Additional file 2 of Cis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits

Additional file 2: Supplementary Table 1. Candidate CpG sites for cocaine use after meta-analysis to combine results for the 450K and EPIC cohorts (Ntotal = 811). Supplementary Table 2. Selected SNP-CpG pairs (FDR < 0.05) after meta-analysis to combine results for the 450K and EPIC cohorts (Ntotal = 811) and clumping. Supplementary Table 3. Enriched Ingenuity Canonical Pathways identified using genes mapped by the meQTLs for cocaine-related DNA methylations. Supplementary Table 4. Significant traits associated with the top index meQTLs. A total of 60 studies reached the significance level after bonferroni correction (p < 1.7e-7), which consisted of 36 phenotypes (phenotype number). Supplementary Table 5. meQTLs trait enrichment using associates downloaded from GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ). MeQTLs for cocaine-related DNA methylations were used. Supplementary Table 6. Candidate CpG sites for cocaine use frequency (high frequency users VS low frequency users). Supplementary Table 7. MeQTLs (FDR < 0.05) for cocaine use frequency-related DNA methylations after meta-analysis to combine results for the 450K and EPIC cohorts (Ntotal = 293) and clumping. Supplementary Table 8.  Enriched Ingenuity Canonical Pathways identified using genes mapped by the meQTLs for cocaine use frequency-related DNA methylations. Supplementary Table 9. Significant traits associated with the top index meQTLs for cocaine use frequency-related DNA methylations. A total of 4 studies reached the significance level after bonferroni correction (p < 7.0e-7), which consisted of 4 phenotypes (phenotype number). Supplementary Table 10. meQTLs trait enrichment using associates downloaded from GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ). MeQTLs for cocaine use frequency-related DNA methylations were used

Additional file 1 of Cis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits

Additional file 1: Supplementary Figure 1. Global ancestry estimates by ADMIXTURE. 2,504 residents with African (AFR), East Asian (EAS), European (EUR), and South Asian (SAS) ancestry from the 1000 Genomes Project were used as the reference genotype panel to infer the super populations membership. Results were plotted for (A) samples in the Veterans Aging Cohort Study (VACS) cohort (n = 2244) with respect to reference samples, (B) a subset of the VACS cohort with admixed ancestral information (n = 105) with respect to reference samples. The reference and VACS samples were separated by the black line. Supplementary Figure 2. Inferred global ancestry of the VACS samples. Scatter plot of the genotype principal component analysis (PCA) results (PC1 and PC2) for the VACS cohort and 1000 Genome Project were plotted. The color indicated the super population of 1000 Genome reference samples (dots), and the inferred global ancestry of the VACS samples (triangles). Supplementary Figure 3. Selection of candidate CpGs associated with cocaine use in the Veterans Aging Cohort Study (VACS) samples. (A) Manhattan plot and (B) QQ plot (genomic inflation λ = 1.196) after meta-analysis to combine results from the 450K and EPIC cohorts. A total of 224 candidate CpG sites were identified. The red line indicates the p-value threshold used to identify candidate CU-associated CpG sites (p-value < 0.0001). Supplementary Figure 4. Two representative patterns of genetic effects by cocaine use for the meQTLs identified. (A-B): the distribution of methylation by the genotype among cocaine non-users and users. The patterns in the 450K cohort and EPIC cohort were plotted separately. (A) The genetic effect of rs13233191 on the methylation of cg17914838. (B) The genetic effect of rs7834638 on the methylation of cg21175976. CU: cocaine use; meQTL: methylation quantitative trait loci

Adjusted Odds Ratio of Association Between ED Visit and Continuity of Care and Levels of Continuity.

<p>The first row illustrates the adjusted odds ratio between ED visits and continuity of care for individuals with at least one visit having continuity with their primary care provider. The second and third rows are the adjusted odds ratio for individuals who had high and medium levels of continuity and are compared to individuals with low levels of continuity, indicated by the asterisk (*).</p

Association Between Continuity and Emergency Department Utilization, Stratified by Total Number of Primary Care Visits.

<p>AOR, adjusted odds ratio; CI, confidence interval.</p><p>*The adjusted odds ratio is the association between continuity and emergency department utilization based on the same covariates used in the model as outlined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096356#pone-0096356-t003" target="_blank">Table 3</a>.</p

Association Between Levels of Continuity of Care and Emergency Department Utilization for Patients with ≥1 Continuity Visit.

<p>Abbreviations: ED, emergency department; PY, person-year; CI, confidence interval; IQR, interquartile range; OR, odds ratio; AOR, adjusted odds ratio; COPD, chronic obstructive pulmonary disease; TIA, transient ischemic attack; PTSD, post-traumatic stress disorder; ED, emergency department; PCP, primary care provider.</p>a<p>The adjusted model excluded 1289 (9.6%) patients because they were missing one or more variables in the model.</p>b<p>Reference value.</p>c<p>Because of the small number of veterans in these groups, they were combined: active military personnel, CAV/NPS, ChampVA spouse and children, non-Veteran humanitarian groups, merchant marines, and Tricare.</p

Association Between Continuity of Care and Emergency Department Utilization.

<p>Abbreviations: ED, emergency department; PY, per-year; CI, confidence interval; IQR, interquartile range; OR, odds ratio; AOR, adjusted odds ratio; COPD, chronic obstructive pulmonary disease; TIA, transient ischemic attack; PTSD, post-traumatic stress disorder; ED, emergency department; PCP, primary care provider.</p>a<p>The adjusted model excluded 1289 (9.6%) patients because they were missing one or more variables in the model.</p>b<p>Continuity of care levels were excluded because of potential for colinearity with the main outcome of interest, overall continuity.</p>c<p>Reference value.</p>d<p>Because of the small number of veterans in these groups, they were combined: active military personnel, CAV/NPS, ChampVA spouse and children, non-Veteran humanitarian groups, merchant marines, and Tricare.</p

Patient Demographics by Continuity and Stratification by Continuity of Care Levels.

<p>Abbreviations: IQR, interquartile range; MI, myocardial infarction; COPD, chronic obstructive pulmonary disease; TIA, transient ischemic attack; PTSD, post-traumatic stress disorder; ED, emergency department.</p>a<p>Sixty-nine percent were missing this variable.</p>b<p>Composite of major depression, bipolar, schizophrenia and PTSD.</p>c<p>Because of the small number of veterans in these groups, they were combined: active military personnel, CAV/NPS, ChampVA spouse and children, non-Veteran humanitarian groups, merchant marines, and Tricare.</p>d<p>Among ED visits that took place between Monday and Friday.</p

Patient Demographics by Emergency Department Visits.

<p>Abbreviations: IQR, interquartile range; MI, myocardial infarction; COPD, chronic obstructive pulmonary disease; TIA, transient ischemic attack; PTSD, post-traumatic stress disorder; PCP, primary care provider.</p>a<p>Sixty-nine percent (69%) were missing this variable.</p>b<p>Composite of major depression, bipolar, schizophrenia and PTSD.</p>c<p>Because of the small number of veterans in these groups, they were combined: active military personnel, CAV/NPS, ChampVA spouse and children, non-Veteran humanitarian groups, merchant marines, and Tricare.</p

Potential Patient Visit Interactions with a Provider and/or Trainee and the Relationship with Continuity.

<p>PCP: primary care provider. Grey boxes indicate visits with continuity of care and white boxes indicate visits without continuity.</p

Modelling Short-Term Maximum Individual Exposure from Airborne Hazardous Releases in Urban Environments. Part ΙI: Validation of a Deterministic Model with Wind Tunnel Experimental Data

The capability to predict short-term maximum individual exposure is very important for several applications including, for example, deliberate/accidental release of hazardous substances, odour fluctuations or material flammability level exceedance. Recently, authors have proposed a simple approach relating maximum individual exposure to parameters such as the fluctuation intensity and the concentration integral time scale. In the first part of this study (Part I), the methodology was validated against field measurements, which are governed by the natural variability of atmospheric boundary conditions. In Part II of this study, an in-depth validation of the approach is performed using reference data recorded under truly stationary and well documented flow conditions. For this reason, a boundary-layer wind-tunnel experiment was used. The experimental dataset includes 196 time-resolved concentration measurements which detect the dispersion from a continuous point source within an urban model of semi-idealized complexity. The data analysis allowed the improvement of an important model parameter. The model performed very well in predicting the maximum individual exposure, presenting a factor of two of observations equal to 95%. For large time intervals, an exponential correction term has been introduced in the model based on the experimental observations. The new model is capable of predicting all time intervals giving an overall factor of two of observations equal to 100%