Among many improved convolutional neural network (CNN) architectures in the optical image classification, only a few were applied in synthetic aperture radar (SAR) automatic target recognition (ATR). One main reason is that direct transfer of these advanced architectures for the optical images to the SAR images easily yields overfitting due to its limited data set and less features relative to the optical images. Thus, based on the characteristics of the SAR image, we proposed a novel deep convolutional neural network architecture named umbrella. Its framework consists of two alternate CNN-layer blocks. One block is a fusion of six 3-layer paths, which is used to extract diverse level features from different convolution layers. The other block is composed of convolution layers and pooling layers are mainly utilized to reduce dimensions and extract hierarchical feature information. The combination of the two blocks could extract rich features from different spatial scale and simultaneously alleviate overfitting. The performance of the umbrella model was validated by the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set. This architecture could achieve higher than 99% accuracy for the classification of 10-class targets and higher than 96% accuracy for the classification of 8 variants of the T72 tank, even in the case of diverse positions located by targets. The accuracy of our umbrella is superior to the current networks applied in the classification of MSTAR. The result shows that the umbrella architecture possesses a very robust generalization capability and will be potential for SAR-ART.

Metabotropic glutamate receptors of class C GPCRs exist as constitutive dimers, which play important roles in activating excitatory synapses of the central nervous system. However, the activation mechanism induced by agonists has not been clarified in experiments. To address the problem, we used microsecond all-atom molecular dynamics (MD) simulation couple with protein structure network (PSN) to explore the glutamate-induced activation for the mGluR1 homodimer. The results indicate that glutamate binding stabilizes not only the closure of Venus flytrap domains but also the polar interaction of LB2–LB2, in turn keeping the extracelluar domain in the active state. The activation of the extracelluar domain drives transmembrane domains (TMDs) of the two protomers closer and induces asymmetric activation for the TMD domains of the two protomers. One protomer with lower binding affinity to the agonist is activated, while the other protomer with higher binding energy is still in the inactive state. The PSN analysis identifies the allosteric regulation pathway from the ligand-binding pocket in the extracellular domain to the G-protein binding site in the intracellular TMD region and further reveals that the asymmetric activation is attributed to a combination of trans-pathway and cis-pathway regulations from two glumatates, rather than a single activation pathway. These observations could provide valuable molecular information for understanding of the structure and the implications in drug efficacy for the class C GPCR dimers.

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