Internet-Initiated Sexual Assault Among U.S. Adolescents Reported in Newspapers, 1996–2007

This article provides an assessment over time of the incidence of newspaper-reported Internet-initiated sexual assaults among U.S. adolescents undergoing adjudication from 1996 to 2007. Of 812 newspaper reports of adjudicated Internet-initiated sexual assault, most (79.2%) victims were female, and the median age was 14 years. The incidence rate of these reports increased over the 12-year period for females but remained steady for males. The frequency of these assaults was much less than reported for other types of sexual assaults in this age group. These estimates hopefully will assist in a greater understanding of these assaults, aid in interventions to decrease their occurrence, and guide effective policymaking that will reduce all types of sexual assault among adolescents.     

Screens and teens with migraines: visually sensitive learners in contemporary digital classrooms

Technology-rich school classrooms incorporate digital media in the form of computers and interactive whiteboards into the visual learning environment. Whilst evidence-based research shows use of technology improves academic outcomes for high school students in general, there are limited data available on the consequences of digital media use for high school students with migraine. This article highlights the historical issues with light-emitting media, the physical parameters that are changed by adoption of these digital media into the classroom and some of the adverse effects caused by visual light stimulation. The article concludes by calling for further social research to better understand adjustments needed by students with migraine in the digital media classroom, and the policies needed to support image parameter guidelines for schools. In this article, the term visual light sensitivity refers to any student’s abnormal sensitivity to optically sighted light leading to negative responses, including that of migraine.     

Trajectory inference and parameter estimation in stochastic models with temporally aggregated data

Stochastic models are of fundamental importance in many scientific and engineering applications. For example, stochastic models provide valuable insights into the causes and consequences of intra-cellular fluctuations and inter-cellular heterogeneity in molecular biology. The chemical master equation can be used to model intra-cellular stochasticity in living cells, but analytical solutions are rare and numerical simulations are computationally expensive. Inference of system trajectories and estimation of model parameters from observed data are important tasks and are even more challenging. Here, we consider the case where the observed data are aggregated over time. Aggregation of data over time is required in studies of single cell gene expression using a luciferase reporter, where the emitted light can be very faint and is therefore collected for several minutes for each observation. We show how an existing approach to inference based on the linear noise approximation (LNA) can be generalised to the case of temporally aggregated data. We provide a Kalman filter (KF) algorithm which can be combined with the LNA to carry out inference of system variable trajectories and estimation of model parameters. We apply and evaluate our method on both synthetic and real data scenarios and show that it is able to accurately infer the posterior distribution of model parameters in these examples. We demonstrate how applying standard KF inference to aggregated data without accounting for aggregation will tend to underestimate the process noise and can lead to biased parameter estimates.