The shells of pearl oysters, Pinctada fucata, are composed of calcite and aragonite and possess remarkable mechanical properties. These shells are formed under the regulation of macromolecules, especially shell matrix proteins (SMPs). Identification of diverse SMPs will lay a foundation for understanding biomineralization process. Here, we identified 72 unique SMPs using liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of proteins extracted from the shells of P. fucata combined with a draft genome. Of 72 SMPs, 17 SMPs are related to both the prismatic and nacreous layers. Moreover, according to the diverse domains found in the SMPs, we hypothesize that in addition to controlling CaCO3 crystallization and crystal organization, these proteins may potentially regulate the extracellular microenvironment and communicate between cells and the extracellular matrix (ECM). Immunohistological localization techniques identify the SMPs in the mantle, shells and synthetic calcite. Together, these proteomic data increase the repertoires of the shell matrix proteins in P. fucata and suggest that shell formation in P. fucata may involve tight regulation of cellular activities and the extracellular microenvironment.

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A convenient and effective binocular vision system is set up. Gesture information can be accurately extract from the complex environment with the system. The template calibration method is used to calibrate the binocular camera and the parameters of the camera are accurately obtained. In the phase of stereo matching, the BM algorithm is used to quickly and accurately match the images of the left and right cameras to get the parallax of the measured gesture. Combined with triangulation principle, resulting in a more dense depth map. Finally, the depth information is remapped to the original color image to realize three-dimensional reconstruction and three-dimensional cloud image generation. According to the cloud image information, it can be judged that the binocular vision system can effectively segment the gesture from the complex background.

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The brain is the largest and most complex structure in the central nervous system. It dominates all activities in the body, and the lesions in the human body are also reflected in the brain signal. In this paper, the image method is used to assist the brain signal to detect the human lesion. Due to the particularity of medical images, there is no common segmentation method for any medical image, and there is no objective standard to judge whether the segmentation is effective. Medical image segmentation technology is still a bottleneck restricting the development and the application of other related technologies in medical image processing. Based on the above reasons, this paper proposes an improved region growing algorithm based on the fuzzy theory and region growing algorithm. The algorithm is used to segment the medical images of the liver and chest X-ray of different human organs. The improved algorithm uses a threshold segmentation algorithm to assist in the automatic selection of seed points and improves the region growing rules, then morphological post-processing is used to improve the segmentation effect. The experimental results show that the improved region growing algorithm has better segmentation effect under two different organs, which proves that the algorithm has certain applicability, and its accuracy and segmentation quality are better than the traditional region growing algorithm. This algorithm combines the advantages of the threshold method and traditional region growing method. It is feasible in algorithm and has certain application value. INDEX TERMS Medical image segmentation, improved region growing algorithm, applicability method, brain signal.

Sesame (Sesamum indicum L.) is an important oilseed crop and has an indeterminate growth habit. Here we resequenced the genomes of the parents and 120 progeny of an F2 population derived from crossing Yuzhi 11 (indeterminate, Dt) and Yuzhi DS899 (determinate, dt1), and constructed an ultra-dense SNP map for sesame comprised of 3,041 bins including 30,193 SNPs in 13 linkage groups (LGs) with an average marker density of 0.10 cM. Results indicated that the same recessive gene controls the determinacy trait in dt1 and a second determinate line, dt2 (08TP092). The QDt1 locus for the determinacy trait was located in the 18.0 cM–19.2 cM interval of LG8. The target SNP, SiDt27-1, and the determinacy gene, DS899s00170.023 (named here as SiDt), were identified in Scaffold 00170 of the Yuzhi 11 reference genome, based on genetic mapping and genomic association analysis. Unlike the G397A SNP change in the dt1 genotype, the SiDt allele in dt2 line was lost from the genome. This example of map-based gene cloning in sesame provides proof-of-concept of the utility of ultra-dense SNP maps for accurate genome research in sesame.