Animal behavior usually has a hierarchical structure and dynamics. Therefore, to understand how the neural system coordinates with behaviors, neuroscientists need a quantitative description of the hierarchical dynamics of different behaviors. However, the recent end-to-end machine-learning-based methods for behavior analysis mostly focus on recognizing behavioral identities on a static timescale or based on limited observations. These approaches usually lose rich dynamic information on cross-scale behaviors. Here, inspired by the natural structure of animal behaviors, we address this challenge by proposing a parallel and multi-layered framework to learn the hierarchical dynamics and generate an objective metric to map the behavior into the feature space. In addition, we characterize the animal 3D kinematics with our low-cost and efficient multi-view 3D animal motion-capture system. Finally, we demonstrate that this framework can monitor spontaneous behavior and automatically identify the behavioral phenotypes of the transgenic animal disease model. The extensive experiment results suggest that our framework has a wide range of applications, including animal disease model phenotyping and the relationships modeling between the neural circuits and behavior.

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Indoor and outdoor air pollution has been classified as group I carcinogen in humans, but the underlying tumorigenesis remains unclear. Here, we screened for abnormal long noncoding RNAs (lncRNAs) in lung cancers from patients living in Xuanwei city which has the highest lung cancer incidence in China due to smoky coal combustion-generated air pollution. We reported that Xuanwei patients had much more dysregulated lncRNAs than patients from control regions where smoky coal was not used. The lncRNA CAR intergenic 10 (CAR10) was up-regulated in 39/62 (62.9%) of the Xuanwei patients, which was much higher than in patients from control regions (32/86, 37.2%; p=0.002). A multivariate regression analysis showed an association between CAR10 overexpression and air pollution, and a smoky coal combustion-generated carcinogen dibenz[a,h]anthracene up-regulated CAR10 by increasing transcription factor FoxF2 expression. CAR10 bound and stabilized transcription factor Y-box-binding protein 1 (YB-1), leading to up-regulation of the epidermal growth factor receptor (EGFR) and proliferation of lung cancer cells. Knockdown of CAR10 inhibited cell growth in vitro and tumor growth in vivo. These results demonstrate the role of lncRNAs in environmental lung carcinogenesis, and CAR10-YB-1 represents a potential therapeutic target.

Air pollution has been classified as a group 1 carcinogen in humans, but the underlying tumourigenic mechanisms remain unclear. In Xuanwei city of Yunnan Province, the lung cancer incidence is among the highest in China, owing to severe air pollution generated by the combustion of smoky coal, providing a unique opportunity to dissect lung carcinogenesis. To identify abnormal miRNAs critical for air pollution-related tumourigenesis, we performed microRNA microarray analysis in 6 Xuanwei non-small cell lung cancers (NSCLCs) and 4 NSCLCs from control regions where smoky coal was not used. We found 13 down-regulated and 2 up-regulated miRNAs in Xuanwei NSCLCs. Among them, miR-144 was one of the most significantly down-regulated miRNAs. The expanded experiments showed that miR-144 was down-regulated in 45/51 (88.2%) Xuanwei NSCLCs and 34/54 (63%) control region NSCLCs (p = 0.016). MiR-144 interacted with the oncogene Zeb1 at 2 sites in its 3′ untranslated region, and a decrease in miR-144 resulted in increased Zeb1 expression and an epithelial mesenchymal transition phenotype. Ectopic expression of miR-144 suppressed NSCLCs in vitro and in vivo by targeting Zeb1. These results indicate that down-regulation of miR-144 is critical for air pollution-related lung cancer, and the miR-144-Zeb1 signalling pathway could represent a potential therapeutic target.