Compilation of somatic mutations of the CDKN2 gene in human cancers : non-random distribution of base substitutions

The CDKN2 gene, encoding the cyclin-dependent kinase inhibitor p16, is a tumour suppressor gene that maps to chromosome band 9p21-p22. The most common mechanism of inactivation of this gene in human cancers is through homozygous deletion; however, in a smaller proportion of tumours and tumour cell lines intragenic mutations occur. In this study we have compiled a database of over 120 published point mutations in the CDKN2 gene from a wide variety of tumour types. A further 50 deletions, insertions, and splice mutations in CDKN2 have also been compiled. Furthermore, we have standardised the numbering of all mutations according to the full-length 156 amino acid form of p16. From this study we are able to define several hot spots, some of which occur at conserved residues within the ankyrin domains of p16. While many of the hotspots are shared by a number of cancers, the relative importance of each position varies, possibly reflecting the role of different carcinogens in the development of certain tumours. As reported previously, the mutational spectrum of CDKN2 in melanomas differs from that of internal malignancies and supports the involvement of UV in melanoma tumorigenesis. Notably, 52% of all substitutions in melanoma-derived samples occurred at just six nucleotide positions. Nonsense mutations comprise a comparatively high proportion of mutations present in the CDKN2 gene, and possible explanations for this are discussed.

Controls on iron in soils and soil waters of a forested, coastal catchment in subtropical Australia

Soluble organic matter derived from exotic Pinus vegetation forms stronger complexes with iron (Fe) than the soluble organic matter derived from most native Australian species. This has lead to concern about the environmental impacts related to the establishment of extensive exotic Pinus plantations in coastal southeast Queensland, Australia. It has been suggested that the Pinus plantations may enhance the solubility of Fe in soils by increasing the amount of organically complexed Fe. While this remains inconclusive, the environmental impacts of an increased flux of dissolved, organically complexed Fe from soils to the fluvial system and then to sensitive coastal ecosystems are potentially damaging. Previous work investigated a small number of samples, was largely laboratory based and had limited application to field conditions. These assessments lacked field-based studies, including the comparison of the soil water chemistry of sites associated with Pinus vegetation and undisturbed native vegetation. In addition, the main controls on the distribution and mobilisation of Fe in soils of this subtropical coastal region have not been determined. This information is required in order to better understand the relative significance of any Pinus enhanced solubility of Fe. The main aim of this thesis is to determine the controls on Fe distribution and mobilisation in soils and soil waters of a representative coastal catchment in southeast Queensland (Poona Creek catchment, Fraser Coast) and to test the effect of Pinus vegetation on the solubility and speciation of Fe. The thesis is structured around three individual papers. The first paper identifies the main processes responsible for the distribution and mobilisation of labile Fe in the study area and takes a catchment scale approach. Physicochemical attributes of 120 soil samples distributed throughout the catchment are analysed, and a new multivariate data analysis approach (Kohonen’s self organising maps) is used to identify the conditions associated with high labile Fe. The second paper establishes whether Fe nodules play a major role as an iron source in the catchment, by determining the genetic mechanism responsible for their formation. The nodules are a major pool of Fe in much of the region and previous studies have implied that they may be involved in redox-controlled mobilisation and redistribution of Fe. This is achieved by combining a detailed study of a ferric soil profile (morphology, mineralogy and micromorphology) with the distribution of Fe nodules on a catchment scale. The third component of the thesis tests whether the concentration and speciation of Fe in soil solutions from Pinus plantations differs significantly from native vegetation soil solutions. Microlysimeters are employed to collect unaltered, in situ soil water samples. The redox speciation of Fe is determined spectrophotometrically and the interaction between Fe and dissolved organic matter (DOM) is modelled with the Stockholm Humic Model. The thesis provides a better understanding of the controls on the distribution, concentration and speciation of Fe in the soils and soil waters of southeast Queensland. Reductive dissolution is the main mechanism by which mobilisation of Fe occurs in the study area. Labile Fe concentrations are low overall, particularly in the sandy soils of the coastal plain. However, high labile Fe is common in seasonally waterlogged and clay-rich soils which are exposed to fluctuating redox conditions and in organic-rich soils adjacent to streams. Clay-rich soils are most common in the upper parts of the catchment. Fe nodules were shown to have a negligible role in the redistribution of dissolved iron in the catchment. They are formed by the erosion, colluvial transport and chemical weathering of iron-rich sandstones. The ferric horizons, in which nodules are commonly concentrated, subsequently form through differential biological mixing of the soil. Whereas dissolution/ reprecipitation of the Fe cements is an important component of nodule formation, mobilised Fe reprecipitates locally. Dissolved Fe in the soil waters is almost entirely in the ferrous form. Vegetation type does not affect the concentration and speciation of Fe in soil waters, although Pinus DOM has greater acidic functional group site densities than DOM from native vegetation. Iron concentrations are highest in the high DOM soil waters collected from sandy podosols, where they are controlled by redox potential. Iron concentrations are low in soil solutions from clay and iron oxide rich soils, in spite of similar redox potentials. This is related to stronger sorption to the reactive clay and iron oxide mineral surfaces in these soils, which reduces the amount of DOM available for microbial metabolisation and reductive dissolution of Fe. Modelling suggests that Pinus DOM can significantly increase the amount of truly dissolved ferric iron remaining in solution in oxidising conditions. Thus, inputs of ferrous iron together with Pinus DOM to surface waters may reduce precipitation of hydrous ferric oxides and increase the flux of dissolved iron out of the catchment. Such inputs are most likely from the lower catchment, where podosols planted with Pinus are most widely distributed. Significant outcomes other than the main aims were also achieved. It is shown that mobilisation of Fe in podosols can occur as dissolved Fe(II) rather than as Fe(III)-organic complexes. This has implications for the large body of work which assumes that Fe(II) plays a minor role. Also, the first paper demonstrates that a data analysis approach based on Kohonen’s self organising maps can facilitate the interpretation of complex datasets and can help identify geochemical processes operating on a catchment scale.

Analysis of ten years historical weather data in Australia

Typical reference year (TRY) weather data is often used to represent the long term weather pattern for building simulation and design. Through the analysis of ten year historical hourly weather data for seven Australian major capital cities using the frequencies procedure of descriptive statistics analysis (by SPSS software), this paper investigates: • the closeness of the typical reference year (TRY) weather data in representing the long term weather pattern; • the variations and common features that may exist between relatively hot and cold years. It is found that for the given set of input data, in comparison with the other weather elements, the discrepancy between TRY and multiple years is much smaller for the dry bulb temperature, relative humidity and global solar irradiance. The overall distribution patterns of key weather elements are also generally similar between the hot and cold years, but with some shift and/or small distortion. There is little common tendency of change between the hot and the cold years for different weather variables at different study locations.

Three-dimensional geological modelling and multivariate statistical analysis of water chemistry data to analyse and visualise aquifer structure and groundwater composition in the Wairau Plain, Marlborough District, New Zealand

Concerns regarding groundwater contamination with nitrate and the long-term sustainability of groundwater resources have prompted the development of a multi-layered three dimensional (3D) geological model to characterise the aquifer geometry of the Wairau Plain, Marlborough District, New Zealand. The 3D geological model which consists of eight litho-stratigraphic units has been subsequently used to synthesise hydrogeological and hydrogeochemical data for different aquifers in an approach that aims to demonstrate how integration of water chemistry data within the physical framework of a 3D geological model can help to better understand and conceptualise groundwater systems in complex geological settings. Multivariate statistical techniques(e.g. Principal Component Analysis and Hierarchical Cluster Analysis) were applied to groundwater chemistry data to identify hydrochemical facies which are characteristic of distinct evolutionary pathways and a common hydrologic history of groundwaters. Principal Component Analysis on hydrochemical data demonstrated that natural water-rock interactions, redox potential and human agricultural impact are the key controls of groundwater quality in the Wairau Plain. Hierarchical Cluster Analysis revealed distinct hydrochemical water quality groups in the Wairau Plain groundwater system. Visualisation of the results of the multivariate statistical analyses and distribution of groundwater nitrate concentrations in the context of aquifer lithology highlighted the link between groundwater chemistry and the lithology of host aquifers. The methodology followed in this study can be applied in a variety of hydrogeological settings to synthesise geological, hydrogeological and hydrochemical data and present them in a format readily understood by a wide range of stakeholders. This enables a more efficient communication of the results of scientific studies to the wider community.

Incidence and geographic distribution of endemic Burkitt lymphoma in northern Uganda revisited

Endemic Burkitt lymphoma (BL) is etiologically associated with Epstein-Barr virus (EBV) and ecologically linked to Plasmodium falciparum malaria. However, these infections imperfectly correlate with BL epidemiology. To obtain recent epidemiological data, we studied district- and county-specific BL incidence and standardized incidence ratios using data collected from 1997 through 2006 at Lacor Hospital in northern Uganda, where studies were last done more than 30 years ago. Among 500 patients, median age was 6 years (inter-quartile range 5-8) and male-to-female ratio was 1.8:1. Among those known, most presented with abdominal (56%, M: F 1.4:1) vs. only facial tumors (35%, M: F 3.0:1). Abdominal tumors occurred in older (mean age: 7.0 vs. 6.0 years; p<0.001) and more frequently in female children (68% vs. 50%; OR 2.2, 95% CI 1.5-3.5). The age-standardized incidence was 2.4 per 100,000, being 0.6 in 1-4 year olds, 4.1 in 5-9 year olds and 2.8 in 10-14 year olds and varied 3-4-fold across districts. The incidence was lower in districts that were far from Lacor and higher in districts that were close to Lacor. While districts close to Lacor were also more urbanized, the incidence was higher in the nearby perirural areas. We highlight high BL incidence and geographic variation in neighboring districts in northern Uganda. While distance from Lacor clearly influenced the patterns, the incidence was lower in municipal than in surrounding rural areas. Jaw tumors were characterized by young age and male gender, but presentation has shifted away from facial to mostly abdominal. Keywords: Africa, cancer, malaria, Epstein-Barr virus, clustering, epidemiology

Ethnic differences in body fat distribution among Asian pre-pubertal children: A cross-sectional multicenter study

Background Ethnic differences in body fat distribution contribute to ethnic differences in cardiovascular morbidities and diabetes. However few data are available on differences in fat distribution in Asian children from various backgrounds. Therefore, the current study aimed to explore ethnic differences in body fat distribution among Asian children from four countries. Methods A total of 758 children aged 8-10 y from China, Lebanon, Malaysia and Thailand were recruited using a non-random purposive sampling approach to enrol children encompassing a wide BMI range. Height, weight, waist circumference (WC), fat mass (FM, derived from total body water [TBW] estimation using the deuterium dilution technique) and skinfold thickness (SFT) at biceps, triceps, subscapular, supraspinale and medial calf were collected. Results After controlling for height and weight, Chinese and Thai children had a significantly higher WC than their Lebanese and Malay counterparts. Chinese and Thais tended to have higher trunk fat deposits than Lebanese and Malays reflected in trunk SFT, trunk/upper extremity ratio or supraspinale/upper extremity ratio after adjustment for age and total body fat. The subscapular/supraspinale skinfold ratio was lower in Chinese and Thais compared with Lebanese and Malays after correcting for trunk SFT. Conclusions Asian pre-pubertal children from different origins vary in body fat distribution. These results indicate the importance of population-specific WC cut-off points or other fat distribution indices to identify the population at risk of obesity-related health problems.

Association between prostinogen (KLK15) genetic variants and prostate cancer risk and aggressiveness in Australia and a meta-analysis of GWAS data

Background: Kallikrein 15 (KLK15)/Prostinogen is a plausible candidate for prostate cancer susceptibility. Elevated KLK15 expression has been reported in prostate cancer and it has been described as an unfavorable prognostic marker for the disease. Objectives: We performed a comprehensive analysis of association of variants in the KLK15 gene with prostate cancer risk and aggressiveness by genotyping tagSNPs, as well as putative functional SNPs identified by extensive bioinformatics analysis. Methods and Data Sources: Twelve out of 22 SNPs, selected on the basis of linkage disequilibrium pattern, were analyzed in an Australian sample of 1,011 histologically verified prostate cancer cases and 1,405 ethnically matched controls. Replication was sought from two existing genome wide association studies (GWAS): the Cancer Genetic Markers of Susceptibility (CGEMS) project and a UK GWAS study. Results: Two KLK15 SNPs, rs2659053 and rs3745522, showed evidence of association (p, 0.05) but were not present on the GWAS platforms. KLK15 SNP rs2659056 was found to be associated with prostate cancer aggressiveness and showed evidence of association in a replication cohort of 5,051 patients from the UK, Australia, and the CGEMS dataset of US samples. A highly significant association with Gleason score was observed when the data was combined from these three studies with an Odds Ratio (OR) of 0.85 (95% CI = 0.77-0.93; p = 2.7610 24). The rs2659056 SNP is predicted to alter binding of the RORalpha transcription factor, which has a role in the control of cell growth and differentiation and has been suggested to control the metastatic behavior of prostate cancer cells. Conclusions: Our findings suggest a role for KLK15 genetic variation in the etiology of prostate cancer among men of European ancestry, although further studies in very large sample sets are necessary to confirm effect sizes.

Phylogeography of Echinocladius martini Cranston (Diptera: Chironomidae) in closed forest streams of eastern Australia

The eastern Australian rainforests have experienced several cycles of range contraction and expansion since the late Miocene that are closely correlated with global glaciation events. Together with ongoing aridification of the continent, this has resulted in current distributions of native closed forest that are highly fragmented along the east coast. Several closed forest endemic taxa exhibit patterns of population genetic structure that are congruent with historical isolation of populations in discrete refugia and reflect evolutionary histories dramatically affected by vicariance. Currently, limited data are available regarding the impact of these past climatic fluctuations on freshwater invertebrate taxa. The non-biting midge species Echinocladius martini Cranston is distributed along the east coast and inhabits predominantly montane streams in closed forest habitat. Phylogeographic structure in E. martini was resolved here at a continental scale by incorporating data from a previous pilot study and expanding the sampling design to encompass populations in the Wet Tropics of north-eastern Queensland, south-east Queensland, New South Wales and Victoria. Patterns of phylogeographic structure revealed several deeply divergent mitochondrial lineages from central and south-eastern Australia that were previously unrecognised and were geographically endemic to closed forest refugia. Estimated divergence times were congruent with late Miocene onset of rainforest contractions across the east coast of Australia. This suggested that dispersal and gene flow among E. martini populations isolated in refugia has been highly restricted historically. Moreover, these data imply, in contrast to existing preconceptions about freshwater invertebrates, that this taxon may be acutely susceptible to habitat fragmentation.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Analysis of acoustic emission data for accurate damage assessment for structural health monitoring applications

Structural health monitoring (SHM) refers to the procedure used to assess the condition of structures so that their performance can be monitored and any damage can be detected early. Early detection of damage and appropriate retrofitting will aid in preventing failure of the structure and save money spent on maintenance or replacement and ensure the structure operates safely and efficiently during its whole intended life. Though visual inspection and other techniques such as vibration based ones are available for SHM of structures such as bridges, the use of acoustic emission (AE) technique is an attractive option and is increasing in use. AE waves are high frequency stress waves generated by rapid release of energy from localised sources within a material, such as crack initiation and growth. AE technique involves recording these waves by means of sensors attached on the surface and then analysing the signals to extract information about the nature of the source. High sensitivity to crack growth, ability to locate source, passive nature (no need to supply energy from outside, but energy from damage source itself is utilised) and possibility to perform real time monitoring (detecting crack as it occurs or grows) are some of the attractive features of AE technique. In spite of these advantages, challenges still exist in using AE technique for monitoring applications, especially in the area of analysis of recorded AE data, as large volumes of data are usually generated during monitoring. The need for effective data analysis can be linked with three main aims of monitoring: (a) accurately locating the source of damage; (b) identifying and discriminating signals from different sources of acoustic emission and (c) quantifying the level of damage of AE source for severity assessment. In AE technique, the location of the emission source is usually calculated using the times of arrival and velocities of the AE signals recorded by a number of sensors. But complications arise as AE waves can travel in a structure in a number of different modes that have different velocities and frequencies. Hence, to accurately locate a source it is necessary to identify the modes recorded by the sensors. This study has proposed and tested the use of time-frequency analysis tools such as short time Fourier transform to identify the modes and the use of the velocities of these modes to achieve very accurate results. Further, this study has explored the possibility of reducing the number of sensors needed for data capture by using the velocities of modes captured by a single sensor for source localization. A major problem in practical use of AE technique is the presence of sources of AE other than crack related, such as rubbing and impacts between different components of a structure. These spurious AE signals often mask the signals from the crack activity; hence discrimination of signals to identify the sources is very important. This work developed a model that uses different signal processing tools such as cross-correlation, magnitude squared coherence and energy distribution in different frequency bands as well as modal analysis (comparing amplitudes of identified modes) for accurately differentiating signals from different simulated AE sources. Quantification tools to assess the severity of the damage sources are highly desirable in practical applications. Though different damage quantification methods have been proposed in AE technique, not all have achieved universal approval or have been approved as suitable for all situations. The b-value analysis, which involves the study of distribution of amplitudes of AE signals, and its modified form (known as improved b-value analysis), was investigated for suitability for damage quantification purposes in ductile materials such as steel. This was found to give encouraging results for analysis of data from laboratory, thereby extending the possibility of its use for real life structures. By addressing these primary issues, it is believed that this thesis has helped improve the effectiveness of AE technique for structural health monitoring of civil infrastructures such as bridges.

Combinatorial design of key distribution mechanisms for wireless sensor networks

Secure communications in wireless sensor networks operating under adversarial conditions require providing pairwise (symmetric) keys to sensor nodes. In large scale deployment scenarios, there is no prior knowledge of post deployment network configuration since nodes may be randomly scattered over a hostile territory. Thus, shared keys must be distributed before deployment to provide each node a key-chain. For large sensor networks it is infeasible to store a unique key for all other nodes in the key-chain of a sensor node. Consequently, for secure communication either two nodes have a key in common in their key-chains and they have a wireless link between them, or there is a path, called key-path, among these two nodes where each pair of neighboring nodes on this path have a key in common. Length of the key-path is the key factor for efficiency of the design. This paper presents novel deterministic and hybrid approaches based on Combinatorial Design for deciding how many and which keys to assign to each key-chain before the sensor network deployment. In particular, Balanced Incomplete Block Designs (BIBD) and Generalized Quadrangles (GQ) are mapped to obtain efficient key distribution schemes. Performance and security properties of the proposed schemes are studied both analytically and computationally. Comparison to related work shows that the combinatorial approach produces better connectivity with smaller key-chain sizes.

miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data

miRDeep and its varieties are widely used to quantify known and novel micro RNA (miRNA) from small RNA sequencing (RNAseq). This article describes miRDeep*, our integrated miRNA identification tool, which is modeled off miRDeep, but the precision of detecting novel miRNAs is improved by introducing new strategies to identify precursor miRNAs. miRDeep* has a user-friendly graphic interface and accepts raw data in FastQ and Sequence Alignment Map (SAM) or the binary equivalent (BAM) format. Known and novel miRNA expression levels, as measured by the number of reads, are displayed in an interface, which shows each RNAseq read relative to the pre-miRNA hairpin. The secondary pre-miRNA structure and read locations for each predicted miRNA are shown and kept in a separate figure file. Moreover, the target genes of known and novel miRNAs are predicted using the TargetScan algorithm, and the targets are ranked according to the confidence score. miRDeep* is an integrated standalone application where sequence alignment, pre-miRNA secondary structure calculation and graphical display are purely Java coded. This application tool can be executed using a normal personal computer with 1.5 GB of memory. Further, we show that miRDeep* outperformed existing miRNA prediction tools using our LNCaP and other small RNAseq datasets. miRDeep* is freely available online at http://www.australianprostatecentre.org/research/software/mirdeep-star

A new database compilation of whole-rock chemical and geochronological data of igneous rocks in Queensland : a new resource for HDR geothermal resource exploration

The Geothermal industry in Australia and Queensland is in its infancy and for hot dry rock (HDR) geothermal energy, it is very much in the target identification and resource definition stages. As a key effort to assist the geothermal industry and exploration for HDR in Queensland, we are developing a comprehensive and new integrated geochemical and geochronological database on igneous rocks. To date, around 18,000 igneous rocks have been analysed across Queensland for chemical and/or age information. However, these data currently reside in a number of disparate datasets (e.g., Ozchron, Champion et al., 2007, Geological Survey of Queensland, journal publications, and unpublished university theses). The goal of this project is to collate and integrate these data on Queensland igneous rocks to improve our understanding of high heat producing granites in Queensland, in terms of their distribution (particularly in the subsurface), dimensions, ages, and controlling factors in their genesis.

Usability of small crisis data sets in the absence of big data

Big data is big news in almost every sector including crisis communication. However, not everyone has access to big data and even if we have access to big data, we often do not have necessary tools to analyze and cross reference such a large data set. Therefore this paper looks at patterns in small data sets that we have ability to collect with our current tools to understand if we can find actionable information from what we already have. We have analyzed 164390 tweets collected during 2011 earthquake to find out what type of location specific information people mention in their tweet and when do they talk about that. Based on our analysis we find that even a small data set that has far less data than a big data set can be useful to find priority disaster specific areas quickly.