Bayesian Inference of Social Norms as Shared Constraints on Behavior

People act upon their desires, but often, also act in adherence to implicit social norms. How do people infer these unstated social norms from others' behavior, especially in novel social contexts? We propose that laypeople have intuitive theories of social norms as behavioral constraints shared across different agents in the same social context. We formalize inference of norms using a Bayesian Theory of Mind approach, and show that this computational approach provides excellent predictions of how people infer norms in two scenarios. Our results suggest that people separate the influence of norms and individual desires on others' actions, and have implications for modelling generalizations of hidden causes of behavior.Comment: 7 pages, 5 figures, to appear in CogSci 2019, code available at https://github.com/ztangent/norms-cogsci1

Ion-mediated RNA structural collapse: effect of spatial confinement

RNAs are negatively charged molecules residing in macromolecular crowding cellular environments. Macromolecular confinement can influence the ion effects in RNA folding. In this work, using the recently developed tightly bound ion model for ion fluctuation and correlation, we investigate the confinement effect on the ion-mediated RNA structural collapse for a simple model system. We found that, for both Na$^+$ and Mg$^{2+}$, ion efficiencies in mediating structural collapse/folding are significantly enhanced by the structural confinement. Such an enhancement in the ion efficiency is attributed to the decreased electrostatic free energy difference between the compact conformation ensemble and the (restricted) extended conformation ensemble due to the spatial restriction.Comment: 22 pages, 5 figure

Targeting PH domain proteins for cancer therapy

Targeted therapy has been one of the most promising treatment options for cancer during the past decade. Discoveries of potent and selective small molecule inhibitors are critical to new and promising targeted therapy. Pleckstrin Homology (PH) domain proteins are one of the biggest protein families in the human proteome. However, no drugs have been achieved to the late development stages, let alone getting to the market. Thus, a deeper understanding of this protein family is required and there is an urgent need to develop novel small molecule compounds targeting these proteins. Studies of PH domains began around two decades ago and a lot of efforts have been focused on their structures and functions. However, not much is known about their role in cancers, except a few proteins such as AKT. In order to delineate the roles of PH domain proteins in cancers, we performed a comprehensive analysis of 313 PH domain proteins using 13 types of most common cancers in TCGA. From this analysis, we identified the most frequently upregulated and mutated PH domain proteins. Interestingly, we found Tiam1, a guanine nucleotide exchange factor (GEF) specific for Rac1 activation, was overexpressed in several cancers, particularly neuroendocrine prostate cancer. Targeting PH domain proteins remains to be a significant challenge for multiple reasons. First, the binding pockets of most PH domain proteins are unknown due to lacking of PH-PIPs complex crystal structures. Second, these binding pockets are positively charged, which makes it really difficult to design small molecule inhibitors targeting these sites. In order to address these issues, we performed structural sequence alignment of available PH domain structures to identify conserved residues. Also, ensemble docking was performed in order to address the flexibility of the proteins. Through these efforts, we identified two scaffolds as Tiam1 small molecule inhibitors. These inhibitors showed binding affinity to the PH domain using surface plasmon resonance (SPR) assay and inhibition of Rac1 activation in prostate cancer cells. Also, these compounds inhibited prostate cancer cell proliferation and migration in vitro

Online-offline activities and game-playing behaviors of avatars in a massive multiplayer online role-playing game

Massive multiplayer online role-playing games (MMORPGs) are very popular in China, which provides a potential platform for scientific research. We study the online-offline activities of avatars in an MMORPG to understand their game-playing behavior. The statistical analysis unveils that the active avatars can be classified into three types. The avatars of the first type are owned by game cheaters who go online and offline in preset time intervals with the online duration distributions dominated by pulses. The second type of avatars is characterized by a Weibull distribution in the online durations, which is confirmed by statistical tests. The distributions of online durations of the remaining individual avatars differ from the above two types and cannot be described by a simple form. These findings have potential applications in the game industry.Comment: 6 EPL pages including 10 eps figure