Characterisation and application of a bovine U6 promoter for expression of short hairpin RNAs

Background
The use of small interfering RNA (siRNA) molecules in animals to achieve double-stranded RNA-mediated interference (RNAi) has recently emerged as a powerful method of sequence-specific gene knockdown. As DNA-based expression of short hairpin RNA (shRNA) for RNAi may offer some advantages over chemical and in vitro synthesised siRNA, a number of vectors for expression of shRNA have been developed. These often feature polymerase III (pol. III) promoters of either mouse or human origin.
Results
To develop a shRNA expression vector specifically for bovine RNAi applications, we identified and characterised a novel bovine U6 small nuclear RNA (snRNA) promoter from bovine sequence data. This promoter is the putative bovine homologue of the human U6-8 snRNA promoter, and features a number of functional sequence elements that are characteristic of these types of pol. III promoters. A PCR based cloning strategy was used to incorporate this promoter sequence into plasmid vectors along with shRNA sequences for RNAi. The promoter was then used to express shRNAs, which resulted in the efficient knockdown of an exogenous reporter gene and an endogenous bovine gene.
Conclusion
We have mined data from the bovine genome sequencing project to identify a functional bovine U6 promoter and used the promoter sequence to construct a shRNA expression vector. The use of this native bovine promoter in shRNA expression is an important component of our future development of RNAi therapeutic and transgenic applications in bovine species.

Who's afraid of designer babies?

Australian scientists in Sydney made medical history by creating a life-saving brother for a child with an incurable genetic disease. Features the case of a Tasmanian couple who, with their help, have this life-saving baby, at their third try. The pioneering IVF treatment, which is legal only in N.S.W., hit the news headlines, sparking an ethical storm. Examines the moral and ethical issues that genetic manipulation raises, including the potential uses and misuses of genetic technology. Raises also the spectre of an extreme form of eugenics, as seen in the World War II Nazi push to create a master race through human genetics. Some view eugenics as another form of PGD. Presents the diverse views of eminent ethicists and the Catholic Church, world wide.

The mundiad : sic transit gloria mundi

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Antibody-mediated protection of plants against infection from potyviruses

Transgenic plants expressing single-chain antibodies have been produced to investigate the feasibility of antibody-mediated broad-spectrum protection against plant virus infections. This study indicates that protection against a wide range of plant viruses can be achieved in transgenic plants expressing a single antibody construct.

The decline of the notion of soul and the rise of the concept of self

This thesis examines the ethics of genetic engineering from the frame of reference of the human individual.

Microbial biosynthesis of biopolymers and applications in the biopharmaceutical, biomedical and food industries

Biopolymers can be produced through a variety of mechanisms. They can be derived from microbial systems, extracted from higher organisms such as plants, or synthesized chemically from basic biological building blocks. A wide range of emerging applications rely on all three of these production techniques. In recent years, considerable attention has been given to biopolymers produced by microbes. It is on the microbial level where the tools of genetic engineering can be most readily applied. A number of novel materials are now being developed or introduced into the market. Biopolymers are being developed for use as medical materials, packaging, cosmetics, food additives, clothing fabrics, water treatment chemicals, industrial plastics, absorbents, biosensors, and even data storage elements. This review identifies the possible commercial applications and describes the various methods of production of microbial biopolymers.

Production and characterization of hypoallergenic chicken eggs

This project aimed to develop novel therapeutics and methods to tackle egg allergy. In this study, recombinant variants of egg allergens were produced that may be used in future immunotherapy. Genetic engineering was also used to develop new strategies to produce non-allergenic chicken eggs.

Dissecting the function of essential rhoptry genes of the malaria parasite

This thesis explored the role of essential Rhoptry-proteins (RAPs) in parasites that cause malaria. Utilising genetic engineering approaches, this work provides the first formal proof that RAPs are not involved in the invasion of host erythrocytes and instead presents evidence supporting a post-invasion role. This changes the existing notion of RAPs as potential vaccine-candidates to being potential drug-targets.

Evolutionary multiobjective optimization in engineering management: an empirical study on bridge deck rehabilitation

There exist multiple objectives in engineering management such as minimum cost and maximum service capacity. Although solution methods of multiobjective optimization problems have undergone continual development over the past several decades, the methods available to date are not particularly robust, and none of them performs well on the broad classes. Because genetic algorithms work with a population of points, they can capture a number of solutions simultaneously, and easily incorporate the concept of Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with the rehabilitation planning of bridge decks at a network level by minimizing the rehabilitation cost and deterioration degree simultaneously.

Evolutionary multiobjective optimization in engineering management: an empirical application in infrastructure systems

Generally multiple objectives exist in transportation infrastructure management, such as minimum cost and maximum service capacity. Although solution methoak of multiobjective optimization problems have undergone continual development over the part several decades, the methods available to date are not particularly robust, and none of them perform well on the broad classes. Because genetic algorithms work with apopulation ofpoints, they can capture a number of solutions simultaneously, and easily incorporate the concept of a Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with an empirical application for the rehabilitation planning of bridge decks, at a network level, by minimizing the rehabilitation cost and deterioration degree simultaneously.

Using immune genetic algorithm to optimize the reactive power

A novel algorithm, immune genetic algorithm (IGA) is proposed for reactive power optimization of power system. While retaining excellent characteristics of genetic algorithm (GA), through imitating the biological immune system, the algorithm evaluates and selects the optimal solutions by the affinities between antigens and antibodies. With the regulation of the activating and suppressing of antibodies, IGA can achieve the dynamic balance between individual diversity and population convergence, and avoid getting into the local optimal solution. The proposed IGA is applied to the IEEE 30-bus system, and the results show that it is superior to the GA with good population convergence and fast computing speed.

Fuzzy ARTMAP and a hybrid chaos genetic algorithm for medical pattern classification

In this paper, an Evolutionary Artificial Neural Network (EANN), which combines the Fuzzy ARTMAP (FAM) neural network and a hybrid Chaos Genetic Algorithm (CGA), is proposed for undertaking pattern classification tasks. The hybrid CGA is a modified version of the hybrid real-coded genetic algorithms that includes a Chaotic Mapping Operator (CMO) in its search and adaptation process. It is used to evolve the connection weights in FAM, and the resulting EANN is known as FAM-hybrid CGA. The CMO in the hybrid CGA is used to generate a group of chromosomes that incorporates the characteristics of chaos. The chromosomes are then adapted with an arbitrary small amount of variation in every generation. As the evolution procedure proceeds, chromosomes with considerable differences are produced. Such chromosomes, which are located at different regions of interest in the solution space, are able to provide good solutions to undertake search and adaption problems. The effectiveness of the proposed FAM-hybrid CGA model is first evaluated using benchmark medical data sets from the UCI machine learning repository. Its applicability to medical decision support is then demonstrated using a real database of patient records with suspected Acute Coronary Syndrome. The results indicate that FAM-hybrid CGA is able to outperform its neural network counterpart (i.e., FAM), and it can be employed as a useful pattern classification tool for tackling medical decision support tasks.

A genetic fuzzy system to model pedestrian walking path in a built environment

A study on the pedestrian's steering behaviour through a built environment in normal circumstances is presented in this paper. The study focuses on the relationship between the environment and the pedestrian's walking trajectory. Owing to the ambiguity and vagueness of the relationship between the pedestrians and the surrounding environment, a genetic fuzzy system is proposed for modelling and simulation of the pedestrian's walking trajectory confronting the environmental stimuli. We apply the genetic algorithm to search for the optimum membership function parameters of the fuzzy model. The proposed system receives the pedestrian's perceived stimuli from the environment as the inputs, and provides the angular change of direction in each step as the output. The environmental stimuli are quantified using the Helbing social force model. Attractive and repulsive forces within the environment represent various environmental stimuli that influence the pedestrian's walking trajectory at each point of the space. To evaluate the effectiveness of the proposed model, three experiments are conducted. The first experimental results are validated against real walking trajectories of participants within a corridor. The second and third experimental results are validated against simulated walking trajectories collected from the AnyLogic® software. Analysis and statistical measurement of the results indicate that the genetic fuzzy system with optimised membership functions produces more accurate and stable prediction of heterogeneous pedestrians' walking trajectories than those from the original fuzzy model. © 2014 Elsevier B.V. All rights reserved.

Optimisation of nonlinear motion cueing algorithm based on genetic algorithm

Motion cueing algorithms (MCAs) are playing a significant role in driving simulators, aiming to deliver the most accurate human sensation to the simulator drivers compared with a real vehicle driver, without exceeding the physical limitations of the simulator. This paper provides the optimisation design of an MCA for a vehicle simulator, in order to find the most suitable washout algorithm parameters, while respecting all motion platform physical limitations, and minimising human perception error between real and simulator driver. One of the main limitations of the classical washout filters is that it is attuned by the worst-case scenario tuning method. This is based on trial and error, and is effected by driving and programmers experience, making this the most significant obstacle to full motion platform utilisation. This leads to inflexibility of the structure, production of false cues and makes the resulting simulator fail to suit all circumstances. In addition, the classical method does not take minimisation of human perception error and physical constraints into account. Production of motion cues and the impact of different parameters of classical washout filters on motion cues remain inaccessible for designers for this reason. The aim of this paper is to provide an optimisation method for tuning the MCA parameters, based on nonlinear filtering and genetic algorithms. This is done by taking vestibular sensation error into account between real and simulated cases, as well as main dynamic limitations, tilt coordination and correlation coefficient. Three additional compensatory linear blocks are integrated into the MCA, to be tuned in order to modify the performance of the filters successfully. The proposed optimised MCA is implemented in MATLAB/Simulink software packages. The results generated using the proposed method show increased performance in terms of human sensation, reference shape tracking and exploiting the platform more efficiently without reaching the motion limitations.