Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads

Technology has dramatically changed the way criminals conduct their illicit activities. Specifically, the Internet has become a major facilitator of online human sex trafficking. Traffickers are using these technologies to market their victims which presents new challenges for efforts to combat sex trafficking. This study used knowledge management principles and natural language processing methods to develop an improved ontology of online sex trafficking ads. The language of these ads is constantly evolving; therefore, this study explored the role of a new type of indicator, emoticons, to the ontology of human trafficking indicators

A statistical analysis of time trends in atmospheric ethane

Ethane is the most abundant non-methane hydrocarbon in the Earth's atmosphere and an important precursor of tropospheric ozone through various chemical pathways. Ethane is also an indirect greenhouse gas (global warming potential), influencing the atmospheric lifetime of methane through the consumption of the hydroxyl radical (OH). Understanding the development of trends and identifying trend reversals in atmospheric ethane is therefore crucial. Our dataset consists of four series of daily ethane columns obtained from ground-based FTIR measurements. As many other decadal time series, our data are characterized by autocorrelation, heteroskedasticity, and seasonal effects. Additionally, missing observations due to instrument failure or unfavorable measurement conditions are common in such series. The goal of this paper is therefore to analyze trends in atmospheric ethane with statistical tools that correctly address these data features. We present selected methods designed for the analysis of time trends and trend reversals. We consider bootstrap inference on broken linear trends and smoothly varying nonlinear trends. In particular, for the broken trend model, we propose a bootstrap method for inference on the break location and the corresponding changes in slope. For the smooth trend model we construct simultaneous confidence bands around the nonparametrically estimated trend. Our autoregressive wild bootstrap approach, combined with a seasonal filter, is able to handle all issues mentioned above

Crossreactive T Cells Spotlight the Germline Rules for αβ T Cell-Receptor Interactions with MHC Molecules

SummaryTo test whether highly crossreactive αβ T cell receptors (TCRs) produced during limited negative selection best illustrate evolutionarily conserved interactions between TCR and major histocompatibility complex (MHC) molecules, we solved the structures of three TCRs bound to the same MHC II peptide (IAb-3K). The TCRs had similar affinities for IAb-3K but varied from noncrossreactive to extremely crossreactive with other peptides and MHCs. Crossreactivity correlated with a shrinking, increasingly hydrophobic TCR-ligand interface, involving fewer TCR amino acids. A few CDR1 and CDR2 amino acids dominated the most crossreactive TCR interface with MHC, including Vβ8 48Y and 54E and Vα4 29Y, arranged to impose the familiar diagonal orientation of TCR on MHC. These interactions contribute to MHC binding by other TCRs using related V regions, but not usually so dominantly. These data show that crossreactive TCRs can spotlight the evolutionarily conserved features of TCR-MHC interactions and that these interactions impose the diagonal docking of TCRs on MHC