Acute electronic cigarette use: nicotine delivery and subjective effects in regular users

Rationale Electronic cigarettes are becoming increasingly popular among smokers worldwide. Commonly reported reasons for use include the following: to quit smoking, to avoid relapse, to reduce urge to smoke, or as a perceived lower-risk alternative to smoking. Few studies, however, have explored whether electronic cigarettes (e-cigarettes) deliver measurable levels of nicotine to the blood. Objective This study aims to explore in experienced users the effect of using an 18-mg/ml nicotine first-generation e-cigarette on blood nicotine, tobacco withdrawal symptoms, and urge to smoke. Methods Fourteen regular e-cigarette users (three females), who are abstinent from smoking and e-cigarette use for 12 h, each completed a 2.5 h testing session. Blood was sampled, and questionnaires were completed (tobacco-related withdrawal symptoms, urge to smoke, positive and negative subjective effects) at four stages: baseline, 10 puffs, 60 min of ad lib use and a 60-min rest period. Results Complete sets of blood were obtained from seven participants. Plasma nicotine concentration rose significantly from a mean of 0.74 ng/ml at baseline to 6.77 ng/ml 10 min after 10 puffs, reaching a mean maximum of 13.91 ng/ml by the end of the ad lib puffing period. Tobacco-related withdrawal symptoms and urge to smoke were significantly reduced; direct positive effects were strongly endorsed, and there was very low reporting of adverse effects. Conclusions These findings demonstrate reliable blood nicotine delivery after the acute use of this brand/model of e-cigarette in a sample of regular users. Future studies might usefully quantify nicotine delivery in relation to inhalation technique and the relationship with successful smoking cessation/harm reduction.

Uncovering Dependence Clusters and Linchpin Functions

Dependence clusters are (maximal) collections of mutually dependent source code entities according to some dependence relation. Their presence in software complicates many maintenance activities including testing, refactoring, and feature extraction. Despite several studies finding them common in production code, their formation, identification, and overall structure are not well understood, partly because of challenges in approximating true dependences between program entities. Previous research has considered two approximate dependence relations: a fine-grained statement-level relation using control and data dependences from a program’s System Dependence Graph and a coarser relation based on function-level controlflow reachability. In principal, the first is more expensive and more precise than the second. Using a collection of twenty programs, we present an empirical investigation of the clusters identified by these two approaches. In support of the analysis, we consider hybrid cluster types that works at the coarser function-level but is based on the higher-precision statement-level dependences. The three types of clusters are compared based on their slice sets using two clustering metrics. We also perform extensive analysis of the programs to identify linchpin functions – functions primarily responsible for holding a cluster together. Results include evidence that the less expensive, coarser approaches can often be used as e�ective proxies for the more expensive, finer-grained approaches. Finally, the linchpin analysis shows that linchpin functions can be e�ectively and automatically identified.