Materials and technologies for energy conversion, saving and storage: A global network enabling capacity-building for sustainable energy in developing countries

Access to energy is a fundamental component of poverty abatement. People who live in homes without electricity are often dependent on dirty, time-consuming and disproportionately expensive solid fuel sources for heating and cooking. [1] In developing countries, the Human Development Index (HDI), which comprises measures of standard of living, longevity and educational attainment, increases rapidly with per capita electricity use. [2] For these reasons the United Nations has been making a concerted effort to promote global access to energy, first by naming 2012 the Year of Sustainable Energy for All, [3] and now by declaring 2014-2024 the Decade of Sustainable Energy for All. [4]

Gene patent practice across plant and human genomes

The uses of genetic sequences to inform, enable or create products or services for human biomedicine are substantially different from their uses in crop-based agriculture. Here, we explore what similarities and differences may emerge in patent use and strategies, and map patent-disclosed sequences onto three important plant genomes: maize (corn), rice and soybean. We focus on those referenced in the granted patent claims to compare their uses to the approach used in human gene patenting.

The ownership question of plant gene and genome intellectual properties

The restructuring of the crop agriculture industry over the past two decades has enabled patent holders to exclude, prevent and deter others from using certain research tools and delay or block further follow-on inventions

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

The experience of forging new emergency nursing curriculum development, through a course advisory network

Queensland University of Technology (QUT), School of Nursing (SoN), has offered a postgraduate Graduate Certificate in Emergency Nursing since 2003, for registered nurses practising in an emergency clinical area, who fulfil key entry criteria. Feedback from industry partners and students evidenced support for flexible and extended study pathways in emergency nursing. Therefore, in the context of a growing demand for emergency health services and the need for specialist qualified staff, it was timely to review and redevelop our emergency specialist nursing courses. The QUT postgraduate emergency nursing study area is supported by a course advisory group, whose aim is to provide input and focus development of current and future course planning. All members of the course advisory were invited to form an expert panel to review current emergency course documents. A half day “brainstorm session”, planning and development workshop was held to review the emergency courses to implement changes from 2009. Results from the expert panel planning day include: proposal for a new emergency specialty unit; incorporation of the College of Emergency Nurses (CENA) Standards for Emergency Nursing Specialist in clinical assessment; modification of the present core emergency unit; enhancing the focus of the two other units that emergency students undertake; and opening the emergency study area to the Graduate Diploma in Nursing (Emergency Nursing) and Master of Nursing (Emergency Nursing). The conclusion of the brainstorm session resulted in a clearer conceptualisation, of the study pathway for students. Overall, the expert panel group of enthusiastic emergency educators and clinicians provided viable options for extending the career progression opportunities for emergency nurses. In concluding, the opportunity for collaboration across university and clinical settings has resulted in the design of a course with exciting potential and strong clinical relevance.

Structures of three different neutral polysaccharides of Acinetobacter baumannii, NIPH190, NIPH201, and NIPH615, assigned to K30, K45, and K48 capsule types, respectively, based on capsule biosynthesis gene clusters

Neutral capsular polysaccharides (CPSs) were isolated from Acinetobacter baumannii NIPH190, NIPH201, and NIPH615. The CPSs were found to contain common monosaccharides only and to be branched with a side-chain 1→3-linked β-d-glucopyranose residue. Structures of the oligosaccharide repeat units (K units) of the CPSs were elucidated by 1D and 2D 1H and 13C NMR spectroscopy. Novel CPS biosynthesis gene clusters, designated KL30, KL45, and KL48, were found at the K locus in the genome sequences of NIPH190, NIPH201, and NIPH615, respectively. The genetic content of each gene cluster correlated with the structure of the CPS unit established, and therefore, the capsular types of the strains studied were designated as K30, K45, and K48, respectively. The initiating sugar of each K unit was predicted, and glycosyltransferases encoded by each gene cluster were assigned to the formation of the linkages between sugars in the corresponding K unit.

Oxygen transport in a three-dimensional microvascular network incorporated with early tumour growth and preexisting vessel cooption: Numerical simulation study

We propose a dynamic mathematical model of tissue oxygen transport by a preexisting three-dimensional microvascular network which provides nutrients for an in situ cancer at the very early stage of primary microtumour growth. The expanding tumour consumes oxygen during its invasion to the surrounding tissues and cooption of host vessels. The preexisting vessel cooption, remodelling and collapse are modelled by the changes of haemodynamic conditions due to the growing tumour. A detailed computational model of oxygen transport in tumour tissue is developed by considering (a) the time-varying oxygen advection diffusion equation within the microvessel segments, (b) the oxygen flux across the vessel walls, and (c) the oxygen diffusion and consumption with in the tumour and surrounding healthy tissue. The results show the oxygen concentration distribution at different time points of early tumour growth. In addition, the influence of preexisting vessel density on the oxygen transport has been discussed. The proposed model not only provides a quantitative approach for investigating the interactions between tumour growth and oxygen delivery, but also is extendable to model other molecules or chemotherapeutic drug transport in the future study.

Association of microRNA 17–92 cluster host gene (MIR17HG) polymorphisms with breast cancer

Breast cancer incidence and mortality rates are increasing despite our current knowledge on the disease. Ninety-five percent of breast cancer cases correspond to sporadic forms of the disease and are believed to involve an interaction between environmental and genetic determinants. The microRNA 17–92 cluster host gene (MIR17HG) has been shown to regulate expression of genes involved in breast cancer development and progression. Study of single-nucleotide polymorphisms (SNPs) located in this cluster gene could help provide a further understanding of its role in breast cancer. Therefore, this study investigated six SNPs in the MIR17HG using two independent Australian Caucasian case–control populations (GRC-BC and GU-CCQ BB populations) to determine association to breast cancer susceptibility. Genotyping was undertaken using chip-based matrix assisted laser desorption ionisation time-of-flight (MALDI-TOF) mass spectrometry (MS). We found significant association between rs4824505 and breast cancer at the allelic level in both study cohorts (GRC-BC p = 0.01 and GU-CCQ BB p = 0.03). Furthermore, haplotypic analysis of results from our combined population determined a significant association between rs4824505/rs7336610 and breast cancer susceptibility (p = 5 × 10−4). Our study is the first to show that the A allele of rs4824505 and the AC haplotype of rs4824505/rs7336610 are associated with risk of breast cancer development. However, definitive validation of this finding requires larger cohorts or populations in different ethnical backgrounds. Finally, functional studies of these SNPs could provide a deeper understanding of the role that MIR17HG plays in the pathophysiology of breast cancer.

Optimized group replacement scheduling for water pipeline network

Replacement of deteriorated water pipes is a capital-intensive activity for utility companies. Replacement planning aims to minimize total costs while maintaining a satisfactory level of service and is usually conducted for individual pipes. Scheduling replacement in groups is seen to be a better method and has the potential to provide benefits such as the reduction of maintenance costs and service interruptions. However, developing group replacement schedules is a complex task and often beyond the ability of a human expert, especially when multiple or conflicting objectives need to be catered for, such as minimization of total costs and service interruptions. This paper describes the development of a novel replacement decision optimization model for group scheduling (RDOM-GS), which enables multiple group-scheduling criteria by integrating new cost functions, a service interruption model, and optimization algorithms into a unified procedure. An industry case study demonstrates that RDOM-GS can improve replacement planning significantly and reduce costs and service interruptions.

Effects of increasing neuromuscular electrical stimulation current intensity on cortical sensorimotor network activation: A time domain fNIRS study

Neuroimaging studies have shown neuromuscular electrical stimulation (NMES)-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and secondary somatosensory area (S2), as well as regions of the prefrontal cortex (PFC) known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI), and with reference to voluntary (VOL) wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O₂Hb) and deoxygenated (HHb) hemoglobin concentrations were estimated. The O₂Hb and HHb maps obtained using the General Linear Model (NIRS-SPM) analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O₂Hb and corresponding decrease in HHb) in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2). However, the level and area of contralateral sensorimotor network (including PFC) activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions.

Comparison of decision tree, support vector machines, and Bayesian network approaches for classification of falls in Parkinson’s disease

Being able to accurately predict the risk of falling is crucial in patients with Parkinson’s dis- ease (PD). This is due to the unfavorable effect of falls, which can lower the quality of life as well as directly impact on survival. Three methods considered for predicting falls are decision trees (DT), Bayesian networks (BN), and support vector machines (SVM). Data on a 1-year prospective study conducted at IHBI, Australia, for 51 people with PD are used. Data processing are conducted using rpart and e1071 packages in R for DT and SVM, con- secutively; and Bayes Server 5.5 for the BN. The results show that BN and SVM produce consistently higher accuracy over the 12 months evaluation time points (average sensitivity and specificity > 92%) than DT (average sensitivity 88%, average specificity 72%). DT is prone to imbalanced data so needs to adjust for the misclassification cost. However, DT provides a straightforward, interpretable result and thus is appealing for helping to identify important items related to falls and to generate fallers’ profiles.

Micro-CT based characterisation of changes to the vascular network following closed soft tissue trauma and cryotherapy

This project has investigated how the architecture of the blood vessels supplying nutrients to skeletal muscles is affected by muscle contusion injuries, and how it changes during healing with or without initial treatment of the injury by icing. In order to do this, we used contrast agents to visualise blood vessels in 3D with micro-computed tomography imaging. This research significantly contributes to the fields of orthopaedics, traumatology and sports medicine, as it improves our understanding of muscle contusion injuries. Furthermore, the methods developed in this thesis may help to improve the diagnosis and monitoring of these injuries.

Mammalian reverse genetics without crossing reveals Nr3a as a short-sleeper gene

The identification of molecular networks at the system level in mammals is accelerated by next-generation mammalian genetics without crossing, which requires both the efficient production of whole-body biallelic knockout (KO) mice in a single generation and high-performance phenotype analyses. Here, we show that the triple targeting of a single gene using the CRISPR/Cas9 system achieves almost perfect KO efficiency (96%–100%). In addition, we developed a respiration-based fully automated noninvasive sleep phenotyping system, the Snappy Sleep Stager (SSS), for high-performance (95.3% accuracy) sleep/wake staging. Using the triple-target CRISPR and SSS in tandem, we reliably obtained sleep/wake phenotypes, even in double-KO mice. By using this system to comprehensively analyze all of the N-methyl-D-aspartate (NMDA) receptor family members, we found Nr3a as a short-sleeper gene, which is verified by an independent set of triple-target CRISPR. These results demonstrate the application of mammalian reverse genetics without crossing to organism-level systems biology in sleep research.

Dynamic Bayesian network inferencing for non-homogeneous complex systems

Dynamic Bayesian Networks (DBNs) provide a versatile platform for predicting and analysing the behaviour of complex systems. As such, they are well suited to the prediction of complex ecosystem population trajectories under anthropogenic disturbances such as the dredging of marine seagrass ecosystems. However, DBNs assume a homogeneous Markov chain whereas a key characteristics of complex ecosystems is the presence of feedback loops, path dependencies and regime changes whereby the behaviour of the system can vary based on past states. This paper develops a method based on the small world structure of complex systems networks to modularise a non-homogeneous DBN and enable the computation of posterior marginal probabilities given evidence in forwards inference. It also provides an approach for an approximate solution for backwards inference as convergence is not guaranteed for a path dependent system. When applied to the seagrass dredging problem, the incorporation of path dependency can implement conditional absorption and allows release from the zero state in line with environmental and ecological observations. As dredging has a marked global impact on seagrass and other marine ecosystems of high environmental and economic value, using such a complex systems model to develop practical ways to meet the needs of conservation and industry through enhancing resistance and/or recovery is of paramount importance.