Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

Changes in energy efficiency in Australia: A decomposition of aggregate energy intensity using logarithmic mean Divisia approach

This paper provides an empirical estimation of energy efficiency and other proximate factors that explain energy intensity in Australia for the period 1978-2009. The analysis is performed by decomposing the changes in energy intensity by means of energy efficiency, fuel mix and structural changes using sectoral and sub-sectoral levels of data. The results show that the driving forces behind the decrease in energy intensity in Australia are efficiency effect and sectoral composition effect, where the former is found to be more prominent than the latter. Moreover, the favourable impact of the composition effect has slowed consistently in recent years. A perfect positive association characterizes the relationship between energy intensity and carbon intensity in Australia. The decomposition results indicate that Australia needs to improve energy efficiency further to reduce energy intensity and carbon emissions. © 2012 Elsevier Ltd.

Decomposition of energy-related CO2 emissions in Australia: Challenges and policy implications

Changes in energy-related CO2 emissions aggregate intensity, total CO2 emissions and per-capita CO2 emissions in Australia are decomposed by using a Logarithmic Mean Divisia Index (LMDI) method for the period 1978-2010. Results indicate improvements in energy efficiency played a dominant role in the measured 17% reduction in CO2 emissions aggregate intensity in Australia over the period. Structural changes in the economy, such as changes in the relative importance of the services sector vis-à-vis manufacturing, have also played a major role in achieving this outcome. Results also suggest that, without these mitigating factors, income per capita and population effects could well have produced an increase in total emissions of more than 50% higher than actually occurred over the period. Perhaps most starkly, the results indicate that, without these mitigating factors, the growth in CO2 emissions per capita could have been over 150% higher than actually observed. Notwithstanding this, the study suggests that, for Australia to meet its Copenhagen commitment, the relative average per annum effectiveness of these mitigating factors during 2010-2020 probably needs to be almost three times what it was in the 2005-2010 period-a very daunting challenge indeed for Australia's policymakers.

Changes in CO2 emissions over business cycle recessions and expansions in the United States: A decomposition analysis

This paper examines the asymmetry of changes in CO2 emissions over business cycle recessions and expansions using yearly data from 1949 and monthly data from 1973 for the United States (US). In addition, decomposition analysis is applied to investigate the relative roles of various proximate contributing factors to observed changes in total and per capita CO2 emissions and emissions intensity, over business cycle phases. The results suggest, inter alia, that aggregate emissions and emissions intensity reduce much faster in contractions than they increase in expansions. In addition, unlike the three previous expansions, in the most recent post-GFC US expansion, emissions per capita have continued to decline, and at a rate very similar to the rate of reduction in preceding contractions. This suggests the real possibility that the most recent contraction may have had an ongoing impact on the path of per capita emissions well beyond the immediate impact experienced during the contraction itself.

Quarantine and surveillance strategies for plant pathogen detection and control

Incursions of plant pests and diseases pose serious threats to food security, agricultural productivity and the natural environment. One of the challenges in confidently delimiting and eradicating incursions is how to choose from an arsenal of surveillance and quarantine approaches in order to best control multiple dispersal pathways. Anthropogenic spread (propagules carried on humans or transported on produce or equipment) can be controlled with quarantine measures, which in turn can vary in intensity. In contrast, environmental spread processes are more difficult to control, but often have a temporal signal (e.g. seasonality) which can introduce both challenges and opportunities for surveillance and control. This leads to complex decisions regarding when, where and how to search. Recent modelling investigations of surveillance performance have optimised the output of simulation models, and found that a risk-weighted randomised search can perform close to optimally. However, exactly how quarantine and surveillance strategies should change to reflect different dispersal modes remains largely unaddressed. Here we develop a spatial simulation model of a plant fungal-pathogen incursion into an agricultural region, and its subsequent surveillance and control. We include structural differences in dispersal via the interplay of biological, environmental and anthropogenic connectivity between host sites (farms). Our objective was to gain broad insights into the relative roles played by different spread modes in propagating an invasion, and how incorporating knowledge of these spread risks may improve approaches to quarantine restrictions and surveillance. We find that broad heuristic rules for quarantine restrictions fail to contain the pathogen due to residual connectivity between sites, but surveillance measures enable early detection and successfully lead to suppression of the pathogen in all farms. Alternative surveillance strategies attain similar levels of performance by incorporating environmental or anthropogenic dispersal risk in the prioritisation of sites. Our model provides the basis to develop essential insights into the effectiveness of different surveillance and quarantine decisions for fungal pathogen control. Parameterised for authentic settings it will aid our understanding of how the extent and resolution of interventions should suitably reflect the spatial structure of dispersal processes.

The role of MicroRNAs in stress response in the resurrection plant Tripogon loliiformis

Research in this thesis focussed on the improvement of agricultural crops in increasing water use efficiency that impacts global crop productivity. The study identified key genetic regulatory mechanisms that the resurrection plant Tripogon loliiformis utilises to tolerate desiccation. Due to the conserved nature of the pathways involved, this information can be transferred for the enhancement of drought tolerance and water use efficiency in agricultural crops. Specifically this study used high throughput sequencing, microscopy and plant transformation to further the understanding of post-transcriptional regulatory mechanisms. It was shown that T. loliiformis uses microRNAs to regulate pro-survival autophagy pathways to tolerate desiccation.

Trehalose accumulation triggers autophagy during plant desiccation

Global climate change, increasingly erratic weather and a burgeoning global population are significant threats to the sustainability of future crop production. There is an urgent need for the development of robust measures that enable crops to withstand the uncertainty of climate change whilst still producing maximum yields. Resurrection plants possess the unique ability to withstand desiccation for prolonged periods, can be restored upon watering and represent great potential for the development of stress tolerant crops. Here, we describe the remarkable stress characteristics of Tripogon loliiformis, an uncharacterised resurrection grass and close relative of the economically important cereals, rice, sorghum, and maize. We show that T. loliiformis survives extreme environmental stress by implementing autophagy to prevent Programmed Cell Death. Notably, we identified a novel role for trehalose in the regulation of autophagy in T.loliiformis. Transcriptome, Gas Chromatography Mass Spectrometry, immunoblotting and confocal microscopy analyses directly linked the accumulation of trehalose with the onset of autophagy in dehydrating and desiccated T. loliiformis shoots. These results were supported in vitro with the observation of autophagosomes in trehalose treated T. loliiformis leaves; autophagosomes were not detected in untreated samples. Presumably, once induced, autophagy promotes desiccation tolerance in T.loliiformis , by removal of cellular toxins to suppress programmed cell death and the recycling of nutrients to delay the onset of senescence. These findings illustrate how resurrection plants manipulate sugar metabolism to promote desiccation tolerance and may provide candidate genes that are potentially useful for the development of stress tolerant crops.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.

The complete mitochondrial genome of the tarnished plant bug, Lygus lineolaris (Heteroptera: Miridae)

The complete mitochondrial genome of the tarnished plant bug, Lygus lineolaris, comprised 17,027 bp. The genome contained 13 protein coding regions, 22 tRNA genes and 2 ribosomal RNA genes. The gene arrangement corresponded to the common order found among insect mtDNAs which was considered to be the ancestral arrangement. The protein coding genes started with ATN and stopped with TAA or TAG. The nucleotide distribution was 76.0% A + T. The control region contained two repeat regions, one was 24 bp and the other was 161 bp. The Genbank accession for the complete L. lineolaris mt genome is EU401991.

A generic individual-based spatially explicit model as a novel tool for investigating insect-plant interactions: A case study of the behavioural ecology of frugivorous Tephritidae

Computational modelling of mechanisms underlying processes in the real world can be of great value in understanding complex biological behaviours. Uptake in general biology and ecology has been rapid. However, it often requires specific data sets that are overly costly in time and resources to collect. The aim of the current study was to test whether a generic behavioural ecology model constructed using published data could give realistic outputs for individual species. An individual-based model was developed using the Pattern-Oriented Modelling (POM) strategy and protocol, based on behavioural rules associated with insect movement choices. Frugivorous Tephritidae (fruit flies) were chosen because of economic significance in global agriculture and the multiple published data sets available for a range of species. The Queensland fruit fly (Qfly), Bactrocera tryoni, was identified as a suitable individual species for testing. Plant canopies with modified architecture were used to run predictive simulations. A field study was then conducted to validate our model predictions on how plant architecture affects fruit flies’ behaviours. Characteristics of plant architecture such as different shapes, e.g., closed-canopy and vase-shaped, affected fly movement patterns and time spent on host fruit. The number of visits to host fruit also differed between the edge and centre in closed-canopy plants. Compared to plant architecture, host fruit has less contribution to effects on flies’ movement patterns. The results from this model, combined with our field study and published empirical data suggest that placing fly traps in the upper canopy at the edge should work best. Such a modelling approach allows rapid testing of ideas about organismal interactions with environmental substrates in silico rather than in vivo, to generate new perspectives. Using published data provides a saving in time and resources. Adjustments for specific questions can be achieved by refinement of parameters based on targeted experiments.

Unusual RNA plant virus integration in the soybean genome leads to the production of small RNAs

Horizontal gene transfer (HGT) is known to be a major force in genome evolution. The acquisition of genes from viruses by eukaryotic genomes is a well-studied example of HGT, including rare cases of non-retroviral RNA virus integration. The present study describes the integration of cucumber mosaic virus RNA-1 into soybean genome. After an initial metatranscriptomic analysis of small RNAs derived from soybean, the de novo assembly resulted a 3029-nt contig homologous to RNA-1. The integration of this sequence in the soybean genome was confirmed by DNA deep sequencing. The locus where the integration occurred harbors the full RNA-1 sequence followed by the partial sequence of an endogenous mRNA and another sequence of RNA-1 as an inverted repeat and allowing the formation of a hairpin structure. This region recombined into a retrotransposon located inside an exon of a soybean gene. The nucleotide similarity of the integrated sequence compared to other Cucumber mosaic virus sequences indicates that the integration event occurred recently. We described a rare event of non-retroviral RNA virus integration in soybean that leads to the production of a double-stranded RNA in a similar fashion to virus resistance RNAi plants.

Decomposition analysis to examine Australia’s 2030 GHGs emissions target: How hard will it be to achieve?

The Australian government has recently pledged a reduction in GHGs emissions of 26–28% below the 2005 level by 2030. How big is the challenge for the country to achieve this target in terms of its present emissions profile, recent historical trends, and the contributions to those trends from key proximate factors contributing to emissions? In this paper, we attempt a quantitative judgement of the challenge by using decomposition analysis. Based on the analysis it appears the announced target will be quite challenging to achieve if the average annual mitigating effects from economic restructuring, energy efficiency improvements and movement towards less emissions-intensive energy sources in evidence over 2002–2013 continued through to 2030; however, if the contribution from these mitigating sources in evidence over 2006–2013 can be sustained, achievement of the target will be much less challenging. The challenge for government then will be to provide a policy framework to ensure the more pronounced beneficial impacts of the mitigating factors evidenced during 2006–2013 can be maintained over the years to 2030.

Missing pieces in the puzzle of plant microRNAs

Plant microRNAs (miRNAs) are important regulatory switches. Recent advances have revealed many regulatory layers between the two essential processes, miRNA biogenesis and function. However, how these multilayered regulatory processes ultimately control miRNA gene regulation and connects miRNAs and plant responses with the surrounding environment is still largely unknown. In this opinion article, we propose that the miRNA pathway is highly dynamic and plastic. The apparent flexibility of the miRNA pathway in plants appears to be controlled by a number recently identified proteins and poorly characterized signaling cascades. We further propose that altered miRNA accumulation can be a direct consequence of the rewiring of interactions between proteins that function in the miRNA pathway, an avenue that remains largely unexplored.