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Independent interaction terms were included in the mixed model to determine if complications (i.e., psychotic symptoms × sampling year and SUD × sampling year) influenced the stability of cortisol over time.
Monthly rainfall distribution of the sampling year (2015) in Rangareddy district is presented in Fig. 4.
Sampling dates over the whole GBR, Tully and Fitzroy are identified in various colours per sampling year.
Figure 12 Areas of high chl-a biomass (identified as secondary plume waters) for each sampling year.
Before adjustment for multiple comparisons, lifetime psychotic symptoms significantly interacted with cortisol sampling year to predict logAUC (p < 0.0001).
We used Indicator Species Analysis to identify BSC taxon groups that were strongly associated with each sampling year.
Replicate sampling plot (Replication), and replicate sampling plot nested within each sampling year (Year/Replication) were set up as the random effect for former and latter models, respectively.
Salivary cortisol levels were averaged over the three consecutive sampling days at each time point for each sampling year to account for day to day variation.
Before adjustment for multiple comparisons, there was also a significant interaction between SUD and cortisol sampling year predicting logAUC (p = 0.03) (Table 3).
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There were no significant differences (Two-Way ANOSIM; depth nested within sampling year) between sampling years.
Subsequently to this pilot sampling, year-round samples were collected to determine the temporal variation in levels of bacterial contamination.
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