Journal of Oceanology and Limnology - Blepharipoda liberate Shen is a commercially valuable seafood species that has important ecological significance in Shandong Province, China. Although B.... 相似文献
Decapterus maruadsi is a commercially important species in China, but has been heavily exploited in some areas. There is a growing need to develop microsatellites promoting its genetic research for the adequate management of this fishery resources. The recently developed specific-locus amplified fragment sequencing (SLAF-seq) is an efficient and high-resolution method for genome-wide microsatellite markers discovery. In this study, 28 905 microsatellites (mono- to hexa-nucleotide repeats) were identified using SLAF-seq technology, of which di-nucleotide was the most frequent (13 590, 47.02%), followed by mono-nucleotide (8 138, 28.15%), tri-nucleotide (5 727, 19.81%), tetra-nucleotide (1 104, 3.82%), pentanucleotide (234, 0.81%), and hexa-nucleotide (112, 0.39%). One hundred and thirty-two microsatellite loci (di- and tri-nucleotide) were randomly selected for amplification and polymorphism, of which 49 were highly polymorphic and well-resolved. The average number of alleles per locus was 13.63, ranging from 4 to 25, and allele sizes varied between 110 bp and 309 bp. The observed heterozygosity ( Ho ) and expected heterozygosity ( He ) ranged from 0.233 to 1.000 and from 0.374 to 0.959, with mean values of 0.738 and 0.836, respectively. The polymorphism information content (PIC) ranged from 0.341 to 0.941 (mean=0.806). However, 12 loci deviated from Hardy-Weinberg equilibrium. Furthermore, transferability tests were also successful in validating the utility of the developed markers in five phylogenetically related species of family Carangidae. A total of 48 microsatellite markers were successfully cross-amplified in Decapterus macarellus, Decapterus macrosoma, Decapterus kurroides, Trachurus japonicus, and Selaroides leptolepis. The present microsatellites provided the first known set of microsatellite DNA markers for D. maruadsi, D. macarellus, D. kurroides, and D. macrosoma, and would be useful for further population genetic and molecular phylogeny studies as well as help with the fisheries management formulation and implementation of the understudied species.
The river centerline is a basic hydrological characteristic. Most prior studies have used remote sensing data to extract the river centerline from the open water region in a pure water pixel region. Extracting this type of river is relatively easy. However, extracting the centerline of a micro-river, which is mainly composed of mixed water pixels, is challenging. This paper presents a novel method, called the Multiple Direction Integration Algorithm (MDIA), to extract the river centerline using an image-enhancing method combined with river morphology. MDIA can be applied to regions mainly composed of pure water pixels, as well as to regions consisting of mixed water pixels in the index image. The method first calculates the normalized difference vegetation index (NDVI) and enhances the river linear structure using a Hessian matrix. Second, a small window is constructed as a circular structural element. In the window region, the local threshold is automatically obtained using water-oriented clustering segmentation and prior river knowledge to judge the pixel type. After completing the river centerline extraction in the current window, the next detecting window is generated to continue judgment. The structural element automatically executes river centerline judgment until the entire river centerline is extracted. The Landsat 8 images of six regions with different geomorphologies were chosen to analyze the method’s performance. The test sites include high mountain region, low mountain region, plains region with farmland and a residential region. The experimental results show that the optimal threshold of the processing results ranged from 0.2 to 0.3. In this range, the user’s accuracy is 0.813 to 0.997, and the producer’s accuracy is 0.981 to 1. The MDIA effectively and correctly extracts the river network in mixed-pixel regions. The presented method provides an effective algorithm for river centerline extraction that can be used to expand and update river datasets and provide reliable river centerline data for relevant hydrology studies. 相似文献
The extraction of partition lines for long and narrow patches (LN patches) is an important yet difficult problem in the generalization of thematic data. When current methods are used to process polygons with irregular shapes or complex branch convergence zones, the extracted line structural features tend to be inaccurate and topologically erroneous. In this article, we propose an improved partition lines extraction algorithm of constrained Delaunay triangulation to counter these issues. The proposed method aims to maintain consistency between the extracted line structure characteristics and the actual object structure, especially for complex branch convergence zones. First, we describe three types of aggregation patterns (Type A, B, and C aggregation zones) that occur in partition line extractions for LN patches of complex branch convergence zones using Delaunay triangulation. Then, a partition line extraction algorithm that accounts for the direction between the edges of triangles and the distance of nodes in aggregation zones is proposed. Finally, we test our method for a dataset relating to Guizhou Province, China. Compared with the current method that uses quantitative indicators and visualization, the results indicate that our method not only has applicability for simple situations but also is superior for preserving structural features of complex branch convergence zones. 相似文献
