Escalation and deceleration of offending behaviours from adolescence to early adulthood

Relatively little longitudinal research is available in Australia to describe I the age/crime relationship in much detail, particularly patterns of offending occurring during the transition from adolescence to early adulthood. This paper addresses this issue using self-reported criminal involvement from a school-based sample, a group of socially disadvantaged individuals, and a group of officially identified offenders. The findings support the widespread research that rates of offending peak during adolescence, at which time offending is widespread, and that the criminal career is of relatively short duration. However, the results also demonstrate that the age/crime curve is not a unitary phenomenon. The type of offending behaviour being considered, the gender of the population, and the perpetrator's exposure to the criminal justice system contribute to the variability in the curve. In this study, the prevalence and mean level of overall offending for the total sample was higher during early adulthood than adolescence for vehicle offences and drug-use, rates of theft were similar in both periods, and vandalism and serious offending were lower. In addition, socially disadvantaged young people reported involvement in crime that peaked and desisted earlier in the life course compared to the school-based sample, and gender differences within these groups were also found. For the school-based sample, offending for females began and desisted earlier than for males, but within the at-risk group, the opposite was true. Implications for crime-prevention programming are discussed.

Genes for the majority of group A streptococcal virulence factors and extracellular surface proteins do not confer an increased propensity to cause invasive disease

Background. The factors behind the reemergence of severe, invasive group A streptococcal (GAS) diseases are unclear, but it could be caused by altered genetic endowment in these organisms. However, data from previous studies assessing the association between single genetic factors and invasive disease are often conflicting, suggesting that other, as-yet unidentified factors are necessary for the development of this class of disease. Methods. In this study, we used a targeted GAS virulence microarray containing 226 GAS genes to determine the virulence gene repertoires of 68 GAS isolates (42 associated with invasive disease and 28 associated with noninvasive disease) collected in a defined geographic location during a contiguous time period. We then employed 3 advanced machine learning methods (genetic algorithm neural network, support vector machines, and classification trees) to identify genes with an increased association with invasive disease. Results. Virulence gene profiles of individual GAS isolates varied extensively among these geographically and temporally related strains. Using genetic algorithm neural network analysis, we identified 3 genes with a marginal overrepresentation in invasive disease isolates. Significantly, 2 of these genes, ssa and mf4, encoded superantigens but were only present in a restricted set of GAS M-types. The third gene, spa, was found in variable distributions in all M-types in the study. Conclusions. Our comprehensive analysis of GAS virulence profiles provides strong evidence for the incongruent relationships among any of the 226 genes represented on the array and the overall propensity of GAS to cause invasive disease, underscoring the pathogenic complexity of these diseases, as well as the importance of multiple bacteria and/ or host factors.

Regional patterns of agricultural land use and deforestation in Colombia

An expanding human population and associated demands for goods and services continues to exert an increasing pressure on ecological systems. Although the rate of expansion of agricultural lands has slowed since 1960, rapid deforestation still occurs in many tropical countries, including Colombia. However, the location and extent of deforestation and associated ecological impacts within tropical countries is often not well known. The primary aim of this study was to obtain an understanding of the spatial patterns of forest conversion for agricultural land uses in Colombia. We modeled native forest conversion in Colombia at regional and national-levels using logistic regression and classification trees. We investigated the impact of ignoring the regional variability of model parameters, and identified biophysical and socioeconomic factors that best explain the current spatial pattern and inter-regional variation in forest cover. We validated our predictions for the Amazon region using MODIS satellite imagery. The regional-level classification tree that accounted for regional heterogeneity had the greatest discrimination ability. Factors related to accessibility (distance to roads and towns) were related to the presence of forest cover, although this relationship varied regionally. In order to identify areas with a high risk of deforestation, we used predictions from the best model, refined by areas with rural population growth rates of > 2%. We ranked forest ecosystem types in terms of levels of threat of conversion. Our results provide useful inputs to planning for biodiversity conservation in Colombia, by identifying areas and ecosystem types that are vulnerable to deforestation. Several of the predicted deforestation hotspots coincide with areas that are outstanding in terms of biodiversity value.

Particle number emissions and source signatures of an industrial facility

The work presented was conducted within the scope of a larger study investigating impacts of the Stuart Oil Shale project, a facility operating to the north of the industrial city of Gladstone, Australia. The aims of the investigations were threefold: (a) the identification of the plant signatures in terms of particle size distributions in the submicrometer range (13-830 nm) through stack measurements, (b) exploring the applicability of these signatures in tracing the source contributions at locations of interest, at a distance from the plant, and (c) assessing the contribution of the plant to the total particle number concentration at locations of interest. The stack measurements conducted for three different conditions of plant operation showed that the particle size distributions were bimodal with average modal count median diameters (CMDs) of 24 (SD 4) and 52 (SD 9) nm. The average of all the particle size distributions recorded within the plant sector at a site located 4.5 km from the plant, over the sampling period when the plant was operating, also showed a bimodal distribution. The modal CMDs in this case were 27 and 50 nm, similar to those at the stack. This bimodal size distribution is distinct from the size distribution of the most common ambient anthropogenic emission source, which is vehicle emissions, and can be considered as a signature of this source. The average contribution of the plant (for plant sector winds) was estimated to be (10.0 +/- 3.8) x 10(2) particles cm(-3) and constituted approximately a 50% increase over the local particle ambient concentration for plant sector winds. This increase in particle number concentration compared to the local background concentration, while high compared to the clean environment concentration, is not significant when compared to concentrations generally encountered in the urban environment of Brisbane.

An electrical heart model incorporating real geometry and motion

Abstract—This paper describes an electrical model of the ventricles incorporating real geometry and motion. Cardiac geometry and motion is obtained from segmentations of multipleslice MRI time sequences. A static heart model developed previously is deformed to match the observed geometry using a novel shape registration algorithm. The resulting electrocardiograms and body surface potential maps are compared to a static simulation in the resting heart. These results demonstrate that introducing motion into the cardiac model modifies the ECG during the T wave at peak contraction of the ventricles.

ANNA: A new prediction method for bioassessment programs

1. Cluster analysis of reference sites with similar biota is the initial step in creating River Invertebrate Prediction and Classification System (RIVPACS) and similar river bioassessment models such as Australian River Assessment System (AUSRIVAS). This paper describes and tests an alternative prediction method, Assessment by Nearest Neighbour Analysis (ANNA), based on the same philosophy as RIVPACS and AUSRIVAS but without the grouping step that some people view as artificial. 2. The steps in creating ANNA models are: (i) weighting the predictor variables using a multivariate approach analogous to principal axis correlations, (ii) calculating the weighted Euclidian distance from a test site to the reference sites based on the environmental predictors, (iii) predicting the faunal composition based on the nearest reference sites and (iv) calculating an observed/expected (O/E) analogous to RIVPACS/AUSRIVAS. 3. The paper compares AUSRIVAS and ANNA models on 17 datasets representing a variety of habitats and seasons. First, it examines each model's regressions for Observed versus Expected number of taxa, including the r(2), intercept and slope. Second, the two models' assessments of 79 test sites in New Zealand are compared. Third, the models are compared on test and presumed reference sites along a known trace metal gradient. Fourth, ANNA models are evaluated for western Australia, a geographically distinct region of Australia. The comparisons demonstrate that ANNA and AUSRIVAS are generally equivalent in performance, although ANNA turns out to be potentially more robust for the O versus E regressions and is potentially more accurate on the trace metal gradient sites. 4. The ANNA method is recommended for use in bioassessment of rivers, at least for corroborating the results of the well established AUSRIVAS- and RIVPACS-type models, if not to replace them.

Overseas Visitor Deaths in Australia: 1997-2000

Reassuring visitors about their health and safety is particularly important for tourist destinations since the terrorist attacks of 11 September 2001. This study examined the deaths of 1513 overseas visitors to Australia over a four-year period, and found that most deaths (76%) were due to natural causes.Among the accidental deaths, the main causes were motor vehicle crashes and water-related incidents. The study findings support a widely held view that Australia is a safe destination for overseas visitors. It also provides a safety benchmark for other tourist destinations.