Intron-mediated improvement of a selectable marker gene for plant transformation using Agrobacterium tumefaciens

In binary vectors, the antibiotic resistance gene used for selection of transformed plant cells is also usually expressed in the transforming Agrobacterium cells. This expression gives the bacterium antibiotic resistance, an unnecessary advantage on selective medium containing the antibiotic. Insertion of a castor bean catalase-1 (CAT-1) gene intron or a Parasponia andersonii haemoglobin gene intron into the coding region of the selectable marker gene, hph, completely abolished the expression of the gene in Agrobacterium, rendering it susceptible to hygromycin B. Use of these modified binary vectors minimized the overgrowth of Agrobacterium during plant transformation. Both of the introns were correctly spliced in plant cells and significantly enhanced hph gene expression in transformed rice tissue. The presence of these introns in the hph coding sequence not only maintained the selection efficiency of the hph gene, but with the CAT-1 intron also substantially increased the frequency of rice transformation. Transgenic lines with an intron-hph gene generally contained fewer gene copies and produced substantially more mRNA of the predicted size. Our results also indicate that transgenic plants with many copies of the transgene were more likely to show gene silencing than plants with 1-3 copies.

Australian Diabetes Foot Network : practical guideline on the provision of footwear for people with diabetes

Trauma, in the form of pressure and/or friction from footwear, is a common cause of foot ulceration in people with diabetes. These practical recommendations regarding the provision of footwear for people with diabetes were agreed upon following review of existing position statements and clinical guidelines. The aim of this process was not to re-invent existing guidelines but to provide practical guidance for health professionals on how they can best deliver these recommendations within the Australian health system. Where information was lacking or inconsistent, a consensus was reached following discussion by all authors. Appropriately prescribed footwear, used alone or in conjunction with custom-made foot orthoses, can reduce pedal pressures and reduce the risk of foot ulceration. It is important for all health professionals involved in the care of people with diabetes to both assess and make recommendations on the footwear needs of their clients or to refer to health professionals with such skills and knowledge. Individuals with more complex footwear needs (for example those who require custom-made medical grade footwear and orthoses) should be referred to health professionals with experience in the prescription of these modalities and who are able to provide appropriate and timely follow-up. Where financial disadvantage is a barrier to individuals acquiring appropriate footwear, health care professionals should be aware of state and territory based equipment funding schemes that can provide financial assistance. Aboriginal and Torres Strait Islanders and people living in rural and remote areas are likely to have limited access to a broad range of footwear. Provision of appropriate footwear to people with diabetes in these communities needs be addressed as part of a comprehensive national strategy to reduce the burden of diabetes and its complications on the health system.

Nucleotide sequence of coat protein gene for GPV isolate of barley yellow dwarf virus and construction of expression plasmid for plant

GPV is a Chinese serotype isolate of barley yellow dwarf virus (BYDV) that has no reaction with antiserum of MAV, PAV, SGV, RPV and RMV The sequence of the coat protein (CP) of GPV isolate of BYDV was identified and its amino acid sequence was deduced. The coding region for the putative GPV CP is 603 bases nucleotides and encodes a Mr 22 218 (22 ku) protein. The same as MAV, PAV and RPV, GPV contained a second ORF within the coat protein coding region. This protein of 17 024 Mr (17 ku) is thought to correspond to the Virion protein genome linked (Vpg). Sequence comparisons of the CP coding region between the GPV isolate of BYDV and other isolates of BYDV have been done. The nucleotide and amino acid sequence homology of GPV has a greater identity to the sequence of RPV than those of PAV and MAV. The GPV CP sequence stored 83.7% of nucleotide similarity and 77.5% of deduced amino acid similarity, whereas that of the PAV and MAV shared 56.9%, 53.2% and 44.1%, 43.8% respectively. According to BYDV-GPV CP sequence, two primers were designed. The cDNA of CP was produced by RT-PCR. Full-length cDNA of CP was inserted into plasmid to construct expression plasmids named pPPI1, pPPI2 and pPPI5 based on different promoters. The recombinant plasmids were identified by using α-32P-dATP labelled CP probe, α-32P-ATP labelled GPV RNA probe and sequencing to confirm real GPV CP gene cDNA in plasmids.

Sequence and identification of the barley yellow dwarf virus coat protein gene

The nucleotide sequence of the coat protein gene of barley yellow dwarf virus (BYDV, PAV serotype) was determined, and the amino acid sequence was deduced. The open reading frame, encoding a protein of relative molecular mass (Mr) 22,047, was confirmed as the coat protein gene by comparison with amino acid sequences of tryptic peptides derived from dissociated virions. In addition, a fragment of this gene expressed in Escherichia coli produced a product which was recognized by antibodies prepared against purified BYDV virions. An overlapping reading frame encoding an Mr 17,147 protein is contained completely within the coat protein gene. © 1988.

Brisbane Macroscopic Fundamental Diagram : empirical findings on network partitioning and incident detection

The Macroscopic Fundamental Diagram (MFD) relates space-mean density and flow, and the existence with dynamic features was confirmed in congested urban network in downtown Yokohama with real data set. Since the MFD represents the area-wide network traffic performances, studies on perimeter control strategies and an area traffic state estimation utilizing the MFD concept has been reported. However, limited works have been reported on real world example from signalised arterial network. This paper fuses data from multiple sources (Bluetooth, Loops and Signals) and develops a framework for the development of the MFD for Brisbane, Australia. Existence of the MFD in Brisbane arterial network is confirmed. Different MFDs (from whole network and several sub regions) are evaluated to discover the spatial partitioning in network performance representation. The findings confirmed the usefulness of appropriate network partitioning for traffic monitoring and incident detections. The discussion addressed future research directions

Health and guardianship law : the decision of X v the Sydney Children's Hospitals Network [2013] NSWCA 320 : mature minors and the refusal of blood products

This recent decision of the New South Wales Court of Appeal considers the scope of the parens patriae jurisdiction in cases where the jurisdiction is invoked for the protection of a Gillick competent minor. As outlined below, in certain circumstances the law recognises that mature minors are able to make their own decisions concerning medical treatment. However, there have been a number of Commonwealth decisions which have addressed the issue of whether mature minors are able to refuse medical procedures in circumstances where refusal will result in the minor dying. Ultimately, this case confirms that the minor does not necessarily have a right to make autonomous decisions; the minor’s right to exercise his or her autonomous decision only exists when such decision accords with what is deemed to be in his or her best interests.

Association between xenobiotic gene polymorphisms and non-Hodgkin's lymphoma risk

The last four decades have seen a significant increase in the incidence of non-Hodgkin's lymphoma (NHL) as a possible result of increasing environmental carcinogen exposure, particularly pesticides and solvents. Based on the increasing evidence for an association between carcinogen exposure-related cancer risk and xenobiotic gene polymorphisms, we have undertaken a case-control study of xenobiotic gene polymorphisms in individuals with a diagnosis of NHL. Polymorphisms of six xenobiotic genes (CYP1A1, GSTT1, GSTM1, PON1, NAT1, NAT2) were characterized in 169 individuals with NHL and 205 normal controls using polymerase chain reaction-based methods. Polymorphic frequencies were compared using Fisher's exact tests, and odds ratios for NHL risk were calculated. Among the NHL group, the incidence of GSTT1 null and PON1 BB genotypes were significantly increased compared with controls, 34% vs 14%, and 24% vs 11% respectively. Adjusted odds ratios calculated from multivariate analyses demonstrated that GSTT1 null conferred a fourfold increase in NHL risk (OR = 4.27; 95% CI, 2.40-7.61, P < 0.001) and PON1 BB a 2.9-fold increase (OR = 2.92; 95% CI, 1.49-5.72, P = 0.002). Furthermore, GSTT1 null combined with PON1 BB or GSTM1 null conferred an additional risk of NHL. This is the first time that a PON1 gene polymorphism has been shown to be associated with cancer risk. We conclude that the two polymorphisms, GSTT1 null and PON1 BB, are common genetic traits that pose low individual risk but may be important determinants of overall population NHL risk, particularly among groups exposed to NHL-related carcinogens.

Effects of wireless sensor network uncertainties on output-only modal-based damage identification

The use of Wireless Sensor Networks (WSNs) for Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data synchronization error and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research examining effects of uncertainties of generic WSN platform and verifying the capability of SHM-oriented WSNs, particularly on demanding SHM applications like modal analysis and damage identification of real civil structures. This article first reviews the major technical uncertainties of both generic and SHM-oriented WSN platforms and efforts of SHM research community to cope with them. Then, effects of the most inherent WSN uncertainty on the first level of a common Output-only Modal-based Damage Identification (OMDI) approach are intensively investigated. Experimental accelerations collected by a wired sensory system on a benchmark civil structure are initially used as clean data before being contaminated with different levels of data pollutants to simulate practical uncertainties in both WSN platforms. Statistical analyses are comprehensively employed in order to uncover the distribution pattern of the uncertainty influence on the OMDI approach. The result of this research shows that uncertainties of generic WSNs can cause serious impact for level 1 OMDI methods utilizing mode shapes. It also proves that SHM-WSN can substantially lessen the impact and obtain truly structural information without having used costly computation solutions.

Validation of β-actin used as endogenous control for gene expression analysis in mechanobiology studies : amendments

We write in response to the letter by Liu et al. [1] commenting on our article, ‘‘Mesenchymal Stem Cells Regulate Angiogenesis According to Their Mechanical Environment’’ [2]. The study by Liu et al. demonstrates that the commonly used endogeneous reference gene (ERG), b-actin, is upregulated by mechanical loading, indicating a potential bias in the determined target gene expression when normalizing to b-actin, such as in our report on unchanged vascular endothelial growth factor (VEGF) and hypoxia-inducible factors (HIF)-1a mRNA levels in mechanically loaded mesenchymal stem cells (MSCs).

Building adaptable agent-based models – application to the electricity distribution network

Agent-based modelling (ABM), like other modelling techniques, is used to answer specific questions from real world systems that could otherwise be expensive or impractical. Its recent gain in popularity can be attributed to some degree to its capacity to use information at a fine level of detail of the system, both geographically and temporally, and generate information at a higher level, where emerging patterns can be observed. This technique is data-intensive, as explicit data at a fine level of detail is used and it is computer-intensive as many interactions between agents, which can learn and have a goal, are required. With the growing availability of data and the increase in computer power, these concerns are however fading. Nonetheless, being able to update or extend the model as more information becomes available can become problematic, because of the tight coupling of the agents and their dependence on the data, especially when modelling very large systems. One large system to which ABM is currently applied is the electricity distribution where thousands of agents representing the network and the consumers’ behaviours are interacting with one another. A framework that aims at answering a range of questions regarding the potential evolution of the grid has been developed and is presented here. It uses agent-based modelling to represent the engineering infrastructure of the distribution network and has been built with flexibility and extensibility in mind. What distinguishes the method presented here from the usual ABMs is that this ABM has been developed in a compositional manner. This encompasses not only the software tool, which core is named MODAM (MODular Agent-based Model) but the model itself. Using such approach enables the model to be extended as more information becomes available or modified as the electricity system evolves, leading to an adaptable model. Two well-known modularity principles in the software engineering domain are information hiding and separation of concerns. These principles were used to develop the agent-based model on top of OSGi and Eclipse plugins which have good support for modularity. Information regarding the model entities was separated into a) assets which describe the entities’ physical characteristics, and b) agents which describe their behaviour according to their goal and previous learning experiences. This approach diverges from the traditional approach where both aspects are often conflated. It has many advantages in terms of reusability of one or the other aspect for different purposes as well as composability when building simulations. For example, the way an asset is used on a network can greatly vary while its physical characteristics are the same – this is the case for two identical battery systems which usage will vary depending on the purpose of their installation. While any battery can be described by its physical properties (e.g. capacity, lifetime, and depth of discharge), its behaviour will vary depending on who is using it and what their aim is. The model is populated using data describing both aspects (physical characteristics and behaviour) and can be updated as required depending on what simulation is to be run. For example, data can be used to describe the environment to which the agents respond to – e.g. weather for solar panels, or to describe the assets and their relation to one another – e.g. the network assets. Finally, when running a simulation, MODAM calls on its module manager that coordinates the different plugins, automates the creation of the assets and agents using factories, and schedules their execution which can be done sequentially or in parallel for faster execution. Building agent-based models in this way has proven fast when adding new complex behaviours, as well as new types of assets. Simulations have been run to understand the potential impact of changes on the network in terms of assets (e.g. installation of decentralised generators) or behaviours (e.g. response to different management aims). While this platform has been developed within the context of a project focussing on the electricity domain, the core of the software, MODAM, can be extended to other domains such as transport which is part of future work with the addition of electric vehicles.

Modelling for the electricity distribution network

Global awareness for cleaner and renewable energy is transforming the electricity sector at many levels. New technologies are being increasingly integrated into the electricity grid at high, medium and low voltage levels, new taxes on carbon emissions are being introduced and individuals can now produce electricity, mainly through rooftop photovoltaic (PV) systems. While leading to improvements, these changes also introduce challenges, and a question that often rises is ‘how can we manage this constantly evolving grid?’ The Queensland Government and Ergon Energy, one of the two Queensland distribution companies, have partnered with some Australian and German universities on a project to answer this question in a holistic manner. The project investigates the impact the integration of renewables and other new technologies has on the physical structure of the grid, and how this evolving system can be managed in a sustainable and economical manner. To aid understanding of what the future might bring, a software platform has been developed that integrates two modelling techniques: agent-based modelling (ABM) to capture the characteristics of the different system units accurately and dynamically, and particle swarm optimization (PSO) to find the most economical mix of network extension and integration of distributed generation over long periods of time. Using data from Ergon Energy, two types of networks (3 phase, and Single Wired Earth Return or SWER) have been modelled; three-phase networks are usually used in dense networks such as urban areas, while SWER networks are widely used in rural Queensland. Simulations can be performed on these networks to identify the required upgrades, following a three-step process: a) what is already in place and how it performs under current and future loads, b) what can be done to manage it and plan the future grid and c) how these upgrades/new installations will perform over time. The number of small-scale distributed generators, e.g. PV and battery, is now sufficient (and expected to increase) to impact the operation of the grid, which in turn needs to be considered by the distribution network manager when planning for upgrades and/or installations to stay within regulatory limits. Different scenarios can be simulated, with different levels of distributed generation, in-place as well as expected, so that a large number of options can be assessed (Step a). Once the location, sizing and timing of assets upgrade and/or installation are found using optimisation techniques (Step b), it is possible to assess the adequacy of their daily performance using agent-based modelling (Step c). One distinguishing feature of this software is that it is possible to analyse a whole area at once, while still having a tailored solution for each of the sub-areas. To illustrate this, using the impact of battery and PV can have on the two types of networks mentioned above, three design conditions can be identified (amongst others): · Urban conditions o Feeders that have a low take-up of solar generators, may benefit from adding solar panels o Feeders that need voltage support at specific times, may be assisted by installing batteries · Rural conditions - SWER network o Feeders that need voltage support as well as peak lopping may benefit from both battery and solar panel installations. This small example demonstrates that no single solution can be applied across all three areas, and there is a need to be selective in which one is applied to each branch of the network. This is currently the function of the engineer who can define various scenarios against a configuration, test them and iterate towards an appropriate solution. Future work will focus on increasing the level of automation in identifying areas where particular solutions are applicable.

Targeting nuclear factor-kappa B to overcome resistance to chemotherapy

Intrinsic or acquired resistance to chemotherapeutic agents is a common phenomenon and a major challenge in the treatment of cancer patients. Chemoresistance is defined by a complex network of factors including multi-drug resistance proteins, reduced cellular uptake of the drug, enhanced DNA repair, intracellular drug inactivation, and evasion of apoptosis. Pre-clinical models have demonstrated that many chemotherapy drugs, such as platinum-based agents, antracyclines, and taxanes, promote the activation of the NF-κB pathway. NF-κB is a key transcription factor, playing a role in the development and progression of cancer and chemoresistance through the activation of a multitude of mediators including anti-apoptotic genes. Consequently, NF-κB has emerged as a promising anti-cancer target. Here, we describe the role of NF-κB in cancer and in the development of resistance, particularly cisplatin. Additionally, the potential benefits and disadvantages of targeting NF-κB signaling by pharmacological intervention will be addressed.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific sub-regions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.