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.
We study the machine learning method for classifying the basic shape of space debris in both simulated and observed data experiments, where light curves are used as the input features. In the dataset for training and testing, simulated light curves are derived from four types of debris within different shapes and materials. Observed light curves are extracted from Mini-Mega TORTORA (MMT) database which is a publicly accessible source of space object photometric records. The experiments employ the deep convolutional neural network, make comparisons with other machine learning algorithms, and the results show CNN (Convolutional Neural Network) is better. In simulational experiments, both types of cylinder can be distinguished perfectly, and two other types of satellite have around 90% probability to be classified. Rockets and defunct satellites can achieve 99% success rate in binary classification, but in further sub-classes classifications, the rate becomes relatively lower. 相似文献
