Evolution of sexual segregation in mammalian herbivores: kangaroos as marsupial models

Sexual segregation is best known in sexually dimorphic ungulates. Many hypotheses have been proposed to explain the evolution of sexual segregation in ungulates, but all are reducible to the influence of two factors: body size and sex-specific reproductive strategy. Definitive tests of these hypotheses are lacking in ungulates because these factors are confounded, all males being somewhat larger than females. Kangaroos represent a parallel radiation of terrestrial herbivores, but their populations are composed of a spectrum of adult body sizes, ranging from small males the same size as females to large males more than twice the size. We exploited this heteromorphism to assess the independent influences of size and sex in these ungulate analogues. We conducted a preliminary study of western grey kangaroos (Macropus fuliginosus) in north-western Victoria, Australia. Adult males predominately occupied grassland habitat, whereas females occurred mostly in lakebed, woodland and shrubland. Single-sex groups occurred more often than expected during the non-mating season. The diet of large males had the highest proportion of grass, and females had the least. These initial results indicate that both size and sex influence segregation in this species, confirming the worth of kangaroos as marsupial models for research into the evolution of sexual segregation.

Models of survivorship care provision in adult patients with haematological cancer : an integrative literature review

Purpose: Increasing numbers of haematology cancer survivors warrants identification of the most effective model of survivorship care to survivors from a diverse range of haematological cancers with aggressive treatment regimens. This review aimed to identify models of survivorship care to support the needs of haematology cancer survivors. Methods: An integrative literature review method utilised a search of electronic databases (CINAHL, Medline, PsycInfo, PubMed, EMBASE, PsycArticles, Cochrane Library) for eligible articles (up to July 2014). Articles were included if they proposed or reported the use of a model of care for haematology cancer survivors. Results: Fourteen articles were included in this review. Eight articles proposed and described models of care and six reported the use of a range of survivorship models of care in haematology cancer survivors. No randomised controlled trials or literature reviews were found to have been undertaken specifically with this cohort of cancer survivors. There was variation in the models described and who provided the survivorship care. Conclusion: Due to the lack of studies evaluating the effectiveness of models of care, it is difficult to determine the best model of care for haematology cancer survivors. Many different models of care are being put into practice before robust research is conducted. Therefore well-designed high quality pragmatic randomised controlled trials are required to inform clinical practice.

Effects of strain artefacts arising from a pre-defined callus domain in models of bone healing mechanobiology

Iterative computational models have been used to investigate the regulation of bone fracture healing by local mechanical conditions. Although their predictions replicate some mechanical responses and histological features, they do not typically reproduce the predominantly radial hard callus growth pattern observed in larger mammals. We hypothesised that this discrepancy results from an artefact of the models’ initial geometry. Using axisymmetric finite element models, we demonstrated that pre-defining a field of soft tissue in which callus may develop introduces high deviatoric strains in the periosteal region adjacent to the fracture. These bone-inhibiting strains are not present when the initial soft tissue is confined to a thin periosteal layer. As observed in previous healing models, tissue differentiation algorithms regulated by deviatoric strain predicted hard callus forming remotely and growing towards the fracture. While dilatational strain regulation allowed early bone formation closer to the fracture, hard callus still formed initially over a broad area, rather than expanding over time. Modelling callus growth from a thin periosteal layer successfully predicted the initiation of hard callus growth close to the fracture site. However, these models were still susceptible to elevated deviatoric strains in the soft tissues at the edge of the hard callus. Our study highlights the importance of the initial soft tissue geometry used for finite element models of fracture healing. If this cannot be defined accurately, alternative mechanisms for the prediction of early callus development should be investigated.

Three dimensional in-vitro models for studying cancer angiogenesis

Introduction Hydrogels prepared from star-shaped poly(ethylene glycol) (PEG) and maleimide-functionalized heparin provide a potential matrix for use in developing three dimensional (3D) models. We have previously demonstrated that these hydrogels support the cultivation of human umbilical vein endothelial cells (HUVECs). We extend this body of work to study the ability to create an extracellular matrix (ECM)-like model to study breast and prostate cancer cell growth in 3D. Also, we investigate the ability to produce a tri-culture mimicking tumour angiogenesis with cancer spheroids, HUVECs and mesenchymal stem cells (MSCs). Materials and Methods The breast cancer cell lines, MCF-7 and MDA-MB-231, and prostate cancer cell lines, LNCaP and PC3, were seeded into starPEG-heparin hydrogels and grown for 14 Days to analyze the effects of varying hydrogel stiffness on spheroid development. Resulting hydrogel constructs were analyzed via proliferation assays, light microscopy, and immunostaining. Cancer cell lines were then seeded into starPEG-heparin hydrogels functionalized with growth factors as spheroids with HUVECs and MSCs and grown as a tri-culture. Cultures were analyzed via immunostaining and observed using confocal microscopy. Results Cultures prepared in MMP-cleavable starPEG-heparin hydrogels display spheroid formation in contrast to adherent growth on tissue culture plastic. Small differences were visualized in cancer spheroid growth between different gel stiffness across the range of cell lines. Cancer cell lines were able to be co-cultivated with HUVECs and MSC. Interaction was visualized between tumours and HUVECs via confocal microscopy. Further studies intend to further optimize and mimic the ECM environment of in-situ tumour angiogenesis. Discussion Our results confirm the suitability of hydrogels constructed from starPEG-heparin for HUVEC and MSC co-cultivation with cancer cell lines to study cell-cell and cell-matrix interactions in a 3D environment. This represents a step forward in the development of 3D culture models to study the pathomechanisms of breast and prostate cancer.

Mathematical models of calcium and tight junctions in normal and reconstructed epidermis

This project investigated the calcium distributions of the skin, and the growth patterns of skin substitutes grown in the laboratory, using mathematical models. The research found that the calcium distribution in the upper layer of the skin is controlled by three different mechanisms, not one as previously thought. The research also suggests that tight junctions, which are adhesions between neighbouring skin cells, cannot be solely responsible for the differences in the growth patterns of skin substitutes and normal skin.

Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods which have been recently employed to analyse PNSD data, however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K-means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and Silhouette width validation values and the K-means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K-means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectra to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.

Charged particles and cluster ions produced during cooking activities

Previous studies showed that a significant number of the particles present in indoor air are generated by cooking activities, and measured particle concentrations and exposures have been used to estimate the related human dose. The dose evaluation can be affected by the particle charge level which is usually not considered in particle deposition models. To this purpose, in this paper we show, for the very first time, the electric charge of particles generated during cooking activities and thus extending the interest on particle charging characterization to indoor micro-environments, so far essentially focused on outdoors. Particle number, together with positive and negative cluster ion concentrations, was monitored using a condensation particle counter and two air ion counters, respectively, during different cooking events. Positively-charged particle distribution fractions during gas combustion, bacon grilling, and eggplant grilling events were measured by two Scanning Mobility Particle Sizer spectrometers, used with and without a neutralizer. Finally, a Tandem Differential Mobility Analyzer was used to measure the charge specific particle distributions of bacon and eggplant grilling experiments, selecting particles of 30, 50, 80 and 100 nm in mobility diameter. The total fraction of positively-charged particles was 4.0%, 7.9%, and 5.6% for gas combustion, bacon grilling, and eggplant grilling events, respectively, then lower than other typical outdoor combustion-generated particles.

The Fleet Store : devising a public retail exhibition to promote creative, innovative and sustainable business models for emerging fashion designers

1.Description of the Work The Fleet Store was devised as a creative output to establish an exhibition linked to a fashion business model where emerging designers were encouraged to research new and innovative strategies for creating design-driven and commercial collections for a public consumer. This was a project that was devised to break down the perceptions of emerging fashion designers that designing commercial collections linked to a sustainable business model is a boring and unnecessary process. The focus was to demystify the business of fashion and to link its importance to a design-driven and public outcome that is more familiar to fashion designers. The criterion for participation was that all designers had to be registered as a business with the Australian Taxation Office. Designers were chosen from the Creative Enterprise Australia Fashion Business Incubator, the QUT fashion graduate alumni and current QUT fashion design and double degree (fashion and business) students with existing businesses. The project evolved from a series of collaborative workshops where designers were introduced to new and innovative creative industries’ business models and the processes, costings and timings involved to create a niche, sustainable business for a public exhibition of design-driven commercial collections. All designers initiated their own business infra-structure but were then introduced to the concept of collaboration for successful and profitable exhibition and business outcomes. Collaborative strategies such as crowd funding, crowd sourcing, peer to peer mentoring and manufacturing were all researched, and strategies for the establishment of the retail exhibition were all devised in a collaborative environment. All participants also took on roles outside their ‘designer’ background to create a retail exhibition that was creative but also had critical mass and aesthetic for the consumer. The Fleet Store ‘popped up’ for 2 weeks (10 days), in a heritage-listed building in an inner city location. Passers-by were important, but the main consumer was enlisted by the use of interest and investment from crowd sourcing, crowd funding, ethical marketing, corporate social responsibility projects and collaborative public relations and social media strategies. The research has furthered discussion on innovative strategies for emerging fashion designers to initiate and maintain sustainable businesses and suggests that collaboration combined with a design-driven and business focus can create a sustainable and economically viable retail exhibition. 2. Research Statement Research Background The research field involved developing a new ethical, design-driven, collaborative and sustainable model for fashion design practice and management. The research asked can a public, design-driven, collaborative retail exhibition create a platform for promoting creative, innovative and sustainable business models for emerging fashion designers. The methodology was primarily practice-led as all participants were designers in their own right and the project manager acted as a mentor and curator to guide the process and analyse the potential of the research question. The Fleet Store offers new knowledge in design practice and management; with the creation of a model where design outcomes and business models are inextricably linked to the success of the creative output. Key innovations include extending the commercialisation of emerging fashion businesses by creating a curated retail gallery for collaborative and sustainable strategies to support niche fashion designer labels. This has contributed to a broader conversation on how to nurture and sustain competitive Australian fashion designers/labels. Research Contribution and Significance The Fleet Store has contributed to a growing body of research into innovative and sustainable business models for niche fashion and creative industries’ practitioners. All participants have maintained their business infra-structure and many are currently growing their businesses, using the strategies tested for the Fleet Store. The exhibition space was visited by over 1,000 people and sales of $27,000 were made in 10 days of opening. (Follow up sales of $3,000 has also been reported.) Three of the designers were ‘discovered’ from the exhibition and have received substantial orders from high profile national buyers and retailers for next season delivery. Several participants have since collaborated to create other pop up retail environments and are now mentoring other emerging designers on the significance of a collaborative retail exhibition to consolidate niche business models for emerging fashion designers.

Supervised latent dirichlet allocation models for efficient activity representation

Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of efficiently representing the extracted features for classification to improve the overall performance. We introduce two generative supervised topic models, maximum entropy discrimination LDA (MedLDA) and class- specific simplex LDA (css-LDA), to encode the raw features suitable for discriminative SVM based classification. Unsupervised LDA models disconnect topic discovery from the classification task, hence yield poor results compared to the baseline Bag-of-words framework. On the other hand supervised LDA techniques learn the topic structure by considering the class labels and improve the recognition accuracy significantly. MedLDA maximizes likelihood and within class margins using max-margin techniques and yields a sparse highly discriminative topic structure; while in css-LDA separate class specific topics are learned instead of common set of topics across the entire dataset. In our representation first topics are learned and then each video is represented as a topic proportion vector, i.e. it can be comparable to a histogram of topics. Finally SVM classification is done on the learned topic proportion vector. We demonstrate the efficiency of the above two representation techniques through the experiments carried out in two popular datasets. Experimental results demonstrate significantly improved performance compared to the baseline Bag-of-features framework which uses kmeans to construct histogram of words from the feature vectors.

Multi-objective models and techniques for analysing the absolute capacity of railway networks

Railway capacity determination and expansion are very important topics. In prior research, the competition between different entities such as train services and train types, on different network corridors however have been ignored, poorly modelled, or else assumed to be static. In response, a comprehensive set of multi-objective models have been formulated in this article to perform a trade-off analysis. These models determine the total absolute capacity of railway networks as the most equitable solution according to a clearly defined set of competing objectives. The models also perform a sensitivity analysis of capacity with respect to those competing objectives. The models have been extensively tested on a case study and their significant worth is shown. The models were solved using a variety of techniques however an adaptive E constraint method was shown to be most superior. In order to identify only the best solution, a Simulated Annealing meta-heuristic was implemented and tested. However a linearization technique based upon separable programming was also developed and shown to be superior in terms of solution quality but far less in terms of computational time.

Accounting for management costs in sensitivity analyses of matrix population models

Traditional sensitivity and elasticity analyses of matrix population models have been used to inform management decisions, but they ignore the economic costs of manipulating vital rates. For example, the growth rate of a population is often most sensitive to changes in adult survival rate, but this does not mean that increasing that rate is the best option for managing the population because it may be much more expensive than other options. To explore how managers should optimize their manipulation of vital rates, we incorporated the cost of changing those rates into matrix population models. We derived analytic expressions for locations in parameter space where managers should shift between management of fecundity and survival, for the balance between fecundity and survival management at those boundaries, and for the allocation of management resources to sustain that optimal balance. For simple matrices, the optimal budget allocation can often be expressed as simple functions of vital rates and the relative costs of changing them. We applied our method to management of the Helmeted Honeyeater (Lichenostomus melanops cassidix; an endangered Australian bird) and the koala (Phascolarctos cinereus) as examples. Our method showed that cost-efficient management of the Helmeted Honeyeater should focus on increasing fecundity via nest protection, whereas optimal koala management should focus on manipulating both fecundity and survival simultaneously. These findings are contrary to the cost-negligent recommendations of elasticity analysis, which would suggest focusing on managing survival in both cases. A further investigation of Helmeted Honeyeater management options, based on an individual-based model incorporating density dependence, spatial structure, and environmental stochasticity, confirmed that fecundity management was the most cost-effective strategy. Our results demonstrate that decisions that ignore economic factors will reduce management efficiency. ©2006 Society for Conservation Biology.

Managing and learning with multiple models: Objectives and optimization algorithms

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. © 2010 Elsevier Ltd.