents of Sr decreased the well-ordered mesopores as well as their surface area/pore volume. Sr2+ ions could be released from Sr-MBG scaffolds in a controlled way. The incorporation of Sr into MBG scaffolds has significantly stimulated ALP activity and osteogenesis/cementogenesis-related gene expression of PDLCs. Furthermore, Sr-MBG scaffolds in simulated body fluids environment still maintained excellent apatite-mineralization ability. The study suggests that the incorporation of Sr into MBG scaffolds is a viable way to stimulate the biological response of PDLCs. Sr-MBG scaffolds are a promising bioactive material for periodontal tissue engineering application.

Sustainable development as a meta-context for engineering education

At the end of the first decade of the twenty-first century, there is unprecedented awareness of the need for a transformation in development, to meet the needs of the present while also preserving the ability of future generations to meet their own needs. However, within engineering, educators still tend to regard such development as an ‘aspect’ of engineering rather than an overarching meta-context, with ad hoc and highly variable references to topics. Furthermore, within a milieu of interpretations there can appear to be conflicting needs for achieving sustainable development, which can be confusing for students and educators alike. Different articulations of sustainable development can create dilemmas around conflicting needs for designers and researchers, at the level of specific designs and (sub-) disciplinary analysis. Hence sustainability issues need to be addressed at a meta-level using a whole of system approach, so that decisions regarding these dilemmas can be made. With this appreciation, and in light of curriculum renewal challenges that also exist in engineering education, this paper considers how educators might take the next step to move from sustainable development being an interesting ‘aspect’ of the curriculum, to sustainable development as a meta-context for curriculum renewal. It is concluded that capacity building for such strategic considerations is critical in engineering education.

Engineering of vascularized adipose constructs

Adipose tissue engineering offers a promising alternative to the current surgical techniques for the treatment of soft tissue defects. It is a challenge to find the appropriate scaffold that not only represents a suitable environment for cells but also allows fabrication of customized tissue constructs, particularly in breast surgery. We investigated two different scaffolds for their potential use in adipose tissue regeneration. Sponge-like polyurethane scaffolds were prepared by mold casting with methylal as foaming agent, whereas polycaprolactone scaffolds with highly regular stacked-fiber architecture were fabricated with fused deposition modeling. Both scaffold types were seeded with human adipose tissuederived precursor cells, cultured and implanted in nude mice using a femoral arteriovenous flow-through vessel loop for angiogenesis. In vitro, cells attached to both scaffolds and differentiated into adipocytes. In vivo, angiogenesis and adipose tissue formation were observed throughout both constructs after 2 and 4 weeks, with angiogenesis being comparable in seeded and unseeded constructs. Fibrous tissue formation and adipogenesis were more pronounced on polyurethane foam scaffolds than on polycaprolactone prototyped scaffolds. In conclusion, both scaffold designs can be effectively used for adipose tissue engineering.

Towards semantic search and inference in electronic medical records : an approach using concept-based information retrieval

Background This paper presents a novel approach to searching electronic medical records that is based on concept matching rather than keyword matching. Aim The concept-based approach is intended to overcome specific challenges we identified in searching medical records. Method Queries and documents were transformed from their term-based originals into medical concepts as defined by the SNOMED-CT ontology. Results Evaluation on a real-world collection of medical records showed our concept-based approach outperformed a keyword baseline by 25% in Mean Average Precision. Conclusion The concept-based approach provides a framework for further development of inference based search systems for dealing with medical data.

Visual route recognition with a handful of bits

In this paper we use a sequence-based visual localization algorithm to reveal surprising answers to the question, how much visual information is actually needed to conduct effective navigation? The algorithm actively searches for the best local image matches within a sliding window of short route segments or 'sub-routes', and matches sub-routes by searching for coherent sequences of local image matches. In contract to many existing techniques, the technique requires no pre-training or camera parameter calibration. We compare the algorithm's performance to the state-of-the-art FAB-MAP 2.0 algorithm on a 70 km benchmark dataset. Performance matches or exceeds the state of the art feature-based localization technique using images as small as 4 pixels, fields of view reduced by a factor of 250, and pixel bit depths reduced to 2 bits. We present further results demonstrating the system localizing in an office environment with near 100% precision using two 7 bit Lego light sensors, as well as using 16 and 32 pixel images from a motorbike race and a mountain rally car stage. By demonstrating how little image information is required to achieve localization along a route, we hope to stimulate future 'low fidelity' approaches to visual navigation that complement probabilistic feature-based techniques.

Group dynamics and software engineering

This paper reports on an experiment that was conducted to determine the extent to which group dynamics impacts on the effectiveness of software development teams. The experiment was conducted on software engineering project students at the Queensland University of Technology (QUT).

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

Capping computation time and storage requirements for appearance-based localization with CAT-SLAM

Appearance-based localization is increasingly used for loop closure detection in metric SLAM systems. Since it relies only upon the appearance-based similarity between images from two locations, it can perform loop closure regardless of accumulated metric error. However, the computation time and memory requirements of current appearance-based methods scale linearly not only with the size of the environment but also with the operation time of the platform. These properties impose severe restrictions on longterm autonomy for mobile robots, as loop closure performance will inevitably degrade with increased operation time. We present a set of improvements to the appearance-based SLAM algorithm CAT-SLAM to constrain computation scaling and memory usage with minimal degradation in performance over time. The appearance-based comparison stage is accelerated by exploiting properties of the particle observation update, and nodes in the continuous trajectory map are removed according to minimal information loss criteria. We demonstrate constant time and space loop closure detection in a large urban environment with recall performance exceeding FAB-MAP by a factor of 3 at 100% precision, and investigate the minimum computational and memory requirements for maintaining mapping performance.