Transcript and metabolite profiling for the evaluation of tobacco tree and poplar as feedstock for the bio-based industry

The global demand for food, feed, energy and water poses extraordinary challenges for future generations. It is evident that robust platforms for the exploration of renewable resources are necessary to overcome these challenges. Within the multinational framework MultiBioPro we are developing biorefinery pipelines to maximize the use of plant biomass. More specifically, we use poplar and tobacco tree (Nicotiana glauca) as target crop species for improving saccharification, isoprenoid, long chain hydrocarbon contents, fiber quality, and suberin and lignin contents. The methods used to obtain these outputs include GC-MS, LC-MS and RNA sequencing platforms. The metabolite pipelines are well established tools to generate these types of data, but also have the limitations in that only well characterized metabolites can be used. The deep sequencing will allow us to include all transcripts present during the developmental stages of the tobacco tree leaf, but has to be mapped back to the sequence of Nicotiana tabacum. With these set-ups, we aim at a basic understanding for underlying processes and at establishing an industrial framework to exploit the outcomes. In a more long term perspective, we believe that data generated here will provide means for a sustainable biorefinery process using poplar and tobacco tree as raw material. To date the basal level of metabolites in the samples have been analyzed and the protocols utilized are provided in this article.

Computation of ECG signal features using MCMC modelling in software and FPGA reconfigurable hardware

Computational optimisation of clinically important electrocardiogram signal features, within a single heart beat, using a Markov-chain Monte Carlo (MCMC) method is undertaken. A detailed, efficient data-driven software implementation of an MCMC algorithm has been shown. Initially software parallelisation is explored and has been shown that despite the large amount of model parameter inter-dependency that parallelisation is possible. Also, an initial reconfigurable hardware approach is explored for future applicability to real-time computation on a portable ECG device, under continuous extended use.

A taxonomy of computation offloading in mobile cloud computing

The ability of cloud computing to provide almost unlimited storage, backup and recovery, and quick deployment contributes to its widespread attention and implementation. Cloud computing has also become an attractive choice for mobile users as well. Due to limited features of mobile devices such as power scarcity and inability to cater computationintensive tasks, selected computation needs to be outsourced to the resourceful cloud servers. However, there are many challenges which need to be addressed in computation offloading for mobile cloud computing such as communication cost, connectivity maintenance and incurred latency. This paper presents taxonomy of the computation offloading approaches which aim to address the challenges. The taxonomy provides guidelines to identify research scopes in computation offloading for mobile cloud computing. We also outline directions and anticipated trends for future research.

Computational science for undergraduate biologists via QUT.Bio.Excel

Molecular biology is a scientific discipline which has changed fundamentally in character over the past decade to rely on large scale datasets – public and locally generated - and their computational analysis and annotation. Undergraduate education of biologists must increasingly couple this domain context with a data-driven computational scientific method. Yet modern programming and scripting languages and rich computational environments such as R and MATLAB present significant barriers to those with limited exposure to computer science, and may require substantial tutorial assistance over an extended period if progress is to be made. In this paper we report our experience of undergraduate bioinformatics education using the familiar, ubiquitous spreadsheet environment of Microsoft Excel. We describe a configurable extension called QUT.Bio.Excel, a custom ribbon, supporting a rich set of data sources, external tools and interactive processing within the spreadsheet, and a range of problems to demonstrate its utility and success in addressing the needs of students over their studies.

Vertical graphene gas- and bio-sensors via catalyst-free, reactive plasma reforming of natural honey

A rapid reforming of natural honey exposed to reactive low-temperature Ar + H2 plasmas produced high-quality, ultra-thin vertical graphenes, without any metal catalyst or external heating. This transformation is only possible in the plasma and fails in similar thermal processes. The process is energy-efficient, environmentally benign, and is much cheaper than common synthesis methods based on purified hydrocarbon precursors. The graphenes retain the essential minerals of natural honey, feature reactive open edges and reliable gas- and bio-sensing performance.

Reactive plasma-aided RF sputtering deposition of hydroxyapatite bio-implant coatings

The plasma-assisted RF sputtering deposition of a biocompatible, functionally graded calcium phosphate bioceramic on a Ti6A14 V orthopedic alloy is reported. The chemical composition and presence of hydroxyapatite (HA), CaTiO3, and CaO mineral phases can be effectively controlled by the process parameters. At higher DC biases, the ratio [Ca]/[P] and the amount of CaO increase, whereas the HA content decreases. Optical emission spectroscopy suggests that CaO+ is the dominant species that responds to negative DC bias and controls calcium content. Biocompatibility tests in simulated body fluid confirm a positive biomimetic response evidenced by in-growth of an apatite layer after 24 h of immersion.

Stoichiometry, crystallinity, and nano-scale surface morphology of the graded calcium phosphate-based bio-ceramic interlayer on Ti-Al-V

A plasma-assisted concurrent Rf sputtering technique for fabrication of biocompatible, functionally graded CaP-based interlayer on Ti-6Al-4V orthopedic alloy is reported. Each layer in the coating is designed to meet a specific functionality. The adherent to the metal layer features elevated content of Ti and supports excellent ceramic-metal interfacial stability. The middle layer features nanocrystalline structure and mimics natural bone apatites. The technique allows one to reproduce Ca/P ratios intrinsic to major natural calcium phosphates. Surface morphology of the outer, a few to few tens of nanometers thick, layer, has been tailored to fit the requirements for the bio-molecule/protein attachment factors. Various material and surface characterization techniques confirm that the optimal surface morphology of the outer layer is achieved for the process conditions yielding nanocrystalline structure of the middle layer. Preliminary cell culturing tests confirm the link between the tailored nano-scale surface morphology, parameters of the middle nanostructured layer, and overall biocompatibility of the coating.

Reactive species in RF plasma sputtering deposition of nanocrystalline calcium phosphate bio-active films

Optical emission of reactive plasma species during the synthesis of functionally graded calcium phosphate-based bioactive films has been investigated. The coatings have been deposited on Ti-6Al-4V orthopedic alloy by co-sputtering of hydroxyapatite (HA) and titanium targets in reactive plasmas of Ar + H2O gas mixtures. The species, responsible for the Ca-P-Ti film growth have been non-intrusively monitored in situ by a high-resolution optical emission spectroscopy (OES). It is revealed that the optical emission originating from CaO species dominates throughout the deposition process. The intensities of CaO, PO and CaPO species are strongly affected by variations of the operating pressure, applied RF power, and DC substrate bias. The optical emission intensity (OEI) of reaction species can efficiently be controlled by addition of H2O reactant.

On secure multi-party computation in black-box groups

We study the natural problem of secure n-party computation (in the passive, computationally unbounded attack model) of the n-product function f G (x 1,...,x n ) = x 1 ·x 2 ⋯ x n in an arbitrary finite group (G,·), where the input of party P i is x i  ∈ G for i = 1,...,n. For flexibility, we are interested in protocols for f G which require only black-box access to the group G (i.e. the only computations performed by players in the protocol are a group operation, a group inverse, or sampling a uniformly random group element). Our results are as follows. First, on the negative side, we show that if (G,·) is non-abelian and n ≥ 4, then no ⌈n/2⌉-private protocol for computing f G exists. Second, on the positive side, we initiate an approach for construction of black-box protocols for f G based on k-of-k threshold secret sharing schemes, which are efficiently implementable over any black-box group G. We reduce the problem of constructing such protocols to a combinatorial colouring problem in planar graphs. We then give two constructions for such graph colourings. Our first colouring construction gives a protocol with optimal collusion resistance t < n/2, but has exponential communication complexity O(n*2t+1^2/t) group elements (this construction easily extends to general adversary structures). Our second probabilistic colouring construction gives a protocol with (close to optimal) collusion resistance t < n/μ for a graph-related constant μ ≤ 2.948, and has efficient communication complexity O(n*t^2) group elements. Furthermore, we believe that our results can be improved by further study of the associated combinatorial problems.

Defining the spatiotemporal surveillance space for alien species’ invasions using approximate Bayesian computation

The spatiotemporal dynamics of an alien species invasion across a real landscape are typically complex. While surveillance is an essential part of a management response, planning surveillance in space and time present a difficult challenge due to this complexity. We show here a method for determining the highest probability sites for occupancy across a landscape at an arbitrary point in the future, based on occupancy data from a single slice in time. We apply to the method to the invasion of Giant Hogweed, a serious weed in the Czech republic and throughout Europe.

Interpreting scratch assays using pair density dynamics and approximate Bayesian computation

Quantifying the impact of biochemical compounds on collective cell spreading is an essential element of drug design, with various applications including developing treatments for chronic wounds and cancer. Scratch assays are a technically simple and inexpensive method used to study collective cell spreading; however, most previous interpretations of scratch assays are qualitative and do not provide estimates of the cell diffusivity, D, or the cell proliferation rate,l. Estimating D and l is important for investigating the efficacy of a potential treatment and provides insight into the mechanism through which the potential treatment acts. While a few methods for estimating D and l have been proposed, these previous methods lead to point estimates of D and l, and provide no insight into the uncertainty in these estimates. Here, we compare various types of information that can be extracted from images of a scratch assay, and quantify D and l using discrete computational simulations and approximate Bayesian computation. We show that it is possible to robustly recover estimates of D and l from synthetic data, as well as a new set of experimental data. For the first time, our approach also provides a method to estimate the uncertainty in our estimates of D and l. We anticipate that our approach can be generalized to deal with more realistic experimental scenarios in which we are interested in estimating D and l, as well as additional relevant parameters such as the strength of cell-to-cell adhesion or the strength of cell-to-substrate adhesion.

100 futures now : designing sustainable futures

This presentation tells the story of an initiative in middle schooling at Kelvin Grove State College that begins in the Art studios, but reaches out to other disciplines and approaches, and to community and industry partners. It is inspired by the potential of 'future thinking' to become a compelling focus in contemporary art and design. Ethically it espouses a simple premise": every student in our classrooms now has a stake in creating livable, democratic and creative futures. Every student has the potential to be an active force in making that future. "100 Futures Now" is a project that envisages creative and imaginative students working in collaboration with artists and designers to visualize amazing futures and communicate their vision through art and design. "100 Futures Now" is one in a series of innovative curriculum initiatives at Kelvin Grove State College designed to build sustainable practice in arts education with the support of partners in industry and universities and with resident artists and designers. The model blends elements of art and design methodology to focus on the critical and creative thinking skills prioritised in ACARA and 21st century curriculum. The organisers are developing a sustainable model for working with resident artists that goes beyond a single arts intervention or extension/enrichment experience. In this model artists and designers are collaborators in the design of learning experiences that support future programs. This model also looks to transfer the benefits of residencies to the wider school community (in this case to middle schooling curriculum) and to teachers in other curriculum areas, and not exclusively to the immediate target group. In "100 Futures Now", story-making is the engine that powers the creative process. For this reason the program uses a series of imaginative scenarios, including those of speculative fiction and science, as departure points for inquiry, and applies the methodologies of arts and design practice to explore and express student story telling and story making. The story-making responses of student teams will naturally be expressed multimodally through visual art, design artifacts, installation, performance and digital works. The project’s focus on narratives and its modes of communication (performance/installation) are inspired by the work of experimental contemporary design practices and the speculative scenarios of U.K. based designers Anthony Dunne and Fiona Raby. Thanks to the support of an Arts Queensland Artist-in -Residence grant in 2014, resident artists and designers who work with a diversity of ideas and approaches ranging over science, bio-ethics, biodiversity, behavior and ethics, ambient sound, urbanism, food, and wearable design, will work with middle school students as catalysts for deeper thinking and creative action. All these rich fields for future speculation will become triggers for team inquiry into the deeper connections between the past, the present, and future challenges such as climate, waste, energy, sustainability and resilience. These imagined futures will form the platform for a critical, sustainability/design futures approach that will involve questioning assumptions and empowering students as agents rather than consumers of change.

Approximate Bayesian Computation for astronomical model analysis : a case study in galaxy demographics and morphological transformation at high redshift

Approximate Bayesian Computation’ (ABC) represents a powerful methodology for the analysis of complex stochastic systems for which the likelihood of the observed data under an arbitrary set of input parameters may be entirely intractable – the latter condition rendering useless the standard machinery of tractable likelihood-based, Bayesian statistical inference [e.g. conventional Markov chain Monte Carlo (MCMC) simulation]. In this paper, we demonstrate the potential of ABC for astronomical model analysis by application to a case study in the morphological transformation of high-redshift galaxies. To this end, we develop, first, a stochastic model for the competing processes of merging and secular evolution in the early Universe, and secondly, through an ABC-based comparison against the observed demographics of massive (Mgal > 1011 M⊙) galaxies (at 1.5 < z < 3) in the Cosmic Assembly Near-IR Deep Extragalatic Legacy Survey (CANDELS)/Extended Groth Strip (EGS) data set we derive posterior probability densities for the key parameters of this model. The ‘Sequential Monte Carlo’ implementation of ABC exhibited herein, featuring both a self-generating target sequence and self-refining MCMC kernel, is amongst the most efficient of contemporary approaches to this important statistical algorithm. We highlight as well through our chosen case study the value of careful summary statistic selection, and demonstrate two modern strategies for assessment and optimization in this regard. Ultimately, our ABC analysis of the high-redshift morphological mix returns tight constraints on the evolving merger rate in the early Universe and favours major merging (with disc survival or rapid reformation) over secular evolution as the mechanism most responsible for building up the first generation of bulges in early-type discs.

Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets

Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated sampling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1–28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model diversity for thorough exploration of the model space. This algorithm was applied to a validating example to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission examples of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.

Biologically inspired SLAM using Wi-Fi

Wi-Fi is a commonly available source of localization information in urban environments but is challenging to integrate into conventional mapping architectures. Current state of the art probabilistic Wi-Fi SLAM algorithms are limited by spatial resolution and an inability to remove the accumulation of rotational error, inherent limitations of the Wi-Fi architecture. In this paper we leverage the low quality sensory requirements and coarse metric properties of RatSLAM to localize using Wi-Fi fingerprints. To further improve performance, we present a novel sensor fusion technique that integrates camera and Wi-Fi to improve localization specificity, and use compass sensor data to remove orientation drift. We evaluate the algorithms in diverse real world indoor and outdoor environments, including an office floor, university campus and a visually aliased circular building loop. The algorithms produce topologically correct maps that are superior to those produced using only a single sensor modality.