Hormone replacement therapy and breast cancer: estimate of risk: Education debate

The risk of breast cancer arises from a combination of genetic susceptibility and environmental factors. Recent studies show that type and duration of use of hormone replacement therapy affect a women's risk of developing breast cancer.1-7 The women's health initiative trial was stopped early because of excess adverse cardiovascular events and invasive breast cancer with oestrogen and progestogen.6 The publicity increased public awareness of the risks of hormone replacement therapy, and this was heightened by the publication of the million women study.2 However, the recently published oestrogen only arm of the women's health initiative trial suggests that this formulation may reduce the risk of breast cancer.8 To help make sense of the often confusing information,9 women and clinicians need individual rather than population risk data. We have produced estimates that can be used to calculate individual risk for women living up to the age of 79 and suggest the risk

Development of a genetic method to estimate effective spawner numbers in the NPF and Queensland tiger prawn fisheries

OBJECTIVES: 1. To critically evaluate a variety of mathematical methods of calculating effective population size (Ne) by conducting comprehensive computer simulations and by analysis of empirical data collected from the Moreton Bay population of tiger prawns. 2. To lay the groundwork for the application of the technology in the NPF. 3. To produce software for the calculation of Ne, and to make it widely available.

Integrating high and moderate spatial resolution image data to estimate forest age structure

The collection of spatial information to quantify changes to the state and condition of the environment is a fundamental component of conservation or sustainable utilization of tropical and subtropical forests, Age is an important structural attribute of old-growth forests influencing biological diversity in Australia eucalypt forests. Aerial photograph interpretation has traditionally been used for mapping the age and structure of forest stands. However this method is subjective and is not able to accurately capture fine to landscape scale variation necessary for ecological studies. Identification and mapping of fine to landscape scale vegetative structural attributes will allow the compilation of information associated with Montreal Process indicators lb and ld, which seek to determine linkages between age structure and the diversity and abundance of forest fauna populations. This project integrated measurements of structural attributes derived from a canopy-height elevation model with results from a geometrical-optical/spectral mixture analysis model to map forest age structure at a landscape scale. The availability of multiple-scale data allows the transfer of high-resolution attributes to landscape scale monitoring. Multispectral image data were obtained from a DMSV (Digital Multi-Spectral Video) sensor over St Mary's State Forest in Southeast Queensland, Australia. Local scene variance levels for different forest tapes calculated from the DMSV data were used to optimize the tree density and canopy size output in a geometric-optical model applied to a Landsat Thematic Mapper (TU) data set. Airborne laser scanner data obtained over the project area were used to calibrate a digital filter to extract tree heights from a digital elevation model that was derived from scanned colour stereopairs. The modelled estimates of tree height, crown size, and tree density were used to produce a decision-tree classification of forest successional stage at a landscape scale. The results obtained (72% accuracy), were limited in validation, but demonstrate potential for using the multi-scale methodology to provide spatial information for forestry policy objectives (ie., monitoring forest age structure).

MRI based diffusion and perfusion predictive model to estimate stroke evolution

In this study we present a novel automated strategy for predicting infarct evolution, based on MR diffusion and perfusion images acquired in the acute stage of stroke. The validity of this methodology was tested on novel patient data including data acquired from an independent stroke clinic. Regions-of-interest (ROIs) defining the initial diffusion lesion and tissue with abnormal hemodynamic function as defined by the mean transit time (MTT) abnormality were automatically extracted from DWI/PI maps. Quantitative measures of cerebral blood flow (CBF) and volume (CBV) along with ratio measures defined relative to the contralateral hemisphere (r(a)CBF and r(a)CBV) were calculated for the MTT ROIs. A parametric normal classifier algorithm incorporating these measures was used to predict infarct growth. The mean r(a)CBF and r(a)CBV values for eventually infarcted MTT tissue were 0.70 +/-0.19 and 1.20 +/-0.36. For recovered tissue the mean values were 0.99 +/-0.25 and 1.87 +/-0.71, respectively. There was a significant difference between these two regions for both measures (P

Population balance modelling of granulation with a physically based coalescence kernel

It was previously published by the authors that granules can either coalesce through Type I (when granules coalesce by viscous dissipation in the surface liquid layer before their surfaces touch) or Type II (when granules are slowed to a halt during rebound, after their surfaces have made contact) (AIChE J. 46 (3) (2000) 529). Based on this coalescence mechanism, a new coalescence kernel for population balance modelling of granule growth is presented. The kernel is constant such that only collisions satisfying the conditions for one of the two coalescence types are successful. One constant rate is assigned to each type of coalescence and zero is for the case of rebound. As the conditions for Types I and II coalescence are dependent on granule and binder properties, the coalescence kernel is thus physically based. Simulation results of a variety of binder and granule materials show good agreement with experimental data. (C) 2002 Elsevier Science Ltd. All rights reserved.

The effect of water, brine and ethanol flotation on the quality and shelf life of macadamia kernels 2. Kernel pieces

Raw macadamia kernel pieces were immersed in water (specific gravity 1.00 g/cm(3)), brine (SG 1.02 g/cm(3)) or ethanol solution (SG 0.97 g/cm(3)) for 30 or 60 s, then re-dried to below 1.5% moisture (wet basis) and stored under vacuum for 0, 4 and 12 months. Flotation in water had no effect on the quality or shelf life of the kernel pieces over 12 months storage, as measured by sensory evaluation of the kernels and chemical analysis of the kernel oil. Immersion in a salt solution caused unacceptable changes in quality during storage, increasing as storage time increased. Flotation in dilute ethanol also caused unacceptable quality changes during storage. Therefore, only flotation of macadamia kernel pieces in water can be recommended for commercial operations. Microbiological concerns with such a process still need to be addressed.

A liberation model for comminution based on probability theory

Mineral processing plants use two main processes; these are comminution and separation. The objective of the comminution process is to break complex particles consisting of numerous minerals into smaller simpler particles where individual particles consist primarily of only one mineral. The process in which the mineral composition distribution in particles changes due to breakage is called 'liberation'. The purpose of separation is to separate particles consisting of valuable mineral from those containing nonvaluable mineral. The energy required to break particles to fine sizes is expensive, and therefore the mineral processing engineer must design the circuit so that the breakage of liberated particles is reduced in favour of breaking composite particles. In order to effectively optimize a circuit through simulation it is necessary to predict how the mineral composition distributions change due to comminution. Such a model is called a 'liberation model for comminution'. It was generally considered that such a model should incorporate information about the ore, such as the texture. However, the relationship between the feed and product particles can be estimated using a probability method, with the probability being defined as the probability that a feed particle of a particular composition and size will form a particular product particle of a particular size and composition. The model is based on maximizing the entropy of the probability subject to mass constraints and composition constraint. Not only does this methodology allow a liberation model to be developed for binary particles, but also for particles consisting of many minerals. Results from applying the model to real plant ore are presented. A laboratory ball mill was used to break particles. The results from this experiment were used to estimate the kernel which represents the relationship between parent and progeny particles. A second feed, consisting primarily of heavy particles subsampled from the main ore was then ground through the same mill. The results from the first experiment were used to predict the product of the second experiment. The agreement between the predicted results and the actual results are very good. It is therefore recommended that more extensive validation is needed to fully evaluate the substance of the method. (C) 2003 Elsevier Ltd. All rights reserved.