Science Advances

Supplementary Materials

This PDF file includes:

  • S1. Description of data sets and guide for data analysis
  • S2. Supplementary methods
  • S3. Table summary of pharmaceutically active pollutants
  • table S1.1. Summary of observations by Case and Exp. Group for the GSA.csv data set.
  • table S1.2. Summary of the variables included in the GSA.csv data set.
  • table S1.3. Summary of observations by Case and Exp. Group for GSA_MIX_MEDIAN data set.
  • table S1.4. Summary of the variables included in the GSA_MIX_MEDIAN.csv data set.
  • table S1.5. Summary of observations by Exp. Group for ANTIBIOTIC_EC50.csv data set.
  • table S1.6. ANTIBIOTIC_EC50.csv data set.
  • table S1.7. Summary of observations by Exp. Group for biofilm.csv data set.
  • table S1.8. biofilm.csv data set.
  • table S1.9. P values of Wald test for significance of time variable of the ANODEV test for each univariate GLMs fitted to each end point (Phos, Gluc, F0, Ymax, F, Yeff) for each treatment with time as explanatory variable.
  • table S2.1. Number of observations for calculating each occurrence of statistical descriptors.
  • table S2.2. Stability of PPCPs under experimental conditions.
  • fig. S1.1. Q-Q plot for normal distribution for control observations (n = 880).
  • fig. S1.2. Notched box plot for Lum of control observations by Experiment.
  • fig. S1.3. hovPlots for bioluminescence as a function of the Experiment for Control observations.
  • fig. S1.4. hovPlots for bioluminescence as a function of the Experiment for Treatment observations.
  • fig. S1.5. Notched box plot for Lum of controls (n = 880), individual exposure to PPCPs (n = 768), and exposure to mixtures of PPCPs (n = 1080).
  • fig. S1.6. 95% family-wise confidence level for the difference of means according to anova.2_glht model.
  • fig. S1.7. Notched box plot for Lum of control observations (n = 880), divided into two aleatory groups C1 (n = 440) and C2 (n = 1080).
  • fig. S1.8. Notched box plot for Lum for treatment observations shorted by L level (1, 2).
  • fig. S1.9. Boxplots of Lum as a function of PPCPs (C1 to C16).
  • fig. S1.10. Histogram (left panel), normal Q-Q plot (central panel) and jack after jackknife plot (right panel) for bootstrapped medians of Lum values (R = 999) for “treatment” = 1.
  • fig. S1.11. Bootstrapped (R = 999) medians and 95% CIs for the 180 treatments.
  • fig. S1.12. Tolerance level in the estimation of median values for lum for the 180 mixture effects.
  • fig. S1.13. EE μ*-σ plot for the 17 studied input factors (16 PPCPs and light intensity).
  • fig. S1.14. Ranked input factors by importance (μ*).
  • fig. S1.15. EE μ*-σ plot for the 17 studied input factors (16 PPCPs and light intensity).
  • fig. S1.16. Diagnostic plots of Anov.1 model.
  • fig. S1.17. Diagnostic plots of Anov.4 model.
  • fig. S1.18. Dose ranges of PPCPs (in nanograms per liter).
  • fig. S1.19. Histogram of the frequency distribution of the total sum of the 16 PPCPs in the 180 mixtures.
  • fig. S1.20. Rose plot presenting the relative abundance (counts) of each PPCP (variable) and level (value) of each PPCP in the 180 mixtures.
  • fig. S1.21. Rose plot presenting the mean concentration (value) of each PPCP (variable) in the 180 mixtures.
  • fig. S1.22. Dose-response data and fitted LL5 drm models (chem.1) to the experimental data according to CA model.
  • fig. S1.23. Schematic representation of the calculation sequence required to predict chemical mixture effects.
  • fig. S1.24. The 21 dose-response models (LL.5 models) fitted to the in silico–predicted dose-response patterns of the 21 unique combinations of C10, C11, and C14.
  • fig. S1.25. Observed versus predicted bioluminescence values of A. CPB4337 to the 180 low-dose mixtures of PPCPs.
  • fig. S1.26. FITEVAL report for the predicted versus experimental low-dose mixture effects of PPCPs.
  • fig. S1.27. Response of six community-level end points measured along time as a function of treatment on model freshwater benthic microbial communities.
  • fig. S1.28. Residual versus fits plot to check the quadratic mean-variance assumption of negative binomial regression (with different metabolic end points coded in different colors).
  • References (73109)

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Other Supplementary Material for this manuscript includes the following:

  • S4 (Microsoft Excel format). Composition of the 180 mixtures

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