Variation in the fatty acid composition of the blubber in Cape fur seals (Arctocephalus pusillus pusillus) and the implications for dietary interpretation

Analysis of the fatty acid (FA) composition of blubber is a valuable tool in interpreting the diet of marine mammals. This technique is based on the principle that particular FA present in prey can be incorporated largely untransformed into predator adipose tissue stores, thereby providing biochemical signatures with which to identify prey species. Several studies of phocid seals and cetaceans have documented vertical stratification in the FA composition of blubber such that inferences about diet may vary greatly depending on the layer of the blubber that is analysed. It is not known whether blubber in otariid seals (fur seals and sea lions) also displays vertical stratification in FA composition. Furthermore, it is not known whether the FA composition of blubber is uniform in these species. In the present study, the vertical and regional variation in FA composition of blubber was investigated in seven adult female Cape fur seals (Arctocephalus pusillus pusillus). The proportion of monounsaturated fatty acids (MUFA) was greater in the outer (43.6±1.3%) than inner portion (40.9±1.2%; t20=5.59, P<0.001) whereas the proportions were greater in the inner than outer portions for saturated fatty acids (23.6±0.5% and 21.9±0.6%, respectively, t20 = 5.31, P<0.001) and polyunsaturated fatty acids (PUFA, 35.5±0.7% and 34.5±0.7%, respectively, t20 = 3.81, P < 0.001). There was an inverse relationship between MUFA and PUFA in the blubber, independent of sampling location. In addition, with the exception of the inner portion from non-lactating females, blubber from the mammary area had the highest proportions of 18:1ω9c and total MUFA, followed by blubber from the rump and neck, suggesting that the deposition and mobilisation of blubber lipids may not be uniform around the body in otariid seals. These results support the need for blubber tissue to be sampled from the same site on animals, and to the full depth of the blubber layer, to minimise variation in FA profiles that could occur if different sites and depths were sampled. Such standardisation of sampling will further aid in interpreting diet in otariid seals using the FA Signature Analysis approach.

Extraction of structured programs from specification proofs

We present a method using an extended logical system for obtaining programs from specifications written in a sublanguage of CASL. These programs are “correct” in the sense that they satisfy their specifications. The technique we use is to extract programs from proofs in formal logic by techniques due to Curry and Howard. The logical calculus, however, is novel because it adds structural rules corresponding to the standard ways of modifying specifications: translating (renaming), taking unions, and hiding signatures. Although programs extracted by the Curry-Howard process can be very cumbersome, we use a number of simplifications that ensure that the programs extracted are in a language close to a standard high-level programming language. We use this to produce an executable refinement of a given specification and we then provide a method for producing a program module that maximally respects the original structure of the specification. Throughout the paper we demonstrate the technique with a simple example.

Plant natriuretic peptide active site determination and effects on cGMP and cell volume regulation

Natriuretic peptides (NP) were first identified in animals where they play a role in the regulation of salt and water balance. This regulation is partly mediated by intracellular changes in cyclic GMP (cGMP). NP immunoanalogues occur in many plants and have been isolated, with two NP encoding genes characterised in Arabidopsis thaliana L. (AtPNP-A and AtPNP-B). Part of AtPNP-A contains the region with homology to human atrial (A)NP. We report here on the effects of recombinant AtPNP-A and smaller synthetic peptides within the ANP-homologous region with a view to identifying the biologically active domain of the molecule. Furthermore, we investigated interactions between AtPNP-A and the hormone, abscisic acid (ABA). ABA does not significantly affect Arabidopsis mesophyll protoplast volume regulation, whereas AtPNP-A and synthetic peptides promote water uptake into the protoplasts causing swelling. This effect is promoted by the membrane permeable cGMP analogue, 8-Br-cGMP, and inhibited by guanylate cyclase inhibitors indicating that increases in cGMP are an essential component of the plant natriuretic peptides (PNP) signalling cascade. ABA does not induce cGMP transients and does not affect AtPNP-A dependent cGMP increases, hence the two regulators differ in their second messenger signatures. Interestingly, AtPNP-A significantly delays and reduces the extent of ABA stimulated stomatal closure that is also based on cell volume regulation. We conclude that a complex interplay between observed PNP effects (stomatal opening and protoplast swelling) and ABA is likely to be cell type specific.

Terrestrial detritus supports the food webs in lowland intermittent streams of south-eastern Australia : a stable isotope study

1. Large amounts of terrestrial detritus enter many low-order forested streams, and this organic
material is often the major basal resource in the metazoan food webs of such systems. However,
despite their apparently low biomass, algae are the dominant food of organisms in a number of
aquatic communities which conventionally would have been presumed to be dependent on
allochthonous detritus, particularly those in the tropics and also in lowland intermittent streams
in arid Australia.
2. The dual stable isotope signatures (d13C and d15N) of potential primary food sources were
compared with the isotopic signatures of common aquatic animals in lowland intermittent
streams in south-eastern Australia, in both spring and summer, to determine whether
allochthonous detritus was an important nutritional resource in these systems. The isotopic
signatures of the major potential allochthonous plant food sources (Eucalyptus, Phalaris and
Juncus) overlapped, but were distinct from algae and the dominant macrophytes growing in the
study reaches. The isotopic signatures of biofilm were more spatially and temporally variable
than those of the other basal resources.
3. Despite allochthonous detritus having relatively high C : N ratios compared to other
potential basal resources, results from ISOSOURCE mixing model calculations demonstrated
that this detritus, and the associated biofilm, were the major energy sources assimilated by
macroinvertebrate primary consumers in both spring and summer. The importance of these
energy sources was also reflected in animals higher in the food web, including predatory
macroinvertebrates and fish. These resources were supplemented by autochthonous sources of
higher nutritional value (i.e. filamentous algae and macrophytes, which had relatively low
C : N ratios) when they became more prolific as the streams dried to disconnected pools in
summer.
4. The results highlight the importance of allochthonous detritus (particularly from Eucalyptus)
as a dependable energy source for benthic macroinvertebrates and fish in lowland intermittent
streams of south-eastern Australia. This contrasts with previous stable isotope studies
conducted in lowland intermittent streams in arid Australia, which have reported that the fauna
are primarily dependent on autochthonous algae.

Retrospective application of remote sensing to the Tasmanian lakeland

This thesis describes the research undertaken for a degree of Master of Science in a retrospective study of airborne remotely sensed data registered in 1990 and 1993, and field captured data of aquatic humus concentrations for ~ 45 lakes in Tasmania. The aim was to investigate and describe the relationship between the remotely sensed data and the field data and to test the hypothesis that the remotely sensed data would establish further evidence of a limnological corridor of change running north-west to south- east. The airborne remotely sensed data consisted of data captured by the CSIRO Ocean Colour Scanner (OCS) and a newly developed Canadian scanner, a compact airborne spectrographic imager (CASI). The thesis investigates the relationship between the two kinds of data sources. The remotely sensed data was collected with the OCS scanner in 1990 (during one day) and with both the OCS and the CASI in 1993 (during three days). The OCS scanner registers data in 9 wavelength bands between 380 nm and 960 nm with a 10-20 nm bandwidth, and the CASI in 288 wavelength bands between 379.57 nm and 893.5 nm (ie. spectral mode) with a spectral resolution of 2.5 nm. The remotely sensed data were extracted from the original tapes with the help of the CSIRO and supplied software and digital sample areas (band value means) for each lake were subsequently extracted for data manipulation and statistical analysis. Field data was captured concurrently with the remotely sensed data in 1993 by lake hopping using a light aircraft with floats. The field data used for analysis with the remotely sensed data were the laboratory determined g440 values from the 1993 water samples collated with g440 values determined from earlier years. No spectro-radiometric data of the lakes, data of incoming irradiance or ancillary climatic data were captured during the remote sensing missions. The sections of the background chapter in the thesis provide a background to the research both in regards to remote sensing of water quality and the relationship between remotely sensed spectral data and water quality parameters, as well as a description of the Tasmanian lakes flown. The lakes were divided into four groups based on results from previous studies and optical parameters, especially aquatic humus concentrations as measured from field captured data. The four groups consist of the ‘green” clear water lakes mostly situated on the Central Plateau, the ‘brown” highly dystrophic lakes in western Tasmania, the ‘corridor” lakes situated along a corridor of change lying approximately between the two lines denoting the Jurassic edge and 1200 mm isohyet, and the ‘eastern, turbid” lakes make up the fourth group. The analytical part of the research work was mostly concerned with manipulating and analysing the CASI data because of its higher spectral resolution. The research explores methods to apply corrections to this data to reduce the disturbing effects of varying illumination and atmospheric conditions. Three different methods were attempted. In the first method two different standardisation formulas are applied to the data as well as ‘day correction” factors calculated from data from one of the lakes, Lake Rolleston, which had data captured for all three days of the remote sensing operations. The standardisation formulas were also applied to the OCS data. In second method an attempt to reduce the effects of the atmosphere was performed using spectro-radiometric captured in 1988 for one of the lakes flown, Great Lake. All the lake sample data were time normalised using general irradiance data obtained from the University of Tasmania and the sky portion as calculated from Great Lake upwelling irradiance data was then subtracted. The last method involved using two different band ratios to eliminate atmospheric effects. Statistical analysis was applied to the data resulting from the three methods to try to describe the relationship between the remotely sensed data and the field captured data. Discriminant analysis, cluster analysis and factor analysis using principal component analysis (pea) were applied to the remotely sensed data and the field data. The factor scores resulting from the pca were regressed against the field collated data of g440 as were the values resulting from last method. The results from the statistical analysis of the data from the first method show that the lakes group well (100%) against the predetermined groups using discriminant analysis applied to the remotely sensed CASI data. Most variance in the data are contained in the first factor resulting from pca regardless of data manipulation method. Regression of the factor scores against g440 field data show a strong non- linear relationship and a one-sided linear regression test is therefore considered an inappropriate analysis method to describe the dataset relationships. The research has shown that with the available data, correction and analysis methods, and within the scope of the Masters study, it was not possible to establish the relationships between the remotely sensed data and the field measured parameters as hoped. The main reason for this was the failure to retrieve remotely sensed lake signatures adequately corrected for atmospheric noise for comparison with the field data. This in turn is a result of the lack of detailed ancillary information needed to apply available established methods for noise reduction - to apply these methods we require field spectroradiometric measurements and environmental information of the varying conditions both within the study area and within the time frame of capture of the remotely sensed data.

An automatic application signature construction system for unknown traffic

Identifying applications and classifying network traffic flows according to their source applications are critical for a broad range of network activities. Such a decision can be based on packet header fields, packet payload content, statistical characteristics of traffic and communication patterns of network hosts. However, most present techniques rely on some sort of apriori knowledge, which means they require labor-intensive preprocessing before running and cannot deal with previously unknown applications. In this paper, we propose a traffic classification system based on application signatures, with a novel approach to fully automate the process of deriving signatures from unidentified traffic. The key idea is to integrate statistics-based flow clustering with payload-based signature matching method, so as to eliminate the requirement of pre-labeled training data sets. We evaluate the efficiency of our approach using real-world traffic trace, and the results indicate that signature classifiers built from clustered data and pre-labeled data are able to achieve similar high accuracy better than 99%.

Malware variant detection using similarity search over sets of control flow graphs

Static detection of polymorphic malware variants plays an important role to improve system security. Control flow has shown to be an effective characteristic that represents polymorphic malware instances. In our research, we propose a similarity search of malware using novel distance metrics of malware signatures. We describe a malware signature by the set of control flow graphs the malware contains. We propose two approaches and use the first to perform pre-filtering. Firstly, we use a distance metric based on the distance between feature vectors. The feature vector is a decomposition of the set of graphs into either fixed size k-sub graphs, or q-gram strings of the high-level source after decompilation. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flow graphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.

Combining image regions and human activity for indirect object recognition in indoor wide-angle views

Traditional methods of object recognition are reliant on shape and so are very difficult to apply in cluttered, wideangle and low-detail views such as surveillance scenes. To address this, a method of indirect object recognition is proposed, where human activity is used to infer both the location and identity of objects. No shape analysis is necessary. The concept is dubbed 'interaction signatures', since the premise is that a human will interact with objects in ways characteristic of the function of that object - for example, a person sits in a chair and drinks from a cup. The human-centred approach means that recognition is possible in low-detail views and is largely invariant to the shape of objects within the same functional class. This paper implements a Bayesian network for classifying region patches with object labels, building upon our previous work in automatically segmenting and recognising a human's interactions with the objects. Experiments show that interaction signatures can successfully find and label objects in low-detail views and are equally effective at recognising test objects that differ markedly in appearance from the training objects.

Learning people movement model from multiple cameras for behaviour recognition

In surveillance systems for monitoring people behaviours, it is important to build systems that can adapt to the signatures of people's tasks and movements in the environment. At the same time, it is important to cope with noisy observations produced by a set of cameras with possibly different characteristics. In previous work, we have implemented a distributed surveillance system designed for complex indoor environments [1]. The system uses the Abstract Hidden Markov mEmory Model (AHMEM) for modelling and specifying complex human behaviours that can take place in the environment. Given a sequence of observations from a set of cameras, the system employs approximate probabilistic inference to compute the likelihood of different possible behaviours in real-time. This paper describes the techniques that can be used to learn the different camera noise models and the human movement models to be used in this system. The system is able to monitor and classify people behaviours as data is being gathered, and we provide classification results showing the system is able to identify behaviours of people from their movement signatures.

Polymorphic malware detection using Hierarchical Hidden Markov Model

Binary signatures have been widely used to detect malicious software on the current Internet. However, this approach is unable to achieve the accurate identification of polymorphic malware variants, which can be easily generated by the malware authors using code generation engines. Code generation engines randomly produce varying code sequences but perform the same desired malicious functions. Previous research used flow graph and signature tree to identify polymorphic malware families. The key difficulty of previous research is the generation of precisely defined state machine models from polymorphic variants. This paper proposes a novel approach, using Hierarchical Hidden Markov Model (HHMM), to provide accurate inductive inference of the malware family. This model can capture the features of self-similar and hierarchical structure of polymorphic malware family signature sequences. To demonstrate the effectiveness and efficiency of this approach, we evaluate it with real malware samples. Using more than 15,000 real malware, we find our approach can achieve high true positives, low false positives, and low computational cost.

Discovery of activity structures using the hierarchical hidden Markov model

In building a surveillance system for monitoring people behaviours, it is important to understand the typical patterns of people's movement in the environment. This task is difficult when dealing with high-level behaviours. The flat model such as the hidden Markov model (HMM) is inefficient in differentiating between signatures of such behaviours. This paper examines structure learning for high-level behaviours using the hierarchical hidden Markov model (HHMM).We propose a two-phase learning algorithm in which the parameters of the behaviours at low levels are estimated first and then the structures and parameters of the behaviours at high levels are learned from multi-camera training data. Our algorithm is then evaluated using data from a real environment, demonstrating the robustness of the learned structure in recognising people's behaviour.

The role of the ord arid intrusion in the historical and contemporary genetic division of long-tailed finch subspecies in northern Australia

The effect of separation by biogeographic features followed by secondary contact can blur taxonomic boundaries and produce complex genetic signatures. We analyzed population structure and gene flow across the range of the long-tailed finch (Poephila acuticauda) in northern Australia (1) to test the hypothesis that Ord Arid Intrusion acted as the causative barrier that led to divergence of P. acuticauda subspecies, (2) to determine whether genetic data support the presence of a gradual cline across the range or a sudden shift, both of which have been suggested based on morphological data, and (3) to estimate levels of contemporary gene flow within this species complex. We collected samples from 302 individuals from 10 localities. Analyses of 12 microsatellite loci and sequence data from 333 base pairs of the mitochondrial control region were used to estimate population structure and gene flow, using analysis of molecular variance (AMOVA), haplotype network analysis, frequency statistics, and clustering methods. Mitochondrial sequence data indicated the presence of three genetic groups (regions) across the range of P. acuticauda. Genetic diversity was highest in the east and lowest in the west. The Ord Arid Intrusion appears to have functioned as a biogeographic barrier in the past, according to mtDNA evidence presented here and evidence from previous studies. The absence of isolation by distance between adjacent regions and the lack of population genetic structure of mtDNA within regions indicates that genetic changes across the range of P. acuticauda subspecies are characterized by discrete breaks between regions. While microsatellite data indicate a complete absence of genetic structure across this species’ range, it appears unlikely that this results from high levels of gene flow. Mitochondrial data do not support the presence of contemporary gene flow across the range of this species.

From food to offspring down : tissue-specific discrimination and turn-over of stable isotopes in herbivorous waterbirds and other avian foraging guilds

Isotopic discrimination and turn-over are fundamental to the application of stable isotope ecology in animals. However, detailed information for specific tissues and species are widely lacking, notably for herbivorous species. We provide details on tissue-specific carbon and nitrogen discrimination and turn-over times from food to blood, feathers, claws, egg tissues and offspring down feathers in four species of herbivorous waterbirds. Source-to-tissue discrimination factors for carbon (δ¹³C) and nitrogen stable isotope ratios (δ¹⁵N) showed little variation across species but varied between tissues. Apparent discrimination factors ranged between −0.5 to 2.5‰ for δ¹³C and 2.8 to 5.2‰ for δ¹⁵N, and were more similar between blood components than between keratinous tissues or egg tissue. Comparing these results with published data from other species we found no effect of foraging guild on discrimination factors for carbon but a significant foraging-guild effect for nitrogen discrimination factors.

Turn-over of δ¹³C in tissues was most rapid in blood plasma, with a half-life of 4.3 d, whereas δ¹³C in blood cells had a half-life of approximately 32 d. Turn-over times for albumen and yolk in laying females were similar to those of blood plasma, at 3.2 and 6.0 d respectively. Within yolk, we found decreasing half-life times of δ¹³C from inner yolk (13.3 d) to outer yolk (3.1 d), related to the temporal pattern of tissue formation.

We found similarities in tissue-specific turn-over times across all avian species studied to date. Yet, while generalities regarding discrimination factors and tissue turn-over times can be made, a large amount of variation remains unexplained.

Mitochondrial DNA offers unique insights into invasion history of the common starling

Mitochondrial DNA (mtDNA) can be a powerful genetic marker for tracing origins and history of invasive populations. Here, we use mtDNA to address questions relevant to the understanding of invasion pathways of common starlings (Sturnus vulgaris) into Western Australia (WA) and discuss the utility of this marker to provide information useful to invasive species management. Mitochondrial sequence data indicate two geographically restricted genetic groups within Australia. Evidence of dispersal from genetically distinct sources outside the sampled range of starlings in Australia suggests increased vigilance by management agencies may be required to prevent further incursions from widely separated localities. Overall, genetic diversity in Australia was lower than in samples from the native range. Within Australia, genetic diversity was lowest in the most recently colonized area in the west, indicating that demographic bottlenecks have occurred in this area. Evidence of restricted dispersal between localities on the edge of the range expansion (ERE) in WA and other Australian sampling localities suggests that localized control within the ERE may be effective in preventing further range expansion. Signatures of spatial and demographic expansion are present in mismatch analyses from sampling localities located at the ERE, but neutrality indices did not support this finding, suggesting that the former may be more sensitive to recent expansion. Additionally, mismatch analyses support the presence of admixture, which is likely to have occurred pre-introduction. We compare our findings with those from a microsatellite study of the same samples and discuss how the mtDNA analyses used here offer valuable and unique insights into the invasion history of introduced species.

A novel sensing technique for monitoring pitting corrosion of stainless steel type 316L

A novel electrochemically integrated multi-electrode array namely the wire beam electrode(WBE) in combination with noise signatures analysis has been designed to monitor pittingcorrosion of one of the best corrosion resistance ferrous alloys, stainless steel type 316L.From the direct correlation of electrochemical potential noise signatures and galvanic currentdistribution maps during pitting corrosion processes, two characteristic noise patterns wereobserved prior to stable pit formation: (i) the characteristic ‘peak’ of rapid potential transient,towards less negative direction, followed by recovery (termed noise signature I) was found tocorrelate with the disappearance of unstable anode; (ii) the characteristic noise pattern ofquick potential changes towards less negative direction followed by no recovery (termed noisesignature II) was found to correspond with the massive disappearance of minor anodes leadingto formation of highly localized major anodes in the galvanic current distribution maps.