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Abstract The skin is our outer permeability and immune defense barrier against myriad external assaults. Aryl hydrocarbon receptor (AhR) senses environmental factors and regulates barrier robustness and immune homeostasis. AhR agonists have been approved by the FDA for psoriasis treatment and are in clinical trials for the treatment of atopic dermatitis (AD), but the underlying mechanism of action remains poorly defined. Here, we report thatOVOL1/Ovol1is a conserved and direct transcriptional target of AhR in epidermal keratinocytes. We show that OVOL1/Ovol1 influences AhR-mediated regulation of keratinocyte gene expression and thatOVOL1/Ovol1ablation in keratinocytes impairs the barrier-promoting function of AhR, exacerbating AD-like inflammation. Mechanistically, we have identified Ovol1’s direct downstream targets genome-wide and provided in vivo evidence supporting the role ofId1as a functional target in barrier maintenance, disease suppression, and neutrophil accumulation. Furthermore, our findings reveal that an IL-1/dermal γδT cell axis exacerbates type 2 and 3 immune responses downstream of barrier perturbation inOvol1-deficient AD skin. Finally, we present data suggesting the clinical relevance of OVOL1 and ID1 functions in human AD skin. Our study highlights a keratinocyte-intrinsic AhR-Ovol1-Id1 regulatory axis that promotes both epidermal and immune homeostasis in the context of skin inflammation, identifying new therapeutic targets. 

The massively available data about user engagement with online information service systems provides a gold mine about users' latent intents. It calls for quantitative user behavior modeling. In this paper, we study the problem by looking into users' sequential interactive behaviors. Inspired by the concepts of episodic memory and semantic memory in cognitive psychology, which describe how users' behaviors are differently influenced by past experience, we propose a Long- and Short-term Hawkes Process model. It models the short-term dependency between users' actions within a period of time via a multi-dimensional Hawkes process and the long-term dependency between actions across different periods of time via a one dimensional Hawkes process. Experiments on two real-world user activity log datasets (one from an e-commerce website and one from a MOOC website) demonstrate the effectiveness of our model in capturing the temporal dependency between actions in a sequence of user behaviors. It directly leads to improved accuracy in predicting the type and the time of the next action. Interestingly, the inferred dependency between actions in a sequence sheds light on the underlying user intent behind direct observations and provides insights for downstream applications.