Bem1p contributes to secretory pathway polarization through a direct interaction with Exo70p.

The exocyst serves to tether secretory vesicles to cortical sites specified by polarity determinants, in preparation for fusion with the plasma membrane. Although most exocyst components are brought to these sites by riding on secretory vesicles as they are actively transported along actin cables, Exo70p displays actin-independent localization to these sites, implying an interaction with a polarity determinant. Here we show that Exo70p directly and specifically binds to the polarity determinant scaffold protein Bem1p. The interaction involves multiple domains of both Exo70p and Bem1p. Mutations in Exo70p that disrupt its interaction with Bem1, without impairing its interactions with other known binding partners, lead to the loss of actin-independent localization. Synthetic genetic interactions confirm the importance of the Exo70p-Bem1p interaction, although there is some possible redundancy with Sec3p and Sec15p, other exocyst components that also interact with polarity determinants. Similar to Sec3p, the actin-independent localization of Exo70p requires a synergistic interaction with the phosphoinositide PI(4,5)P2

Articulated Pose Estimation Using Hierarchical Exemplar-Based Models

Exemplar-based models have achieved great success on localizing the parts of semi-rigid objects. However, their efficacy on highly articulated objects such as humans is yet to be explored. Inspired by hierarchical object representation and recent application of Deep Convolutional Neural Networks (DCNNs) on human pose estimation, we propose a novel formulation that incorporates both hierarchical exemplar-based models and DCNNs in the spatial terms. Specifically, we obtain more expressive spatial models by assuming independence between exemplars at different levels in the hierarchy; we also obtain stronger spatial constraints by inferring the spatial relations between parts at the same level. As our method strikes a good balance between expressiveness and strength of spatial models, it is both effective and generalizable, achieving state-of-the-art results on different benchmarks: Leeds Sports Dataset and CUB-200-2011.Comment: 8 pages, 6 figure