Meta-analysis of microarray data identifies GAS6 expression as an independent predictor of poor survival in ovarian cancer

Seeking new biomarkers for epithelial ovarian cancer, the fifth most common cause of death from all cancers in women and the leading cause of death from gynaecological malignancies, we performed a meta-analysis of three independent studies and compared the results in regard to clinicopathological parameters. This analysis revealed that GAS6 was highly expressed in ovarian cancer and therefore was selected as our candidate of choice. GAS6 encodes a secreted protein involved in physiological processes including cell proliferation, chemotaxis, and cell survival. We performed immunohistochemistry on various ovarian cancer tissues and found that GAS6 expression was elevated in tumour tissue samples compared to healthy control samples (P < 0.0001). In addition, GAS6 expression was also higher in tumours from patients with residual disease compared to those without. Our data propose GAS6 as an independent predictor of poor survival, suggesting GAS6, both on the mRNA and on the protein level, as a potential biomarker for ovarian cancer. In clinical practice, the staining of a tumour biopsy for GAS6 may be useful to assess cancer prognosis and/or to monitor disease progression.

A study, by fire-assay and ICP-MS, of the distribution of rhodium, platinum, and gold in iron-age pottery

Forty-six archaeological specimens were treated by fire-assay and subsequently analysed by ICP-MS for selected precious metals: Ph, Pt and Au. The investigation was prompted by the possibility that archaeological samples could serve as "indicators" of the precious metal composition of the clays from the excavated sites. Therefore, the experimentally obtained concentrations were carefully studied to determine if there were anomalous levels of these precious metals in the deposits from which the specimens originated. Furthermore, the analytical data were used to establish if it was feasible to distinguish ancient potsherds based on precious metal concentrations, for employment as a basis in provenance studies.

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.

Multi-agent systems for modeling high penetration photovoltaic system impacts in distribution networks

This paper reviews the use of multi-agent systems to model the impacts of high levels of photovoltaic (PV) system penetration in distribution networks and presents some preliminary data obtained from the Perth Solar City high penetration PV trial. The Perth Solar City trial consists of a low voltage distribution feeder supplying 75 customers where 29 consumers have roof top photovoltaic systems. Data is collected from smart meters at each consumer premises, from data loggers at the transformer low voltage (LV) side and from a nearby distribution network SCADA measurement point on the high voltage side (HV) side of the transformer. The data will be used to progressively develop MAS models.

Seasonal occurrence and distribution of Bryde's whales in the Hauraki Gulf, New Zealand

The Hauraki Gulf is a large, shallow embayment located north of Auckland City (36°51′S, 174°46′E), New Zealand. Bryde's whales (Balaenoptera edeni) are the most frequently observed balaenopterid in these waters. To assess the use of the Hauraki Gulf for this species, we examined the occurrence and distribution in relation to environmental parameters. Data were collected from a platform of opportunity during 674 daily surveys between March 2003 and February 2006. A total of 760 observations of Bryde's whales were recorded throughout the study period during 371 surveys. The number of Bryde's whales sighted/day was highest in winter, coinciding with the coolest median sea-surface temperature (14.6°C). Bryde's whales were recorded throughout the Hauraki Gulf in water depths ranging from 12.1–59.8 m (mean = 42.3, SD = 5.1). Cow–calf pairs were most frequently observed during the austral autumn in water depths of 29.9–53.9 m (mean = 40.8, SD = 5.2). Data from this study suggest Bryde's whales in the Hauraki Gulf exhibit a mix of both “inshore” and “offshore” characteristics from the Bryde's whales examined off the coast of South Africa. Based on complete mitochondrial DNA sequences, Sasaki et al. (2006) recognized two sister species of Bryde's whales: Balaenoptera brydei and B. edeni, with the latter including small-type, more coastal Bryde's whales from Japan, Hong Kong, and Australia. Their samples and samples in previous analyses of small-type whales, all originated from eastern and southeastern Asia. These authors did not include the forms of Bryde's whales that occur in other regions, e.g., in the Pacific off Peru (Valdivia et al. 1981), in the Atlantic off Brazil (Best 1977) and in the western Indian Ocean off South Africa (Best 1977). Recent genetic analysis using mtDNA from the “inshore” and “offshore” forms from South Africa confirms the offshore form is B. brydei, and establishes that the inshore form is more closely related to B. brydei than to B. edeni (Penry 2010). These different forms do vary considerably in their habitat use and ecology (refer to Table 1 for a detailed comparison between the South African inshore and offshore forms, as described by Best (1967, 1977) and the Bryde's whales from New Zealand (Wiseman 2008). Recent genetic analysis on the Bryde's whales in the Hauraki Gulf suggests they are B. brydei (Wiseman 2008). However, pending resolution of the uncertainty within and between species of this genus, we follow the Society of Marine Mammal's committee on taxonomy, who state that B. edeni applies to all Bryde's whales.

Comparative genomics of koala, cattle and sheep strains of Chlamydia pecorum

Background Chlamydia pecorum is an important pathogen of domesticated livestock including sheep, cattle and pigs. This pathogen is also a key factor in the decline of the koala in Australia. We sequenced the genomes of three koala C. pecorum strains, isolated from the urogenital tracts and conjunctiva of diseased koalas. The genome of the C. pecorum VR629 (IPA) strain, isolated from a sheep with polyarthritis, was also sequenced. Results Comparisons of the draft C. pecorum genomes against the complete genomes of livestock C. pecorum isolates revealed that these strains have a conserved gene content and order, sharing a nucleotide sequence similarity > 98%. Single nucleotide polymorphisms (SNPs) appear to be key factors in understanding the adaptive process. Two regions of the chromosome were found to be accumulating a large number of SNPs within the koala strains. These regions include the Chlamydia plasticity zone, which contains two cytotoxin genes (toxA and toxB), and a 77 kbp region that codes for putative type III effector proteins. In one koala strain (MC/MarsBar), the toxB gene was truncated by a premature stop codon but is full-length in IPTaLE and DBDeUG. Another five pseudogenes were also identified, two unique to the urogenital strains C. pecorum MC/MarsBar and C. pecorum DBDeUG, respectively, while three were unique to the koala C. pecorum conjunctival isolate IPTaLE. An examination of the distribution of these pseudogenes in C. pecorum strains from a variety of koala populations, alongside a number of sheep and cattle C. pecorum positive samples from Australian livestock, confirmed the presence of four predicted pseudogenes in koala C. pecorum clinical samples. Consistent with our genomics analyses, none of these pseudogenes were observed in the livestock C. pecorum samples examined. Interestingly, three SNPs resulting in pseudogenes identified in the IPTaLE isolate were not found in any other C. pecorum strain analysed, raising questions over the origin of these point mutations. Conclusions The genomic data revealed that variation between C. pecorum strains were mainly due to the accumulation of SNPs, some of which cause gene inactivation. The identification of these genetic differences will provide the basis for further studies to understand the biology and evolution of this important animal pathogen. Keywords: Chlamydia pecorum; Single nucleotide polymorphism; Pseudogene; Cytotoxin

The regional distribution of anxiety disorders : implications for the Global Burden of Disease Study, 2010

Anxiety disorders are increasingly acknowledged as a global health issue however an accurate picture of prevalence across populations is lacking. Empirical data are incomplete and inconsistent so alternate means of estimating prevalence are required to inform estimates for the new Global Burden of Disease Study 2010. We used a Bayesian meta-regression approach which included empirical epidemiological data, expert prior information, study covariates and population characteristics. Reported are global and regional point prevalence for anxiety disorders in 2010. Point prevalence of anxiety disorders differed by up to three-fold across world regions, ranging between 2.1% (1.8-2.5%) in East Asia and 6.1% (5.1-7.4%) in North Africa/Middle East. Anxiety was more common in Latin America; high income regions; and regions with a history of recent conflict. There was considerable uncertainty around estimates, particularly for regions where no data were available. Future research is required to examine whether variations in regional distributions of anxiety disorders are substantive differences or an artefact of cultural or methodological differences. This is a particular imperative where anxiety is consistently reported to be less common, and where it appears to be elevated, but uncertainty prevents the reporting of conclusive estimates.

Smart coordinated distribution system control to enable high level penetration of rooftop PVs

This thesis addresses voltage violation problem, the most critical issue associated with high level penetration of photovoltaic (PV) in electricity distribution network. A coordinated control algorithm using the reactive power from PV inverter and integrated battery energy storage has been developed and investigated in different network scenarios in the thesis. Probable variations associated with solar generation, end-user participation and network parameters are also considered. Furthermore, a unified data model and well-defined communication protocol to ensure the smooth coordination between all the components during the operation of the algorithm is described. Finally this thesis incorporated the uncertainties of solar generation using probabilistic load flow analysis.

The BAD project: data mining, database and prediction of protein adsorption on surfaces

Protein adsorption at solid-liquid interfaces is critical to many applications, including biomaterials, protein microarrays and lab-on-a-chip devices. Despite this general interest, and a large amount of research in the last half a century, protein adsorption cannot be predicted with an engineering level, design-orientated accuracy. Here we describe a Biomolecular Adsorption Database (BAD), freely available online, which archives the published protein adsorption data. Piecewise linear regression with breakpoint applied to the data in the BAD suggests that the input variables to protein adsorption, i.e., protein concentration in solution; protein descriptors derived from primary structure (number of residues, global protein hydrophobicity and range of amino acid hydrophobicity, isoelectric point); surface descriptors (contact angle); and fluid environment descriptors (pH, ionic strength), correlate well with the output variable-the protein concentration on the surface. Furthermore, neural network analysis revealed that the size of the BAD makes it sufficiently representative, with a neural network-based predictive error of 5% or less. Interestingly, a consistently better fit is obtained if the BAD is divided in two separate sub-sets representing protein adsorption on hydrophilic and hydrophobic surfaces, respectively. Based on these findings, selected entries from the BAD have been used to construct neural network-based estimation routines, which predict the amount of adsorbed protein, the thickness of the adsorbed layer and the surface tension of the protein-covered surface. While the BAD is of general interest, the prediction of the thickness and the surface tension of the protein-covered layers are of particular relevance to the design of microfluidics devices.

A recommendation system for predicting risks across multiple business process instances

This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a business process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we suggest to the participant the action to perform which minimizes the predicted process risk. Risks are predicted by traversing decision trees generated from the logs of past process executions, which consider process data, involved resources, task durations and other information elements like task frequencies. When applied in the context of multiple process instances running concurrently, a second technique is employed that uses integer linear programming to compute the optimal assignment of resources to tasks to be performed, in order to deal with the interplay between risks relative to different instances. The recommendation system has been implemented as a set of components on top of the YAWL BPM system and its effectiveness has been evaluated using a real-life scenario, in collaboration with risk analysts of a large insurance company. The results, based on a simulation of the real-life scenario and its comparison with the event data provided by the company, show that the process instances executed concurrently complete with significantly fewer faults and with lower fault severities, when the recommendations provided by our recommendation system are taken into account.

Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods which have been recently employed to analyse PNSD data, however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K-means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and Silhouette width validation values and the K-means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K-means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectra to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.

Education paths for documentary distribution: DAF, ATOM and the study guides that bind them

An expanding education market targeted through ‘bridging material’ enabling cineliteracies has the potential to offer Australian producers with increased distribution opportunities, educators with targeted teaching aids and students with enhanced learning outcomes. For Australian documentary producers, the key to unlocking the potential of the education sector is engaging with its curriculum-based requirements at the earliest stages of pre-production. Two key mechanisms can lead to effective educational engagement; the established area of study guides produced in association with the Australian Teachers of Media (ATOM) and the emerging area of philanthropic funding coordinated by the Documentary Australia Foundation (DAF). DAF has acted as a key financial and cultural philanthropic bridge between individuals, foundations, corporations and the Australian documentary sector for over 14 years. DAF does not make or commission films but through management and receipt of grants and donations provides ‘expertise, information, guidance and resources to help each sector work together to achieve their goals’. The DAF application process also requires film-makers to detail their ‘Education and Outreach Strategy’ for each film with 582 films registered and 39 completed as of June 2014. These education strategies that can range from detailed to cursory efforts offer valuable insights into the Australian documentary sector's historical and current expectations of education as a receptive and dynamic audience for quality factual content. A recurring film-maker education strategy found in the DAF data is an engagement with ATOM to create a study guide for their film. This study guide then acts as a ‘bridging material’ between content and education audience. The frequency of this effort suggests these study guides enable greater educator engagement with content and increased interest and distribution of the film to educators. The paper Education paths for documentary distribution: DAF, ATOM and the study guides that bind them will address issues arising out of the changing needs of the education sector and the impact targeting ‘cineliteracy’ outcomes may have for Australian documentary distribution.

The validity of personal disturbance scale (DSSI/sAD) in people with diabetes mellitus, using longitudinal data

Despite being used since 1976, Delusions-Symptoms-States-Inventory/states of Anxiety and Depression (DSSI/sAD) has not yet been validated for use among people with diabetes. The aim of this study was to examine the validity of the personal disturbance scale (DSSI/sAD) among women with diabetes using Mater-University of Queensland Study of Pregnancy (MUSP) cohort data. The DSSI subscales were compared against DSM-IV disorders, the Mental Component Score of the Short Form 36 (SF-36 MCS), and Center for Epidemiologic Studies Depression Scale (CES-D). Factor analyses, odds ratios, receiver operating characteristic (ROC) analyses and diagnostic efficiency tests were used to report findings. Exploratory factor analysis and fit indices confirmed the hypothesized two-factor model of DSSI/sAD. We found significant variations in the DSSI/sAD domain scores that could be explained by CES-D (DSSI-Anxiety: 55%, DSSI-Depression: 46%) and SF-36 MCS (DSSI-Anxiety: 66%, DSSI-Depression: 56%). The DSSI subscales predicted DSM-IV diagnosed depression and anxiety disorders. The ROC analyses show that although the DSSI symptoms and DSM-IV disorders were measured concurrently the estimates of concordance remained only moderate. The findings demonstrate that the DSSI/sAD items have similar relationships to one another in both the diabetes and non-diabetes data sets which therefore suggest that they have similar interpretations.

Open problem: Shifting experts on easy data

A number of online algorithms have been developed that have small additional loss (regret) compared to the best “shifting expert”. In this model, there is a set of experts and the comparator is the best partition of the trial sequence into a small number of segments, where the expert of smallest loss is chosen in each segment. The regret is typically defined for worst-case data / loss sequences. There has been a recent surge of interest in online algorithms that combine good worst-case guarantees with much improved performance on easy data. A practically relevant class of easy data is the case when the loss of each expert is iid and the best and second best experts have a gap between their mean loss. In the full information setting, the FlipFlop algorithm by De Rooij et al. (2014) combines the best of the iid optimal Follow-The-Leader (FL) and the worst-case-safe Hedge algorithms, whereas in the bandit information case SAO by Bubeck and Slivkins (2012) competes with the iid optimal UCB and the worst-case-safe EXP3. We ask the same question for the shifting expert problem. First, we ask what are the simple and efficient algorithms for the shifting experts problem when the loss sequence in each segment is iid with respect to a fixed but unknown distribution. Second, we ask how to efficiently unite the performance of such algorithms on easy data with worst-case robustness. A particular intriguing open problem is the case when the comparator shifts within a small subset of experts from a large set under the assumption that the losses in each segment are iid.