Australian National University

About: Australian National University is a education organization based out in Canberra, Australian Capital Territory, Australia. It is known for research contribution in the topics: Population & Galaxy. The organization has 34419 authors who have published 109261 publications receiving 4315448 citations. The organization is also known as: The Australian National University & ANU.

Improving Intrinsic Water-Use Efficiency and Crop Yield.

Abstract: Greater yield per unit rainfall is one of the most important challenges in dryland agriculture. Improving intrinsic water-use efficiency (W(T)), the ratio of CO(2) assimilation rate to transpiration rate at the stomata, may be one means of achieving this goal. Carbon isotope discrimination (Delta(13)C) is recognized as a reliable surrogate for W(T) and there have now been numerous studies which have examined the relationship between crop yield and W(T) (measured as Delta(13)C). These studies have shown the relationship between yield and W(T) to be highly variable. The impact on crop yield of genotypic variation in W(T) will depend on three factors: (i) the impact of variation in W(T) on crop growth rate, (ii) the impact of variation in W(T) on the rate of crop water use, and (iii) how growth and water use interact over the crop's duration to produce grain yield. The relative importance of these three factors will differ depending on the crop species being grown and the nature of the cropping environment. Here we consider these interactions using (i) the results of field trials with bread wheat (Triticum aestivum L.), durum wheat (T. turgidum L.), and barley (Hordeum vulgare L.) that have examined the association between yield and Delta(13)C and (ii) computer simulations with the SIMTAG wheat crop growth model. We present details of progress in breeding to improve W(T) and yield of wheat for Australian environments where crop growth is strongly dependent on subsoil moisture stored from out-of-season rains and assess other opportunities to improve crop yield using W(T).

Scrutinizing Exotic Cosmological Models Using ESSENCE Supernova Data Combined with Other Cosmological Probes

Abstract: The first cosmological results from the ESSENCE supernova survey (Wood-Vasey and coworkers) are extended to a wider range of cosmological models including dynamical dark energy and nonstandard cosmological models. We fold in a greater number of external data sets such as the recent Higher-z release of high-redshift supernovae (Riess and coworkers), as well as several complementary cosmological probes. Model comparison statistics such as the Bayesian and Akaike information criteria are applied to gauge the worth of models. These statistics favor models that give a good fit with fewer parameters. Based on this analysis, the preferred cosmological model is the flat cosmological constant model, where the expansion history of the universe can be adequately described with only one free parameter describing the energy content of the universe. Among the more exotic models that provide good fits to the data, we note a preference for models whose best-fit parameters reduce them to the cosmological constant model.

Estimates of the Regional Distribution of Sea Level Rise over the 1950–2000 Period

Abstract: TOPEX/Poseidon satellite altimeter data are used to estimate global empirical orthogonal functions that are then combined with historical tide gauge data to estimate monthly distributions of large-scale sea level variability and change over the period 1950–2000. The reconstruction is an attempt to narrow the current broad range of sea level rise estimates, to identify any pattern of regional sea level rise, and to determine any variation in the rate of sea level rise over the 51-yr period. The computed rate of global-averaged sea level rise from the reconstructed monthly time series is 1.8 ± 0.3 mm yr−1. With the decadal variability in the computed global mean sea level, it is not possible to detect a significant increase in the rate of sea level rise over the period 1950–2000. A regional pattern of sea level rise is identified. The maximum sea level rise is in the eastern off-equatorial Pacific and there is a minimum along the equator, in the western Pacific, and in the eastern Indian Ocean. A g...

Pyevolve: a toolkit for statistical modelling of molecular evolution

Abstract: Examining the distribution of variation has proven an extremely profitable technique in the effort to identify sequences of biological significance. Most approaches in the field, however, evaluate only the conserved portions of sequences – ignoring the biological significance of sequence differences. A suite of sophisticated likelihood based statistical models from the field of molecular evolution provides the basis for extracting the information from the full distribution of sequence variation. The number of different problems to which phylogeny-based maximum likelihood calculations can be applied is extensive. Available software packages that can perform likelihood calculations suffer from a lack of flexibility and scalability, or employ error-prone approaches to model parameterisation. Here we describe the implementation of PyEvolve, a toolkit for the application of existing, and development of new, statistical methods for molecular evolution. We present the object architecture and design schema of PyEvolve, which includes an adaptable multi-level parallelisation schema. The approach for defining new methods is illustrated by implementing a novel dinucleotide model of substitution that includes a parameter for mutation of methylated CpG's, which required 8 lines of standard Python code to define. Benchmarking was performed using either a dinucleotide or codon substitution model applied to an alignment of BRCA1 sequences from 20 mammals, or a 10 species subset. Up to five-fold parallel performance gains over serial were recorded. Compared to leading alternative software, PyEvolve exhibited significantly better real world performance for parameter rich models with a large data set, reducing the time required for optimisation from ~10 days to ~6 hours. PyEvolve provides flexible functionality that can be used either for statistical modelling of molecular evolution, or the development of new methods in the field. The toolkit can be used interactively or by writing and executing scripts. The toolkit uses efficient processes for specifying the parameterisation of statistical models, and implements numerous optimisations that make highly parameter rich likelihood functions solvable within hours on multi-cpu hardware. PyEvolve can be readily adapted in response to changing computational demands and hardware configurations to maximise performance. PyEvolve is released under the GPL and can be downloaded from http://cbis.anu.edu.au/software .