Classification of survey participants as never, former, and current smokers/e-cig users.

Population Assessment of Tobacco and Health (PATH) Study Waves 1–4.5 respondents were classified into never, former, and current smoking/e-cig use. A single participant’s classification could differ between the two products: e.g., former smoker/current e-cig user. In youth survey Waves 1–2 and adult survey Wave 1, regarding electronic products, participants were asked only about “electronic-cigarettes.” In all other survey waves, participants were asked about e-product use, which included "e-cigarettes, vape pens, personal vaporizers and mods, e-cigars, e-pipes, e-hookah or hookah pens." For this analysis, we considered an affirmative response to either of these questions as indicative of e-cig use. *Youth in Wave 1 were asked to attest to ever use instead of ever “fairly regular” use of e-cigs. In our analysis, youth in Wave 1 who attested to never having used e-cigs were classified as never e-cig users. We classified all other youth as “undefined” for Wave 1 e-cig use and excluded them from the analysis in that wave.</p

Supporting information.

IntroductionEstimates of initiation, cessation, and relapse rates of tobacco cigarette smoking and e-cigarette use can facilitate projections of longer-term impact of their use. We aimed to derive transition rates and apply them to validate a microsimulation model of tobacco that newly incorporated e-cigarettes.MethodsWe fit a Markov multi-state model (MMSM) for participants in Waves 1–4.5 of the Population Assessment of Tobacco and Health (PATH) longitudinal study. The MMSM had nine cigarette smoking and e-cigarette use states (current/former/never use of each), 27 transitions, two sex categories, and four age categories (youth: 12-17y; adults: 18-24y/25-44y/≥45y). We estimated transition hazard rates, including initiation, cessation, and relapse. We then validated the Simulation of Tobacco and Nicotine Outcomes and Policy (STOP) microsimulation model, by: (a) using transition hazard rates derived from PATH Waves 1–4.5 as inputs, and (b) comparing STOP-projected prevalence of smoking and e-cigarette use at 12 and 24 months to empirical data from PATH Waves 3 and 4. We compared the goodness-of-fit of validations with “static relapse” and “time-variant relapse,” wherein relapse rates did not or did depend on abstinence duration.ResultsPer the MMSM, youth smoking and e-cigarette use was generally more volatile (lower probability of maintaining the same e-cigarette use status over time) than that of adults. Root-mean-squared error (RMSE) for STOP-projected versus empirical prevalence of smoking and e-cigarette use was DiscussionA microsimulation model incorporating smoking and e-cigarette use transition rates from a MMSM accurately projected downstream prevalence of product use. The microsimulation model structure and parameters provide a foundation for estimating the behavioral and clinical impact of tobacco and e-cigarette policies.</div

Annual cumulative transition probabilities according to the continuous-time Markov multi-state model for (a) youth and (b) adults.

Data are presented as the probability, among those in the indicated prior state (row), of being in the indicated subsequent state (column) one year later. In parentheses are the 95% confidence intervals. More likely transitions are pictured in orange, while less likely transitions are pictured in blue. Note that participants may experience more than one instantaneous transition in sequence within the year, meaning that some transitions that cannot occur instantaneously (e.g., NSNE to FSFE) are nonetheless allowed as annual cumulative transitions.</p

Continuous time Markov model-estimated baseline transition hazard rates and adjusted hazard rate ratios.

Continuous time Markov model-estimated baseline transition hazard rates and adjusted hazard rate ratios.</p

Allowed and disallowed instantaneous transitions in the continuous-time Markov multi-state model of cigarette smoking and e-cig use states.

Continuous time Markov multi-state models do not require exact transition times to be observed and allow multiple transitions to occur between observations. They therefore require allowed instantaneous transitions to be specified. Allowed instantaneous transitions from the indicated pre-transition smoking and e-cig use state (row) to the indicated post-transition state (column) are in green. Disallowed instantaneous transitions are in white. We disallowed transitions that would entail never users going directly to a former use state and those that would entail former users or current users going to a never use state. Cells in gray are those reflecting staying in the same state.</p

Additional file 3: of Transcriptome analysis of pig intestinal cell monolayers infected with Cryptosporidium parvum asexual stages

Table S1. Host cell genes differentially expressed at a FDR < 0.05. (XLSX 92 kb

Additional file 4: of Transcriptome analysis of pig intestinal cell monolayers infected with Cryptosporidium parvum asexual stages

Table S2. KEGG pathways significantly enriched in C. parvum infected cell monolayers. (XLSX 15 kb

Additional file 1: of Transcriptome analysis of pig intestinal cell monolayers infected with Cryptosporidium parvum asexual stages

Figure S1. S. scrofa FPKM values from two replicate cell cultures infected with C. parvum (correlation coefficient R 2 = 0.968). (TIFF 56 kb

Additional file 2: of Transcriptome analysis of pig intestinal cell monolayers infected with Cryptosporidium parvum asexual stages

Figure S2. Micrographs of immunofluorescently labelled infected (left) and control monolayers of IPEC-J2 cells viewed with 400× magnification. (PDF 28 kb

Additional file 5: of Transcriptome analysis of pig intestinal cell monolayers infected with Cryptosporidium parvum asexual stages

Table S3. Pathway analysis of genes upregulated in uninfected monolayers. (XLSX 17 kb