Abstract Preventing the widespread occurrence of stripe rust in wheat largely depends on the identification of new stripe rust resistance genes and the breeding of cultivars with durable resistance. We obtained a wheat–tetraploid Thinopyrumelongatum 6E (6D) substitution line and determined that chromosome 6E contains genetic material conferring superior resistance to stripe rust at the adult stage. In this study, three novel wheat–tetraploid Th. elongatum translocation lineswere generated from the offspring of a cross between common wheat and the 6E (6D) substitution line. Genomic in situ hybridization (GISH), fluorescence in situ hybridization chromosome painting (FISH painting), repetitive sequential FISH, and 55K SNP analyses indicated that K227-48, K242-82, and K246-6 contained 42 chromosomes and were 6ES·6DL, 2DL·6EL, and 6DS·6ELtranslocation lines, respectively. The assessment of stripe rust resistance revealed that K227-48 was susceptible to a mixture of Pst races, whereas the 6EL lines K242-82 and K246-6 exhibited adult plant resistance to stripe rust. Thus, this resistance was due to the 6EL chromosome. The overall good agronomic performance of K246-6 implies this line may be a useful germplasm resource for wheat breeding programs. Furthermore, 34 PCR-based markers for chromosome6EL were developed using the whole-genome sequence of diploid Th. elongatum. This novel translocation line may be applicable for breeding wheat lines resistant to stripe rust. Additionally, themarkers developed in this study will enablethe accurate tracing of tetraploid Th. elongatum chromosome 6E and the mapping of additional favorable genes on 6EL.
Abstract Background Availability of information on the genetic diversity and population structure of germplasm facilitates its use in wheat breeding programs. Recently, with the development of next-generation sequencing technology, genotyping-by-sequencing (GBS) has been used as a high-throughput and cost-effective molecular tool for examination of the genetic diversity of wheat breeding lines. In this study, GBS was used to characterize a population of 180 accessions of common wheat originating from Asia and Europe between the latitudes 30° and 45°N.Results In total, 24,767 high-quality single-nucleotide polymorphism (SNP) markers were used for analysis of genetic diversity and population structure. The B genome contained the highest number of SNPs, followed by the A and D genomes. The polymorphism information content ranged from 0.1 to 0.4, with an average of 0.26. The distribution of SNPs markers on the 21 chromosomes ranged from 243 on chromosome 4D to 2,337 on chromosome 3B. Structure and cluster analyses divided the panel of accessions into two subgroups (G1 and G2). G1 principally consisted of European and partial Asian accessions, and G2 comprised mainly accessions from the Middle East and partial Asia. Molecular analysis of variance showed that the genetic variation was greater within groups (99%) than between groups (1%). Comparison of the two subgroups indicated that G1 and G2 contained a high level of genetic diversity. The genetic diversity of G2 was higher as indicated by the Shannon’s information index ( I ) = 0.512, diversity index ( h ) = 0.334, observed heterozygosity ( H o ) = 0.226, and unbiased diversity index (uh) = 0.338.Conclusion The present results will not only help breeders to understand the genetic diversity of wheat germplasm on the Eurasian continent between the latitudes of 30° and 45°N, but also provide valuable information for wheat genetic improvement through introgression of novel genetic variation in this region.
Abstract Background The genus Pseudoroegneria (Nevski) Á. Löve with the St genome accounts for more than 60% of perennial Triticeae speciation. However, the strong dominant character of the St genome results in challenging to distinguish each species even genus based on single or combined morphological traits, the phylogeny and taxonomy of the St-containing polyploid genera remain under controversy. Results In this study, we used nuclear and chloroplast DNA-based phylogenetic analyses to reveal the systematic relationships of the St-containing polyploidy species. The maximum likelihood (ML) tree based on nuclear ribosomal internal transcribed spacer region (nrITS) and three single-copy nuclear genes data (Acc1 + Pgk1 + DMC1) showed that St-containing polyploid species were separated into StStHH, StStYY, StStYYHH, StStYYPP, StStYYWW, StStPP, and StStEE genome types and polyploidy species in Caucasus, America and Australia have unique polyploidization events. The ML tree for the chloroplast DNA fragments (matK + rbcL + trnL-trnF) displayed that the P genome serves as a maternal donor of Kengyilia melanthera and K. dingqinensis from the Hengduan Mountains region, while the St or StY genome serves as the maternal donor of other St genome containing species. Herein, we reported the genome constitution of Kengyilia tibetica, K. changduensis, and K. dingqinensis with the StStYYPP genome for the first time. Conclusions The St-containing polyploid species should be treated as distinct genera according to different genome constitutions, St-containing polyploid species experienced independent allopolyploidization events in different distribution regions and the St-containing polyploid species had two relatively independent maternal origins from the P genome or St/StYgenome. Besides, the Xp genome may have contributed to the unknown Y genome formation.
In the coal mining process, various types of tramp materials will be mixed into the raw coal, which will affect the quality of the coal and endanger the normal operation of the equipment. Automatic detection of tramp materials objects is an important process and basis for efficient coal sorting. However, previous research has focused on the detection of gangue, ignoring the detection of other types of tramp materials, especially small targets. Because the initial Single Shot MultiBox Detector (SSD) lacks the efficient use of feature maps, it is difficult to obtain stable results when detecting tramp materials objects. In this article, an object detection algorithm based on feature fusion and dense convolutional network is proposed, which is called tramp materials in raw coal single-shot detector (TMRC-SSD), to detect five types of tramp materials such as gangue, bolt, stick, iron sheet, and iron chain. In this algorithm, a modified DenseNet is first designed and a four-stage feature extractor is used to down-sample the feature map stably. After that, we use the dilation convolution and multi-branch structure to enrich the receptive field. Finally, in the feature fusion module, we designed cross-layer feature fusion and attention fusion modules to realize the semantic interaction of feature maps. The experiments show that the module we designed is effective. This method is better than the existing model. When the input image is 300 × 300 pixels, it can reach 96.12% MAP and 24FPS. Especially in the detection of small objects, the detection accuracy has increased by 4.1 to 95.57%. The experimental results show that this method can be applied to the actual detection of tramp materials objects in raw coal.
The morphological changes of the retinal blood vessels in retinal images are important indicators for diseases like diabetes, hypertension and glaucoma. Thus the accurate segmentation of blood vessel is of diagnostic value. In this paper, we present a novel method to segment retinal blood vessels to overcome the variations in contrast of large and thin vessels. This method uses adaptive local thresholding to produce a binary image then extract large connected components as large vessels. The residual fragments in the binary image including some thin vessel segments (or pixels), are classified by Support Vector Machine (SVM). The tracking growth is applied to the thin vessel segments to form the whole vascular network. The proposed algorithm is tested on DRIVE database, and the average sensitivity is over 77% while the average accuracy reaches 93.2%. In this paper, we distinguish large vessels by adaptive local thresholding for their good contrast. Then identify some thin vessel segments with bad contrast by SVM, which can be lengthened by tracking. This proposed method can avoid heavy computation and manual intervention.
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