Chitosan-collagen scaffolds with nano/microfibrous architecture for skin tissue engineering

In this study, a hierarchical nano/microfibrous chitosan/collagen scaffold that approximates structural and functional attributes of native extracellular matrix (ECM), has been developed for applicability in skin tissue engineering. Scaffolds were produced by electrospinning of chitosan followed by imbibing of collagen solution, freeze-drying and subsequent cross-linking of two polymers. Scanning electron microscopy showed formation of layered scaffolds with nano/microfibrous architechture. Physico-chemical properties of scaffolds including tensile strength, swelling behavior and biodegradability were found satisfactory for intended application. 3T3 fibroblasts and HaCaT keratinocytes showed good in vitro cellular response on scaffolds thereby indicating the matrices′ cytocompatible nature. Scaffolds tested in an ex vivo human skin equivalent (HSE) wound model, as a preliminary alternative to animal testing, showed keratinocyte migration and wound re-epithelization — a pre-requisite for healing and regeneration. Taken together, the herein proposed chitosan/collagen scaffold, shows good potential for skin tissue engineering.

Intelligent reliability analysis with incomplete covariates

The reliability analysis is crucial to reducing unexpected down time, severe failures and ever tightened maintenance budget of engineering assets. Hazard based reliability methods are of particular interest as hazard reflects the current health status of engineering assets and their imminent failure risks. Most existing hazard models were constructed using the statistical methods. However, these methods were established largely based on two assumptions: one is the assumption of baseline failure distributions being accurate to the population concerned and the other is the assumption of effects of covariates on hazards. These two assumptions may be difficult to achieve and therefore compromise the effectiveness of hazard models in the application. To address this issue, a non-linear hazard modelling approach is developed in this research using neural networks (NNs), resulting in neural network hazard models (NNHMs), to deal with limitations due to the two assumptions for statistical models. With the success of failure prevention effort, less failure history becomes available for reliability analysis. Involving condition data or covariates is a natural solution to this challenge. A critical issue for involving covariates in reliability analysis is that complete and consistent covariate data are often unavailable in reality due to inconsistent measuring frequencies of multiple covariates, sensor failure, and sparse intrusive measurements. This problem has not been studied adequately in current reliability applications. This research thus investigates such incomplete covariates problem in reliability analysis. Typical approaches to handling incomplete covariates have been studied to investigate their performance and effects on the reliability analysis results. Since these existing approaches could underestimate the variance in regressions and introduce extra uncertainties to reliability analysis, the developed NNHMs are extended to include handling incomplete covariates as an integral part. The extended versions of NNHMs have been validated using simulated bearing data and real data from a liquefied natural gas pump. The results demonstrate the new approach outperforms the typical incomplete covariates handling approaches. Another problem in reliability analysis is that future covariates of engineering assets are generally unavailable. In existing practices for multi-step reliability analysis, historical covariates were used to estimate the future covariates. Covariates of engineering assets, however, are often subject to substantial fluctuation due to the influence of both engineering degradation and changes in environmental settings. The commonly used covariate extrapolation methods thus would not be suitable because of the error accumulation and uncertainty propagation. To overcome this difficulty, instead of directly extrapolating covariate values, projection of covariate states is conducted in this research. The estimated covariate states and unknown covariate values in future running steps of assets constitute an incomplete covariate set which is then analysed by the extended NNHMs. A new assessment function is also proposed to evaluate risks of underestimated and overestimated reliability analysis results. A case study using field data from a paper and pulp mill has been conducted and it demonstrates that this new multi-step reliability analysis procedure is able to generate more accurate analysis results.

An evaluation framework for cross-lingual link discovery

Cross-Lingual Link Discovery (CLLD) is a new problem in Information Retrieval. The aim is to automatically identify meaningful and relevant hypertext links between documents in different languages. This is particularly helpful in knowledge discovery if a multi-lingual knowledge base is sparse in one language or another, or the topical coverage in each language is different; such is the case with Wikipedia. Techniques for identifying new and topically relevant cross-lingual links are a current topic of interest at NTCIR where the CrossLink task has been running since the 2011 NTCIR-9. This paper presents the evaluation framework for benchmarking algorithms for cross-lingual link discovery evaluated in the context of NTCIR-9. This framework includes topics, document collections, assessments, metrics, and a toolkit for pooling, assessment, and evaluation. The assessments are further divided into two separate sets: manual assessments performed by human assessors; and automatic assessments based on links extracted from Wikipedia itself. Using this framework we show that manual assessment is more robust than automatic assessment in the context of cross-lingual link discovery.

Social support promotes psychological well-being following a natural disaster

Receiving emotional support has consistently been demonstrated as an important factor associated with mental health but sparse research has investigated giving support in addition to receiving it or the types of support that predict well-being. In this paper the relationship between giving and receiving instrumental and emotional social support and psychological well-being during and following a natural disaster is investigated. A survey administered between four and six months after fatal floods was conducted with 200 community members consisting of men (n = 68) and women (n = 132) aged between 17 and 87 years. Social support experiences were assessed using the 2-Way Social Support Scale (2-Way SSS; Shakespeare-Finch & Obst, 2011) and eudemonic well-being was measured using the Psychological Well-Being Scale (PWBS; Ryff & Keyes, 1995). Hierarchical multiple regression analyses were used to examine expected relationships and to explore the differential effects of the four factors of the 2-Way SSS. Results indicated that social support shared significant positive associations with domains of psychological well-being, especially with regards to interpersonal relationships. Receiving and giving emotional support were respectively the strongest unique predictors of psychological well-being. However, receiving instrumental support predicted less autonomy. Results highlight the importance of measuring social support as a multidimensional construct and affirm that disaster response policy and practice should focus on emotional as well as instrumental needs in order to promote individual and community psychosocial health following a flooding crisis.

An ontology-based method for sparsity problem in tag recommendation

Tags or personal metadata for annotating web resources have been widely adopted in Web 2.0 sites. However, as tags are freely chosen by users, the vocabularies are diverse, ambiguous and sometimes only meaningful to individuals. Tag recommenders may assist users during tagging process. Its objective is to suggest relevant tags to use as well as to help consolidating vocabulary in the systems. In this paper we discuss our approach for providing personalized tag recommendation by making use of existing domain ontology generated from folksonomy. Specifically we evaluated the approach in sparse situation. The evaluation shows that the proposed ontology-based method has improved the accuracy of tag recommendation in this situation.

Recognising facial expressions with noisy data

Techniques to improve the automated analysis of natural and spontaneous facial expressions have been developed. The outcome of the research has applications in several fields including national security (eg: expression invariant face recognition); education (eg: affect aware interfaces); mental and physical health (eg: depression and pain recognition).

A fuzzy TOPSIS based multi criteria performance measurement model for lean supply chain

Increasing global competition, rapid technological changes, advances in manufacturing and information technology and discerning customers are forcing supply chains to adopt improvement practices that enable them to deliver high quality products at a lower cost and in a shorter period of time. A lean initiative is one of the most effective approaches toward achieving this goal. In the lean improvement process, it is critical to measure current and desired performance level in order to clearly evaluate the lean implementation efforts. Many attempts have tried to measure supply chain performance incorporating both quantitative and qualitative measures but failed to provide an effective method of measuring improvements in performances for dynamic lean supply chain situations. Therefore, the necessity of appropriate measurement of lean supply chain performance has become imperative. There are many lean tools available for supply chains; however, effectiveness of a lean tool depends on the type of the product and supply chain. One tool may be highly effective for a supply chain involved in high volume products but may not be effective for low volume products. There is currently no systematic methodology available for selecting appropriate lean strategies based on the type of supply chain and market strategy This thesis develops an effective method to measure the performance of supply chain consisting of both quantitative and qualitative metrics and investigates the effects of product types and lean tool selection on the supply chain performance Supply chain performance matrices and the effects of various lean tools over performance metrics mentioned in the SCOR framework have been investigated. A lean supply chain model based on the SCOR metric framework is then developed where non- lean and lean as well as quantitative and qualitative metrics are incorporated in appropriate metrics. The values of appropriate metrics are converted into triangular fuzzy numbers using similarity rules and heuristic methods. Data have been collected from an apparel manufacturing company for multiple supply chain products and then a fuzzy based method is applied to measure the performance improvements in supply chains. Using the fuzzy TOPSIS method, which chooses an optimum alternative to maximise similarities with positive ideal solutions and to minimise similarities with negative ideal solutions, the performances of lean and non- lean supply chain situations for three different apparel products have been evaluated. To address the research questions related to effective performance evaluation method and the effects of lean tools over different types of supply chains; a conceptual framework and two hypotheses are investigated. Empirical results show that implementation of lean tools have significant effects over performance improvements in terms of time, quality and flexibility. Fuzzy TOPSIS based method developed is able to integrate multiple supply chain matrices onto a single performance measure while lean supply chain model incorporates qualitative and quantitative metrics. It can therefore effectively measure the improvements for supply chain after implementing lean tools. It is demonstrated that product types involved in the supply chain and ability to select right lean tools have significant effect on lean supply chain performance. Future study can conduct multiple case studies in different contexts.

Virtual world cultural probe

We have developed a virtual world environment for eliciting expert information from stakeholders. The intention is that the virtual world prompts the user to remember more about their work processes. Our example shows a sparse visualisation of the University of Vienna Department of Computer Science, our collaborators in this project.

Impact of extracellular matrix derived from osteoarthritis subchondral bone osteoblasts on osteocytes : role of integrinβ1 and focal adhesion kinase signaling cues

INTRODUCTION: Our recent study indicated that subchondral bone pathogenesis in osteoarthritis (OA) is associated with osteocyte morphology and phenotypic abnormalities. However, the mechanism underlying this abnormality needs to be identified. In this study we investigated the effect of extracellular matrix (ECM) produced from normal and OA bone on osteocytic cells function. METHODS: De-cellularized matrices, resembling the bone provisional ECM secreted from primary human subchondral bone osteoblasts (SBOs) of normal and OA patients were used as a model to study the effect on osteocytic cells. Osteocytic cells (MLOY4 osteocyte cell line) cultured on normal and OA derived ECMs were analyzed by confocal microscopy, scanning electron microscopy (SEM), cell attachment assays, zymography, apoptosis assays, qRT-PCR and western blotting. The role of integrinβ1 and focal adhesion kinase (FAK) signaling pathways during these interactions were monitored using appropriate blocking antibodies. RESULTS: The ECM produced by OA SBOs contained less mineral content, showed altered organization of matrix proteins and matrix structure compared with the matrices produced by normal SBOs. Culture of osteocytic cells on these defective OA ECM resulted in a decrease of integrinβ1 expression and the de-activation of FAK cell signaling pathway, which subsequently affected the initial osteocytic cell's attachment and functions including morphological abnormalities of cytoskeletal structures, focal adhesions, increased apoptosis, altered osteocyte specific gene expression and increased Matrix metalloproteinases (MMP-2) and -9 expression. CONCLUSION: This study provides new insights in understanding how altered OA bone matrix can lead to the abnormal osteocyte phenotypic changes, which is typical in OA pathogenesis.

Gifted students with a reading disability : the impact of lived experience

Gifted students who have a reading disability have learning characteristics that set them apart from their peers. The ability to read impacts upon all areas of the formal curriculum in which print-based texts are common. Therefore, the full intellectual development of gifted students with a reading disability can be repressed because their access to learning opportunities is reduced. When the different learning needs caused by concomitant giftedness and reading disability are not met, it can have serious implications for both academic achievement and the social-emotional wellbeing of these students. In order to develop a deeper understanding of this vulnerable group of students, this study investigated the learning characteristics of gifted students with a reading disability. Furthermore, it investigated how the learning characteristics of these students impact upon their lived experiences. Since achievement and motivation have been shown to be closely linked to self-efficacy, self-efficacy theory underpinned the conceptual framework of the study. The study used a descriptive case study approach to document the lived experiences of gifted students with a reading disability. Nine participants aged between 11 and 18, who were formally identified as gifted with a reading disability, took part in the study. Data sources in the case study database included: cognitive assessments, such as WISC assessments, Stanford Binet 5, or the Raven's Standard Progressive Matrices; the WIAT II reading assessment; the Reader Self-Perception Scale; document reviews; parent and teacher checklists designed to gain information about the students' learning characteristics; and semi-structured interviews with students. The study showed that gifted students with a reading disability display a complex profile of learning strengths and weaknesses. As a result, they face a daily struggle of trying to reconcile the confusion of being able to complete some tasks to a high level, while struggling to read. The study sheds light on the myriad of issues faced by the students at school. It revealed that when the particular learning characteristics and needs of gifted students with a reading disability are recognised and met, these students can experience academic success, and avoid the serious social-emotional complications cited in previous studies. Indeed, rather than suffering from depression, disengagement from learning, and demotivation, these students were described as resilient, independent, determined, goal oriented and motivated to learn and persevere. Notably, the students in the study had developed effective coping strategies for dealing with the daily challenges they faced. These strategies are outlined in the thesis together with the advice students offered for helping other gifted students with a reading disability to succeed. Their advice is significant for all teachers who wish to nurture the potential of those students who face the challenge of being gifted with a reading disability, and for the parents of these students. This research advances knowledge pertaining to the theory of self-efficacy, and self-efficacy in reading specifically, by showing that although gifted students with a reading disability have low self-efficacy, the level is not the same for all aspects of reading. Furthermore, despite low self-efficacy in reading these students remained motivated. The study also enhances existing knowledge in the areas of gifted education and special education because it documents the lived experience of gifted students with a specific learning disability in reading from the students' perspectives. Based on a synthesis of the literature and research findings, an Inclusive Pathway Model is proposed that describes a framework to support gifted students with a reading disability so that they might achieve, and remain socially and emotionally well-adjusted. The study highlights the importance of clear identification protocols (such as the use of a range of assessment sources, discussions with students and parents, and an awareness of the characteristics of gifted students with a reading disability) and support mechanisms for assisting students (for example, differentiated reading instruction and the use of assistive technology).

Chemotherapy-induced nausea and vomiting : a review to inform clinical practice

Chemotherapy-induced nausea and vomiting (CINV) is a common sideeffect of cytotoxic treatment and despite the widespread use of anti-emetic medication, it continues to affect a significant proportion of patients with up to 23% and 73% of chemotherapy patients still experiencing vomiting and nausea symptoms, respectively. This is of particular concern in oncology patients as nausea and vomiting may result in malnutrition, decreased quality of life and in extreme cases, treatment stoppage. Therefore, the primary aim of this paper was to inform clinicians on the current literature regarding CINV including its effect on the patient, its pathophysiology, and current treatment options. In addition, this review will also discuss the usage of dietetic interventions as well as less utilised, novel interventions such as oral ginger extracts in the treatment of CINV. In order to address these issues, a systematic literature search was conducted using Pubmed, CINAHL, MEDLINE, Embase, and Health Source (Nursing/Academic Edition). A key finding of this review was that common dietary strategies (e.g. eating slowly, avoiding fatty foods) seem to be solely based on professional opinion as no clinical trials investigating these strategies were identified. In contrast, ginger extracts were found to possess several viable mechanisms that interact with CINV progression including 5-HT3, Substance P and acetylcholine receptor antagonism; anti-inflammatory and antioxidant properties; and gastrointestinal motility and gastric emptying modulation. In conclusion, research investigating dietetic interventions in the management of CINV is sparse and requires further investigation while novel intervention such as ginger, possess multiple mechanisms that may benefit CINV management. This review will discuss the prevalence and significance of CINV, dietetic and novel treatment options, and provide implications for clinical practise and future research.

Ergodic capacity and outage performance of amplify-and-forward protocols

This thesis analyses the performance bounds of amplify-and-forward relay channels which are becoming increasingly popular in wireless communication applications. The statistics of cascaded Nakagami-m fading model which is a major obstacle in evaluating the outage of wireless networks is analysed using Mellin transform. Furthermore, the upper and the lower bounds for the ergodic capacity of the slotted amplify-and-forward relay channel, for finite and infinite number of relays are derived using random matrix theory. The results obtained will enable wireless network designers to optimize the network resources, benefiting the consumers.

Advance transit oriented development typology : case study in Brisbane, Australia

Internationally, transit oriented development (TOD) is characterised by moderate to high density development with diverse land use patterns and well connected street networks centred around high frequency transit stops (bus and rail). Although different TOD typologies have been developed in different contexts, they are based on subjective evaluation criteria derived from the context in which they are built and typically lack a validation measure. Arguably there exist sets of TOD characteristics that perform better in certain contexts, and being able to optimise TOD effectiveness would facilitate planning and supporting policy development. This research utilises data from census collection districts (CCDs) in Brisbane with different sets of TOD attributes measured across six objectively quantified built environmental indicators: net employment density, net residential density, land use diversity, intersection density, cul-de-sac density, and public transport accessibility. Using these measures, a Two Step Cluster Analysis was conducted to identify natural groupings of the CCDs with similar profiles, resulting in four unique TOD clusters: (a) residential TODs, (b) activity centre TODs, (c) potential TODs, and; (d) TOD non-suitability. The typologies are validated by estimating a multinomial logistic regression model in order to understand the mode choice behaviour of 10,013 individuals living in these areas. Results indicate that in comparison to people living in areas classified as residential TODs, people who reside in non-TOD clusters were significantly less likely to use public transport (PT) (1.4 times), and active transport (4 times) compared to the car. People living in areas classified as potential TODs were 1.3 times less likely to use PT, and 2.5 times less likely to use active transport compared to using the car. Only a little difference in mode choice behaviour was evident between people living in areas classified as residential TODs and activity centre TODs. The results suggest that: (a) two types of TODs may be suitable for classification and effect mode choice in Brisbane; (b) TOD typology should be developed based on their TOD profile and performance matrices; (c) both bus stop and train station based TODs are suitable for development in Brisbane.

Real-time video event detection in crowded scenes using MPEG derived features : a multiple instance learning approach

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors are used to build particle trajectories that represent the motion of objects in uniform video clips, and the MPEG DCT coefficients are used to compute a foreground map to remove background particles. Trajectories are transformed into the Fourier domain, and the Fourier representations are quantized into visual words using the K-Means algorithm. The proposed MIL algorithm models the scene as a linear combination of independent events, where each event is a distribution of visual words. Experimental results show that the proposed approaches achieve promising results for event detection compared to the state-of-the-art.

Design, implementation and multisite evaluation of a system suitability protocol for the quantitative assessment of instrument performance in liquid chromatography-multiple reaction monitoring-MS (LC-MRM-MS)

Multiple reaction monitoring (MRM) mass spectrometry coupled with stable isotope dilution (SID) and liquid chromatography (LC) is increasingly used in biological and clinical studies for precise and reproducible quantification of peptides and proteins in complex sample matrices. Robust LC-SID-MRM-MS-based assays that can be replicated across laboratories and ultimately in clinical laboratory settings require standardized protocols to demonstrate that the analysis platforms are performing adequately. We developed a system suitability protocol (SSP), which employs a predigested mixture of six proteins, to facilitate performance evaluation of LC-SID-MRM-MS instrument platforms, configured with nanoflow-LC systems interfaced to triple quadrupole mass spectrometers. The SSP was designed for use with low multiplex analyses as well as high multiplex approaches when software-driven scheduling of data acquisition is required. Performance was assessed by monitoring of a range of chromatographic and mass spectrometric metrics including peak width, chromatographic resolution, peak capacity, and the variability in peak area and analyte retention time (RT) stability. The SSP, which was evaluated in 11 laboratories on a total of 15 different instruments, enabled early diagnoses of LC and MS anomalies that indicated suboptimal LC-MRM-MS performance. The observed range in variation of each of the metrics scrutinized serves to define the criteria for optimized LC-SID-MRM-MS platforms for routine use, with pass/fail criteria for system suitability performance measures defined as peak area coefficient of variation <0.15, peak width coefficient of variation <0.15, standard deviation of RT <0.15 min (9 s), and the RT drift <0.5min (30 s). The deleterious effect of a marginally performing LC-SID-MRM-MS system on the limit of quantification (LOQ) in targeted quantitative assays illustrates the use and need for a SSP to establish robust and reliable system performance. Use of a SSP helps to ensure that analyte quantification measurements can be replicated with good precision within and across multiple laboratories and should facilitate more widespread use of MRM-MS technology by the basic biomedical and clinical laboratory research communities.