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Many of the problems associated with alcohol occur after a single drinking event (e.g. drink driving, assault). These acute alcohol problems have a huge global impact and account for a large percentage of unintentional and intentional injuries in the world. Nonetheless, alcohol research and preventive interventions rarely focus on drinking at the event-level since drinking events are complex, dynamic, and methodologically challenging to observe. This exploratory study provides an example of how event-level data may be collected, analyzed, and interpreted. The drinking behavior of twenty undergraduate students enrolled at a large Midwestern public university was observed during a single bar crawl event that is organized by students annually. Alcohol use was monitored with transdermal alcohol devices coupled with ecological momentary assessments and geospatial data. "Small N, Big Data" studies have the potential to advance health behavior theory and to guide real-time interventions. However, such studies generate large amounts of within subject data that can be challenging to analyze and present. This study examined how to visually display event-level data and also explored the relationship between some basic indicators and alcohol consumption.

In this proof of concept study, chemical threat agent (CTA) samples were classified to their sources with accuracies of 87-100% by applying a random forest statistical pattern recognition technique to analytical data acquired by comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometric detection (GC × GC-TOFMS). Three organophosphate pesticides, chlorpyrifos, dichlorvos, and dicrotophos, were used as the model CTAs, with data collected for 4-6 sources per CTA and 7-10 replicate analyses per source. The analytical data were also evaluated to determine tentatively identified chemical attribution signatures for the CTAs by comparing samples from different sources according to either the presence/absence of peaks or the relative responses of peaks. These results demonstrate that GC × GC-TOFMS analysis in combination with a random forest technique can be useful in sample classification and signature identification for pesticides. Furthermore, the results suggest that this combination of analytical chemistry and statistical approaches can be applied to forensic analysis of other chemicals for similar purposes.