SummaryGenome‐wide association studies (GWASs) combining high‐throughput genome resequencing and phenotyping can accelerate the dissection of genetic architecture and identification of genes for plant complex traits. In this study, we developed a rapeseed genomic variation map consisting of 4 542 011 SNPs and 628 666 INDELs. GWAS was performed for three seed‐quality traits, including erucic acid content (EAC), glucosinolate content (GSC) and seed oil content (SOC) using 3.82 million polymorphisms in an association panel. Six, 49 and 17 loci were detected to be associated with EAC, GSC and SOC in multiple environments, respectively. The mean total contribution of these loci in each environment was 94.1% for EAC and 87.9% for GSC, notably higher than that for SOC (40.1%). A high correlation was observed between phenotypic variance and number of favourable alleles for associated loci, which will contribute to breeding improvement by pyramiding these loci. Furthermore, candidate genes were detected underlying associated loci, based on functional polymorphisms in gene regions where sequence variation was found to correlate with phenotypic variation. Our approach was validated by detection of well‐characterized FAE1 genes at each of two major loci for EAC on chromosomes A8 and C3, along with MYB28 genes at each of three major loci for GSC on chromosomes A9, C2 and C9. Four novel candidate genes were detected by correlation between GSC and SOC and observed sequence variation, respectively. This study provides insights into the genetic architecture of three seed‐quality traits, which would be useful for genetic improvement of B. napus.

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Frame insertion and deletion are common inter-frame forgery in digital videos. In this paper, an efficient method based on quotients of correlation coefficients between local binary patterns (LBPs) coded frames is proposed. This method is composed of two parts: feature extraction and abnormal point detection. In the feature extraction, each frame of a video is coded by LBP. Then, quotients of correlation coefficients among sequential LBP-coded frames are calculated. In the abnormal point detection, insertion and deletion localization is achieved by using Tchebyshev inequality twice followed by abnormal points detection based on decision-thresholding. Experimental results show that our method has high detection accuracy and low computational complexity.

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BackgroundAllelic variation at the FRIGIDA (FRI) locus is a major contributor to natural variation of flowering time and vernalization requirement in Arabidopsis thaliana. Dominant FRI inhibits flowering by activating the expression of the MADS box transcriptional repressor FLOWERING LOCUS C (FLC), which represses flowering prior to vernalization. Four FRI orthologues had been identified in the domesticated amphidiploid Brassica napus. Linkage and association studies had revealed that one of the FRI orthologues, BnaA3.FRI, contributes to flowering time variation and crop type differentiation.ResultsSequence analyses indicated that three out of the four BnaFRI paralogues, BnaA3.FRI, BnaA10.FRI and BnaC3.FRI, contained a large number of polymorphic sites. Haplotype analysis in a panel of 174 B. napus accessions using PCR markers showed that all the three paralogues had a biased distribution of haplotypes in winter type oilseed rape (P < 0.01). Association analysis indicated that only BnaA3.FRI contributes to flowering time variation in B. napus. In addition, transgenic functional complementation demonstrated that mutations in the coding sequence of BnaA3.FRI lead to weak alleles, and subsequently to flowering time variation.ConclusionThis study for the first time provides a molecular basis for flowering time control by BnaA3.FRI in B. napus, and will facilitate predictive oilseed rape breeding to select varieties with favorable flowering time and better adaption to latitude and seasonal shifts due to changing climate.Electronic supplementary materialThe online version of this article (10.1186/s12870-018-1253-1) contains supplementary material, which is available to authorized users.

Abstract.With the ongoing development of rendering technology, computer graphics (CG) are sometimes so photorealistic that to distinguish them from photographic images (PG) by human eyes has become difficult. To this end, many methods have been developed for automatic CG and PG classification. In this paper, we explore the statistical difference of uniform gray-scale invariant local binary patterns (LBP) to distinguish CG from PG with the help of support vector machines (SVM). We select YCbCr as the color model. The original JPEG coefficients of Y and Cr components, and their prediction errors are used for LBP calculation. From each 2-D array, we obtain 59 LBP features. In total, four groups of 59 features are obtained from each image. The proposed features have been tested with thousands of CG and PG. Classification accuracy reaches 98.3% with SVM and outperforms the state-of-the-art works.