Plasma CXCL10, sCD163 and sCD14 Levels Have Distinct Associations with Antiretroviral Treatment and Cardiovascular Disease Risk Factors

We investigate the associations of three established plasma biomarkers in the context of HIV and treatment-related variables including a comprehensive cardiovascular disease risk assessment, within a large ambulatory HIV cohort. Patients were recruited in 2010 to form the Royal Perth Hospital HIV/CVD risk cohort. Plasma sCD14, sCD163 and CXCL10 levels were measured in 475 consecutive patients with documented CVD risk (age, ethnicity, gender, smoking, blood pressure, BMI, fasting metabolic profile) and HIV treatment history including immunological/virological outcomes. The biomarkers assessed showed distinct associations with virological response: CXCL10 strongly correlated with HIV-1 RNA (p0.2). Associations between higher sCD163 and protease inhibitor therapy (p = 0.05) and lower sCD14 with integrase inhibitor therapy (p = 0.02) were observed. Levels of sCD163 were also associated with CVD risk factors (age, ethnicity, HDL, BMI), with a favourable influence of Framingham score <10% (p = 0.04). Soluble CD14 levels were higher among smokers (p = 0.002), with no effect of other CVD risk factors, except age (p = 0.045). Our findings confirm CXCL10, sCD163 and sCD14 have distinct associations with different aspects of HIV infection and treatment. Levels of CXCL10 correlated with routinely monitored variables, sCD163 levels reflect a deeper level of virological suppression and influence of CVD risk factors, while sCD14 levels were not associated with routinely monitored variables, with evidence of specific effects of smoking and integrase inhibitor therapy warranting further investigation

Determinants of tree assemblage composition at the mesoscale within a subtropical eucalypt forest.

A variety of environmental processes, including topography, edaphic and disturbance factors can influence vegetation composition. The relative influence of these patterns has been known to vary with scale, however, few studies have focused on environmental drivers of composition at the mesoscale. This study examined the relative importance of topography, catchment flow and soil in influencing tree assemblages in Karawatha Forest Park; a South-East Queensland subtropical eucalypt forest embedded in an urban matrix that is part of the Terrestrial Ecosystem Research Network South-East Queensland Peri-urban SuperSite. Thirty-three LTER plots were surveyed at the mesoscale (909 ha), where all woody stems ≥1.3 m high rooted within plots were sampled. Vegetation was divided into three cohorts: small (≥1-10 cm DBH), intermediate (≥10-30 cm DBH), and large (≥30 cm DBH). Plot slope, aspect, elevation, catchment area and location and soil chemistry and structure were also measured. Ordinations and smooth surface modelling were used to determine drivers of vegetation assemblage in each cohort. Vegetation composition was highly variable among plots at the mesoscale (plots systematically placed at 500 m intervals). Elevation was strongly related to woody vegetation composition across all cohorts (R2: 0.69-0.75). Other topographic variables that explained a substantial amount of variation in composition were catchment area (R2: 0.43-0.45) and slope (R2: 0.23-0.61). Soil chemistry (R2: 0.09-0.75) was also associated with woody vegetation composition. While species composition differed substantially between cohorts, the environmental variables explaining composition did not. These results demonstrate the overriding importance of elevation and other topographic features in discriminating tree assemblage patterns irrespective of tree size. The importance of soil characteristics to tree assemblages was also influenced by topography, where ridge top sites were typically drier and had lower soil nutrient levels than riparian areas

Fire management on private conservation lands : knowledge, perceptions and actions of landholders in eastern Australia

Fire is an important natural disturbance process within the Australian landscape, but the complex and hazardous nature of fire creates a conservation management dilemma. For landholders of private conservation lands, management for conservation of biodiversity and risk reduction is complicated. Private conservation landholders in eastern Australia directed far less effort towards fire management than other conservation management actions, despite clearly acknowledging the risk and associated responsibilities of fire management on their lands. Nonetheless, landholders did undertake actions to reduce fuel hazards and prepare for wildfire events on their land. Despite the established role and benefits of fire to many ecosystems in the region, landholder understanding of the ecological role of fire was generally poor. Few landholders were aware of ecologically appropriate fire regimes for the vegetation types on their property, and few undertook fire management actions to achieve ecological outcomes. Site-specific obstacles, lack of fire management knowledge and experience, and legal and containment concerns contributed to the low level of fire management observed. There is a need for property-specific fire management planning across all private conservation lands, to further integrate ecological fire requirements into biodiversity management, and prioritise actions that aim to improve conservation outcomes while safeguarding life and property.<br /

Longitudinal Trends in Western Australian HIV-1 Sequence Diversity and Viral Transmission Networks and Their Influence on Clinical Parameters: 2000–2014

We examined baseline HIV-1 protease and reverse transcriptase sequences and HIV clinical parameters from 1,021 consecutive patients (814 male, 207 female) through the Royal Perth Hospital HIV service to investigate HIV-1 subtype diversity and local phylogenetic networks from 2000 to 2014. HIV-1 subtype B virus sequences were demonstrated in 619 (61%) of cases, with increasing non-B HIV-1 subtypes from 23.2% (2000-2003) to 48% (2008-2011) and 43% (2012-2014) (p 2: 135/211; 64% vs. 13/69; 19%; p = 0.001), including one cluster of 53 HIV-1 B subtype sequences that evolved from 2008 to 2014. Non-B subtype HIV-1 was associated with lower baseline CD4 T cell count (p = 0.005) but not plasma HIV-1 RNA levels (p = 0.31), suggesting relatively delayed diagnosis. Baseline viral load was strongly associated with calendar time [mean 18,620 copies/ml in 2000-2003; 75,858 copies/ml in 2012-2014 (p 2) in adjusted analyses (p = 0.03). This study identifies a number of temporal trends over the past 15 years, including an increasing prevalence of non-B subtype HIV-1 that highlights the growing influence of migration and travel on the Australian HIV-1 epidemic and the associated increased role of heterosexual HIV-1 transmission in this context. At the same time, these data indicate that local transmission within predominantly male networks remains a challenging issue for HIV-1 prevention

The total species richness of each genus within each size cohort among the 33 plots at KFP.

<p>Plant abundance for each genus is also shown.</p><p>The total species richness of each genus within each size cohort among the 33 plots at KFP.</p

Plot layout within Karawatha Forest Park (KFP) within the TERN SEQ Peri-urban SuperSite.

<p>The midlines (thick black lines), and 10 m contour lines (thin grey lines with values in metres) demonstrate the positions for thirty three PPBio LTER plots surveyed in this study. The inset shows the location of KFP (star) within Queensland, Australia. Each midline starting point was placed systematically on a grid. These grid locations are also displayed as a combination of a letter and number.</p

Relationship between environmental predictor variables.

<p>Pearson's correlation coefficients are shown.</p><p>Relationship between environmental predictor variables.</p

Proportion of trees in selected genera for each cohort at Karawatha.

<p>Proportion of trees in selected genera for each cohort at Karawatha.</p

Correlations of 14 soil variables with the three major axes of the soil PCA analysis.

<p>Correlations of 14 soil variables with the three major axes of the soil PCA analysis.</p

Surface modelling statistics for each cohort and significant environmental variable.

<p>R² value and P-value are shown. CA is catchment area.</p><p>*, <i>P</i><0.05;</p><p>**, <i>P</i><0.01;</p><p>***, <i>P</i><0.001; NS is <i>P</i>>0.05.</p><p>Surface modelling statistics for each cohort and significant environmental variable.</p