Introduction Urinary biomarkers are useful in characterizing exposure to harmful and potentially harmful constituents of tobacco products and linking exposure to health outcomes. However, the consistency/reproducibility of many urinary biomarkers over long periods is unknown.Methods Among people who exclusively used cigarettes in the Population Assessment of Tobacco and Health Study Waves 1, 2, 4, and 5 (ranging from 746 to 1361 subjects), we used weighted models to estimate variance components and intra-class correlation coefficients (ICC) for 15 biomarkers of exposure for urine samples collected 3-5 years apart, creatinine-only-adjusted and also adjusted for demographic and behavioral predictors.Results In models adjusted only for creatinine, ICC values of biomarkers ranged from 0.41 (95% confidence interval (CI): 0.32, 0.49) (N-acetyl-S-(2-carbamoylethyl)-L-cysteine) to 0.73 (95% CI: 0.65, 0.81) (4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol), varying within each chemical class. For models adjusted for predictors, associations between biomarkers and predictors were similar for samples collected 3-5 years and 1 year apart. Predictor-adjusted ICCs for samples collected 3-5 years apart ranged from 0.29 (95% CI: 0.17, 0.40) (N-Acetyl-S-(2-carbamoylethyl)-L-cysteine) to 0.63 (95% CI: 0.56, 0.69) (N-Acetyl-S-(2-hydroxyethyl)-L-cysteine) and appeared not different from those for samples collected 1 year apart.Conclusions Even for 3 or 5 years between urine sample collection, unadjusted biomarkers of exposure showed fair to excellent reproducibility. Similar consistency between 1 year and 3-5 years between collections was found when including predictors in the model.

Supplementary Table S8 shows the results of weighted multiple linear regression analysis of log-transformed urinary nicotine N’-oxide with study covariates

Supplementary Table S35 shows the results of weighted multiple linear regression analysis of log-transformed urinary N‐acetyl‐S‐(4‐hydroxy‐2‐methyl‐2‐buten‐1‐yl)‐L‐cysteine (4HMBeMA) with study covariates

Supplementary Table S27 shows the results of weighted multiple linear regression analysis of log-transformed urinary N-acetyl‐S‐(3‐hydroxypropyl)‐L‐cysteine (3HPMA) with study covariates

Supplementary Table S31 shows the results of weighted multiple linear regression analysis of log-transformed urinary N‐acetyl‐S‐(N‐methylcarbamoyl)‐L‐cysteine (MCaMA) with study covariates

Supplementary Table S20 shows the results of weighted multiple linear regression analysis of log-transformed urinary N‐acetyl‐S‐(2‐carbamoyl‐2‐hydroxyethyl)‐L‐cysteine (2CaHEMA) with study covariates

Supplementary Table S40 shows regression coefficients of time since last smoked a cigarette referenced to “within the last hour” from weighted multiple linear regression analysis of log-transformed urinary biomarkers for each evaluated in this study and the intra-class correlation coefficients from this alternative model

Supplementary Table S32 shows the results of weighted multiple linear regression analysis of log-transformed urinary N‐acetyl‐S‐(benzyl)‐L‐cysteine (BzMA) with study covariates

Supplementary Table S22 shows the results of weighted multiple linear regression analysis of log-transformed urinary N‐acetyl‐S‐(1‐cyano‐2‐hydroxyethyl)‐L‐cysteine (1CyHEMA) with study covariates

Supplementary Table S21 shows the results of weighted multiple linear regression analysis of log-transformed urinary N‐acetyl‐S‐(2‐cyanoethyl)‐L‐cysteine (2CyEMA) with study covariates

Supplementary Table S33 shows the results of weighted multiple linear regression analysis of log-transformed urinary N‐acetyl‐S‐(2‐hydroxyethyl)‐L‐cysteine (2HEMA) with study covariates

Supplementary Table S10 shows the results of weighted multiple linear regression analysis of log-transformed urinary anabasine with study covariates

Supplementary Table S42 shows intra-class correlation coefficients resulting from the full mixed model analysis for all analytes and from the mixed model analysis when no covariates were included in the model.

AbstractBackground: Biomarkers of exposure are tools for understanding the impact of tobacco use on health outcomes if confounders like demographics, use behavior, biological half-life, and other sources of exposure are accounted for in the analysis. Methods: We performed multiple regression analysis of longitudinal measures of urinary biomarkers of alkaloids, tobacco-specific nitrosamines, polycyclic aromatic hydrocarbons, volatile organic compounds (VOC), and metals to examine the sample-to-sample consistency in Waves 1 and 2 of the Population Assessment of Tobacco and Health (PATH) Study including demographic characteristics and use behavior variables of persons who smoked exclusively. Regression coefficients, within- and between-person variance, and intra-class correlation coefficients (ICC) were compared with biomarker smoking/nonsmoking population mean ratios and biological half-lives. Results: Most biomarkers were similarly associated with sex, age, race/ethnicity, and product use behavior. The biomarkers with larger smoking/nonsmoking population mean ratios had greater regression coefficients related to recency of exposure. For VOC and alkaloid metabolites, longer biological half-life was associated with lower within-person variance. For each chemical class studied, there were biomarkers that demonstrated good ICCs. Conclusions: For most of the biomarkers of exposure reported in the PATH Study, for people who smoke cigarettes exclusively, associations are similar between urinary biomarkers of exposure and demographic and use behavior covariates. Biomarkers of exposure within-subject consistency is likely associated with nontobacco sources of exposure and biological half-life. Impact: Biomarkers measured in the PATH Study provide consistent sample-to-sample measures from which to investigate the association of adverse health outcomes with the characteristics of cigarettes and their use.

Supplementary Table S30 shows the results of weighted multiple linear regression analysis of log-transformed urinary phenylglyoxylic acid (PhGA) with study covariates

Supplementary Table S12 shows the results of weighted multiple linear regression analysis of log-transformed urinary 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) with study covariates

Supplementary Table S6 shows the results of weighted multiple linear regression analysis of log-transformed urinary trans-3’-hydroxycotinine with study covariates

Entity linking (EL) aims to find entities that the textual mentions refer to from a knowledge base (KB). The performance of current distantly supervised EL methods is not satisfactory under the condition of low-quality candidate generation. In this paper, we consider the scenario where multiple KBs are available, and for each KB, there is an EL model corresponding to it. We propose the selection consistency constraint (SCC), that is, for one sample, the entities selected from multiple KBs should be consistent if these selections are all correct. In this work, we aim to utilize the SCC to improve the performance of each EL model (not the combination of multiple EL models) under low-quality candidate generation. Specifically, we define an SCC model from two different aspects: minimizing probability and upper bound, which are used to introduce the SCC into the training of EL models. The experimental results show that our method, jointly training multiple EL models with the SCC model, outperforms the baseline which trains multiple EL models separately, and it has low cost.

课堂教学质量提升是一项持续性的系统工程,需要整体协同着力.分析了当前课堂教学中存在"学为中心"理念落地不实、教与学能力成长催生不力、教与学诊断咨询不足及教管学关系和谐不够等问题.提出课堂教学质量提升的策略,即理清教学共生互动的依存关系,强化研教促学,促进课堂教学机制由"教为中心"向"教学共生"转变;探求师生实质成长的内在动因,实化导教助学,促进教学能力培养由"以督为主"向"督导并行"转变;剖析教学过程建构的底层逻辑,深化教学观察,促进课堂评估范式从"授课考评"向"教学诊断"转变;挖掘质量持续提升的外部动力,优化教学环境,促进质量保障定位由"监控为主"向"服务为要"转变,进而持续提高课堂教学质量.