Pain regulation of endokinin A/B or endokinin C/D on chimeric peptide MCRT in mice.

The present study focused on the interactive pain regulation of endokinin A/B (EKA/B, the common C-terminal decapeptide in EKA and EKB) or endokinin C/D (EKC/D, the common C-terminal duodecapeptide in EKC and EKD) on chimeric peptide MCRT (YPFPFRTic-NH2, based on YPFP-NH2 and PFRTic-NH2) at the supraspinal level in mice. Results demonstrated that the co-injection of nanomolar EKA/B and MCRT showed moderate regulation, whereas 30 pmol EKA/B had no effect on MCRT. The combination of EKC/D and MCRT produced enhanced antinociception, which was nearly equal to the sum of the mathematical values of single EKC/D and MCRT. Mechanism studies revealed that pre-injected naloxone attenuated the combination significantly compared with the equivalent analgesic effects of EKC/D alone, suggesting that EKC/D and MCRT might act on two totally independent pathways. Moreover, based on the above results and previous reports, we made two reasonable hypotheses to explain the cocktail-induced analgesia, which may potentially pave the way to explore the respective regulatory mechanisms of EKA/B, EKC/D, and MCRT and to better understand the complicated pain regulation of NK1 and µ opioid receptors, as follows: (1) MCRT and endomorphin-1 possibly activated different µ subtypes; and (2) picomolar EKA/B might motivate the endogenous NPFF system after NK1 activation.

A novel biologically inspired local feature descriptor.

Local feature descriptor is a fundamental representation for image patch which has been extensively used in many computer vision applications. In this paper, different from state-of-the-art features, a novel biologically inspired local descriptor (BILD) is proposed based on the visual information processing mechanism of ventral pathway in human brain. The local features used for constructing BILD are extracted by a two-layer network, which corresponds to the simple-to-complex cell hierarchy in the primary visual cortex (V1). It works in a similar way as the simple cell and complex cell do to get responses by applying the lateral inhibition from different orientations and operating an improved cortical pooling. To enhance the distinctiveness of BILD, we combine the local features from different orientations. Extensive evaluations have been performed for image matching and object recognition. Experimental results reveal that our proposed BILD outperforms many widely used descriptors such as SIFT and SURF, which demonstrate its efficiency for representing local regions.

Synthesis and diuretic activities of pseudoproline-containing analogues of the insect kinin core pentapeptide.

C-2 dimethylated/unmethylated thiazolidine-4-carboxylic acid and C-2 dimethylated oxazolidine-4-carboxylic acid were introduced into the insect kinin core pentapeptide in place of Pro(3) , yielding three new analogues. NMR analysis revealed that the peptide bond of Phe(2) -pseudoproline (ΨPro)(3) is practically 100% in cis conformation in the case of dimethylated pseudoproline-containing analogues, about 50% cis for the thiazolidine-4-carboxylic acid analogue and about 33% cis for the parent Pro(3) peptide. The diuretic activities are consistent with the population of cis conformation of the Phe(2) -ΨPro(3) /Pro(3) peptide bonds, and the results confirm a cis Phe-Pro bond as bioactive conformation.

Large disparity motion layer extraction via topological clustering.

In this paper, we present a robust and efficient approach to extract motion layers from a pair of images with large disparity motion. First, motion models are established as: 1) initial SIFT matches are obtained and grouped into a set of clusters using our developed topological clustering algorithm; 2) for each cluster with no less than three matches, an affine transformation is estimated with least-square solution as tentative motion model; and 3) the tentative motion models are refined and the invalid models are pruned. Then, with the obtained motion models, a graph cuts based layer assignment algorithm is employed to segment the scene into several motion layers. Experimental results demonstrate that our method can successfully segment scenes containing objects with large interframe motion or even with significant interframe scale and pose changes. Furthermore, compared with the previous method invented by Wills and its modified version, our method is much faster and more robust.