Development and application of perfect SSR markers in cotton

Background:This study aimed to develop a set of perfect simple sequence repeat(SSR)markers with a single copy in the cotton genome,to construct a DNA fingerprint database suitable for authentication of cotton cultivars.We optimized the polymerase chain reaction(PCR)system for multi-platform compatibility and improving detection efficiency.Based on the reference genome of upland cotton and 10×resequencing data of 48 basic cotton germplasm lines,single-copy polymorphic SSR sites were identified and developed as diploidization SSR markers.The SSR markers were detected by denaturing polyacrylamide gel electrophoresis(PAGE)for initial screening,then fluorescence capillary electrophoresis for secondary screening.The final perfect SSR markers were evaluated and verified using 210 lines from different sources among Chinese cotton regional trials.Results:Using bioinformatics techniques,1246 SSR markers were designed from 26626 single-copy SSR loci.Adopting a stepwise(primary and secondary)screening strategy,a set of 60 perfect SSR markers was selected with high amplification efficiency and stability,easy interpretation of peak type,multiple allelic variations,high polymorphism information content(PIC)value,uniform chromosome distribution,and single-copy characteristics.A multiplex PCR system was established with ten SSR markers using capillary electrophoresis detection.Conclusions:A set of perfect SSR markers of cotton was developed and a high-throughput SSR marker detection system was established.This study lays a foundation for large-scale and standardized construction of a cotton DNA fingerprint database for authentication of cotton varieties.

Fine-grained classification of grape leaves via a pyramid residual convolution neural network

The value of grape cultivars varies.The use of a mixture of cultivars can negate the benefits of improved cultivars and hamper the protection of genetic resources and the identification of new hybrid cultivars.Classifying cultivars based on their leaves is therefore highly practical.Transplanted grape seedlings take years to bear fruit,but leaves mature in months.Foliar morphology differs among cultivars,so identifying cultivars based on leaves is feasible.Different cultivars,however,can be bred from the same parents,so the leaves of some cultivars can have similar morphologies.In this work,a pyramid residual convolution neural network was developed to classify images of eleven grape cultivars.The model extracts multi-scale feature maps of the leaf images through the convolution layer and enters them into three residual convolution neural networks.Features are fused by adding the value of the convolution kernel feature matrix to enhance the attention on the edge and center regions of the leaves and classify the images.The results indicated that the average accuracy of the model was 92.26%for the proposed leaf dataset.The proposed model is superior to previous models and provides a reliable method for the fine-grained classification and identification of plant cultivars.