thermalqualitydata

The thermal quality in study sites. time.tset: number of hours the thermal model was between 32.2 C and 26.0 C (preferred temperature range of ornate tree lizards) for each julian day a model recorded data site: number (1:10) identifying the site hab: character identifying whether the thermal model was in the upland (u) or wash (w) month: number identifying the mont

arthropodpitfalldata

Arthropod prey abundance at study sites in two habitats. arthropods: number of arthropod prey caught in each pitfall trap (over 24 hours) month: number identifying month the trap was set site: number (1:10) identifying the site where the trap was set hab: character identifying whether the trap was set in the upland (u) or wash (w

Additional file 1 of A survey of severe asthma in Canada: results from the CASCADE practice reflective program

Additional file 1: Appendix S1. Survey Questionnaires

Reactivity Switch of Platinum with Gallium: From Reverse Water Gas Shift to Methanol Synthesis

The development of efficient catalysts for the hydrogenation of CO2 to methanol using “green” H2 is foreseen to be a key step to close the carbon cycle. In this study, we show that small and narrowly distributed alloyed PtGa nanoparticles supported on silica, prepared via a surface organometallic chemistry (SOMC) approach, display notable activity for the hydrogenation of CO2 to methanol, reaching a 7.2 molCH3OH h–1 molPt–1 methanol formation rate with a 54% intrinsic CH3OH selectivity. This reactivity sharply contrasts with what is expected for Pt, which favors the reverse water gas shift reaction, albeit with poor activity (2.6 molCO2 h–1 molPt–1). In situ XAS studies indicate that ca. 50% of Ga is reduced to Ga0 yielding alloyed PtGa nanoparticles, while the remaining 50% persist as isolated GaIII sites. The PtGa catalyst slightly dealloys under CO2 hydrogenation conditions and displays redox dynamics with PtGa–GaOx interfaces responsible for promoting both the CO2 hydrogenation activity and methanol selectivity. Further tailoring the catalyst interface by using a carbon support in place of silica enables to improve the methanol formation rate by a factor of ∼5

Rank abundance graphs used to differentiate hotspot and coldspot values from background values.

<p>One linear trend within the background values were characteristic of St Kilda whilst two linear trends were primarily seen at Noarlunga. <b>St Kilda: A</b> VLP 1, microplate 2. <b>B</b> HDNA 1, microplate 2. <b>Noarlunga: C</b> VLP 1, microplate 3. <b>D</b> HDNA 2, microplate 2.</p

Single vertical profile of VLP 1 and LDNA populations at Noarlunga.

<p><b>A</b> Microplate three showing little to no association. <b>B</b> Microplate one showing association. Gap in profile indicates a missing data point.</p

Two-dimensional contour plots showing the highest change in heterogeneity due to the presence of hotspots and coldspots within bacterial and viral subpopulations.

<p>Hotspots and coldspots seen across a distance of 6.3×11.3 cm using Surfer 10 (Golden Software, Inc.). Noarlunga: A VLP 2 showing a 2585-fold change in abundance over 0.9 cm. B LDNA showing a 12.9-fold change in abundance over 0.9 cm. St Kilda: C VLP 1 showing a maximum 10.52-fold change in heterogeneity seen over 0.9 cm. D HDNA 2 showing a maximum 45.2-fold change in heterogeneity seen over 0.9 cm. There were a range of heterogeneities over 0.9 cm (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102805#pone.0102805.s004" target="_blank">Fig. S4</a>) indicating a variety of intensities for hotspots and coldspots. Abundance levels are indicated by a colour intensity scale in units of cells/particles ml⁻¹. Solid red circles indicate areas of abundance higher than the maximum contour level selected. A minimum contour interval value of at least 1000 was chosen based on maximum machine error. The faint gridlines show sample interval.</p

Comparison of total mean bacterial and viral populations at Noarlunga via vertical depth profiles.

<p>Total mean bacterial and total mean viral population abundance within all three microplates. <b>A</b> Noarlunga, (n = 1350). <b>B</b> St Kilda, (n = 1045). Error bars represent the 95% confidence intervals obtained from all three replicates (n = 12).</p

Significant Moran correlograms of non-randomly distributed bacterial subpopulations at St Kilda.

<p><b>A</b> LDNA, microplate 2. <b>B</b> LDNA, microplate 3. <b>C</b> HDNA 2, microplate 2. <b>D</b> Total bacteria, microplate 2. Filled and unfilled data points indicate significant and non-significant Moran’s I values (p≤0.01). Only sample points with ≥30 pairs of values were included.</p

Identification of bacterial and viral subpopulations via flow cytometry.

<p>Flow cytometric cytograms of side-scatter light versus green fluorescence (SYBR Green) and histograms of green fluorescence (SYBR Green). <b>Noarlunga: </b><b>A</b> cytogram <b>B</b> histogram; <b>St Kilda: </b><b>C</b> cytogram <b>D</b> histogram showing two distinct viral populations (VLP 1 and VLP 2) and three distinct bacterial populations (LDNA, HDNA 1 and HDNA 2).</p