During pregnancy, recreational drug-using women stop taking ecstasy (3,4-methylenedioxy-N-methylamphetamine) and reduce alcohol consumption but continue to smoke tobacco and cannabis

While recreational drug use in UK women is prevalent, to date there is little prospective data on patterns of drug use in recreational drug-using women immediately before and during pregnancy. A total of 121 participants from a wide range of backgrounds were recruited to take part in the longitudinal Development and Infancy Study (DAISY) study of prenatal drug use and outcomes. Eighty-six of the women were interviewed prospectively while pregnant and/or soon after their infant was born. Participants reported on use immediately before and during pregnancy and on use over their lifetime. Levels of lifetime drug use of the women recruited were high, with women reporting having used at least four different illegal drugs over their lifetime. Most users of cocaine, 3,4-methylenedioxy-N-methylamphetamine (MDMA) and other stimulants stopped using these by the second trimester and levels of use were low. However, in pregnancy, 64% of the sample continued to use alcohol, 46% tobacco and 48% cannabis. While the level of alcohol use reduced substantially, average tobacco and cannabis levels tended to be sustained at pre-pregnancy levels even into the third trimester (50 cigarettes and/or 11 joints per week). In sum, while the use of ‘party drugs’ and alcohol seems to reduce, levels of tobacco and cannabis use are likely to be sustained throughout pregnancy. The data provide polydrug profiles that can form the basis for the development of more realistic animal models.

Distress Tolerance among Students Referred for Treatment Following Violation of Campus Cannabis Use Policy: Relations to Use, Problems, and Motivation

Students referred to treatment after violating campus drug policies represent a high-risk group. Identification of factors related to these students’ cannabis use could inform prevention and treatment efforts. Distress tolerance (DT) is negatively related to substance-related behaviors and may be related to high-risk cannabis use vulnerability factors that can impact treatment outcome. Thus, the current study tested whether DT was related to cannabis use frequency, cannabis-related problems, and motivation to change cannabis use among 88 students referred for treatment after violating campus cannabis policies. DT was robustly, negatively related to cannabis use and related problems. DT was also significantly, negatively correlated with coping, conformity, and expansion motives. DT was directly and indirectly related to cannabis problems via coping (not conformity or expansion) motives. Motives did not mediate the relation of DT to cannabis use frequency. DT may be an important target in treatment with students who violate campus cannabis policies.

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.