Optimising profitability and environmental trade-offs in managing cropping systems using differential evolution

Whilst traditional optimisation techniques based on mathematical programming techniques are in common use, they suffer from their inability to explore the complexity of decision problems addressed using agricultural system models. In these models, the full decision space is usually very large while the solution space is characterized by many local optima. Methods to search such large decision spaces rely on effective sampling of the problem domain. Nevertheless, problem reduction based on insight into agronomic relations and farming practice is necessary to safeguard computational feasibility. Here, we present a global search approach based on an Evolutionary Algorithm (EA). We introduce a multi-objective evaluation technique within this EA framework, linking the optimisation procedure to the APSIM cropping systems model. The approach addresses the issue of system management when faced with a trade-off between economic and ecological consequences.

Genetic algorithm optimization of adaptive multi-scale GLCM features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

An update on genetic, structural and functional studies of arylamine N-acetyltransferases in eucaryotes and procaryotes

Arylamine N-acetyltransferase (NAT) was first identified as the inactivator of the anti-tubercular drug isoniazid, The enzyme was shown to catalyse the transfer of an acetyl group from acetyl-CoA to the terminal nitrogen of the hydrazine drug. The rate of inactivation of isoniazid was polymorphically distributed in the population and was one of the first examples of pharmacogenetic variation, NAT was identified recently in Mycobacterium tuberculosis and is a candidate for; modulating the response to isoniazid, Genome sequences have revealed many homologous members of this unique family of enzymes. The first three-dimensional structure of a member of the NAT family identifies a catalytic triad consisting of aspartate, histidine and cysteine proposed to form the activation mechanism. So far, all procaryotic NATs resemble the human enzyme which acetylates isoniazid (NAT2), Human NAT2 is characteristic of drug-metabolizing enzymes: it is found in liver and intestine, In humans and other mammals, there are up to three different isoenzymes. If only one isoenzyme is present, it is like human NAT1. Human NAT1 and its murine equivalent specifically acetylate the folate catabolite p-amino-benzoylglutamate. NAT1 and its murine homologue each have a ubiquitous tissue distribution and are expressed early in development at the blastocyst stage, During murine embryonic development, NAT is expressed in the developing neural tube. The proposed endogenous role of NAT in folate metabolism, and its multi-allelic nature, indicate that its role in development should be assessed further.

Genetic association and cellular function of MC1R variant alleles in human pigmentation

We have examined MC1R variant allele frequencies in the general population of South East Queensland and in a collection of adolescent dizygotic and monozygotic twins and family members to define statistical associations with hair and skin color, freckling, and mole count. Results of these studies are consistent with a linear recessive allelic model with multiplicative penetrance in the inheritance of red hair. Four alleles, D84E, R151C, R160W, and D294H, are strongly associated with red hair and fair skin with multinomial regression analysis showing odds ratios of 63, 118, 50, and 94, respectively. An additional three low-penetrance alleles V60L, V92M, and R163Q have odds ratios 6, 5, and 2 relative to the wild-type allele. To address the cellular effects of MC1R variant alleles in signal transduction, we expressed these receptors in permanently transfected HEK293 cells. Measurement of receptor activity via induction of a cAMP-responsive luciferase reporter gene found that the R151C and R160W receptors were active in the presence of NDP-MSH ligand, but at much reduced levels compared with that seen with the wild-type receptor. The ability to stimulate phosphorylation of the cAMP response element binding protein (CREB) transcription factor was also apparent in all stimulated MC1R variant allele-expressing HEK293 cell extracts as assessed by immunoblotting. In contrast, human melanoma cell lines showed wide variation in the their ability to undergo cAMP-mediated CREB phosphorylation. Culture of human melanocytes of known MC1R genotype may provide the best experimental approach to examine the functional consequences for each MC1R variant allele. With this objective, we have established more than 300 melanocyte cell strains of defined MC1R genotype.

An Australian twin study of the genetic basis of preeclampsia and eclampsia

OBJECTIVE: We investigated maternal versus fetal genetic causes of preeclampsia and eclampsia by assessing concordance between monozygotic and dizygotic female co-twins, between female partners of male monozygotic and dizygotic twin pairs, and between female twins and partners of their male co-twins in dizygotic opposite-sex pairs. STUDY DESIGN: Two large birth cohorts of volunteer Australian female twin pairs (N = 1504 pairs and N = 858 pairs) were screened and interviewed, and available medical and hospital records were obtained and reviewed where indicated, with diagnoses assigned according to predetermined criteria. RESULTS: With strict diagnostic criteria used for preeclampsia and eclampsia, no concordant female twin pairs were found. Collapsing diagnoses of definite, probable, or possible preeclampsia or eclampsia resulted in very low genetic recurrence risk estimates. CONCLUSION: Results from these two cohorts of female twin pairs do not support clear, solely maternal genetic influences on preeclampsia and eclampsia. Numbers of parous female partners of male twins were too low for conclusions to be drawn regarding paternal transmission.

The influence of genetic and environmental factors in estimations of current body size, desired body size, and body dissatisfaction

The objective was to investigate the genetic epidemiology of figural stimuli. Standard figural stimuli were available from 5,325 complete twin pairs: 1,751 (32.9%) were monozygotic females, 1,068 (20.1%) were dizygotic females, 752 (14.1%) were monozygotic males, 495 (9.3%) were dizygotic males, and 1,259 (23.6%) were dizygotic male-female pairs. Univariate twin analyses were used to examine the influences on the individual variation in current body size and ideal body size. These data were analysed separately for men and women in each of five age groups. A factorial analysis of variance, with polychoric correlations between twin pairs as the dependent variable, and age, sex, zygosity, and the three interaction terms (age x sex, age x zygosity, sex x zygosity) as independent variables, was used to examine trends across the whole data set. Results showed genetic influences had the largest impact on the individual variation in current body size measures, whereas non-shared environmental influences were associated with the majority of individual variation in ideal body size. There was a significant main effect of zygosity (heritability) in predicting polychoric correlations for current body size and body dissatisfaction. There was a significant main effect of gender and zygosity in predicting ideal body size, with a gender x zygosity interaction. In common with BMI, heritability is important in influencing the estimation of current body size. Selection of desired body size for both men and women is more strongly influenced by environmental factors.

Xmds: eXtensible multi-dimensional simulator

A software package that efficiently solves a comprehensive range of problems based on coupled complex nonlinear stochastic ODEs and PDEs is outlined. Its input and output syntax is formulated as a subset of XML, thus making a step towards a standard for specifying numerical simulations.

Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials

For the improvement of genetic material suitable for on farm use under low-input conditions, participatory and formal plant breeding strategies are frequently presented as competing options. A common frame of reference to phrase mechanisms and purposes related to breeding strategies will facilitate clearer descriptions of similarities and differences between participatory plant breeding and formal plant breeding. In this paper an attempt is made to develop such a common framework by means of a statistically inspired language that acknowledges the importance of both on farm trials and research centre trials as sources of information for on farm genetic improvement. Key concepts are the genetic correlation between environments, and the heterogeneity of phenotypic and genetic variance over environments. Classic selection response theory is taken as the starting point for the comparison of selection trials (on farm and research centre) with respect to the expected genetic improvement in a target environment (low-input farms). The variance-covariance parameters that form the input for selection response comparisons traditionally come from a mixed model fit to multi-environment trial data. In this paper we propose a recently developed class of mixed models, namely multiplicative mixed models, also called factor-analytic models, for modelling genetic variances and covariances (correlations). Mixed multiplicative models allow genetic variances and covariances to be dependent on quantitative descriptors of the environment, and confer a high flexibility in the choice of variance-covariance structure, without requiring the estimation of a prohibitively high number of parameters. As a result detailed considerations regarding selection response comparisons are facilitated. ne statistical machinery involved is illustrated on an example data set consisting of barley trials from the International Center for Agricultural Research in the Dry Areas (ICARDA). Analysis of the example data showed that participatory plant breeding and formal plant breeding are better interpreted as providing complementary rather than competing information.

Empirical evidence for ultrametric structure in multi-layer perceptron error surfaces

Combinatorial optimization problems share an interesting property with spin glass systems in that their state spaces can exhibit ultrametric structure. We use sampling methods to analyse the error surfaces of feedforward multi-layer perceptron neural networks learning encoder problems. The third order statistics of these points of attraction are examined and found to be arranged in a highly ultrametric way. This is a unique result for a finite, continuous parameter space. The implications of this result are discussed.

Analysis of genetic diversity within Australian lucerne cultivars and implications for future genetic improvement

Lucerne (Medicago sativa L.) is autotetraploid, and predominantly allogamous. This complex breeding structure maximises the genetic diversity within lucerne populations making it difficult to genetically discriminate between populations. The objective of this study was to evaluate the level of random genetic diversity within and between a selection of Australian-grown lucerne cultivars, with tetraploid M. falcata included as a possible divergent control source. This diversity was evaluated using random amplified polymorphic DNA (RAPDs). Nineteen plants from each of 10 cultivars were analysed. Using 11 RAPD primers, 96 polymorphic bands were scored as present or absent across the 190 individuals. Genetic similarity estimates (GSEs) of all pair-wise comparisons were calculated from these data. Mean GSEs within cultivars ranged from 0.43 to 0.51. Cultivar Venus (0.43) had the highest level of intra-population genetic diversity and cultivar Sequel HR (0.51) had the lowest level of intra-population genetic diversity. Mean GSEs between cultivars ranged from 0.31 to 0.49, which overlapped with values obtained for within-cultivar GSE, thus not allowing separation of the cultivars. The high level of intra- and inter-population diversity that was detected is most likely due to the breeding of synthetic cultivars using parents derived from a number of diverse sources. Cultivar-specific polymorphisms were only identified in the M. falcata source, which like M. sativa, is outcrossing and autotetraploid. From a cluster analysis and a principal components analysis, it was clear that M. falcata was distinct from the other cultivars. The results indicate that the M. falcata accession tested has not been widely used in Australian lucerne breeding programs, and offers a means of introducing new genetic diversity into the lucerne gene pool. This provides a means of maximising heterozygosity, which is essential to maximising productivity in lucerne.

Identification of research to improve the efficiency of breeding strategies for white clover in Australia - a review

A major challenge faced by today's white clover breeder is how to manage resources within a breeding program. It is essential to utilise these resources with sufficient flexibility to build on past progress from conventional breeding strategies, but also take advantage of emerging opportunities from molecular breeding tools such as molecular markers and transformation. It is timely to review white clover breeding strategies. This background can then be used as a foundation for considering how to continue conventional plant improvement activities and complement them with molecular breeding opportunities. In this review, conventional white clover breeding strategies relevant to the Australian dryland target population environments are considered. Attention is given to: (i) availability of genetic variation, (ii) characterisation of germplasm collections, (iii) quantitative models for estimation of heritability, (iv) the role of multi-environment trials to accommodate genotype-by-environment interactions, (v) interdisciplinary research to understand adaptation to dryland environments, (vi) breeding and selection strategies, and (vii) cultivar structure. Current achievements in biotechnology with specific reference to white clover breeding in Australia are considered, and computer modelling of breeding programs is discussed as a useful integrative tool for the joint evaluation of conventional and molecular breeding strategies and optimisation of resource use in breeding programs. Four areas are identified as future research priorities: (i) capturing the potential genetic diversity among introduced accessions and ecotypes that are adapted to key constraints such as summer moisture stress and the use of molecular markers to assess the genetic diversity, (ii) understanding the underlying physiological/morphological root and shoot mechanisms involved in water use efficiency of white clover, with the objective of identifying appropriate selection criteria, (iii) estimation of quantitative genetic parameters of important morphological/physiological attributes to enable prediction of response to selection in target environments, and (iv) modelling white clover breeding strategies to evaluate the opportunities for integration of molecular breeding strategies with conventional breeding programs.