Obesogenic Environmental Influences on Young Adults: Evidence From Randomized Dormitory Assignment

This study utilizes a natural experiment—conditionally random dormitory assignments of first-year US college students—to investigate the influence of obesogenic environmental factors in explaining changes in weight and exercise behavior during the 2009–2010 academic year. The design addresses potential selection biases resulting from the likelihood that individuals sort into built environments that match their preferences for exercise and healthy eating. We find some evidence that the food environment, specifically access to campus dining, significantly affected the weight of female students in our study. Females assigned to dormitories where the nearest campus dining hall was closed on the weekends gained about 1 lb less over the course of the year than females assigned to dormitories near dining halls that were open 7 days a week. We also find some evidence that female who lived in close proximity to a grocery store gained less weight over the course of the year. Finally, females who lived closer to campus gym reported more frequent exercise over the course of the year. We do not find significant effects of the built environment on weight changes of males in our sample, but we are cautious to draw strong conclusions from this because the male weight change in our sample was quite small

A Dynamic Quantitative Microbial Risk Assessment for Norovirus in Potable Reuse System

This study describes the results of a dynamic quantitative microbial risk assessment (QMRA) for norovirus (NoV) that was used to evaluate the relative significance of foodborne, person-to-person, and person-to-sewage-to-person transmission pathways. This last pathway was incorporated into simulated potable reuse systems to evaluate the adequacy of typical treatment trains, operational conditions, and regulatory frameworks. The results confirm that secondary and foodborne transmission dominate the overall risk calculation and that waterborne NoV likely contributes no appreciable public health risk, at least in the scenarios modeled in this study. De facto reuse with an environmental buffer storage time of at least 30 days was comparable or even superior to direct potable reuse (DPR) when compound failures during advanced treatment were considered in the model. Except during these low-probability failure events, DPR generally remained below the 10−4 annual risk benchmark for drinking water. Based on system feedback and the time-dependent pathogen load to the community\u27s raw sewage, this model estimated median raw wastewater NoV concentrations of 107–108 genome copies per liter (gc/L), which is consistent with high-end estimates in recent literature

Automatic Extraction of Narrative Structure from Long Form Text

Automatic understanding of stories is a long-time goal of artificial intelligence and natural language processing research communities. Stories literally explain the human experience. Understanding our stories promotes the understanding of both individuals and groups of people; various cultures, societies, families, organizations, governments, and corporations, to name a few. People use stories to share information. Stories are told –by narrators– in linguistic bundles of words called narratives. My work has given computers awareness of narrative structure. Specifically, where are the boundaries of a narrative in a text. This is the task of determining where a narrative begins and ends, a non-trivial task, because people rarely tell one story at a time. People don’t specifically announce when we are starting or stopping our stories: We interrupt each other. We tell stories within stories. Before my work, computers had no awareness of narrative boundaries, essentially where stories begin and end. My programs can extract narrative boundaries from novels and short stories with an F1 of 0.65. Before this I worked on teaching computers to identify which paragraphs of text have story content, with an F1 of 0.75 (which is state of the art). Additionally, I have taught computers to identify the narrative point of view (POV; how the narrator identifies themselves) and diegesis (how involved in the story’s action is the narrator) with F1 of over 0.90 for both narrative characteristics. For the narrative POV, diegesis, and narrative level extractors I ran annotation studies, with high agreement, that allowed me to teach computational models to identify structural elements of narrative through supervised machine learning. My work has given computers the ability to find where stories begin and end in raw text. This allows for further, automatic analysis, like extraction of plot, intent, event causality, and event coreference. These tasks are impossible when the computer can’t distinguish between which stories are told in what spans of text. There are two key contributions in my work: 1) my identification of features that accurately extract elements of narrative structure and 2) the gold-standard data and reports generated from running annotation studies on identifying narrative structure