Implementing technologies and strategies to maintain resistance to sunflower rust

This project proposes to implement resistance gene pyramiding strategies through close collaboration with Pacific Seeds. These strategies have been developed by Department of Primary Industries and Fisheries (DPI&F) researchers in two previous GRDC projects, DAQ356 and DAQ537. The gene pyramids will be incorporated into elite breeding material using techniques and technologies developed by DPI&F. These include the use of DNA markers. If successful, a range of elite lines/commercial hybrids containing strategic resistance gene pyramids will be available to growers. These lines will provide the industry with a directed strategy to manage the sunflower rust pathogen and reduce the risk of outbreaks of the disease.

Molecular Breeding for Complex Adaptive Traits: How Integrating Crop Ecophysiology and Modelling Can Enhance Efficiency

Progress in crop improvement is limited by the ability to identify favourable combinations of genotypes (G) and management practices (M) in relevant target environments (E) given the resources available to search among the myriad of possible combinations. To underpin yield advance we require prediction of phenotype based on genotype. In plant breeding, traditional phenotypic selection methods have involved measuring phenotypic performance of large segregating populations in multi-environment trials and applying rigorous statistical procedures based on quantitative genetic theory to identify superior individuals. Recent developments in the ability to inexpensively and densely map/sequence genomes have facilitated a shift from the level of the individual (genotype) to the level of the genomic region. Molecular breeding strategies using genome wide prediction and genomic selection approaches have developed rapidly. However, their applicability to complex traits remains constrained by gene-gene and gene-environment interactions, which restrict the predictive power of associations of genomic regions with phenotypic responses. Here it is argued that crop ecophysiology and functional whole plant modelling can provide an effective link between molecular and organism scales and enhance molecular breeding by adding value to genetic prediction approaches. A physiological framework that facilitates dissection and modelling of complex traits can inform phenotyping methods for marker/gene detection and underpin prediction of likely phenotypic consequences of trait and genetic variation in target environments. This approach holds considerable promise for more effectively linking genotype to phenotype for complex adaptive traits. Specific examples focused on drought adaptation are presented to highlight the concepts.

Optimal adaptive sequential spatial sampling of soil using pair-copulas

A spatial sampling design that uses pair-copulas is presented that aims to reduce prediction uncertainty by selecting additional sampling locations based on both the spatial configuration of existing locations and the values of the observations at those locations. The novelty of the approach arises in the use of pair-copulas to estimate uncertainty at unsampled locations. Spatial pair-copulas are able to more accurately capture spatial dependence compared to other types of spatial copula models. Additionally, unlike traditional kriging variance, uncertainty estimates from the pair-copula account for influence from measurement values and not just the configuration of observations. This feature is beneficial, for example, for more accurate identification of soil contamination zones where high contamination measurements are located near measurements of varying contamination. The proposed design methodology is applied to a soil contamination example from the Swiss Jura region. A partial redesign of the original sampling configuration demonstrates the potential of the proposed methodology.

Transcending disciplinary, cultural and national boundaries: Emergent technologies, new education landscape and the cloud workshop project

As technology continues to become more accessible, miniaturised and diffused into the environment, the potential of wearable technology to impact our lives in significant ways becomes increasingly viable. Wearables afford unique interaction, communication and functional capabilities between users, their environment as well as access to information and digital data. Wearables also demand an inter-disciplinary approach and, depending on the purpose, can be fashioned to transcend cultural, national and spatial boundaries. This paper presents the Cloud Workshop project based on the theme of ‘Wearables and Wellbeing; Enriching connections between citizens in the Asia-Pacific region’, initiated through a cooperative partnership between Queensland University of Technology (QUT), Hong Kong Baptist University (HKBU) and Griffith University (GU). The project was unique due to its inter-disciplinary, inter-cultural and inter-national scope that occurred simultaneously between Australia and Hong Kong.

Feature - No pain, no gain: Pleasure and suffering in technologies of leidenschaft

Training for bodybuilding competition is clearly a serious business that inflicts serious demands on the competitor. Not only did Francis commit time and money to compete, but he also arguably put winning before his physical well-being—enduring pain and suffering from his injury. Bodybuilding may seem like an extreme example, but it is not the only activity in which people suffer in pursuit of their goals. Boxers fight each other in the ring; soccer players risk knee and ankle injuries, sometimes playing despite being hurt; and mountaineers risk their lives in dangerous climbs. In the arts there are many examples of people suffering to achieve their goals: Beethoven kept composing, conducting, and performing despite his hearing loss; van Gogh grappled with depression but kept painting, finding fame only posthumously; and Mozart lived the final years of his life impoverished but still composing. These examples show that many great achievements come at a price: severe suffering...

Phylogeography and Adaptive Divergence of Three-spined Stickleback Populations

Genetic studies on phylogeography and adaptive divergence in Northern Hemisphere fish species such as three-spined stickleback (Gasterosteus aculeatus) provide an excellent opportunity to investigate genetic mechanisms underlying population differentiation. According to the theory, the process of population differentiation results from a complex interplay between random and deterministic processes as well historical factors. The main scope in this thesis was to study how historical factors like the Pleistocene ice ages have shaped the patterns molecular diversity in three-spined stickleback populations in Europe and how this information could be utilized in the conservation genetic context. Furthermore, identifying footprints of natural selection at the DNA level might be used in identifying genes involved in evolutionary change. Overall, the results from phylogeographic studies indicate that the three-spined stickleback has colonized the Atlantic basin relatively recently but constitutes three major evolutionary lineages in Europe. In addition, the colonization of freshwater appears to result from multiple and independent invasions by the marine conspecifics. Molecular data together with morphology suggest that the most divergent freshwater populations are located in the Balkan Peninsula and these populations deserve a special conservation genetic status without warranting further taxonomical classification. In order to investigate the adaptive divergence in Fennoscandian three-spined stickleback populations several approaches were used. First, sequence variability in the Eda-gene, coding for the number of lateral plates, was concordant with the previously observed global pattern. Full plated allele is in high frequencies among marine populations whereas low plated allele dominates in the freshwater populations. Second, a microsatellite based genome scan identified both indications of balancing and directional selection in the three-spined stickleback genome, i.e. loci with unusually similar or unusually different allele frequencies over populations. The directionally selected loci were mainly associated with the adaptation to freshwater. A follow up study conducting a more detailed analysis in a chromosome region containing a putatively selected gene locus identified a fairly large genomic region affected by natural selection. However, this region contained several gene predictions, all of which might be the actual target of natural selection. All in all, the phylogeographic and adaptive divergence studies indicate that most of the genetic divergence has occurred in the freshwater populations whereas the marine populations have remained relatively uniform.

trans gene regulation in adaptive evolution: A genetic algorithm model

This is a continuation of earlier studies on the evolution of infinite populations of haploid genotypes within a genetic algorithm framework. We had previously explored the evolutionary consequences of the existence of indeterminate—“plastic”—loci, where a plastic locus had a finite probability in each generation of functioning (being switched “on”) or not functioning (being switched “off”). The relative probabilities of the two outcomes were assigned on a stochastic basis. The present paper examines what happens when the transition probabilities are biased by the presence of regulatory genes. We find that under certain conditions regulatory genes can improve the adaptation of the population and speed up the rate of evolution (on occasion at the cost of lowering the degree of adaptation). Also, the existence of regulatory loci potentiates selection in favour of plasticity. There is a synergistic effect of regulatory genes on plastic alleles: the frequency of such alleles increases when regulatory loci are present. Thus, phenotypic selection alone can be a potentiating factor in a favour of better adaptation.

A Control Strategy for Reference Wave Adaptive Current Generation

Speed control of ac motors requires variable frequency, variable current, or variable voltage supply. Variable frequency supply can be obtained directly from a fixed frequency supply by using a frequency converter or from a dc source using inverters. In this paper a control technique for reference wave adaptive-current generation by modulating the inverter voltage is explained. Extension of this technique for three-phase induction-motor speed control is briefly explained. The oscillograms of the current waveforms obtained from the experimental setup are also shown.

Assimilation of remote sensing and hydrological data using adaptive filtering techniques for watershed modelling

The knowledge of hydrological variables (e. g. soil moisture, evapotranspiration) are of pronounced importance in various applications including flood control, agricultural production and effective water resources management. These applications require the accurate prediction of hydrological variables spatially and temporally in watershed/basin. Though hydrological models can simulate these variables at desired resolution (spatial and temporal), often they are validated against the variables, which are either sparse in resolution (e. g. soil moisture) or averaged over large regions (e. g. runoff). A combination of the distributed hydrological model (DHM) and remote sensing (RS) has the potential to improve resolution. Data assimilation schemes can optimally combine DHM and RS. Retrieval of hydrological variables (e. g. soil moisture) from remote sensing and assimilating it in hydrological model requires validation of algorithms using field studies. Here we present a review of methodologies developed to assimilate RS in DHM and demonstrate the application for soil moisture in a small experimental watershed in south India.

Randomised Evaluation of New Technologies within the Population‐Based Cervical Cancer Screening Programme in Finland: Cross‐Sectional Performance and Validity

A randomised and population-based screening design with new technologies has been applied to the organised cervical cancer screening programme in Finland. In this experiment the women invited to routine five-yearly screening are individually randomised to be screened with automation-assisted cytology, human papillomavirus (HPV) test or conventional cytology. By using the randomised design, the ultimate aim is to assess and compare the long-term outcomes of the different screening regimens. The primary aim of the current study was to evaluate, based on the material collected during the implementation phase of the Finnish randomised screening experiment, the cross-sectional performance and validity of automation-assisted cytology (Papnet system) and primary HPV DNA testing (Hybrid Capture II assay for 13 oncogenic HPV types) within service screening, in comparison to conventional cytology. The parameters of interest were test positivity rate, histological detection rate, relative sensitivity, relative specificity and positive predictive value. Also, the effect of variation in performance by screening laboratory on age-adjusted cervical cancer incidence was assessed. Based on the cross-sectional results, almost no differences were observed in the performance of conventional and automation-assisted screening. Instead, primary HPV screening found 58% (95% confidence interval 19-109%) more cervical lesions than conventional screening. However, this was mainly due to overrepresentation of mild- and moderate-grade lesions and, thus, is likely to result in overtreatment since a great deal of these lesions would never progress to invasive cancer. Primary screening with an HPV DNA test alone caused substantial loss in specificity in comparison to cytological screening. With the use of cytology triage test, the specificity of HPV screening improved close to the level of conventional cytology. The specificity of primary HPV screening was also increased by increasing the test positivity cutoff from the level recommended for clinical use, but the increase was more modest than the one gained with the use of cytology triage. The performance of the cervical cancer screening programme varied widely between the screening laboratories, but the variation in overall programme effectiveness between respective populations was more marginal from the very beginning of the organised screening activity. Thus, conclusive interpretations on the quality or success of screening should not be based on performance parameters only. In the evaluation of cervical cancer screening the outcome should be selected as closely as possible to the true measure of programme effectiveness, which is the number of invasive cervical cancers and subsequent deaths prevented in the target population. The evaluation of benefits and adverse effects of each new suggested screening technology should be performed before the technology becomes an accepted routine in the existing screening programme. At best, the evaluation is performed randomised, within the population and screening programme in question, which makes the results directly applicable to routine use.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

An optimal dynamic inversion-based neuro-adaptive approach for treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an innovative technique is presented to design an automatic drug administration strategy for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used to design the controller (medication dosage). First, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat the nominal model patients (patients who can be described by the mathematical model used here with the nominal parameter values) effectively. However, since the system parameters for a realistic model patient can be different from that of the nominal model patients, simulation studies for such patients indicate that the nominal controller is either inefficient or, worse, ineffective; i.e. the trajectory of the number of cancer cells either shows non-satisfactory transient behavior or it grows in an unstable manner. Hence, to make the drug dosage history more realistic and patient-specific, a model-following neuro-adaptive controller is augmented to the nominal controller. In this adaptive approach, a neural network trained online facilitates a new adaptive controller. The training process of the neural network is based on Lyapunov stability theory, which guarantees both stability of the cancer cell dynamics as well as boundedness of the network weights. From simulation studies, this adaptive control design approach is found to be very effective to treat the CML disease for realistic patients. Sufficient generality is retained in the mathematical developments so that the technique can be applied to other similar nonlinear control design problems as well.

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

An adaptive decomposition method for video

Scalable video coding (SVC) is an emerging standard built on the success of advanced video coding standard (H.264/AVC) by the Joint video team (JVT). Motion compensated temporal filtering (MCTF) and Closed loop hierarchical B pictures (CHBP) are two important coding methods proposed during initial stages of standardization. Either of the coding methods, MCTF/CHBP performs better depending upon noise content and characteristics of the sequence. This work identifies other characteristics of the sequences for which performance of MCTF is superior to that of CHBP and presents a method to adaptively select either of MCTF and CHBP coding methods at the GOP level. This method, referred as "Adaptive Decomposition" is shown to provide better R-D performance than of that by using MCTF or CRBP only. Further this method is extended to non-scalable coders.