Using bulked segregant analysis based on next-generation sequencing, the recessive nulliplex-branch gene was mapped between two SNP markers ~600 kb apart. In a "nulliplex-branch" cotton mutant, most of the flowers arise directly from leaf axils on the main shoot, which usually does not have a fruiting branch. A nulliplex-branch is a useful trait by which to study cotton architecture; however, the genetic basis of this mutant has remained elusive. In this study, bulked segregant analysis combined with next-generation sequencing technology was used to finely map the underlying genes that result in a nulliplex-branch plant. The nulliplex-branch Pima cotton variety, Xinhai-18, was crossed with the normal branch upland cotton line, TM-1, resulting in an F2 population. The nulliplex-branch trait was found to be controlled by the recessive gene gb_nb1. Allelic single-nucleotide polymorphisms (SNPs) were discovered by reduced-representation sequencing between the parents, and their profiles were also characterized in the nulliplex-branch and normal branch bulks constructed using the F2 plants. A candidate ~9.0 Mb-long region comprising 42 SNP markers was found to be associated with gb_nb1, which helped localize it at the ~600-kb interval on Chr 16 by segregation analysis in the F2 population. The closely linked markers with gb_nb1 developed in this study will facilitate the marker-assisted selection of the nulliplex-branch trait, and the fine map constructed will accelerate map-based cloning of gb_nb1.

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Classification is an important technology in data mining, while clonal selection algorithm (CSA) is a very effective classification method. Although CSA brings a new effective tool for solving complex problems, we can not completely say that it over-performs to other algorithms especially in the classification field. A main problem of CSA classifier is that it does not carry attribute imbalance. It uses a pure distance criterion to calculate affinity degree of the antibody and antigen. So we utilize weighting attribute scheme to balance the effects of attributes in classification process and attribute weighted CSA (AWCSA) comes into existence. The efficiency of AWCSA lies mainly in the attribute weighting scheme it uses. In this paper we use differential evolution (DE) algorithm to determine the weights of attributes and then use these weights in AWCSA. We evaluate the performance of new algorithm (DE-AWCSA) on six standard datasets. Experimental results show that this attribute weighting process highly benefits the classification accuracy.

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Background Members of the AT-HOOK MOTIF CONTAINING NUCLEAR LOCALIZED (AHL) family are involved in various plant biological processes via protein-DNA and protein-protein interaction. However, no the systematic identification and analysis of AHL gene family have been reported in cotton. Results To investigate the potential functions of AHLs in cotton, genome-wide identification, expressions and structure analysis of the AHL gene family were performed in this study. 48, 51 and 99 AHL genes were identified from the G.raimondii, G.arboreum and G.hirsutum genome, respectively. Phylogenetic analysis revealed that the AHLs in cotton evolved into 2 clades, Clade-A with 4–5 introns and Clade-B with intronless (excluding AHL20–2). Based on the composition of the AT-hook motif(s) and PPC/DUF 296 domain, AHL proteins were classified into three types (Type-I/−II/−III), with Type-I AHLs forming Clade-B, and the other two types together diversifying in Clade-A. The detection of synteny and collinearity showed that the AHLs expanded with the specific WGD in cotton, and the sequence structure of AHL20–2 showed the tendency of increasing intron in three different Gossypium spp. The ratios of non-synonymous (Ka) and synonymous (Ks) substitution rates of orthologous gene pairs revealed that the AHL genes of G.hirsutum had undergone through various selection pressures, purifying selection mainly in A-subgenome and positive selection mainly in D-subgenome. Examination of their expression patterns showed most of AHLs of Clade-B expressed predominantly in stem, while those of Clade-A in ovules, suggesting that the AHLs within each clade shared similar expression patterns with each other. qRT-PCR analysis further confirmed that some GhAHLs higher expression in stems and ovules. Conclusion In this study, 48, 51 and 99 AHL genes were identified from three cotton genomes respectively. AHLs in cotton were classified into two clades by phylogenetic relationship and three types based on the composition of motif and domain. The AHLs expanded with segmental duplication, not tandem duplication. The expression profiles of GhAHLs revealed abundant differences in expression levels in various tissues and at different stages of ovules development. Our study provided significant insights into the potential functions of AHLs in regulating the growth and development in cotton.

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