Pre-clearing vegetation of the coastal lowlands of the Wet Tropics Bioregion, North Queensland

A pre-clearing vegetation map and digital coverage at approximately 1:50 000 scale for the coastal lowlands (up to about 200 m elevation) of the Wet Tropics Bioregion, North Queensland is presented. The study area covers about 508 000 ha from Cooktown, 420 km south almost to Townsville (latitude 15° 30’–18° 20’ longitude 144° 50’–146° 40’). Data sources included historical aerial photography, early surveyors’ plans, explorers’ journals, previous vegetation maps, and maps of soils and geology. The pre-clearing mapping was built around the remnant vegetation mapping of Stanton & Stanton (2005), and the vegetation classification of this latter work was adopted. Vegetation units were further classified into regional ecosystems compatible with the standard State-wide system used by Queensland government. The digital coverage is part of the current Queensland Herbarium regional ecosystem coverage (Queensland Herbarium and Wet Tropics Management Authority 2005). Coloured maps (1:100 000 scale) of the pre-clearing vegetation of the Herbert, Tully, Innisfail and Macalister/Daintree subregions are on an accompanying CD-ROM. An evaluation of vegetation loss through clearing on the coastal lowlands of the Wet Tropics revealed several nearextinct vegetation communities and regional ecosystems, and many others that are drastically reduced in area. Even ecosystems occurring on poorly drained lands have suffered a surprisingly high level of loss due to the effectiveness of drainage operations. Grassland ecosystems were found to be widespread on the Herbert and Tully floodplains, but are now close to extinction. The lowlands vegetation of the Wet Tropics that remains today continues to be fragmented and degraded despite the introduction of State-wide broad-scale tree-clearing laws in 1999, and the cessation of broadscale tree-clearing in December 2006

Is there an association between spatial accessibility of outpatient care and utilization? Analysis of gynecological and general care

Abstract Background In rural regions with a low population density, distances to health care providers as well as insufficient public transport may be barriers for the accessibility of health care. In this analysis it was examined whether the accessibility of gynecologists and GPs, measured as travel time both by car and public transport has an influence on the utilization of health care in the rural region of Western Pomerania in Northern Germany. Methods Utilization data was obtained from the population based Study of Health in Pomerania (SHIP). Utilization was operationalized by the parameter “at least one physician visit during the last 12 months”. To determine travel times by car and by public transport, network analyses were conducted in a Geographic Information System (GIS). Multivariate logistic regression models were calculated to identify determinants for the utilization of gynecologists and GPs. Results There is no significant association between the accessibility by car or public transport and the utilization of gynecologists and GPs. Significant predictors for the utilization of gynecologists in the regression model including public transport are age (OR 0.960, 95% CI 0.950–0.971, p < 0.0001), social class (OR 1.137, 95% CI 1.084–1.193, p < 0.0001) and having persons ≥18 years in the household (OR 2.315, 95% CI 1.116–4.800, p = 0.0241). Conclusions In the examined region less utilization of gynecologists is not explainable with long travel times by car or public transport

Not Just the Demographic Change--The Impact of Trends in Risk Factor Prevalences on the Prediction of Future Cases of Myocardial Infarction.

Previous predictions of population morbidity consider demographic changes only. To model future morbidity, however, changes in prevalences of risk factors should be considered. We calculated the number of incident cases of first myocardial infarction (MI) in Mecklenburg-Western Pomerania in 2017 considering the effects of demographic changes and trends in the prevalences of major risk factors simultaneously.Data basis of the analysis were two population-based cohorts of the German Study of Health in Pomerania (SHIP-baseline [1997-2001] and the 5-year follow-up and SHIP-Trend-baseline [2008-2011] respectively). SHIP-baseline data were used to calculate the initial coefficients for major risk factors for MI with a Poisson regression model. The dependent variable was the number of incident cases of MI between SHIP-baseline and SHIP-5-year follow-up. Explanatory variables were sex, age, a validated diagnosis of hypertension and/or diabetes, smoking, waist circumference (WC), increased blood levels of triglycerides (TG) and low-density-lipoprotein cholesterol (LDL), and low blood levels of high-density-lipoprotein cholesterol (HDL). Applying the coefficients determined for SHIP baseline to risk factor prevalences, derived from the new cohort SHIP-Trend together with population forecast data, we calculated the projected number of incident cases of MI in 2017.Except for WC and smoking in females, prevalences of risk factors in SHIP-Trend-baseline were lower compared to SHIP-baseline. Based on demographic changes only, the calculated incidence of MI for 2017 compared to the reference year 2006 yields an increase of MI (males: +11.5%, females: +8.0%). However, a decrease of MI (males: -23.7%, females: -17.1%) is shown considering the changes in the prevalences of risk factors in the projection.The predicted number of incident cases of MI shows large differences between models with and without considering changes in the prevalences of major risk factors. Hence, the prediction of incident MI should preferably not only be based on demographic changes

Automated Skull and Cavity Segmentation from Ultra Short TE Sequence Images

In order to achieve an accurate attenuation correction in brain PET images acquired by hybrid PET/MR scanners, it is mandatory to delineate cortical bone and cavities in the MR images. Automated segmentation of the anatomical Ultra short echo time (UTE) MR images into different regions allows to assign them to the corresponding attenuation coefficients. The UTE sequence yields two components obtained by echo times TE=0.07 ms and TE=2.46 ms. UTE images were first normalized by means of a scatterplot-based normalization technique, in which the scatterplot of a given scan is fitted into that of reference‘s. Second, a correction mask was generated to reduce the problem of the head edges resulting in the first component. Third, the fully automatic virtual extraction was realized by developing two methods: the two-class Support Vector Machine (C SVM) -based method and the single-class Support Vector Machine (S SVM)-based method using different kernels. Four datasets were evaluated with the corresponding registered CT scans and with an expert manual segmentation of the cavities. The C SVM-based segmentation of the skull using the RBF kernel reached a Dice coefficient (D) value of 0.83±0.042 (mean ± SD). The S SVM-based segmentation of cavities using the RBF kernel attained a D value of 0.73±0.02. Based on the present results, the following conclusions can be drawn: First with our methods, the fully automatic segmentation of cortical bone and cavities reaches good results. Second, intensity normalization enables the development of the S SVMbased method for segmentation of cortical bone and cavities

Absolute numbers of incident cases of MI in Mecklenburg-Western Pomerania in 2006 and 2017 with and without considering the changes in the prevalences of risk factors between 1997–2001 and 2008–2011.

<p>Absolute numbers of incident cases of MI in Mecklenburg-Western Pomerania in 2006 and 2017 with and without considering the changes in the prevalences of risk factors between 1997–2001 and 2008–2011.</p

Results of the Poisson regression model (dependent variable: MI incidence between SHIP baseline and 5-year-follow-up.

<p>CI: Confidence Interval</p><p>The coefficients of the risk factors of this model were used to compute the MI counts for the 5-year period following the baseline examination of SHIP-Trend.</p

Flow diagram of the analysis strategy (Abbreviations: WC, waist circumference; TG, triglyceride; HDL, high-density-lipoprotein; LDL, low-density-lipoprotein).

<p>Flow diagram of the analysis strategy (Abbreviations: WC, waist circumference; TG, triglyceride; HDL, high-density-lipoprotein; LDL, low-density-lipoprotein).</p

Relative changes of the prevalences of risk factors from SHIP-Trend compared to SHIP prevalences, age- and sex-standardized on the population of from Mecklenburg-Western Pomerania in 2006.

<p>Abbreviations: WC, waist circumference; TG, triglyceride; HDL, high-density-lipoprotein; LDL, low-density-lipoprotein.</p

Flow diagram of the data processing.

<p>Flow diagram of the data processing.</p