Undesirable Effects of Working While in College: Work-School Conflict, Substance Use, and Health.

Inter-role conflict occurs when the demands of one area of an individual's life interfere with the responsibilities of another area. Although a large body of research exists on the health effects of the inter-role conflict between work and family (work-family conflict or WFC), far less exists on the inter-role conflict between work and school (work-school conflict or WSC). Given that over two-thirds of college students have outside employment while enrolled in classes, and the potentially negative effects of WSC, a further understanding of WSC is an important part of supporting student health and well-being as well as the health and well-being of the workforce. This study analyzed data from 2,055 participants drawn from a Web-based survey of four geographically and ethnically diverse educational institutions across the United States. Participants reported their current employment characteristics as well as their alcohol, marijuana, and cigarette use, depressive symptomatology, and physical health. Participants with higher WSC reported higher levels of substance use and depressive symptomatology and worse physical health. Further analyses revealed the pattern of results to be consistent with the notion that the subjective experience of conflict between work and school created an aversive psychological state, and that state impacted substance use and health.

Variation in BAS-BIS profiles across categories of cigarette use.

Cigarette smoking is a major health concern, especially among college students. Research suggests a number of individual difference variables may be useful for identifying people at risk of becoming smokers and their likelihood of successfully quitting. The current study focuses on individual differences relating to Behavioral Inhibition System sensitivity (BIS) and the fun seeking, reward responsiveness, and drive aspects of Behavioral Approach System sensitivity (BAS). The former relates to mitigation of potential threat, whereas the latter three relate to different motivations for approach. Noting that existing literature suggests the considerations influencing whether a person experiments with cigarettes differ from those influencing who becomes a habitual smoker which in turn differ from those influencing whether a person quits smoking, we hypothesized that never smokers, experimenters, smokers, and former smokers would differ from each other on BIS, fun seeking, reward responsiveness, and drive in predictable ways. Moreover, we predicted these groups would differ from each other in terms of member profiles across these four variables. We assessed these predictions in a sample of college students from geographically diverse institutions within the United States (N=1840). The profile for never smokers was characterized by high BIS and low fun seeking, that of experimenters by moderately high BIS, high fun seeking, and moderate reward responsiveness, and that of former smokers by moderate BIS, high fun seeking, high reward responsiveness, and high drive. Contrary to expectations, current smokers were low on all four of these characteristics.

Inferring gene regulatory networks from asynchronous microarray data with AIRnet.

BACKGROUND: Modern approaches to treating genetic disorders, cancers and even epidemics rely on a detailed understanding of the underlying gene signaling network. Previous work has used time series microarray data to infer gene signaling networks given a large number of accurate time series samples. Microarray data available for many biological experiments is limited to a small number of arrays with little or no time series guarantees. When several samples are averaged to examine differences in mean value between a diseased and normal state, information from individual samples that could indicate a gene relationship can be lost. RESULTS: Asynchronous Inference of Regulatory Networks (AIRnet) provides gene signaling network inference using more practical assumptions about the microarray data. By learning correlation patterns for the changes in microarray values from all pairs of samples, accurate network reconstructions can be performed with data that is normally available in microarray experiments. CONCLUSIONS: By focussing on the changes between microarray samples, instead of absolute values, increased information can be gleaned from expression data.