GAM relationships for reptile abundance and and the abundance of <i>C. buchanni</i> with time since last fire, reptile species number, and the abundance of <i>M. obscura</i> and <i>M. greyii</i> with vegetation cover, and <i>M. greyii</i> with sqrt-transformed litter depth.

<p>Adjusted r² values are plotted for all relationships. Values for the abundance of reptiles and species number are rescaled values based on the standardised sqrt-transformed abundance/species number per 10 trap nights.</p

ANOVA F-values for reptile abundance, species richness, diversity and evenness, and individual species abundances showing responses to vegetation type, fire age and the interaction of vegetation type and fire age.

<p>Significant values are in bold (* <i>P</i><0.5, ** <i>P</i><0.01). Letters beside significant values indicate results from post-hoc Tukey HSD tests for vegetation type (B = banksia, M = melaleuca) and fire age category (O = old, >16 YSLF; Y = young, <11 YSLF).</p

Fauna survey sites in the remnant vegetation extent surrounding Perth, Western Australia.

<p>Fauna survey sites in the remnant vegetation extent surrounding Perth, Western Australia.</p

NMDS ordination (Sorensen distance measure) on the assemblage of reptiles.

<p>a) NMDS ordination of 21 reptile species at 30 sites of differing habitat (melaleuca vs banksia) and fire age (old versus young). The ordination is in two dimensions (stress = 0.193), with axis 1 and 2 cumulatively representing 75% variance (r² = 0.441 and 0.310 respectively). b) Correlations of species and habitat variables (r²>0.2) with NMDS ordination.</p

Top-ranking generalised additive models (GAM) for reptile response variables with time since last fire (YSLF) and microhabitat variables within each vegetation type.

<p>Only the models with a ΔAICc<2 and an adjusted r²>0.10 are shown. Variables detected for each model are indicated with an ‘X’. Microhabitat variables include: YSLF = years since last fire, Veg = vegetation cover and Bare = bareground cover.</p

GAM relationships between time since last fire and five microhabitat variables within each vegetation type.

<p>Adjusted r² values are plotted for all relationships. Values for microhabitat variables have been rescaled.</p

ANOVA F-values for plant taxa number, microhabitat variables and vertical vegetation density showing responses to vegetation type, fire age and the interaction of vegetation type and fire age.

<p>Significant values are indicated (* <i>P</i><0.5, ** <i>P</i><0.01) and values approaching significance are identified (∧ 0.06><i>P</i>≥0.05). Letters beside significant values indicate results from post-hoc Tukey HSD tests for vegetation type (B = banksia, M = melaleuca) and fire age category (O = old, >16 YSLF; Y = young, <11 YSLF).</p

GAM relationships for the abundance of <i>C. adelaidensis</i> with time since last fire and bareground cover, and <i>M. obscura</i> with time since last fire in banksia woodlands.

<p>Adjusted r² values are plotted for all relationships. Values for the abundance of reptiles are the rescaled values based on the standardised sqrt-transformed abundance per 10 trap nights.</p

Significant differences in reptile species number and reptile abundances for habitat and fire age categories.

<p>Mean (per 10 trap nights ±95%CI) a) reptile species number b) reptile abundance and c) – e) selected individual species abundances between banksia and melaleuca habitats and f) abundance of <i>Menetia greyii</i> in old and young fire age categories.</p

The REVAMP trial to evaluate HIV resistance testing in sub-Saharan Africa: a case study in clinical trial design in resource limited settings to optimize effectiveness and cost effectiveness estimates

<p><b>Background:</b> In sub-Saharan Africa, rates of sustained HIV virologic suppression remain below international goals. HIV resistance testing, while common in resource-rich settings, has not gained traction due to concerns about cost and sustainability.</p> <p><b>Objective:</b> We designed a randomized clinical trial to determine the feasibility, effectiveness, and cost-effectiveness of routine HIV resistance testing in sub-Saharan Africa.</p> <p><b>Approach:</b> We describe challenges common to intervention studies in resource-limited settings, and strategies used to address them, including: (1) optimizing generalizability and cost-effectiveness estimates to promote transition from study results to policy; (2) minimizing bias due to patient attrition; and (3) addressing ethical issues related to enrollment of pregnant women.</p> <p><b>Methods:</b> The study randomizes people in Uganda and South Africa with virologic failure on first-line therapy to standard of care virologic monitoring or immediate resistance testing. To strengthen external validity, study procedures are conducted within publicly supported laboratory and clinical facilities using local staff. To optimize cost estimates, we collect primary data on quality of life and medical resource utilization. To minimize losses from observation, we collect locally relevant contact information, including Whatsapp account details, for field-based tracking of missing participants. Finally, pregnant women are followed with an adapted protocol which includes an increased visit frequency to minimize risk to them and their fetuses.</p> <p><b>Conclusions:</b> REVAMP is a pragammatic randomized clinical trial designed to test the effectiveness and cost-effectiveness of HIV resistance testing versus standard of care in sub-Saharan Africa. We anticipate the results will directly inform HIV policy in sub-Saharan Africa to optimize care for HIV-infected patients.</p