Friend or foe : exotic flora and ecosystem function

Exotic flora, particularly weeds, are renowned for out-competing and displacing native flora, consequently affecting native fauna and pollinator relationships. Nonetheless, it stands to reason that weeds must provide some compensatory ecological value. This study assessed whether weeds are friend or foe to ecosystem function by considering the quality and quantity of pollen offered by widespread weeds in Australian ecosystems. Using the Honeybee Apis mellifera as a case study, and information derived from highly experienced commercial apiarists, we determined that 32 exotic plants are important pollen sources. Most species offered high to very high quality pollen. Pollen quality varied temporally, spatially and infraspecifically. Fifteen species were considered more beneficial to A. mellifera than others; only seven species were considered less beneficial. Thus, exotic flora contribute pollen resources that are valuable to maintain ecosystem function, particularly at times when flowering native species are few.

An integrative framework for the appraisal of coloration in nature

The world in color presents a dazzling dimension of phenotypic variation. Biological interest in this variation has burgeoned, due to both increased means for quantifying spectral information and heightened appreciation for how animals view the world differently than humans. Effective study of color traits is challenged by how to best quantify visual perception in nonhuman species. This requires consideration of at least visual physiology but ultimately also the neural processes underlying perception. Our knowledge of color perception is founded largely on the principles gained from human psychophysics that have proven generalizable based on comparative studies in select animal models. Appreciation of these principles, their empirical foundation, and the reasonable limits to their applicability is crucial to reaching informed conclusions in color research. In this article, we seek a common intellectual basis for the study of color in nature. We first discuss the key perceptual principles, namely, retinal photoreception, sensory channels, opponent processing, color constancy, and receptor noise. We then draw on this basis to inform an analytical framework driven by the research question in relation to identifiable viewers and visual tasks of interest. Consideration of the limits to perceptual inference guides two primary decisions: first, whether a sensory-based approach is necessary and justified and, second, whether the visual task refers to perceptual distance or discriminability. We outline informed approaches in each situation and discuss key challenges for future progress, focusing particularly on how animals perceive color. Given that animal behavior serves as both the basic unit of psychophysics and the ultimate driver of color ecology/evolution, behavioral data are critical to reconciling knowledge across the schools of color research.

The global biopharma industry and the rise of Indian drug multinationals: implication for Australian generics policy

This article provides a synopsis of the new dynamics of the global biopharma industry. The emergence of global generics companies with capabilities approximating those of 'big pharma' has accelerated the blurring of boundaries between the innovator and generics sectors. Biotechnology-based products form a large and growing segment of prescription drug markets and regulatory pathways for biogenerics are imminent. Indian biopharma multinationals with large-scale efficient manufacturing plants and growing R&D capabilities are now major suppliers of Active Pharmaceutical Ingredients (APIs) and generic drugs across both developed and developing countries. In response to generic competition, innovator companies employ a range of life cycle management techniques, including the launch of 'authorised generics'. The generics segment in Australia will see high growth rates in coming years but the prospect for local manufacturing is bleak. The availability of cheap generics in international markets has put pressure on Pharmaceutical Benefits Scheme (PBS) pricing arrangements, and a new policy direction was announced in November 2006. Lower generics prices will have a negative impact on some incumbent suppliers but industrial renewal policies for the medicines industry in Australia are better focused on higher value R&D activities and niche manufacturing of sophisticated products.

Assessing body condition and energy budget components by scoring abdominal profiles in free-ranging pink-footed geese Anser brachyrhynchus

An abdominal profile index (API) was developed for pink-footed geese Anser brachyrhynchus as a measure of body condition. On basis of carcass analysis of 56 adult geese with known API prior to collection, we found significant linear relationships between API against body mass, abdominal fat and total energy content. Hence, changes in API reflect net energy intake rates. As an example of the applicability of the calibration, we compared APIs of individually marked geese before and after long migration episodes and estimated the cost of flight at 8.9 kJ/km. In addition we estimated gain rates at three major staging sites along the spring flyway indicating an increase in fueling rates with latitude. Calibration of APIs and energy contents offers new opportunities for field studies of waterfowl energetics.

Application of white-beam X-ray microdiffraction for the study of mineralogical phase identification in ancient Egyptian pigments

High-brightness synchrotron X-rays together with precision achromatic focusing optics on beamline 7.3.3 at the Advanced Light Source have been applied for Laue microdiffraction analysis of mineralogical phases in Egyptian pigments. Although this task is usually performed using monochromatic X-ray diffraction, the Laue technique was both faster and more reliable for the present sample. In this approach, white-beam diffraction patterns are collected as the sample is raster scanned across the incident beam (0.8 µm × 0.8 µm). The complex Laue diffraction patterns arising from illumination of multiple grains are indexed using the white-beam crystallographic software package XMAS, enabling a mineralogical map as a function of sample position. This methodology has been applied to determine the mineralogy of colour pigments taken from the ancient Egyptian coffin of Tjeseb, a priestess of the Apis bull dating from the Third Intermediate to Late period, 25th Dynasty to early 26th Dynasty (747 to 600 BC). For all pigments, a ground layer of calcite and quartz was identified. For the blue pigment, cuprorivaite (CuCaSi4O10) was found to be the primary colouring agent with a grain size ranging from ∼10 to 50 µm. In the green and yellow samples, malachite [Cu2(OH)2CO3] and goethite [FeO(OH)] were identified, respectively. Grain sizes from these pigments were significantly smaller. It was possible to index some malachite grains up to ∼20 µm in size, while the majority of goethite grains displayed a nanocrystalline particle size. The inability to obtain a complete mineralogical map for goethite highlights the fact that the incident probe size is considerably larger than the grain size. This limit will continue to improve as the present trend is toward focusing optics approaching the diffraction limit (∼1000× smaller beam area).

A fast malware feature selection approach using a hybrid of multi-linear and stepwise binary logistic regression

Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation anti-virus engines employ a signature-template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current anti-virus engines in detecting malware. In this paper, we propose a stepwise binary logistic regression-based dimensionality reduction techniques for malware detection using application program interface (API) call statistics. Finding the most significant malware feature using traditional wrapper-based approaches takes an exponential complexity of the dimension (m) of the dataset with a brute-force search strategies and order of (m-1) complexity with a backward elimination filter heuristics. The novelty of the proposed approach is that it finds the worst case computational complexity which is less than order of (m-1). The proposed approach uses multi-linear regression and the p-value of each individual API feature for selection of the most uncorrelated and significant features in order to reduce the dimensionality of the large malware data and to ensure the absence of multi-collinearity. The stepwise logistic regression approach is then employed to test the significance of the individual malware feature based on their corresponding Wald statistic and to construct the binary decision the model. When the selected most significant APIs are used in a decision rule generation systems, this approach not only reduces the tree size but also improves classification performance. Exhaustive experiments on a large malware data set show that the proposed approach clearly exceeds the existing standard decision rule, support vector machine-based template approach with complete data and provides a better statistical fitness.