Engrailed transcription factors regulate the expression of guidance cues that pattern retinal axon terminals in the dorsal midbrain. They also act directly to guide axon growth in vitro. We show here that an extracellular En gradient exists in the tectum along the anterior-posterior axis. Neutralizing extracellular Engrailed in vivo with antibodies expressed in the tectum causes temporal axons to map aberrantly to the posterior tectum in chick and Xenopus. Furthermore, posterior membranes from wild-type tecta incubated with anti-Engrailed antibodies or posterior membranes from Engrailed-1 knockout mice exhibit diminished repulsive activity for temporal axons. Since EphrinAs play a major role in anterior-posterior mapping, we tested whether Engrailed cooperates with EphrinA5 in vitro. We find that Engrailed restores full repulsion to axons given subthreshold doses of EphrinA5. Collectively, our results indicate that extracellular Engrailed contributes to retinotectal mapping in vivo by modulating the sensitivity of growth cones to EphrinA.

Subcellular localization of protein synthesis provides a means to regulate the protein composition in far reaches of a cell. This localized protein synthesis gives neuronal processes autonomy to rapidly respond to extracellular stimuli. Locally synthesized axonal proteins enable neurons to respond to guidance cues and can help to initiate regeneration after injury. Most studies of axonal mRNA translation have concentrated on cytoplasmic proteins. While ultrastructural studies suggest that axons do not have rough endoplasmic reticulum or Golgi apparatus, mRNAs for transmembrane and secreted proteins localize to axons. Here, we show that growing axons with protein synthetic activity contain ER and Golgi components needed for classical protein synthesis and secretion. Isolated axons have the capacity to traffic locally synthesized proteins into secretory pathways and inhibition of Golgi function attenuates translation-dependent axonal growth responses. Finally, the capacity for secreting locally synthesized proteins in axons appears to be increased by injury.

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Facial expressions play an important role in conveying the emotional states of human beings. Recently, deep learning approaches have been applied to image recognition field due to the discriminative power of Convolutional Neural Network (CNN). In this paper, we first propose a novel Multi-Region Ensemble CNN (MRE-CNN) framework for facial expression recognition, which aims to enhance the learning power of CNN models by capturing both the global and the local features from multiple human face sub-regions. Second, the weighted prediction scores from each sub-network are aggregated to produce the final prediction of high accuracy. Third, we investigate the effects of different sub-regions of the whole face on facial expression recognition. Our proposed method is evaluated based on two well-known publicly available facial expression databases: AFEW 7.0 and RAF-DB, and has been shown to achieve the state-of-the-art recognition accuracy.

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