Investigating the population structure of Queensland invasive Streptococcus pneumoniae isolates in children: Using a modified multi-locus variable number of tandem repeat analysis and a novel minimum SNPs capsular typing method

This research investigated the use of DNA fingerprinting to characterise the bacteria Streptococcus pneumoniae or pneumococcus, and hence gain insight into the development of new vaccines or antibiotics. Different bacterial DNA fingerprinting methods were studied, and a novel method was developed and validated, which characterises different cell coatings that pneumococci produce. This method was used to study the epidemiology of pneumococci in Queensland before and after the introduction of the current pneumococcal vaccine. This study demonstrated that pneumococcal disease is highly prevalent in children under four years, that the bacteria can `switch' its cell coating to evade the vaccine, and that some DNA fingerprinting methods are more discriminatory than others. This has an impact on understanding which strains are more prone to cause invasive disease. Evidence of the excellent research findings have been published in high impact internationally refereed journals.

Impact of diurnal temperature range on human health: a systematic review

Increasing epidemiological studies have shown that a rapid temperature change within 1 day is an independent risk factor for human health. This paper aimed to systematically review the epidemiological evidence on the relationship between diurnal temperature range (DTR) and human health and to propose future research directions. A literature search was conducted in October 2013 using the databases including PubMed, ScienceDirect, and EBSCO. Empirical studies regarding the relationship between DTR and mortality and morbidity were included. Twenty-five relevant studies were identified, among which, 11 investigated the relationship between DTR and mortality and 14 examined the impact of DTR on morbidity. The majority of existing studies reported that DTR was significantly associated with mortality and morbidity, particularly for cardiovascular and respiratory diseases. Notably, compared with adults, the elderly and children were more vulnerable to DTR effects. However, there were some inconsistencies regarding the susceptible groups, lag time, and threshold of DTR. The impact of DTR on human health may be confounded or modified by season, socioeconomic, and educational status. Further research is needed to further confirm the adverse effects of DTR in different geographical locations; examine the effects of DTR on the health of children aged one or under; explore extreme DTR effects on human health; analyze the difference of DTR effects on human health in different locations and the modified effects of potential confounding factors; and develop detailed preventive measures against large DTR, particularly for susceptible groups

Numerical analysis of a new space-time variable fractional order advection-dispersion equation

Many physical processes appear to exhibit fractional order behavior that may vary with time and/or space. The continuum of order in the fractional calculus allows the order of the fractional operator to be considered as a variable. In this paper, we consider a new space–time variable fractional order advection–dispersion equation on a finite domain. The equation is obtained from the standard advection–dispersion equation by replacing the first-order time derivative by Coimbra’s variable fractional derivative of order α(x)∈(0,1]α(x)∈(0,1], and the first-order and second-order space derivatives by the Riemann–Liouville derivatives of order γ(x,t)∈(0,1]γ(x,t)∈(0,1] and β(x,t)∈(1,2]β(x,t)∈(1,2], respectively. We propose an implicit Euler approximation for the equation and investigate the stability and convergence of the approximation. Finally, numerical examples are provided to show that the implicit Euler approximation is computationally efficient.

A Model to Estimate the Population Contributing to the Wastewater Using Samples Collected on Census Day

An important uncertainty when estimating per capita consumption of, for example, illicit drugs by means of wastewater analysis (sometimes referred to as “sewage epidemiology”) relates to the size and variability of the de facto population in the catchment of interest. In the absence of a day-specific direct population count any indirect surrogate model to estimate population size lacks a standard to assess associated uncertainties. Therefore, the objective of this study was to collect wastewater samples at a unique opportunity, that is, on a census day, as a basis for a model to estimate the number of people contributing to a given wastewater sample. Mass loads for a wide range of pharmaceuticals and personal care products were quantified in influents of ten sewage treatment plants (STP) serving populations ranging from approximately 3500 to 500 000 people. Separate linear models for population size were estimated with the mass loads of the different chemical as the explanatory variable: 14 chemicals showed good, linear relationships, with highest correlations for acesulfame and gabapentin. De facto population was then estimated through Bayesian inference, by updating the population size provided by STP staff (prior knowledge) with measured chemical mass loads. Cross validation showed that large populations can be estimated fairly accurately with a few chemical mass loads quantified from 24-h composite samples. In contrast, the prior knowledge for small population sizes cannot be improved substantially despite the information of multiple chemical mass loads. In the future, observations other than chemical mass loads may improve this deficit, since Bayesian inference allows including any kind of information relating to population size.

Studying change in popular culture: A "middle-range" approach

"This chapter reviews the capacity of the discipline field to account for the velocity and quality of digitally-driven transformations, while making a case for a "middle range" approach that steers between unbridled optimism ("all-change") and determined scepticism ("Continuity") about the potential of such change. The chapter focuses on online screen distribution as a case study, considering the evidence for, and significance of, change in industry structure and the main payers, how content is produced and by whom, the nature of content, and the degree to which online screen distribution has reached thresholds of mainstream popularity."

The significance of controlled conditions in lentiviral vector titration and in the use of multiplicity of infection (MOI) for predicting gene transfer events

Background: Although lentiviral vectors have been widely used for in vitro and in vivo gene therapy researches, there have been few studies systematically examining various conditions that may affect the determination of the number of viable vector particles in a vector preparation and the use of Multiplicity of Infection (MOI) as a parameter for the prediction of gene transfer events. Methods: Lentiviral vectors encoding a marker gene were packaged and supernatants concentrated. The number of viable vector particles was determined by in vitro transduction and fluorescent microscopy and FACs analyses. Various factors that may affect the transduction process, such as vector inoculum volume, target cell number and type, vector decay, variable vector - target cell contact and adsorption periods were studied. MOI between 0-32 was assessed on commonly used cell lines as well as a new cell line. Results: We demonstrated that the resulting values of lentiviral vector titre varied with changes of conditions in the transduction process, including inoculum volume of the vector, the type and number of target cells, vector stability and the length of period of the vector adsorption to target cells. Vector inoculum and the number of target cells determine the frequencies of gene transfer event, although not proportionally. Vector exposure time to target cells also influenced transduction results. Varying these parameters resulted in a greater than 50-fold differences in the vector titre from the same vector stock. Commonly used cell lines in vector titration were less sensitive to lentiviral vector-mediated gene transfer than a new cell line, FRL 19. Within 0-32 of MOI used transducing four different cell lines, the higher the MOI applied, the higher the efficiency of gene transfer obtained. Conclusion: Several variables in the transduction process affected in in vitro vector titration and resulted in vastly different values from the same vector stock, thus complicating the use of MOI for predicting gene transfer events. Commonly used target cell lines underestimated vector titre. However, within a certain range of MOI, it is possible that, if strictly controlled conditions are observed in the vector titration process, including the use of a sensitive cell line, such as FRL 19 for vector titration, lentivector-mediated gene transfer events could be predicted. © 2004 Zhang et al; licensee BioMed Central Ltd.

Comparative genomic analysis of human Chlamydia pneumoniae isolates from respiratory, brain and cardiac tissues

Chlamydia pneumoniae is an obligate intracellular bacterium implicated in a wide range of human diseases including atherosclerosis and Alzheimer's disease. Efforts to understand the relationships between C. pneumoniae detected in these diseases have been hindered by the availability of sequence data for non-respiratory strains. In this study, we sequenced the whole genomes for C. pneumoniae isolates from atherosclerosis and Alzheimer's disease, and compared these to previously published C. pneumoniae genomes. Phylogenetic analyses of these new C. pneumoniae strains indicate two sub-groups within human C. pneumoniae, and suggest that both recombination and mutation events have driven the evolution of human C. pneumoniae. Further fine-detailed analyses of these new C. pneumoniae sequences show several genetically variable loci. This suggests that similar strains of C. pneumoniae are found in the brain, lungs and cardiovascular system and that only minor genetic differences may contribute to the adaptation of particular strains in human disease.

Variable Size Population NSGA-II: VPNSGA-II

Multi-objective optimization is an active field of research with broad applicability in aeronautics. This report details a variant of the original NSGA-II software aimed to improve the performances of such a widely used Genetic Algorithm in finding the optimal Pareto-front of a Multi-Objective optimization problem for the use of UAV and aircraft design and optimsaiton. Original NSGA-II works on a population of predetermined constant size and its computational cost to evaluate one generation is O(mn^2 ), being m the number of objective functions and n the population size. The basic idea encouraging this work is that of reduce the computational cost of the NSGA-II algorithm by making it work on a population of variable size, in order to obtain better convergence towards the Pareto-front in less time. In this work some test functions will be tested with both original NSGA-II and VPNSGA-II algorithms; each test will be timed in order to get a measure of the computational cost of each trial and the results will be compared.

Breastmilk-saliva interactions boost innate immunity by regulating the oral microbiome in early infancy

Introduction Xanthine oxidase (XO) is distributed in mammals largely in the liver and small intestine, but also is highly active in milk where it generates hydrogen peroxide (H2O2). Adult human saliva is low in hypoxanthine and xanthine, the substrates of XO, and high in the lactoperoxidase substrate thiocyanate, but saliva of neonates has not been examined. Results Median concentrations of hypoxanthine and xanthine in neonatal saliva (27 and 19 μM respectively) were ten-fold higher than in adult saliva (2.1 and 1.7 μM). Fresh breastmilk contained 27.3±12.2 μM H2O2 but mixing baby saliva with breastmilk additionally generated >40 μM H2O2, sufficient to inhibit growth of the opportunistic pathogens Staphylococcus aureus and Salmonella spp. Oral peroxidase activity in neonatal saliva was variable but low (median 7 U/L, range 2–449) compared to adults (620 U/L, 48–1348), while peroxidase substrate thiocyanate in neonatal saliva was surprisingly high. Baby but not adult saliva also contained nucleosides and nucleobases that encouraged growth of the commensal bacteria Lactobacillus, but inhibited opportunistic pathogens; these nucleosides/bases may also promote growth of immature gut cells. Transition from neonatal to adult saliva pattern occurred during the weaning period. A survey of saliva from domesticated mammals revealed wide variation in nucleoside/base patterns. Discussion and Conclusion During breast-feeding, baby saliva reacts with breastmilk to produce reactive oxygen species, while simultaneously providing growth-promoting nucleotide precursors. Milk thus plays more than a simply nutritional role in mammals, interacting with infant saliva to produce a potent combination of stimulatory and inhibitory metabolites that regulate early oral–and hence gut–microbiota. Consequently, milk-saliva mixing appears to represent unique biochemical synergism which boosts early innate immunity.

Development and Simulation of a Variable Rate Controller for an Air search and Air Sampling Unmanned Aerial System

This report describes the development and simulation of a variable rate controller for a 6-degree of freedom nonlinear model. The variable rate simulation model represents an off the shelf autopilot. Flight experiment involves risks and can be expensive. Therefore a dynamic model to understand the performance characteristics of the UAS in mission simulation before actual flight test or to obtain parameters needed for the flight is important. The control and guidance is implemented in Simulink. The report tests the use of the model for air search and air sampling path planning. A GUI in which a set of mission scenarios, in which two experts (mission expert, i.e. air sampling or air search and an UAV expert) interact, is presented showing the benefits of the method.

Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models

The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

Fabrication and characterization of plasma polymer thin films from monoterpene alcohols for applications in organic electronics and biotechnology

After more than twenty years of basic and applied research, the use of nanotechnology in the design and manufacture of nanoscale materials is rapidly increasing, particularly in commercial applications that span from electronics across renewable energy areas, and biomedical devices. Novel polymers are attracting significant attention for they promise to provide a low−cost high−performance alternative to existing materials. Furthermore, these polymers have the potential to overcome limitations imposed by currently available materials thus enabling the development of new technologies and applications that are currently beyond our reach. This work focuses on the development of a range of new low−cost environmentally−friendly polymer materials for applications in areas of organic (flexible) electronics, optics, and biomaterials. The choice of the monomer reflects the environmentally−conscious focus of this project. Terpinen−4−ol is a major constituent of Australian grown Melaleuca alternifolia (tea tree) oil, attributed with the oil's antimicrobial and anti−inflammatory properties. Plasma polymerisation was chosen as a deposition technique for it requires minimal use of harmful chemicals and produces no hazardous by−products. Polymer thin films were fabricated under varied process conditions to attain materials with distinct physico−chemical, optoelectrical, biological and degradation characteristics. The resultant materials, named polyterpenol, were extensively characterised using a number of well−accepted and novel techniques, and their fundamental properties were defined. Polyterpenol films were demonstrated to be hydrocarbon rich, with variable content of oxygen moieties, primarily in the form of hydroxyl and carboxyl functionalities. The level of preservation of original monomer functionality was shown to be strongly dependent on the deposition energy, with higher applied power increasing the molecular fragmentation and substrate temperature. Polyterpenol water contact angle contact angle increased from 62.7° for the 10 W samples to 76.3° for the films deposited at 100 W. Polymers were determined to resist solubilisation by water, due to the extensive intermolecular and intramolecular hydrogen bonds present, and other solvents commonly employed in electronics and biomedical processing. Independent of deposition power, the surface topography of the polymers was shown to be smooth (Rq <0.5 nm), uniform and defect free. Hardness of polyterpenol coatings increased from 0.33 GPa for 10 W to 0.51 GPa for 100 W (at 500 μN load). Coatings deposited at higher input RF powers showed less mechanical deformation during nanoscratch testing, with no considerable damage, cracking or delamination observed. Independent of the substrate, the quality of film adhesion improved with RF power, suggesting these coatings are likely to be more stable and less susceptible to wear. Independent of fabrication conditions, polyterpenol thin films were optically transparent, with refractive index approximating that of glass. Refractive index increased slightly with deposition power, from 1.54 (10 W) to 1.56 (100 W) at 500 nm. The optical band gap values declined with increasing power, from 2.95 eV to 2.64 eV, placing the material within the range for semiconductors. Introduction of iodine impurity reduced the band gap of polyterpenol, from 2.8 eV to 1.64 eV, by extending the density of states more into the visible region of the electromagnetic spectrum. Doping decreased the transparency and increased the refractive index from 1.54 to 1.70 (at 500 nm). At optical frequencies, the real part of permittivity (k) was determined to be between 2.34 and 2.65, indicating a potential low-k material. These permittivity values were confirmed at microwave frequencies, where permittivity increased with input RF energy – from 2.32 to 2.53 (at 10 GHz ) and from 2.65 to 2.83 (at 20 GHz). At low frequencies, the dielectric constant was determined from current−voltage characteristics of Al−polyterpenol−Al devices. At frequencies below 100 kHz, the dielectric constant varied with RF power, from 3.86 to 4.42 at 1 kHz. For all samples, the resistivity was in order of 10⁸−10⁹ _m (at 6 V), confirming the insulating nature of polyterpenol material. In situ iodine doping was demonstrated to increase the conductivity of polyterpenol, from 5.05 × 10⁻⁸ S/cm to 1.20 × 10⁻⁶ S/cm (at 20 V). Exposed to ambient conditions over extended period of time, polyterpenol thin films were demonstrated to be optically, physically and chemically stable. The bulk of ageing occurred within first 150 h after deposition and was attributed to oxidation and volumetric relaxation. Thermal ageing studies indicated thermal stability increased for the films manufactured at higher RF powers, with degradation onset temperature associated with weight loss shifting from 150 ºC to 205 ºC for 10 W and 100 W polyterpenol, respectively. Annealing the films to 405 °C resulted in full dissociation of the polymer, with minimal residue. Given the outcomes of the fundamental characterisation, a number of potential applications for polyterpenol have been identified. Flexibility, tunable permittivity and loss tangent properties of polyterpenol suggest the material can be used as an insulating layer in plastic electronics. Implementation of polyterpenol as a surface modification of the gate insulator in pentacene-based Field Effect Transistor resulted in significant improvements, shifting the threshold voltage from + 20 V to –3 V, enhancing the effective mobility from 0.012 to 0.021 cm²/Vs, and improving the switching property of the device from 10⁷ to 10⁴. Polyterpenol was demonstrated to have a hole transport electron blocking property, with potential applications in many organic devices, such as organic light emitting diodes. Encapsulation of biomedical devices is also proposed, given that under favourable conditions, the original chemical and biological functionality of terpinen−4−ol molecule can be preserved. Films deposited at low RF power were shown to successfully prevent adhesion and retention of several important human pathogens, including P. aeruginosa, S. aureus, and S. epidermidis, whereas films deposited at higher RF power promoted bacterial cell adhesion and biofilm formation. Preliminary investigations into in vitro biocompatibility of polyterpenol demonstrated the coating to be non−toxic for several types of eukaryotic cells, including Balb/c mice macrophage and human monocyte type (HTP−1 non-adherent) cells. Applied to magnesium substrates, polyterpenol encapsulating layer significantly slowed down in vitro biodegradation of the metal, thus increasing the viability and growth of HTP−1 cells. Recently, applied to varied nanostructured titanium surfaces, polyterpenol thin films successfully reduced attachment, growth, and viability of P. aeruginosa and S. aureus.

A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: Application to robust clustering

We propose a family of multivariate heavy-tailed distributions that allow variable marginal amounts of tailweight. The originality comes from introducing multidimensional instead of univariate scale variables for the mixture of scaled Gaussian family of distributions. In contrast to most existing approaches, the derived distributions can account for a variety of shapes and have a simple tractable form with a closed-form probability density function whatever the dimension. We examine a number of properties of these distributions and illustrate them in the particular case of Pearson type VII and t tails. For these latter cases, we provide maximum likelihood estimation of the parameters and illustrate their modelling flexibility on simulated and real data clustering examples.

The assessment of ecological condition in south-east Queensland, Australia: An evaluation of reliability across variable environments and surrogate efficacy for biodiversity values

Multimetric ecological condition assessment has become an important biodiversity management tool. This study was the first to examine the reliability of these ecological surrogates across variable environments, and the implications for surrogate efficacy. It was demonstrated that through strategic application and design of the multimetric ecological condition index, the effects of environmental gradients and disturbance regimes can be mitigated, and that ecological condition assessment may serve as a scientifically rigorous approach for conservation planning.

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.