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.

Comparison of the coat protein, movement protein and RNA polymerase gene sequences of Australian, Chinese, and American isolates of barley yellow dwarf virus transmitted by Rhopalosiphum padi

Barley yellow dwarf luteovirus-GPV (BYDV-GPV) is a common problem in Chinese wheat crops but is unrecorded elsewhere. A defining characteristic of GPV is its capacity to be transmitted efficiently by both Schizaphis graminum and Rhopaloshiphum padi. This dual aphid species transmission contrasts with those of BYDV-RPV and BYDV-SGV, globally distributed viruses, which are efficiently transmitted only by Rhopaloshiphum padi and Schizaphis graminum respectively. The viral RNA sequences encoding the coat protein (22K) gene, the movement protein (17K) gene, the region surrounding the conserved GDD motif of the polymerase gene and the intergenic sequences between these genes were determined for GPV and an Australian isolate of BYDV-RPV (RPVa). In all three genes, the sequences of GPV and RPVa were more similar to those of an American isolate of BYDV-RPV (RPVu) than to any other luteovirus for which there is data available. RPVa and RPVu were very similar, especially their coat proteins which had 97% identity at the amino acid level. The coat protein of GPV had 76% and 78% amino acid identity with RPVa and RPVu respectively. The data suggest that RPVu and RPVa are correctly named as strains of the same serotype and that GPV is sufficiently different from either RPV strain to be considered a distinct BYDV type. The coat protein and movement protein genes of GPV are very dissimilar to SGV. The polymerase sequences of RPVu, RPVa and GPV show close affinities with those of the sobemo-like luteoviruses and little similarity with those of the carmo-like luteoviruses. The sequences of the coat proteins, movement proteins and the polymerase segments of BYDV serotypes, other than RPV and GPV, form a cluster that is separate from their counterpart sequences from dicot-infecting luteoviruses. The RPV and GPV isolates consistently fall within a dicot-infecting cluster. This suggests that RPV and GPV evolved from within this group of viruses. Since these other viruses all infect dicots it seems likely that their common ancestor infected a dicot and that RPV and GPV evolved from a virus that switched hosts from a dicot to a monocot.

A Near-Infrared Reflectance Spectroscopy Method for Direct Analysis of Several Chemical Components and Properties of Fruit, for Example, Chinese Hawthorn

Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of Chinese hawthorn (Crataegus pinnatifida Bge. var. major) fruit from three geographical regions as well as for the estimation of the total sugar, total acid, total phenolic content, and total antioxidant activity. Principal component analysis (PCA) was used for the discrimination of the fruit on the basis of their geographical origin. Three pattern recognition methods, linear discriminant analysis, partial least-squares-discriminant analysis, and back-propagation artificial neural networks, were applied to classify and compare these samples. Furthermore, three multivariate calibration models based on the first derivative NIR spectroscopy, partial least-squares regression, back-propagation artificial neural networks, and least-squares-support vector machines, were constructed for quantitative analysis of the four analytes, total sugar, total acid, total phenolic content, and total antioxidant activity, and validated by prediction data sets.

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.

Influence of stop position on spectacle magnification

Purpose To develop and use equations of spectacle magnification when the limiting stop is either the entrance pupil of the eye or an artificial pupil in front of a lens. Methods Spectacle magnification was determined for ophthalmic lenses in air and for water environments. The reference was the retinal image for an uncorrected eye in air with a natural pupil. Results When an artificial pupil is placed in front of lenses, spectacle magnification is hardly affected by lens power, unlike the usual situation where the natural pupil is used. The water environment provides interesting influences in which spectacle magnification is highly sensitive to the distance between the cornea and eye entrance pupil. In water, retinal images are approximately 18% bigger than in air. Wearing air-filled goggles in water increases retinal image size by about 13% compared with that when they are not worn. Conclusions The equations extend earlier understanding of spectacle magnification and should be useful for those wishing to determine magnification of ophthalmic lens systems when artificial pupils and environments such as water are used.

Video analytics for the detection of near-miss incidents on approach to railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events. The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle. Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis. We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.

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 development of community foundations in Australia - recreating the American dream

Community foundations have been described as the fastest growing form of philantrhopy. This report focuses on the development of community foundations in Australia as one especially important example of the growth of community foundations world-wide.

Binding of small mono- and oligomeric integrin ligands to membrane-embedded integrins monitored by surface plasmon-enhanced fluorescence spectroscopy

We recently developed a binding assay format by incorporating native transmembrane receptors into artificial phospholipid bilayers on biosensor devices for surface plasmon resonance spectroscopy. By extending the method to surface plasmon-enhanced fluorescence spectroscopy (SPFS), sensitive recording of the association of even very small ligands is enabled. Herewith, we monitored binding of synthetic mono- and oligomeric RGD-based peptides and peptidomimetics to integrins alphavbeta3 and alphavbeta5, after having confirmed correct orientation and functionality of membrane-embedded integrins. We evaluated integrin binding of RGD multimers linked together via aminohexanoic acid (Ahx) spacers and showed that the dimer revealed higher binding activity than the tetramer, followed by the RGD monomers. The peptidomimetic was also found to be highly active with a slightly higher selectivity toward alphavbeta3. The different compounds were also evaluated in in vitro cell adhesion tests for their capacity to interfere with alphavbeta3-mediated cell attachment to vitronectin. We hereby demonstrated that the different RGD monomers were similarly effective; the RGD dimer and tetramer showed comparable IC50 values, which were, however, significantly higher than those of the monomers. Best cell detachment from vitronectin was achieved by the peptidomimetic. The novel SPFS-binding assay platform proves to be a suitable, reliable, and sensitive method to monitor the binding capacity of small ligands to native transmembrane receptors, here demonstrated for integrins.

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.

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.