Selecting for increased barley grain size

In this study, 120–144 commercial varieties and breeding lines were assessed for grain size attributes including plump grain (>2.8 mm) and retention (>2.5 mm+>2.8 mm). Grain samples were produced from replicated trials at 25 sites across four years. Climatic conditions varied between years as well as between sites. Several of the trial sites were irrigated while the remaining were produced under dryland conditions. A number of the dryland sites suffered severe drought stress. The grain size data was analysed for genetic (G), environmental (E) and genotype by environment (G×E) interactions. All analyses included maturity as a covariate. The genetic effect on grain size was greater than environmental or maturity effects despite some sites suffering terminal moisture stress. The model was used to calculate heritability values for each site used in the study. These values ranged from 89 to 98% for plump grain and 88 to 96% for retention. The results demonstrated that removing the sources of non-heritable variation, such as maturity and field effects, can improve genetic estimates of the retention and plump grain fractions. By partitioning all variance components, and thereby having more robust estimates of genetic differences, plant breeders can have greater confidence in selecting barley genotypes which maintain large, stable grain size across a range of environments.

Validation of a Competitive Enzyme-Linked Immunosorbent Assay for Detection of Babesia bigemina Antibodies in Cattle

A competitive enzyme-linked immunosorbent assay (cELISA) based on a broadly conserved, species-specific, B-cell epitope within the C terminus of Babesia bigemina rhoptry-associated protein 1a was validated for international use. Receiver operating characteristic analysis revealed 16% inhibition as the threshold for a negative result, with an associated specificity of 98.3% and sensitivity of 94.7%. Increasing the threshold to 21% increased the specificity to 100% but modestly decreased the sensitivity to 87.2%. By using 21% inhibition, the positive predictive values ranged from 90.7% (10% prevalence) to 100% (95% prevalence) and the negative predictive values ranged from 97.0% (10% prevalence) to 48.2% (95% prevalence). The assay was able to detect serum antibody as early as 7 days after intravenous inoculation. The cELISA was distributed to five different laboratories along with a reference set of 100 defined bovine serum samples, including known positive, known negative, and field samples. The pairwise concordance among the five laboratories ranged from 100% to 97%, and all kappa values were above 0.8, indicating a high degree of reliability. Overall, the cELISA appears to have the attributes necessary for international application.

Identification of dynamical systems with fractional derivative damping models using inverse sensitivity analysis

The problem of identifying parameters of time invariant linear dynamical systems with fractional derivative damping models, based on a spatially incomplete set of measured frequency response functions and experimentally determined eigensolutions, is considered. Methods based on inverse sensitivity analysis of damped eigensolutions and frequency response functions are developed. It is shown that the eigensensitivity method requires the development of derivatives of solutions of an asymmetric generalized eigenvalue problem. Both the first and second order inverse sensitivity analyses are considered. The study demonstrates the successful performance of the identification algorithms developed based on synthetic data on one, two and a 33 degrees of freedom vibrating systems with fractional dampers. Limited studies have also been conducted by combining finite element modeling with experimental data on accelerances measured in laboratory conditions on a system consisting of two steel beams rigidly joined together by a rubber hose. The method based on sensitivity of frequency response functions is shown to be more efficient than the eigensensitivity based method in identifying system parameters, especially for large scale systems.

Tan spot: A new disease of pyrethrum caused by Microsphaeropsis tanaceti sp. nov

The isolation frequency of Microsphaeropsis sp. in spring in association with necrotic lesions on leaves in Tasmanian pyrethrum (Tanacetum cinerariifolium) fields has increased substantially since first identification in 2001. Examination of morphological features and sequencing of the internal transcribed spacer region (ITS) resulted in the identification of a new species, herein described as Microsphaeropsis tanaceti sp. nov. The pathogenicity of three M. tanaceti isolates to two pyrethrum cultivars was confirmed by inoculating glasshouse-grown plants in three experiments. No significant differences in the susceptibility of the two cultivars to infection by M. tanaceti were found. Symptoms were tan-coloured spots which coalesced around the margins of the leaves. Therefore, the name 'tan spot' is proposed for this new disease of pyrethrum. The sensitivity of seven M. tanaceti isolates to difenoconazole and azoxystrobin, commonly used fungicides for the management of foliar diseases in spring, was assessed under in vitro conditions. Sensitivity testing for difenoconazole was conducted using a mycelial growth assay on potato dextrose agar, whilst testing for sensitivity to azoxystrobin used a conidial germination assay on water agar. Microsphaeropsis tanaceti was found to be more sensitive to azoxystrobin than difenoconazole, with complete inhibition of conidial germination at concentrations above 0.625 µg a.i. mL-1. By comparison, concentrations of 50 µg a.i. difenoconazole mL-1 or greater were required for significant inhibition of mycelial growth. It therefore appears likely that there is currently some control of tan spot as a result of the use of azoxystrobin and to a lesser extent, difenoconazole, for the control of other diseases.

Bile Acid Derived PET-Based Cation Sensors: Molecular Structure Dependence of their Sensitivity

A number of bile acid derived photoinduced electron transfer (PET) based sensors for metal ions are prepared. A general strategy for designing the sensor with a modular nature allows for making different molecules capable of sensing different metal ions by a change in the fluorophore and receptor unit. Keeping the basic molecular structure the same, different bile acid base fluoroionophores were prepared inorder to achieve the highest sensitivity toward the metal ions. Thesensors showed similar binding constants for the same metal ion, but the degree Of fluorescence enhancement upon addition of the metal salts were different. The sensitivities of the sensors towards a certain metal were determined from the observed fluorescence enhancement upon addition of the metal salt.

The sensitivity of updated NIR PLS models for fruit sorting to initial parameter settings

Partial least squares regression models on NIR spectra are often optimised (for wavelength range, mathematical pretreatment and outlier elimination) in terms of calibration terms of validation performance with reference to totally independent populations.

Molecular characterisation, pathogenesis and fungicide sensitivity of Pythium spp. from table beet (Beta vulgaris var. vulgaris) grown in the Lockyer Valley, Queensland

Table beet production in the Lockyer Valley of south-eastern Queensland is known to be adversely affected by soilborne root disease from infection by Pythium spp. However, little is known regarding the species or genotypes that are the causal agents of both pre- and post-emergence damping off. Based on RFLP analysis with HhaI, HinfI and MboI of the PCR amplified ITS region DNA from soil and diseased plant samples, the majority of 130 Pythium isolates could be grouped into three genotypes, designated LVP A, LVP B and LVP C. These groups comprised 43, 41 and 7% of all isolates, respectively. Deoxyribonucleic acid sequence analysis of the ITS region indicated that LVP A was a strain of Pythium aphanidermatum, with greater than 99% similarity to the corresponding P. aphanidermatum sequences from the publicly accessible databases. The DNA sequences from LVP B and LVP C were most closely related to P. ultimum and P. dissotocum, respectively. Lower frequencies of other distinct isolates with unique RFLP patterns were also obtained with high levels of similarity (>97%) to P. heterothallicum, P. periplocum and genotypes of P. ultimum other than LVP B. Inoculation trials of 1- and 4-week-old beet seedlings indicated that compared with isolates of the LVP B genotype, a higher frequency of LVP A isolates caused disease. Isolates with the LVP A, LVP B and LVP C genotypes were highly sensitive to the fungicide Ridomil MZ, which suppressed radial growth on V8 agar between approximately four and thirty fold at 5 μg/mL metalaxyl and 40 μg/mL mancozeb, a concentration far lower than the recommended field application rate.

Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

Comment: Eigenvalue sensitivity method for optimal stabilisation of linear systems

In a letter RauA proposed a new method for designing statefeedback controllers using eigenvalue sensitivity matrices. However, there appears to be a conceptual mistake in the procedure, or else it is unduly restricted in its applicability. In particular the equation — BR~lBTK = A/.I, in which K is a positive-definite symmetric matrix.

Climate change impacts on northern Australian rangeland livestock carrying capacity: a review of issues

Grazing is a major land use in Australia's rangelands. The 'safe' livestock carrying capacity (LCC) required to maintain resource condition is strongly dependent on climate. We reviewed: the approaches for quantifying LCC; current trends in climate and their effect on components of the grazing system; implications of the 'best estimates' of climate change projections for LCC; the agreement and disagreement between the current trends and projections; and the adequacy of current models of forage production in simulating the impact of climate change. We report the results of a sensitivity study of climate change impacts on forage production across the rangelands, and we discuss the more general issues facing grazing enterprises associated with climate change, such as 'known uncertainties' and adaptation responses (e.g. use of climate risk assessment). We found that the method of quantifying LCC from a combination of estimates (simulations) of long-term (>30 years) forage production and successful grazier experience has been well tested across northern Australian rangelands with different climatic regions. This methodology provides a sound base for the assessment of climate change impacts, even though there are many identified gaps in knowledge. The evaluation of current trends indicated substantial differences in the trends of annual rainfall (and simulated forage production) across Australian rangelands with general increases in most of western Australian rangelands ( including northern regions of the Northern Territory) and decreases in eastern Australian rangelands and south-western Western Australia. Some of the projected changes in rainfall and temperature appear small compared with year-to-year variability. Nevertheless, the impacts on rangeland production systems are expected to be important in terms of required managerial and enterprise adaptations. Some important aspects of climate systems science remain unresolved, and we suggest that a risk-averse approach to rangeland management, based on the 'best estimate' projections, in combination with appropriate responses to short-term (1-5 years) climate variability, would reduce the risk of resource degradation. Climate change projections - including changes in rainfall, temperature, carbon dioxide and other climatic variables - if realised, are likely to affect forage and animal production, and ecosystem functioning. The major known uncertainties in quantifying climate change impacts are: (i) carbon dioxide effects on forage production, quality, nutrient cycling and competition between life forms (e.g. grass, shrubs and trees); and (ii) the future role of woody plants including effects of. re, climatic extremes and management for carbon storage. In a simple example of simulating climate change impacts on forage production, we found that increased temperature (3 degrees C) was likely to result in a decrease in forage production for most rangeland locations (e. g. -21% calculated as an unweighted average across 90 locations). The increase in temperature exacerbated or reduced the effects of a 10% decrease/increase in rainfall respectively (-33% or -9%). Estimates of the beneficial effects of increased CO2 (from 350 to 650 ppm) on forage production and water use efficiency indicated enhanced forage production (+26%). The increase was approximately equivalent to the decline in forage production associated with a 3 degrees C temperature increase. The large magnitude of these opposing effects emphasised the importance of the uncertainties in quantifying the impacts of these components of climate change. We anticipate decreases in LCC given that the 'best estimate' of climate change across the rangelands is for a decline (or little change) in rainfall and an increase in temperature. As a consequence, we suggest that public policy have regard for: the implications for livestock enterprises, regional communities, potential resource damage, animal welfare and human distress. However, the capability to quantify these warnings is yet to be developed and this important task remains as a challenge for rangeland and climate systems science.

Parameter estimation and sensitivity analysis of fat deposition models in beef steers using acslXtreme.

The Davis Growth Model (a dynamic steer growth model encompassing 4 fat deposition models) is currently being used by the phenotypic prediction program of the Cooperative Research Centre (CRC) for Beef Genetic Technologies to predict P8 fat (mm) in beef cattle to assist beef producers meet market specifications. The concepts of cellular hyperplasia and hypertrophy are integral components of the Davis Growth Model. The net synthesis of total body fat (kg) is calculated from the net energy available after accounting tor energy needs for maintenance and protein synthesis. Total body fat (kg) is then partitioned into 4 fat depots (intermuscular, intramuscular, subcutaneous, and visceral). This paper reports on the parameter estimation and sensitivity analysis of the DNA (deoxyribonucleic acid) logistic growth equations and the fat deposition first-order differential equations in the Davis Growth Model using acslXtreme (Hunstville, AL, USA, Xcellon). The DNA and fat deposition parameter coefficients were found to be important determinants of model function; the DNA parameter coefficients with days on feed >100 days and the fat deposition parameter coefficients for all days on feed. The generalized NL2SOL optimization algorithm had the fastest processing time and the minimum number of objective function evaluations when estimating the 4 fat deposition parameter coefficients with 2 observed values (initial and final fat). The subcutaneous fat parameter coefficient did indicate a metabolic difference for frame sizes. The results look promising and the prototype Davis Growth Model has the potential to assist the beef industry meet market specifications.

Action learning in partnership with Landcare and catchment management groups to support increased pasture sowings in southern inland Queensland

The incorporation of sown pastures as short-term rotations into the cropping systems of northern Australia has been slow. The inherent chemical fertility and physical stability of the predominant vertisol soils across the region enabled farmers to grow crops for decades without nitrogen fertiliser, and precluded the evolution of a crop–pasture rotation culture. However, as less fertile and less physically stable soils were cropped for extended periods, farmers began to use contemporary farming and sown pasture technologies to rebuild and maintain their soils. This has typically involved sowing long-term grass and grass–legume pastures on the more marginal cropping soils of the region. In partnership with the catchment management authority, the Queensland Murray–Darling Committee (QMDC) and Landcare, a pasture extension process using the LeyGrain™ package was implemented in 2006 within two Grain & Graze projects in the Maranoa-Balonne and Border Rivers catchments in southern inland Queensland. The specific objectives were to increase the area sown to high quality pasture and to gain production and environmental benefits (particularly groundcover) through improving the skills of producers in pasture species selection, their understanding and management of risk during pasture establishment, and in managing pastures and the feed base better. The catalyst for increasing pasture sowings was a QMDC subsidy scheme for increasing groundcover on old cropping land. In recognising a need to enhance pasture knowledge and skills to implement this scheme, the QMDC and Landcare producer groups sought the involvement of, and set specific targets for, the LeyGrain workshop process. This is a highly interactive action learning process that built on the existing knowledge and skills of the producers. Thirty-four workshops were held with more than 200 producers in 26 existing groups and with private agronomists. An evaluation process assessed the impact of the workshops on the learning and skill development by participants, their commitment to practice change, and their future intent to sow pastures. The results across both project catchments were highly correlated. There was strong agreement by producers (>90%) that the workshops had improved knowledge and skills regarding the adaptation of pasture species to soils and climates, enabling a better selection at the paddock level. Additional strong impacts were in changing the attitudes of producers to all aspects of pasture establishment, and the relative species composition of mixtures. Producers made a strong commitment to practice change, particularly in managing pasture as a specialist crop at establishment to minimise risk, and in the better selection and management of improved pasture species (particularly legumes and the use of fertiliser). Producers have made a commitment to increase pasture sowings by 80% in the next 5 years, with fourteen producers in one group alone having committed to sow an additional 4893 ha of pasture in 2007–08 under the QMDC subsidy scheme. The success of the project was attributed to the partnership between QMDC and Landcare groups who set individual workshop targets with LeyGrain presenters, the interactive engagement processes within the workshops themselves, and the follow-up provided by the LeyGrain team for on-farm activities.

Increased reproductive success in older female red kangaroos and the impact of harvesting

Wildlife harvesting has a long history in Australia, including obvious examples of overexploitation. Not surprisingly, there is scepticism that commercial harvesting can be undertaken sustainably. Kangaroo harvesting has been challenged regularly at Administrative Appeals Tribunals and elsewhere over the past three decades. Initially, the concern from conservation groups was sustainability of the harvest. This has been addressed through regular, direct monitoring that now spans > 30 years and a conservative harvest regime with a low risk of overharvest in the face of uncertainty. Opposition to the harvest now continues from animal rights groups whose concerns have shifted from overall harvest sustainability to side effects such as animal welfare, and changes to community structure, genetic composition and population age structure. Many of these concerns are speculative and difficult to address, requiring expensive data. One concern is that older females are the more successful breeders and teach their daughters optimal habitat and diet selection. The lack of older animals in a harvested population may reduce the fitness of the remaining individuals; implying population viability would also be compromised. This argument can be countered by the persistence of populations under harvesting without any obvious impairment to reproduction. Nevertheless, an interesting question is how age influences reproductive output. In this study, data collected from a number of red kangaroo populations across eastern Australia indicate that the breeding success of older females is up to 7-20% higher than that of younger females. This effect is smaller than that of body condition and the environment, which can increase breeding success by up to 30% and 60% respectively. Average age of mature females in a population may be reduced from 9 to 6 years old, resulting in a potential reduction in breeding success of 3-4%. This appears to be offset in harvested populations by improved condition of females from a reduction in kangaroo density. There is an important recommendation for management. The best insurance policy against overharvest and unwanted side effects is not research, which could be never-ending. Rather, it is a harvest strategy that includes safeguards against uncertainty such as harvest reserves, conservative quotas and regular monitoring. Research is still important in fine tuning that strategy and is most usefully incorporated as adaptive management where it can address the key questions on how populations respond to harvesting.

Evaluation of a Drunk Driving Warning System

The Drunk Driving Warning System is an alcohol interlock based on performance of the Critical Tracking Task (CTT). An evaluation was undertaken to determine CTT sensitivity to blood alcohol concentration (BAC), particularly at .05 g/100 ml. Subjects were 36 males in 3 age groups (18, 21 to 25, 35 years and above) divided into 2 alcohol consumption categories ("light" and "heavy"), and scored on 4 training and 2 test days (one alcohol and one placebo). The CTT performance declined as BAC increased and was significantly impaired at .05 BAC. However, performance was too variable for in-vehicle use. Age and alcohol consumption pattern were without effect.

Real-time wide-area loading margin sensitivity (WALMS) in power systems

Loading margin sensitivity (LMS) has been widely used in applications in the realm of voltage stability assessment and control. Typically, LMS is derived based on system equilibrium equations near bifurcation and therefore requires full detailed system model and significant computation effort. Availability of phasor measurement units (PMUs) due to the recent development of wide-area monitoring system (WAMS) provides an alternative computation-friendly approach for calculating LMS. With such motivation, this work proposes measurement-based wide-area loading margin sensitivity (WALMS) in bulk power systems. The proposed sensitivity, with its simplicity, has great potential to be embedded in real-time applications. Moreover, the calculation of the WALMS is not limited to low voltage near bifurcation point. A case study on IEEE 39-bus system verifies the proposed sensitivity. Finally, a voltage control scenario demonstrates the potential application of the WALMS.