Monitoring healing progression and characterizing the mechanical environment in preclinical models for bone tissue engineering

The treatment of large segmental bone defects remains a significant clinical challenge. Due to limitations surrounding the use of bone grafts, tissue-engineered constructs for the repair of large bone defects could offer an alternative. Before translation of any newly developed tissue engineering (TE) approach to the clinic, efficacy of the treatment must be shown in a validated preclinical large animal model. Currently, biomechanical testing, histology, and microcomputed tomography are performed to assess the quality and quantity of the regenerated bone. However, in vivo monitoring of the progression of healing is seldom performed, which could reveal important information regarding time to restoration of mechanical function and acceleration of regeneration. Furthermore, since the mechanical environment is known to influence bone regeneration, and limb loading of the animals can poorly be controlled, characterizing activity and load history could provide the ability to explain variability in the acquired data sets and potentially outliers based on abnormal loading. Many approaches have been devised to monitor the progression of healing and characterize the mechanical environment in fracture healing studies. In this article, we review previous methods and share results of recent work of our group toward developing and implementing a comprehensive biomechanical monitoring system to study bone regeneration in preclinical TE studies.

PolyPatEx : an R package for paternity exclusion in autopolyploids

Microsatellite markers have demonstrated their value for performing paternity exclusion and hence exploring mating patterns in plants and animals. Methodology is well established for diploid species, and several software packages exist for elucidating paternity in diploids; however, these issues are not so readily addressed in polyploids due to the increased complexity of the exclusion problem and a lack of available software. We introduce polypatex, an r package for paternity exclusion analysis using microsatellite data in autopolyploid, monoecious or dioecious/bisexual species with a ploidy of 4n, 6n or 8n. Given marker data for a set of offspring, their mothers and a set of candidate fathers, polypatex uses allele matching to exclude candidates whose marker alleles are incompatible with the alleles in each offspring–mother pair. polypatex can analyse marker data sets in which allele copy numbers are known (genotype data) or unknown (allelic phenotype data) – for data sets in which allele copy numbers are unknown, comparisons are made taking into account all possible genotypes that could arise from the compared allele sets. polypatex is a software tool that provides population geneticists with the ability to investigate the mating patterns of autopolyploids using paternity exclusion analysis on data from codominant markers having multiple alleles per locus.

Natural variation and Biogeography of the melon fruit fly, Zeugodacus cucurbitae (Diptera : Tephritidae), in Southeast-Asia and the west-pacific

This thesis used multidisciplinary approaches which greatly enhance our understanding of population structure and can be particularly powerful tools for resolving variation of melon fly over geographic and temporal scales, and for determining invasive pathways. The results from this thesis reinforce the value of integrating multiple data sets to better understand and resolve natural variation within an important pest to determine whether there are cryptic species, discrete lineages or host races, and to identify dispersal pathways in an invasive pest. These results are instructive for regional biosecurity, trade and quarantine, and provide important background for future area-wide management programmes. The integrative methodology adopted in this thesis is applicable to a variety of other insect pests.

The global burden of injury: Incidence, mortality, disability-adjusted life years and time trends from the Global Burden of Disease study 2013

Background The Global Burden of Diseases (GBD), Injuries, and Risk Factors study used the disability-adjusted life year (DALY) to quantify the burden of diseases, injuries, and risk factors. This paper provides an overview of injury estimates from the 2013 update of GBD, with detailed information on incidence, mortality, DALYs and rates of change from 1990 to 2013 for 26 causes of injury, globally, by region and by country. Methods Injury mortality was estimated using the extensive GBD mortality database, corrections for ill-defined cause of death and the cause of death ensemble modelling tool. Morbidity estimation was based on inpatient and outpatient data sets, 26 cause-of-injury and 47 nature-of-injury categories, and seven follow-up studies with patient-reported long-term outcome measures. Results In 2013, 973 million (uncertainty interval (UI) 942 to 993) people sustained injuries that warranted some type of healthcare and 4.8 million (UI 4.5 to 5.1) people died from injuries. Between 1990 and 2013 the global age-standardised injury DALY rate decreased by 31% (UI 26% to 35%). The rate of decline in DALY rates was significant for 22 cause-of-injury categories, including all the major injuries. Conclusions Injuries continue to be an important cause of morbidity and mortality in the developed and developing world. The decline in rates for almost all injuries is so prominent that it warrants a general statement that the world is becoming a safer place to live in. However, the patterns vary widely by cause, age, sex, region and time and there are still large improvements that need to be made.

Multi-GNSS precise point positioning with raw single-frequency and dual-frequency measurement models

The emergence of multiple satellite navigation systems, including BDS, Galileo, modernized GPS, and GLONASS, brings great opportunities and challenges for precise point positioning (PPP). We study the contributions of various GNSS combinations to PPP performance based on undifferenced or raw observations, in which the signal delays and ionospheric delays must be considered. A priori ionospheric knowledge, such as regional or global corrections, strengthens the estimation of ionospheric delay parameters. The undifferenced models are generally more suitable for single-, dual-, or multi-frequency data processing for single or combined GNSS constellations. Another advantage over ionospheric-free PPP models is that undifferenced models avoid noise amplification by linear combinations. Extensive performance evaluations are conducted with multi-GNSS data sets collected from 105 MGEX stations in July 2014. Dual-frequency PPP results from each single constellation show that the convergence time of undifferenced PPP solution is usually shorter than that of ionospheric-free PPP solutions, while the positioning accuracy of undifferenced PPP shows more improvement for the GLONASS system. In addition, the GLONASS undifferenced PPP results demonstrate performance advantages in high latitude areas, while this impact is less obvious in the GPS/GLONASS combined configuration. The results have also indicated that the BDS GEO satellites have negative impacts on the undifferenced PPP performance given the current “poor” orbit and clock knowledge of GEO satellites. More generally, the multi-GNSS undifferenced PPP results have shown improvements in the convergence time by more than 60 % in both the single- and dual-frequency PPP results, while the positioning accuracy after convergence indicates no significant improvements for the dual-frequency PPP solutions, but an improvement of about 25 % on average for the single-frequency PPP solutions.

Geometry-free stochastic analysis of BDS triple frequency signals

The paper presents a geometry-free approach to assess the variation of covariance matrices of undifferenced triple frequency GNSS measurements and its impact on positioning solutions. Four independent geometryfree/ ionosphere-free (GFIF) models formed from original triple-frequency code and phase signals allow for effective computation of variance-covariance matrices using real data. Variance Component Estimation (VCE) algorithms are implemented to obtain the covariance matrices for three pseudorange and three carrier-phase signals epoch-by-epoch. Covariance results from the triple frequency Beidou System (BDS) and GPS data sets demonstrate that the estimated standard deviation varies in consistence with the amplitude of actual GFIF error time series. The single point positioning (SPP) results from BDS ionosphere-free measurements at four MGEX stations demonstrate an improvement of up to about 50% in Up direction relative to the results based on a mean square statistics. Additionally, a more extensive SPP analysis at 95 global MGEX stations based on GPS ionosphere-free measurements shows an average improvement of about 10% relative to the traditional results. This finding provides a preliminary confirmation that adequate consideration of the variation of covariance leads to the improvement of GNSS state solutions.

Wide area kinematic positioning with BeiDou triple frequency signals

A key challenge of wide area kinematic positioning is to overcome the effects of the varying hardware biases in code signals of the BeiDou system. Based on three geometryfree/ionosphere-free combinations, the elevation-dependent code biases are modelled for all BeiDou satellites. Results from the data sets of 30-day for 5 baselines of 533 to 2545 km demonstrate that the wide-lane (WL) integer-fixing success rates of 98% to 100% can be achieved within 25 min. Under the condition of HDOP of less than 2, the overall RMS statistics show that ionospheric-free WL single-epoch solutions achieve 24 to 50 cm in the horizontal direction. Smoothing processing over the moving window of 20 min reduces the RMS values by a factor of about 2. Considering distance-independent nature, the above results show the potential that reliable and high precision positioning services could be provided in a wide area based on a sparsely distributed ground network.

Understanding who cares: Creating the evidence to address the long-standing policy problem of staff shortages in early childhood education and care

Evidence-based policy is a means of ensuring that policy is informed by more than ideology or expedience. However, what constitutes robust evidence is highly contested. In this paper, we argue policy must draw on quantitative and qualitative data. We do this in relation to a long entrenched problem in Australian early childhood education and care (ECEC) workforce policy. A critical shortage of qualified staff threatens the attainment of broader child and family policy objectives linked to the provision of ECEC and has not been successfully addressed by initiatives to date. We establish some of the limitations of existing quantitative data sets and consider the potential of qualitative studies to inform ECEC workforce policy. The adoption of both quantitative and qualitative methods is needed to illuminate the complex nature of the work undertaken by early childhood educators, as well as the environmental factors that sustain job satisfaction in a demanding and poorly understood working environment.

Comprehending adverbs of doubt and certainty in health communication: A multidimensional scaling approach

This research explored the feasibility of using multidimensional scaling (MDS) analysis in novel combination with other techniques to study comprehension of epistemic adverbs expressing doubt and certainty (e.g., evidently, obviously, probably) as they relate to health communication in clinical settings. In Study 1, Australian English speakers performed a dissimilarity-rating task with sentence pairs containing the target stimuli, presented as "doctors' opinions". Ratings were analyzed using a combination of cultural consensus analysis (factor analysis across participants), weighted-data classical-MDS, and cluster analysis. Analyses revealed strong within-community consistency for a 3-dimensional semantic space solution that took into account individual differences, strong statistical acceptability of the MDS results in terms of stress and explained variance, and semantic configurations that were interpretable in terms of linguistic analyses of the target adverbs. The results confirmed the feasibility of using MDS in this context. Study 2 replicated the results with Canadian English speakers on the same task. Semantic analyses and stress decomposition analysis were performed on the Australian and Canadian data sets, revealing similarities and differences between the two groups. Overall, the results support using MDS to study comprehension of words critical for health communication, including in future studies, for example, second language speaking patients and/or practitioners. More broadly, the results indicate that the techniques described should be promising for comprehension studies in many communicative domains, in both clinical settings and beyond, and including those targeting other aspects of language and focusing on comparisons across different speech communities.

A generic individual-based spatially explicit model as a novel tool for investigating insect-plant interactions: A case study of the behavioural ecology of frugivorous Tephritidae

Computational modelling of mechanisms underlying processes in the real world can be of great value in understanding complex biological behaviours. Uptake in general biology and ecology has been rapid. However, it often requires specific data sets that are overly costly in time and resources to collect. The aim of the current study was to test whether a generic behavioural ecology model constructed using published data could give realistic outputs for individual species. An individual-based model was developed using the Pattern-Oriented Modelling (POM) strategy and protocol, based on behavioural rules associated with insect movement choices. Frugivorous Tephritidae (fruit flies) were chosen because of economic significance in global agriculture and the multiple published data sets available for a range of species. The Queensland fruit fly (Qfly), Bactrocera tryoni, was identified as a suitable individual species for testing. Plant canopies with modified architecture were used to run predictive simulations. A field study was then conducted to validate our model predictions on how plant architecture affects fruit flies’ behaviours. Characteristics of plant architecture such as different shapes, e.g., closed-canopy and vase-shaped, affected fly movement patterns and time spent on host fruit. The number of visits to host fruit also differed between the edge and centre in closed-canopy plants. Compared to plant architecture, host fruit has less contribution to effects on flies’ movement patterns. The results from this model, combined with our field study and published empirical data suggest that placing fly traps in the upper canopy at the edge should work best. Such a modelling approach allows rapid testing of ideas about organismal interactions with environmental substrates in silico rather than in vivo, to generate new perspectives. Using published data provides a saving in time and resources. Adjustments for specific questions can be achieved by refinement of parameters based on targeted experiments.