Aqueous phase synthesis of copper nanoparticles : a link between heavy metal resistance and nanoparticle synthesis ability in bacterial systems

We demonstrate aqueous phase biosynthesis of phase-pure metallic copper nanoparticles (CuNPs) using a silver resistant bacterium Morganella morganii. This is particularly important considering that there has been no report that demonstrates biosynthesis and stabilization of pure copper nanoparticles in the aqueous phase. Electrochemical analysis of bacterial cells exposed to Cu2+ ions provides new insights into the mechanistic aspect of Cu2+ ion reduction within the bacterial cell and indicates a strong link between the silver and copper resistance machinery of bacteria in the context of metal ion reduction. The outcomes of this study take us a step closer towards designing rational strategies for biosynthesis of different metal nanoparticles using microorganisms.

Does a 10-valent pneumococcal-Haemophilus influenzae protein D conjugate vaccine prevent respiratory exacerbations in children with recurrent protracted bacterial bronchitis, chronic suppurative lung disease and bronchiectasis : protocol for a randomised controlled trial

Background Recurrent protracted bacterial bronchitis (PBB), chronic suppurative lung disease (CSLD) and bronchiectasis are characterised by a chronic wet cough and are important causes of childhood respiratory morbidity globally. Haemophilus influenzae and Streptococcus pneumoniae are the most commonly associated pathogens. As respiratory exacerbations impair quality of life and may be associated with disease progression, we will determine if the novel 10-valent pneumococcal-Haemophilus influenzae protein D conjugate vaccine (PHiD-CV) reduces exacerbations in these children. Methods A multi-centre, parallel group, double-blind, randomised controlled trial in tertiary paediatric centres from three Australian cities is planned. Two hundred six children aged 18 months to 14 years with recurrent PBB, CSLD or bronchiectasis will be randomised to receive either two doses of PHiD-CV or control meningococcal (ACYW(135)) conjugate vaccine 2 months apart and followed for 12 months after the second vaccine dose. Randomisation will be stratified by site, age (<6 years and >= 6 years) and aetiology (recurrent PBB or CSLD/bronchiectasis). Clinical histories, respiratory status (including spirometry in children aged >= 6 years), nasopharyngeal and saliva swabs, and serum will be collected at baseline and at 2, 3, 8 and 14 months post-enrolment. Local and systemic reactions will be recorded on daily diaries for 7 and 30 days, respectively, following each vaccine dose and serious adverse events monitored throughout the trial. Fortnightly, parental contact will help record respiratory exacerbations. The primary outcome is the incidence of respiratory exacerbations in the 12 months following the second vaccine dose. Secondary outcomes include: nasopharyngeal carriage of H. influenzae and S. pneumoniae vaccine and vaccine-related serotypes; systemic and mucosal immune responses to H. influenzae proteins and S. pneumoniae vaccine and vaccine-related serotypes; impact upon lung function in children aged >= 6 years; and vaccine safety. Discussion As H. influenzae is the most common bacterial pathogen associated with these chronic respiratory diseases in children, a novel pneumococcal conjugate vaccine that also impacts upon H. influenzae and helps prevent respiratory exacerbations would assist clinical management with potential short- and long-term health benefits. Our study will be the first to assess vaccine efficacy targeting H. influenzae in children with recurrent PBB, CSLD and bronchiectasis.

Design of physiological control and magnetic levitation systems for a total artificial heart

Total Artificial Hearts are mechanical pumps which can be used to replace the failing natural heart. This novel study developed a means of controlling a new design of pump to reproduce physiological flow bringing closer the realisation of a practical artificial heart. Using a mathematical model of the device, an optimisation algorithm was used to determine the best configuration for the magnetic levitation system of the pump. The prototype device was constructed and tested in a mock circulation loop. A physiological controller was designed to replicate the Frank-Starling like balancing behaviour of the natural heart. The device and controller provided sufficient support for a human patient while also demonstrating good response to various physiological conditions and events. This novel work brings the design of a practical artificial heart closer to realisation.

A 22-nt artificial microRNA mediates widespread RNA silencing in Arabidopsis

It is known that 22-nucleotide (nt) microRNAs (miRNAs) derived from asymmetric duplexes trigger phased small-interfering RNA (phasiRNA) production from complementary targets. Here we investigate the efficacy of 22-nt artificial miRNA (amiRNA)-mediated RNA silencing relative to conventional hairpin RNA (hpRNA) and 21-nt amiRNA-mediated RNA silencing. CHALCONE SYNTHASE (CHS) was selected as a target in Arabidopsis thaliana due to the obvious and non-lethal loss of anthocyanin accumulation upon widespread RNA silencing. Over-expression of CHS in the pap1-D background facilitated visual detection of both local and systemic RNA silencing. RNA silencing was initiated in leaf tissues from hpRNA and amiRNA plant expression vectors under the control of an Arabidopsis RuBisCo small subunit 1A promoter (SSU). In this system, hpRNA expression triggered CHS silencing in most leaf tissues but not in roots or seed coats. Similarly, 21-nt amiRNA expression from symmetric miRNA/miRNA* duplexes triggered CHS silencing in all leaf tissues but not in roots or seed coats. However, 22-nt amiRNA expression from an asymmetric duplex triggered CHS silencing in all tissues, including roots and seed coats, in the majority of plant lines. This widespread CHS silencing required RNA-DEPENDENT RNA POLYMERASE6-mediated accumulation of phasiRNAs from the endogenous CHS transcript. These results demonstrate the efficacy of asymmetric 22-nt amiRNA-directed RNA silencing and associated phasiRNA production and activity, in mediating widespread RNA silencing of an endogenous target gene. Asymmetric 22-nt amiRNA-directed RNA silencing requires little modification of existing amiRNA technology and is expected to be effective in suppressing other genes and/or members of gene families.

Vectors and methods for hairpin RNA and artificial microRNA-mediated gene silencing in plants

In plant cells, DICER-LIKE4 processes perfectly double-stranded RNA (dsRNA) into short interfering (si) RNAs, and DICER-LIKE1 generates micro (mi) RNAs from primary miRNA transcripts (pri-miRNA) that form fold-back structures of imperfectly dsRNA. Both si and miRNAs direct the endogenous endonuclease, ARGONAUTE1 to cleave complementary target single-stranded RNAs and either small RNA (sRNA)-directed pathway can be harnessed to silence genes in plants. A routine way of inducing and directing RNA silencing by siRNAs is to express self-complementary single-stranded hairpin RNA (hpRNA), in which the duplexed region has the same sequence as part of the target gene's mRNA. Artificial miRNA (amiRNA)-mediated silencing uses an endogenous pri-miRNA, in which the original miRNA/miRNA* sequence has been replaced with a sequence complementary to the new target gene. In this chapter, we describe the plasmid vector systems routinely used by our research group for the generation of either hpRNA-derived siRNAs or amiRNAs.

Efficient silencing of endogenous microRNAs using artificial microRNAs in Arabidopsis thaliana

We report here that the expression of endogenous microRNAs (miRNAs) can be efficiently silenced in Arabidopsis thaliana (Arabidopsis) using artificial miRNA (amiRNA) technology. We demonstrate that an amiRNA designed to target a mature miRNA directs silencing against all miRNA family members, whereas an amiRNA designed to target the stem-loop region of a miRNA precursor transcript directs silencing against only the individual family member targeted. Furthermore, our results indicate that amiRNAs targeting both the mature miRNA and stem-loop sequence direct RNA silencing through cleavage of the miRNA precursor transcript, which presumably occurs in the nucleus of a plant cell during the initial stages of miRNA biogenesis. This suggests that small RNA (sRNA)-guided RNA cleavage in plants occurs not only in the cytoplasm, but also in the nucleus. Many plant miRNA gene families have been identified via sequencing and bioinformatic analysis, but, to date, only a small tranche of these have been functionally characterized due to a lack of effective forward or reverse genetic tools. Our findings therefore provide a new and powerful reverse-genetic tool for the analysis of miRNA function in plants. © The Author 2010. Published by the Molecular Plant Shanghai Editorial Office in association with Oxford University Press on behalf of CSPP and IPPE, SIBS, CAS.

Portfolio selection using artificial intelligence

The application of artificial intelligence in finance is relatively new area of research. This project employed artificial neural networks (ANNs) that use both fundamental and technical inputs to predict future prices of widely held Australian stocks and use these predicted prices for stock portfolio selection over a long investment horizon. The research involved the creation and testing of a large number of possible network configurations and draws conclusions about ANN architectures and their overall suitability for the purpose of stock portfolio selection.

Firmicutes dominate the bacterial taxa within sugar-cane processing plants

Sugar cane processing sites are characterised by high sugar/hemicellulose levels, available moisture and warm conditions, and are relatively unexplored unique microbial environments. The PhyloChip microarray was used to investigate bacterial diversity and community composition in three Australian sugar cane processing plants. These ecosystems were highly complex and dominated by four main Phyla, Firmicutes (the most dominant), followed by Proteobacteria, Bacteroidetes, and Chloroflexi. Significant variation (p , 0.05) in community structure occurred between samples collected from ‘floor dump sediment’, ‘cooling tower water’, and ‘bagasse leachate’. Many bacterial Classes contributed to these differences, however most were of low numerical abundance. Separation in community composition was also linked to Classes of Firmicutes, particularly Bacillales, Lactobacillales and Clostridiales, whose dominance is likely to be linked to their physiology as ‘lactic acid bacteria’, capable of fermenting the sugars present. This process may help displace other bacterial taxa, providing a competitive advantage for Firmicutes bacteria.

Recent advances in the design of artificial corneas

Purpose of review: Artificial corneas are being developed to meet a shortage of donor corneas as well as to address cases where allografting is contraindicated. A range of artificial corneas has been developed. Here we review several newer designs and especially those inspired by naturally occurring biomaterials found with the human body and elsewhere. Recent findings: Recent trends in the development of artificial corneas indicate a move towards the use of materials derived from native sources including decellularized corneal tissue and tissue substitutes synthesized by corneal cells in vitro when grown either on their own, or in conjunction with novel protein-based scaffolds. Biologically inspired materials are also being considered for implantation on their own with the view to promoting endogenous corneal tissue. Summary: More recent attempts at making artificial corneas have taken a more nature-based or nature-inspired approach. Several will in the near future be likely to be available clinically.

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.

Integrin-functionalized artificial membranes as test platforms for monitoring small integrin ligand binding by surface plasmon-enhanced fluorescence spectroscopy

The design and synthesis of molecularly or supramolecularly defined interfacial architectures have seen in recent years a remarkable growth of interest and scientific research activities for various reasons. On the one hand, it is generally believed that the construction of an interactive interface between the living world of cells, tissue, or whole organisms and the (inorganic or organic) materials world of technical devices such as implants or medical parts requires proper construction and structural (and functional) control of this organism–machine interface. It is still the very beginning of generating a better understanding of what is needed to make an organism tolerate implants, to guarantee bidirectional communication between microelectronic devices and living tissue, or to simply construct interactive biocompatibility of surfaces in general. This exhaustive book lucidly describes the design, synthesis, assembly and characterization, and bio-(medical) applications of interfacial layers on solid substrates with molecularly or supramolecularly controlled architectures. Experts in the field share their contributions that have been developed in recent years.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. 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 and learning algorithm were 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 coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

THE quest for alpha : can artificial neural networks help?

The application of artificial neural networks (ANN) in finance is relatively new area of research. We employed ANNs that used both fundamental and technical inputs to predict future prices of widely held Australian stocks and used these predicted prices for stock portfolio selection over a 10-year period (2001-2011). We found that the ANNs generally do well in predicting the direction of stock price movements. The stock portfolios selected by the ANNs with median accuracy are able to generate positive alpha over the 10-year period. More importantly, we found that a portfolio based on randomly selected network configuration had zero chance of resulting in a significantly negative alpha but a 27% chance of yielding a significantly positive alpha. This is in stark contrast to the findings of the research on mutual fund performance where active fund managers with negative alphas outnumber those with positive alphas.

Understanding chromosome disorders and their implications for special educators

More children are now being diagnosed with chromosome abnormalities. Some chromosome disorder syndromes are relatively well known; while others are so rare that there is only limited evidence about their likely impact on learning and development. For educators, a basic level of knowledge about chromosome abnormalities is important for understanding the literature and communicating with families and professionals. This paper describes chromosomes, and the numerical and structural anomalies that can occur, usually spontaneously during early cell division. Distinctive features of various chromosome syndromes are summarised before a discussion of the rare chromosome disorders that are labelled, not with a syndrome name, but simply by a description of the chromosome number, size and shape. Because of the potential within-group variability that characterises syndromes, and the scarcity of literature about the rare chromosome disorders, expectations for learning and development of individual students need to be based on the range of possible outcomes that may be achievable.

Rational design of artificial cellular niches for tissue engineering

Tissue Engineering is a promising emerging field that studies the intrinsic regenerative potential of the human body and uses it to restore functionality of damaged organs or tissues unable of self-healing due to illness or ageing. In order to achieve regeneration using Tissue Engineering strategies, it is first necessary to study the properties of the native tissue and determine the cause of tissue failure; second, to identify an optimum population of cells capable of restoring its functionality; and third, to design and manufacture a cellular microenvironment in which those specific cells are directed towards the desired cellular functions. The design of the artificial cellular niche has a tremendous importance, because cells will feel and respond to both its biochemical and biophysical properties very differently. In particular, the artificial niche will act as a physical scaffold for the cells, allowing their three-dimensional spatial organization; also, it will provide mechanical stability to the artificial construct; and finally, it will supply biochemical and mechanical cues to control cellular growth, migration, differentiation and synthesis of natural extracellular matrix. During the last decades, many scientists have made great contributions to the field of Tissue Engineering. Even though this research has frequently been accompanied by vast investments during extended periods of time, yet too often these efforts have not been enough to translate the advances into new clinical therapies. More and more scientists in this field are aware of the need of rational experimental designs before carrying out complex, expensive and time-consuming in vitro and in vivo trials. This review highlights the importance of computer modeling and novel biofabrication techniques as critical key players for a rational design of artificial cellular niches in Tissue Engineering.