Longitudinal study of Consumer Price Index (CPI) trends in core and non-core foods in Australia

Objective: This study examined trends in the price of healthy and less-healthy foods from 1989 to 2007 using the Australian Consumer Price Index (CPI).

Methods: CPI food expenditure classes were classified as 'core' or 'non-core'. Trends in the CPI were analysed to examine the rise in prices of core compared with non-core foods.

Results: On average, the CPI for core foods has risen at a slightly higher, though not statistically significant, rate than non-core foods. Furthermore, selected groupings reveal interesting patterns. 'Bread' has risen in price significantly more than 'cakes and biscuits', and 'milk' has risen in price significantly more than 'soft drinks, waters and juices'.

Conclusions and implications: This investigation of food price trends reveals notable differences between core and non-core foods. This should be investigated further to determine the extent to which this contributes to the higher prevalence of diet-related diseases in low socio-economic groups.

Understanding the oil price-exchange rate nexus for the Fiji Islands

In this paper, we examine the relationship between oil price and the Fiji–US exchange rate using daily data for the period 2000–2006. We use the generalised autoregressive conditional heteroskedasticity (GARCH) and exponential GARCH (EGARCH) models to estimate the impact of oil price on the nominal exchange rate. We find that a rise in oil prices leads to an appreciation of the Fijian dollar vis-à-vis the US dollar.

Identifying house price diffusion patterns among Australian state capital cities

Prior research supports the proposition that house price diffusion shows a ripple effect along the spatial dimension. That is, house price changes in one region would reflect in subsequent house price changes in other regions, showing certain linkages among regions. Using the vector autoregression model and the impulse response function, this study investigates house price diffusion among Australia's state capital cities, examining the response of one market to the innovation of other markets and determining the lagged terms for the maximum absolute value of the other markets' responses. The results show that the most important subnational markets in Australia do not point to Sydney, rather towards Canberra and Hobart, while the Darwin market plays a role of buffer. The safest markets are Sydney and Melbourne. This study helps to predict house price movement trends in eight capital cities.

Medical knowledge discovery from a regional asthma dataset

Paediatric asthma represents a significant public health problem. To date, clinical data sets have typically been examined using traditional data analysis techniques. While such traditional statistical methods are invariably widespread, large volumes of data may overwhelm such approaches. The new generation of knowledge discovery techniques may therefore be a more appropriate means of analysis. The primary purpose of this study was to investigate an asthma data set, with the application of various data mining techniques for knowledge discovery. The current study utilises data from an asthma data set (n ≈ 17000). The findings revealed a number of factors and patterns of interest.

Using bioinformatics techniques for gene identification in drug discovery and development

As more and more evidence has become available, the link between gene and emergent disease has been made including cancer, heart disease and parkinsonism. Analyzing the diseases and designing drugs with respect to the gene and protein level obviously help to find the underlying causes of the diseases, and to improve their rate of cure. The development of modern molecular biology, biochemistry, data collection and analysis techniques provides the scientists with a large amount of gene data. To draw a link between genes and their relation to disease outcomes and drug discovery is a big challenge: How to analyze large datasets and extract useful knowledge? Combining bioinformatics with drug discovery is a promising method to tackle this issue. Most techniques of bioinformatics are used in the first two phases of drug discovery to extract interesting information and find important genes and/or proteins for speeding the process of drug discovery, enhancing the accuracy of analysis and reducing the cost. Gene identification is a very fundamental and important technique among them. In this paper, we have reviewed gene identification algorithms and discussed their usage, relationships and challenges in drug discovery and development.

The crossroads approach to information discovery in wireless sensor networks

Wireless sensor networks (WSN) are attractive for information gathering in large-scale data rich environments. In order to fully exploit the data gathering and dissemination capabilities of these networks, energy-efficient and scalable solutions for data storage and information discovery are essential. In this paper, we formulate the information discovery problem as a load-balancing problem, with the combined aim being to maximize network lifetime and minimize query processing delay resulting in QoS improvements. We propose a novel information storage and distribution mechanism that takes into account the residual energy levels in individual sensors. Further, we propose a hybrid push-pull strategy that enables fast response to information discovery queries.

Simulations results prove the proposed method(s) of information discovery offer significant QoS benefits for global as well as individual queries in comparison to previous approaches.

Identifying targets for drug discovery using bioinformatics

Drug discovery is the process of discovering and designing drugs, which includes target identification, target validation, lead identification, lead optimization and introduction of the new drugs to the public. This process is very important, involving analyzing the causes of the diseases and finding ways to tackle them. Objective: The problems we must face include: i) that this process is so long and expensive that it might cost millions of dollars and take a dozen years; and ii) the accuracy of identification of targets is not good enough, which in turn delays the process. Introducing bioinformatics into the drug discovery process could contribute much to it. Bioinformatics is a booming subject combining biology with computer science. It can explore the causes of diseases at the molecular level, explain the phenomena of the diseases from the angle of the gene and make use of computer techniques, such as data mining, machine learning and so on, to decrease the scope of analysis and enhance the accuracy of the results so as to reduce the cost and time. Methods: Here we describe recent studies about how to apply bioinformatics techniques in the four phases of drug discovery, how these techniques improve the drug discovery process and some possible difficulties that should be dealt with. Results: We conclude that combining bioinformatics with drug discovery is a very promising method although it faces many problems currently.

Exploiting affinity propagation for energy-efficient information discovery in sensor networks

Wireless sensor networks (WSN) are attractive for information gathering in large-scale data rich environments. Emerging WSN applications require dissemination of information to interested clients within the network requiring support for differing traffic patterns. Further, in-network query processing capabilities are required for autonomic information discovery. In this paper, we formulate the information discovery problem as a load-balancing problem, with the combined aim being to maximize network lifetime and minimize query processing delay. We propose novel methods for data dissemination, information discovery and data aggregation that are designed to provide significant QoS benefits. We make use of affinity propagation to group "similar" sensors and have developed efficient mechanisms that can resolve both ALL-type and ANY-type queries in-network with improved energy-efficiency and query resolution time. Simulation results prove the proposed method(s) of information discovery offer significant QoS benefits for ALL-type and ANY-type queries in comparison to previous approaches.

Are nominal exchange rates and price levels co-integrated? new evidence from threshold autoregressive and momentum-threshold autoregressive models

The goal of this paper is to examine evidence for co-integration between nominal exchange rates for Canada, the UK, Japan, Germany, Italy and France (G6) vis-à-vis the US dollar, and the relative price ratios using monthly data over the period 1973:01 to 1997:04. Motivated by the fact that exchange rate adjustment may be asymmetric, we allowed for asymmetric adjustment in exchange rates by using the threshold autoregressive model and the momentum threshold autoregressive model. We do not find any evidence of a co-integrating relationship; hence, we fail to establish long-run purchasing power parity.

Forecasting stock index volatility : comparing implied volatility and the intraday high-low price range

The intraday high–low price range offers volatility forecasts similarly efficient to high-quality implied volatility indexes published by the Chicago Board Options Exchange (CBOE) for four stock market indexes: S&P 500, S&P 100, NASDAQ 100, and Dow Jones Industrials. Examination of in-sample and out-of-sample volatility forecasts reveals that neither implied volatility nor intraday high–low range volatility consistently outperforms the other.

Fowl play and the price of petrel: long-living Procellariiformes have peroxidation-resistant membrane composition compared with short-living Galliformes

The membrane pacemaker hypothesis predicts that long-living species will have more peroxidation-resistant membrane lipids than shorter living species. We tested this hypothesis by comparing the fatty acid composition of heart phospholipids from long-living Procellariiformes (petrels and albatrosses) to those of shorter living Galliformes (fowl). The seabirds were obtained from by-catch of commercial fishing operations and the fowl values from published data. The 3.8-fold greater predicted longevity of the seabirds was associated with elevated content of peroxidation-resistant monounsaturates and reduced content of peroxidation-prone polyunsaturates and, consequently, a significantly reduced peroxidation index in heart membrane lipids, compared with fowl. Peroxidation-resistant membrane composition may be an important physiological trait for longevous species.