Activation of 5-HT 2A Receptor Disrupts Rat Maternal Behavior

Serotonin 5-HT2A receptor is widely distributed in the central nervous system and plays an important role in sensorimotor function, emotion regulation, motivation, executive control, learning and memory. We investigated its role in rat maternal behavior, a naturalistic behavior encompassing many psychological functions that the 5-HT2A receptor is involved in. We first showed that activation of 5-HT2A receptor by TCB-2 (a highly selective 5-HT2A agonist, 1, 2.5 or 5.0 mg/kg) disrupted maternal behavior dose-dependently, and this effect was reduced by pretreatment with a 5-HT2A receptor antagonist MDL 100907, but exacerbated by pretreatment with a 5-HT2C receptor antagonist SB242084 and a 5-HT2C receptor agonist MK212, indicating that the maternal disruptive effect of 5-HT2A activation is receptor-specific and can be modulated by 5-HT2C receptor bidirectionally. We then microinjected TCB-2 into two brain regions important for the normal expression of maternal behavior: the medial prefrontal cortex (mPFC) and the medial preoptic area (mPOA) and found that only acute intra-mPFC infusion of TCB-2 suppressed pup retrieval, whereas intra-mPOA had no effect. Finally, using c-Fos immunohistochemistry, we identified that the ventral bed nucleus of stria terminalis (vBNST), the central amygdala (CeA), and the dorsal raphe (DR) were additionally involved in the maternal-disruptive effect of TCB-2. These findings suggest that the 5-HT2A receptor in the mPFC and other maternally related regions is required for the normal expression of maternal behavior through its intrinsic action or interactions with other receptors (e.g. 5-HT2C). Functional disruption of this neuroreceptor system might contribute to postpartum mental disorders (e.g. depression and psychosis) that impair the quality of maternal care

Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection

With the development of depth cameras such as Kinect and Intel Realsense, RGB-D based human detection receives continuous research attention due to its usage in a variety of applications. In this paper, we propose a new Multi-Glimpse LSTM (MG-LSTM) network, in which multi-scale contextual information is sequentially integrated to promote the human detection performance. Furthermore, we propose a feature fusion strategy based on our MG-LSTM network to better incorporate the RGB and depth information. To the best of our knowledge, this is the first attempt to utilize LSTM structure for RGB-D based human detection. Our method achieves superior performance on two publicly available datasets.Comment: ICIP 2017 Ora