Characterizing Anti-Vaping Posts for Effective Communication on Instagram Using Multimodal Deep Learning.

INTRODUCTION: Instagram is a popular social networking platform for sharing photos with a large proportion of youth and young adult users. We aim to identify key features in anti-vaping Instagram image posts associated with high social media user engagement by artificial intelligence. AIMS AND METHODS: We collected 8972 anti-vaping Instagram image posts and hand-coded 2200 Instagram images to identify nine image features such as warning signs and person-shown vaping. We utilized a deep-learning model, the OpenAI: contrastive language-image pre-training with ViT-B/32 as the backbone and a 5-fold cross-validation model evaluation, to extract similar features from the Instagram image and further trained logistic regression models for multilabel classification. Latent Dirichlet Allocation model and Valence Aware Dictionary and sEntiment Reasoner were used to extract the topics and sentiment from the captions. Negative binomial regression models were applied to identify features associated with the likes and comments count of posts. RESULTS: Several features identified in anti-vaping Instagram image posts were significantly associated with high social media user engagement (likes or comments), such as educational warnings and warning signs. Instagram posts with captions about health risks associated with vaping received significantly more likes or comments than those about help quitting smoking or vaping. Compared to the model based on 2200 hand-coded Instagram image posts, more significant features have been identified from 8972 AI-labeled Instagram image posts. CONCLUSION: Features identified from anti-vaping Instagram image posts will provide a potentially effective way to communicate with the public about the health effects of e-cigarette use. IMPLICATIONS: Considering the increasing popularity of social media and the current vaping epidemic, especially among youth and young adults, it becomes necessary to understand e-cigarette-related content on social media. Although pro-vaping messages dominate social media, anti-vaping messages are limited and often have low user engagement. Using advanced deep-learning and statistical models, we identified several features in anti-vaping Instagram image posts significantly associated with high user engagement. Our findings provide a potential approach to effectively communicate with the public about the health risks of vaping to protect public health.

Impact of the FDA flavour enforcement policy on flavoured electronic cigarette use behaviour changes.

INTRODUCTION: This study aims to investigate electronic cigarette (e-cigarette) use behaviour changes after the implementation of the US Food and Drug Administration (FDA) restriction on the sale of all unauthorised flavoured cartridge-based e-cigarettes other than tobacco and menthol flavour on 6 February 2020, as well as factors associated with these changes. METHODS: Through Amazon's Mechanical Turk service, 3533 current adult flavoured e-cigarette users (who were not exclusive tobacco-flavoured or menthol-flavoured e-cigarette users) were recruited for an online survey from 8 July to 29 July 2021. Multiple logistic regression models were used to identify significant factors associated with quitting e-cigarette use, switching to other flavoured electronic nicotine delivery system (ENDS) products, switching to combustible tobacco products, switching to menthol-flavoured e-cigarettes and switching to tobacco-flavoured e-cigarettes. RESULTS: Resulting from the FDA flavour enforcement policy, the top four e-cigarette use behaviour changes were: (1) switching to other flavoured ENDS products such as the tank system or disposable e-cigarettes (29.24%), (2) switching to menthol-flavoured pod systems (18.09%), (3) switching to combustible tobacco products (14.12%) and (4) switching to tobacco-flavoured pod systems (12.03%). There were 4.9% participants who indicated that they quit e-cigarette use. Overall, multiple factors, especially past 30-day use of certain flavours, were associated with different behaviour changes. CONCLUSIONS: The implementation of the FDA flavour enforcement policy on cartridge-based e-cigarette was associated with significant e-cigarette behaviour changes, with multiple factors being associated with these changes. These results provide important information for future regulations of flavoured e-cigarette products.

Perceptions of Oral Nicotine Pouches on Reddit: Observational Study.

BACKGROUND: Oral nicotine pouches are a new form of tobacco-free nicotine products launched in recent years with a variety of flavors. OBJECTIVE: This study aims to examine the public perceptions and discussions of oral nicotine pouches on Reddit, a popular social media platform for sharing user experiences. METHODS: Between February 15, 2019, and February 12, 2021, a total of 2410 Reddit posts related to oral nicotine pouches were obtained over a 2-year period. After the removal of unrelated or commercial posts, 653 Reddit posts related to oral nicotine pouches remained. Topics and sentiments related to oral nicotine pouches on Reddit were hand coded. RESULTS: The number of Reddit posts related to oral nicotine pouches increased during the study period. Content analysis showed that the most popular topic was "sharing product information and user experience" (366/653, 56%), in which sharing oral nicotine pouch products and user experiences were dominant. The next popular topic was "asking product-related questions" (product properties and product recommendations; 115/653, 17.6%), followed by "quitting nicotine products" such as vaping or smoking through use of oral nicotine pouches or quitting the oral nicotine pouches themselves (83/653, 12.7%) and "discussing oral nicotine pouch-related health" symptoms or concerns related to oral nicotine pouches (74/653, 11.3%). The least popular topic was "legality and permissions" related to oral nicotine pouches (15/653, 2.3%). In addition, a greater number of Reddit posts described positive attitudes compared to negative attitudes toward oral nicotine pouches (354/653, 54.2% vs 101/653, 15.5%; P<.001). CONCLUSIONS: Reddit posts overall had a positive attitude toward oral nicotine pouches and users were actively sharing product and user experiences. Our study provides the first insight on up-to-date oral nicotine pouch discussions on social media.

Role of DNA methylation on the association between physical activity and cardiovascular diseases: results from the longitudinal multi-ethnic study of atherosclerosis (MESA) cohort.

BACKGROUND: The complexity of physical activity (PA) and DNA methylation interaction in the development of cardiovascular disease (CVD) is rarely simultaneously investigated in one study. We examined the role of DNA methylation on the association between PA and CVD. RESULTS: The Multi-Ethnic Study of Atherosclerosis (MESA) cohort Exam 5 data with 1065 participants free of CVD were used for final analysis. The quartile categorical total PA variable was created by activity intensity (METs/week). During a median follow-up of 4.0 years, 69 participants developed CVD. Illumina HumanMethylation450 BeadChip was used to provide genome-wide DNA methylation profiles in purified human monocytes (CD14+). We identified 23 candidate DNA methylation loci to be associated with both PA and CVD. We used the structural equation modeling (SEM) approach to test the complex relationships among multiple variables and the roles of mediators. Three of the 23 identified loci (corresponding to genes VPS13D, PIK3CD and VPS45) remained as significant mediators in the final SEM model along with other covariates. Bridged by the three genes, the 2nd PA quartile (ß = - 0.959; 95%CI: - 1.554 to - 0.449) and the 3rd PA quartile (ß = - 0.944; 95%CI: - 1.628 to - 0.413) showed the greatest inverse associations with CVD development, while the 4th PA quartile had a relatively weaker inverse association (ß = - 0.355; 95%CI: - 0.713 to - 0.124). CONCLUSIONS: The current study is among the first to simultaneously examine the relationships among PA, DNA methylation, and CVD in a large cohort with long-term exposure. We identified three DNA methylation loci bridged the association between PA and CVD. The function of the identified genes warrants further investigation in the pathogenesis of CVD.

Analysis of potential associations of JUUL flavours with health symptoms based on user-generated data from Reddit.

BACKGROUND: The rise of the popular e-cigarette, JUUL, has been partly attributed to various teen-friendly e-liquid flavours offered. However, the possible health risks associated with each e-liquid flavour still remain unclear. This research focuses on the possible associations between JUUL flavours and health symptoms using social media data from Reddit. METHODS: Keyword filtering was used to obtain 5,746 JUUL flavour-related posts and 7927 health symptom-related posts from June 2015 to April 2019 from Reddit. Posts from September 2016 to April 2019 were used to conduct temporal analysis for nine health symptom categories and the 8 JUUL flavours. Finally, associations between the JUUL flavours and health symptom categories were examined on the user level using generalised estimating equation models. RESULTS: According to our temporal analysis, Mango and Mint were the most discussed JUUL flavours on Reddit. Respiratory and throat symptoms were the most discussed health issues together with JUUL on Reddit over time. Respiratory symptoms had potential associations with the Mango, Mint and Fruit JUUL flavours. Digestive symptoms had a potential association with the Crème flavour, psychological symptoms had a potential association with the Cucumber flavour, and cardiovascular symptoms had a potential association with the tobacco flavours. CONCLUSIONS: Mango and Mint were the two most mentioned JUUL flavours on Reddit. Certain JUUL flavours were more likely to be mentioned together with certain categories of health symptoms by the same Reddit users. Our findings could prompt further medical research into the health symptoms associated with different e-liquid flavours.

Home smoking and vaping policies among US adults: results from the Population Assessment of Tobacco and Health (PATH) study, wave 3.

We examined the prevalence of home smoking and vaping restrictions among US adults, and compared home policy differences for smoking and vaping among vapers, smokers, and dual users. Secondary data from the Population Assessment of Tobacco and Health (PATH) Study Wave 3 (2015-2016) with 28,148 adults were analyzed using weighted multivariable logistic regression models that account for complex sampling design to compare differences in home policies among non-users, vapers only, smokers only, and dual users. Compared to never-users, current vapers who were ex-smokers and dual users were more likely to allow home vaping (aOR = 11.06, 95% CI: 8.04-15.21; aOR = 6.44, 95% CI: 5.01-8.28) and smoking (aOR = 1.62, 95% CI: 1.19-2.22; aOR = 3.58, 95% CI: 2.88-4.45). Current smokers were more likely to allow vaping (aOR = 3.53, 95% CI: 3.06-4.06) and smoking (aOR = 4.27, 95% CI: 3.73-4.89) inside the home than never-users. Current vapers who never smoked were more likely to allow vaping inside the home than never-users (aOR = 2.45, 95% CI: 1.53-3.93). Vapers reported much lower rates of vape-free home policies relative to both their smoke-free home policies and to vape-free home policies among smokers. Vapers may be using e-cigarettes in hopes of harm reduction, but interpreting "harm reduction" as safe, thus exposing non-users in their homes to second- and thirdhand aerosols. This underscores the need to healthcare providers to extend intervention with vapers to include implementing vape-free home policies.

Cross-Sectional Association of Lifetime Electronic Cigarette Use with Wheezing and Related Respiratory Symptoms in U.S. Adults.

INTRODUCTION: Electronic cigarette use (vaping) has been found to be associated with respiratory symptoms like wheezing or whistling in the chest. Whether or not lifetime vaping occurrences are associated with wheezing has not yet been investigated. METHODS: Population Assessment of Tobacco and Health (PATH) Study Wave 4 data with 22,233 adults collected from December 2016 to January 2018 were used. The cross-sectional association of lifetime vaping occurrences with wheezing and related respiratory symptoms was examined using multivariable weighted logistic regression models considering the complex sampling design. RESULTS: According to the weighted PATH Wave 4 data, about 89.9% adults never vaped, 3.2% adults vaped one time, 3.2% vaped 2-10 times, 1.3% vaped 11-20 times, 1.1% vaped 21-50 times, 0.4% vaped 51-99 times, and 0.9% vaped 100 or more times in their entire life. Compared to adults who never vaped, adults who vaped 2-10 times had a significantly higher association with ever wheezing (aOR = 1.4, 95% CI: 1.1 to 1.6), past 12-month wheezing (aOR = 1.5, 95% CI: 1.2 to 1.9) and the number of wheezing attacks in the past 12 months (aOR = 1.5, 95% CI: 1.2 to 1.8). Adults who vaped 11-20 times and 100 or more times had similar associations with wheezing as that for adults who vaped 2-10 times. Controlling other tobacco use attenuated the associations. CONCLUSIONS: Lifetime vaping occurrences were found to be associated with some definitions of self-reported wheezing in cross-sectional analyses adjusted for other tobacco use. IMPLICATIONS: Using the cross-sectional PATH Wave 4 data with 22,233 adults, we found significant associations between lifetime vaping occurrences and ever wheezing or whistling in the chest, past 12 months wheezing or whistling in the chest, as well as the number of wheezing attacks in the past 12 months. The study results suggest that larger studies with more precise time frames and measures are needed to further understand possible connections between vaping experimentation and wheezing symptoms that could inform our understanding of the health effects of electronic cigarettes and resultant policy decisions.

Cross-Sectional Association Between Lifetime Use of Electronic Cigarettes With or Without Marijuana and Self-Reported Past 12-Month Respiratory Symptoms as well as Lifetime Respiratory Diseases in U.S. Adults.

INTRODUCTION: The use of electronic cigarettes (vaping), especially with marijuana, has become increasingly popular among adults. AIMS AND METHODS: The Population Assessment of Tobacco and Health study Wave 4 data on 33 606 adult participants who indicated ever using electronic cigarettes were included in the study. By controlling for confounding variables (such as age and smoking history), multivariable weighted logistic regression models were used to examine the cross-sectional association between lifetime e-cigarette use with or without marijuana and self-reported past 12-month respiratory symptoms as well as lifetime respiratory diseases. RESULTS: Compared to adults who never vaped, adults who had ever vaped with marijuana had a significantly higher association with self-reported past 12-month respiratory symptoms but not lifetime respiratory diseases. Compared to adults who had ever vaped without marijuana, adults who had ever vaped at least sometimes with marijuana had a significantly greater risk of having wheezing/whistling in the chest (adjusted odds ratio [aOR] = 1.21, 95% confidence interval [CI]: 1.01, 1.44), chest sounded wheezy during or after exercise (aOR = 1.59, 95% CI: 1.31, 1.93), and had a dry cough at night (aOR = 1.35, 95% CI: 1.16, 1.57), while adults who had ever vaped rarely with marijuana had a significantly greater risk of having wheezing/whistling in the chest (aOR = 1.31, 95% CI: 1.06, 1.61), chest sounded wheezy during or after exercise (aOR = 1.24, 95% CI: 1.01, 1.52), and had a dry cough at night (aOR = 1.24, 95% CI: 1.04, 1.47). CONCLUSIONS: Lifetime e-cigarette use with marijuana is associated with self-reported past 12-month respiratory symptoms in adults. IMPLICATIONS: The use of e-cigarettes with marijuana has become prevalent in recent years. Our cross-sectional study suggests that there may be respiratory health symptoms associated with ever vaping with marijuana that is independent of nicotine vaping, which should raise public awareness of potential health risks associated with the use of e-cigarettes with marijuana. Further longitudinal studies on the respiratory health effects of e-cigarette use with marijuana are warranted.

Use of Electronic Cigarettes and Self-Reported Chronic Obstructive Pulmonary Disease Diagnosis in Adults.

INTRODUCTION: Electronic cigarette (e-cigarette) use (vaping) has increased in recent years. Chronic obstructive pulmonary disease (COPD) is the third leading cause of death associated with smoking. AIMS AND METHODS: Based on 2016 and 2017 Behavioral Risk Factor Surveillance System national survey data on 891 242 adult participants who indicated their smoking and vaping status, the cross-sectional association of vaping with self-reported COPD diagnosis was investigated, using univariable and multivariable weighted logistic regression models. RESULTS: Compared to never users, while dual users showed the highest association with self-reported COPD diagnosis (adjusted odds ratio [aOR] = 4.39; 95% confidence interval [CI] = 3.98 to 4.85), current vapers who were either ex-smokers or never smoked showed significantly higher association with self-reported COPD diagnosis (aOR = 3.24; 95% CI = 2.78 to 3.78 and aOR = 1.47; 95% CI = 1.01 to 2.12, respectively). Current vapers who were ex-smokers showed higher association with self-reported COPD diagnosis than ex-smokers who do not vape (aOR = 1.27; 95% CI = 1.09 to 1.48). Dual users showed higher association with self-reported COPD diagnosis than current smokers who do not vape (aOR = 1.16; 95% CI = 1.05 to 1.27). Ex-smokers showed significantly less association with self-reported COPD diagnosis (aOR = 0.67; 95% CI = 0.64 to 0.71) than current smokers. Current vapers who were either ex-smokers or never smoked had less association with self-reported COPD diagnosis compared to current smokers, with aOR = 0.85 (95% CI = 0.73 to 0.99) and aOR = 0.39 (95% CI = 0.27 to 0.56). CONCLUSIONS: Vaping is significantly associated with self-reported COPD diagnosis in adults, even among vapers who never smoked. Whether there is a benefit for COPD of switching from smoking to vaping requires study of the long-term effects of vaping. IMPLICATIONS: With the increase of e-cigarette use in recent years, the health effects of e-cigarettes need to be investigated. While several studies have examined the association of vaping with respiratory symptoms among adolescents, little is known about the association of vaping with susceptibility to COPD among US adults. Using cross-sectional national survey data in adults, our study showed that vaping was significantly associated with self-reported COPD diagnosis. Although our data did not establish the causal relationship between vaping and self-reported COPD diagnosis, this study raises concerns about the observed association between vaping and self-reported COPD diagnosis.

Cross-Sectional Association Between Exclusive and Concurrent Use of Cigarettes, ENDS, and Cigars, the Three Most Popular Tobacco Products, and Wheezing Symptoms Among U.S. Adults.

INTRODUCTION: This study assessed the association of exclusive and concurrent use of cigarettes, electronic nicotine delivery systems (ENDS), and cigars with ever and past 12-month wheezing symptoms among a nationally representative sample of US adult current tobacco users. METHODS: Cross-sectional data from the Population Assessment of Tobacco and Health (PATH) Study Wave 3 (W3) were used. The weighted prevalence of self-reported ever and past 12-month wheezing symptoms for noncurrent users compared with users of cigarettes, ENDS, cigars, and any combination of these products (polytobacco use of these tobacco products) were presented for 28 082 adults. The cross-sectional association of tobacco use with self-reported wheezing symptoms was assessed using weighted multivariable and ordinal logistic regression with consideration of complex sampling design. RESULTS: Significantly higher odds of ever had wheezing or whistling in the chest at any time in the past were observed among current cigarette (adjusted odds ratio: 2.62, 95% confidence intervals [CI]: 2.35, 2.91), ENDS (1.49, 95% CI: 1.14, 1.95), and polytobacco users (2.67, 95% CI: 2.26, 3.16) compared with noncurrent users. No associations were seen for cigar use. Polytobacco use was associated with a higher odds of ever wheezing when compared with exclusive ENDS (1.61, 95% CI: 1.19, 2.17) and exclusive cigar use (2.87, 95% CI: 1.93, 4.26), but not exclusive use of cigarettes. CONCLUSIONS: Ever wheezing is associated with the use of cigarettes, ENDS, and polytobacco use of cigarettes, ENDS, and/or cigars, but not cigar use. The association of polytobacco use and wheezing appears to be driven by cigarette use. IMPLICATIONS: Cross-sectional associations with ever and past 12-month wheezing symptoms were found to be the strongest among cigarette users, exclusively or in combination. Future longitudinal research is needed to better understand how cigarette use interacts with other tobacco and nicotine products and contributes to respiratory symptoms.

User Perceptions of Different Electronic Cigarette Flavors on Social Media: Observational Study.

BACKGROUND: The number of electronic cigarette (e-cigarette) users has been increasing rapidly in recent years, especially among youth and young adults. More e-cigarette products have become available, including e-liquids with various brands and flavors. Various e-liquid flavors have been frequently discussed by e-cigarette users on social media. OBJECTIVE: This study aimed to examine the longitudinal prevalence of mentions of electronic cigarette liquid (e-liquid) flavors and user perceptions on social media. METHODS: We applied a data-driven approach to analyze the trends and macro-level user sentiments of different e-cigarette flavors on social media. With data collected from web-based stores, e-liquid flavors were classified into categories in a flavor hierarchy based on their ingredients. The e-cigarette-related posts were collected from social media platforms, including Reddit and Twitter, using e-cigarette-related keywords. The temporal trend of mentions of e-liquid flavor categories was compiled using Reddit data from January 2013 to April 2019. Twitter data were analyzed using a sentiment analysis from May to August 2019 to explore the opinions of e-cigarette users toward each flavor category. RESULTS: More than 1000 e-liquid flavors were classified into 7 major flavor categories. The fruit and sweets categories were the 2 most frequently discussed e-liquid flavors on Reddit, contributing to approximately 58% and 15%, respectively, of all flavor-related posts. We showed that mentions of the fruit flavor category had a steady overall upward trend compared with other flavor categories that did not show much change over time. Results from the sentiment analysis demonstrated that most e-liquid flavor categories had significant positive sentiments, except for the beverage and tobacco categories. CONCLUSIONS: The most updated information about the popular e-liquid flavors mentioned on social media was investigated, which showed that the prevalence of mentions of e-liquid flavors and user perceptions on social media were different. Fruit was the most frequently discussed flavor category on social media. Our study provides valuable information for future regulation of flavored e-cigarettes.

A Social Media Study on the Associations of Flavored Electronic Cigarettes With Health Symptoms: Observational Study.

BACKGROUND: In recent years, flavored electronic cigarettes (e-cigarettes) have become popular among teenagers and young adults. Discussions about e-cigarettes and e-cigarette use (vaping) experiences are prevalent online, making social media an ideal resource for understanding the health risks associated with e-cigarette flavors from the users' perspective. OBJECTIVE: This study aimed to investigate the potential associations between electronic cigarette liquid (e-liquid) flavors and the reporting of health symptoms using social media data. METHODS: A dataset consisting of 2.8 million e-cigarette-related posts was collected using keyword filtering from Reddit, a social media platform, from January 2013 to April 2019. Temporal analysis for nine major health symptom categories was used to understand the trend of public concerns related to e-cigarettes. Sentiment analysis was conducted to obtain the proportions of positive and negative sentiment scores for all reported health symptom categories. Topic modeling was applied to reveal the topics related to e-cigarettes and health symptoms. Furthermore, generalized estimating equation (GEE) models were used to quantitatively measure potential associations between e-liquid flavors and the reporting of health symptoms. RESULTS: Temporal analysis showed that the Respiratory category was consistently the most discussed health symptom category among all categories related to e-cigarettes on Reddit, followed by the Throat category. Sentiment analysis showed higher proportions of positive sentiment scores for all reported health symptom categories, except for the Cancer category. Topic modeling conducted on all health-related posts showed that 17 of the top 100 topics were flavor related. GEE models showed different associations between the reporting of health symptoms and e-liquid flavor categories, for example, lower association of the Beverage flavors with Respiratory compared with other flavors and higher association of the Fruit flavors with Cardiovascular than other flavors. CONCLUSIONS: This study identified different potential associations between e-liquid flavors and the reporting of health symptoms using social media data. The results of this study provide valuable information for further investigation of the health effects associated with different e-liquid flavors.

Inbreeding drives maize centromere evolution.

Functional centromeres, the chromosomal sites of spindle attachment during cell division, are marked epigenetically by the centromere-specific histone H3 variant cenH3 and typically contain long stretches of centromere-specific tandem DNA repeats (∼1.8 Mb in maize). In 23 inbreds of domesticated maize chosen to represent the genetic diversity of maize germplasm, partial or nearly complete loss of the tandem DNA repeat CentC precedes 57 independent cenH3 relocation events that result in neocentromere formation. Chromosomal regions with newly acquired cenH3 are colonized by the centromere-specific retrotransposon CR2 at a rate that would result in centromere-sized CR2 clusters in 20,000-95,000 y. Three lines of evidence indicate that CentC loss is linked to inbreeding, including (i) CEN10 of temperate lineages, presumed to have experienced a genetic bottleneck, contain less CentC than their tropical relatives; (ii) strong selection for centromere-linked genes in domesticated maize reduced diversity at seven of the ten maize centromeres to only one or two postdomestication haplotypes; and (iii) the centromere with the largest number of haplotypes in domesticated maize (CEN7) has the highest CentC levels in nearly all domesticated lines. Rare recombinations introduced one (CEN2) or more (CEN5) alternate CEN haplotypes while retaining a single haplotype at domestication loci linked to these centromeres. Taken together, this evidence strongly suggests that inbreeding, favored by postdomestication selection for centromere-linked genes affecting key domestication or agricultural traits, drives replacement of the tandem centromere repeats in maize and other crop plants. Similar forces may act during speciation in natural systems.

Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques.

INTRODUCTION: As one of the most prevalent chronic diseases in the United States, diabetes, especially type 2 diabetes, affects the health of millions of people and puts an enormous financial burden on the US economy. We aimed to develop predictive models to identify risk factors for type 2 diabetes, which could help facilitate early diagnosis and intervention and also reduce medical costs. METHODS: We analyzed cross-sectional data on 138,146 participants, including 20,467 with type 2 diabetes, from the 2014 Behavioral Risk Factor Surveillance System. We built several machine learning models for predicting type 2 diabetes, including support vector machine, decision tree, logistic regression, random forest, neural network, and Gaussian Naive Bayes classifiers. We used univariable and multivariable weighted logistic regression models to investigate the associations of potential risk factors with type 2 diabetes. RESULTS: All predictive models for type 2 diabetes achieved a high area under the curve (AUC), ranging from 0.7182 to 0.7949. Although the neural network model had the highest accuracy (82.4%), specificity (90.2%), and AUC (0.7949), the decision tree model had the highest sensitivity (51.6%) for type 2 diabetes. We found that people who slept 9 or more hours per day (adjusted odds ratio [aOR] = 1.13, 95% confidence interval [CI], 1.03-1.25) or had checkup frequency of less than 1 year (aOR = 2.31, 95% CI, 1.86-2.85) had higher risk for type 2 diabetes. CONCLUSION: Of the 8 predictive models, the neural network model gave the best model performance with the highest AUC value; however, the decision tree model is preferred for initial screening for type 2 diabetes because it had the highest sensitivity and, therefore, detection rate. We confirmed previously reported risk factors and also identified sleeping time and frequency of checkup as 2 new potential risk factors related to type 2 diabetes.

Bon-EV: an improved multiple testing procedure for controlling false discovery rates.

BACKGROUND: Stability of multiple testing procedures, defined as the standard deviation of total number of discoveries, can be used as an indicator of variability of multiple testing procedures. Improving stability of multiple testing procedures can help to increase the consistency of findings from replicated experiments. Benjamini-Hochberg's and Storey's q-value procedures are two commonly used multiple testing procedures for controlling false discoveries in genomic studies. Storey's q-value procedure has higher power and lower stability than Benjamini-Hochberg's procedure. To improve upon the stability of Storey's q-value procedure and maintain its high power in genomic data analysis, we propose a new multiple testing procedure, named Bon-EV, to control false discovery rate (FDR) based on Bonferroni's approach. RESULTS: Simulation studies show that our proposed Bon-EV procedure can maintain the high power of the Storey's q-value procedure and also result in better FDR control and higher stability than Storey's q-value procedure for samples of large size(30 in each group) and medium size (15 in each group) for either independent, somewhat correlated, or highly correlated test statistics. When sample size is small (5 in each group), our proposed Bon-EV procedure has performance between the Benjamini-Hochberg procedure and the Storey's q-value procedure. Examples using RNA-Seq data show that the Bon-EV procedure has higher stability than the Storey's q-value procedure while maintaining equivalent power, and higher power than the Benjamini-Hochberg's procedure. CONCLUSIONS: For medium or large sample sizes, the Bon-EV procedure has improved FDR control and stability compared with the Storey's q-value procedure and improved power compared with the Benjamini-Hochberg procedure. The Bon-EV multiple testing procedure is available as the BonEV package in R for download at https://CRAN.R-project.org/package=BonEV .

An evaluation of statistical methods for DNA methylation microarray data analysis.

BACKGROUND: DNA methylation offers an excellent example for elucidating how epigenetic information affects gene expression. ß values and M values are commonly used to quantify DNA methylation. Statistical methods applicable to DNA methylation data analysis span a number of approaches such as Wilcoxon rank sum test, t-test, Kolmogorov-Smirnov test, permutation test, empirical Bayes method, and bump hunting method. Nonetheless, selection of an optimal statistical method can be challenging when different methods generate inconsistent results from the same data set. RESULTS: We compared six statistical approaches relevant to DNA methylation microarray analysis in terms of false discovery rate control, statistical power, and stability through simulation studies and real data examples. Observable differences were noticed between ß values and M values only when methylation levels were correlated across CpG loci. For small sample size (n=3 or 6 in each group), both the empirical Bayes and bump hunting methods showed appropriate FDR control and the highest power when methylation levels across CpG loci were independent. Only the bump hunting method showed appropriate FDR control and the highest power when methylation levels across CpG sites were correlated. For medium (n=12 in each group) and large sample sizes (n=24 in each group), all methods compared had similar power, except for the permutation test whenever the proportion of differentially methylated loci was low. For all sample sizes, the bump hunting method had the lowest stability in terms of standard deviation of total discoveries whenever the proportion of differentially methylated loci was large. The apparent test power comparisons based on raw p-values from DNA methylation studies on ovarian cancer and rheumatoid arthritis provided results as consistent as those obtained in the simulation studies. Overall, these results provide guidance for optimal statistical methods selection under different scenarios. CONCLUSIONS: For DNA methylation studies with small sample size, the bump hunting method and the empirical Bayes method are recommended when DNA methylation levels across CpG loci are independent, while only the bump hunting method is recommended when DNA methylation levels are correlated across CpG loci. All methods are acceptable for medium or large sample sizes.

Developmental regulation of CYCA2s contributes to tissue-specific proliferation in Arabidopsis.

In multicellular organisms, morphogenesis relies on a strict coordination in time and space of cell proliferation and differentiation. In contrast to animals, plant development displays continuous organ formation and adaptive growth responses during their lifespan relying on a tight coordination of cell proliferation. How developmental signals interact with the plant cell-cycle machinery is largely unknown. Here, we characterize plant A2-type cyclins, a small gene family of mitotic cyclins, and show how they contribute to the fine-tuning of local proliferation during plant development. Moreover, the timely repression of CYCA2;3 expression in newly formed guard cells is shown to require the stomatal transcription factors FOUR LIPS/MYB124 and MYB88, providing a direct link between developmental programming and cell-cycle exit in plants. Thus, transcriptional downregulation of CYCA2s represents a critical mechanism to coordinate proliferation during plant development.

Public perceptions of synthetic cooling agents in electronic cigarettes on Twitter.

Amid a potential menthol ban, electronic cigarette (e-cigarette) companies are incorporating synthetic cooling agents like WS-3 and WS-23 to replicate menthol/mint sensations. This study examines public views on synthetic cooling agents in e-cigarettes via Twitter data. From May 2021 to March 2023, we used Twitter Streaming Application Programming Interface (API), to collect tweets related to synthetic cooling agents with keywords such as 'WS-23,' 'ice,' and 'frozen.' The deep learning RoBERTa (Robustly Optimized BERT-Pretraining Approach) model that can be optimized for contextual language understanding was used to classify attitudes expressed in tweets about synthetic cooling agents and identify e-cigarette users. The BERTopic (a topic modeling technique that leverages Bidirectional Encoder Representations from Transformers) deep-learning model, specializing in extracting and clustering topics from large texts, identified major topics of positive and negative tweets. Two proportion Z-tests were used to compare the proportions of positive and negative attitudes between e-cigarette users (vapers) and non-e-cigarette-users (non-vapers). Of 6,940,065 e-cigarettes-related tweets, 5,788 were non-commercial tweets related to synthetic cooling agents. The longitudinal trend analysis showed a clear upward trend in discussions. Vapers posted most of the tweets (73.05%, 4,228/5,788). Nearly half (47.87%, 2,771/5,788) held a positive attitude toward synthetic cooling agents, which is significantly higher than those with a negative attitude (19.92%,1,153/5,788) with a P-value < 0.0001. The likelihood of vapers expressing positive attitudes (60.17%, 2,544/4,228) was significantly higher (P < 0.0001) than that of non-vapers (14.55%, 227/1,560). Conversely, negative attitudes from non-vapers (30%, 468/1,560) were significantly (P < 0.0001) higher than vapers (16.2%, 685/4,228). Prevalent topics from positive tweets included "enjoyment of specific vape flavors," "preference for lush ice vapes," and "liking of minty/icy feelings." Major topics from negative tweets included "disliking certain vape flavors" and "dislike of others vaping around them." On Twitter, vapers are more likely to have a positive attitude toward synthetic cooling agents than non-vapers. Our study provides important insights into how the public perceives synthetic cooling agents in e-cigarettes. These insights are crucial for shaping future U.S. Food and Drug Administration (FDA) regulations aimed at safeguarding public health.

Social Network Analysis of e-Cigarette-Related Social Media Influencers on Twitter/X: Observational Study.

BACKGROUND: An e-cigarette uses a battery to heat a liquid that generates an aerosol for consumers to inhale. e-Cigarette use (vaping) has been associated with respiratory disease, cardiovascular disease, and cognitive functions. Recently, vaping has become increasingly popular, especially among youth and young adults. OBJECTIVE: The aim of this study was to understand the social networks of Twitter (now rebranded as X) influencers related to e-cigarettes through social network analysis. METHODS: Through the Twitter streaming application programming interface, we identified 3,617,766 unique Twitter accounts posting e-cigarette-related tweets from May 3, 2021, to June 10, 2022. Among these, we identified 33 e-cigarette influencers. The followers of these influencers were grouped according to whether or not they post about e-cigarettes themselves; specifically, the former group was defined as having posted at least five e-cigarette-related tweets in the past year, whereas the latter group was defined as followers that had not posted any e-cigarette-related tweets in the past 3 years. We randomly sampled 100 user accounts among each group of e-cigarette influencer followers and created corresponding social networks for each e-cigarette influencer. We compared various network measures (eg, clustering coefficient) between the networks of the two follower groups. RESULTS: Major topics from e-cigarette-related tweets posted by the 33 e-cigarette influencers included advocating against vaping policy (48.0%), vaping as a method to quit smoking (28.0%), and vaping product promotion (24.0%). The follower networks of these 33 influencers showed more connections for those who also post about e-cigarettes than for followers who do not post about e-cigarettes, with significantly higher clustering coefficients for the former group (0.398 vs 0.098; P=.005). Further, networks of followers who post about e-cigarettes exhibited substantially more incoming and outgoing connections than those of followers who do not post about e-cigarettes, with significantly higher in-degree (0.273 vs 0.084; P=.02), closeness (0.452 vs 0.137; P=.04), betweenness (0.036 vs 0.008; P=.001), and out-of-degree (0.097 vs 0.014; P=.02) centrality values. The followers who post about e-cigarettes also had a significantly (P<.001) higher number of followers (n=322) than that of followers who do not post about e-cigarettes (n=201). The number of tweets in the networks of followers who post about e-cigarettes was significantly higher than that in the networks of followers who do not post about e-cigarettes (93 vs 43; P<.001). Two major topics discussed in the networks of followers who post about e-cigarettes included promoting e-cigarette products or vaping activity (55.7%) and vaping being a help for smoking cessation and harm reduction (44.3%). CONCLUSIONS: Followers of e-cigarette influencers who also post about e-cigarettes have more closely connected networks than those of followers who do not themselves post about e-cigarettes. These findings provide a potentially practical intervention approach for future antivaping campaigns.