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Background Manganese (Mn) is an essential microelement in cottonseeds, which is usually determined by the techniques relied on hazardous reagents and complex pretreatment procedures. Therefore a rapid, low-cost, and reagent-free analytical way is demanded to substitute the traditional analytical method. Results The Mn content in cottonseed meal was investigated by near-infrared spectroscopy (NIRS) and chemometrics techniques. Standard normal variate (SNV) combined with first derivatives (FD) was the optimal spectra pre-treatment method. Monte Carlo uninformative variable elimination (MCUVE) and successive projections algorithm method (SPA) were employed to extract the informative variables from the full NIR spectra. The linear and nonlinear calibration models for cottonseed Mn content were developed. Finally, the optimal model for cottonseed Mn content was obtained by MCUVE-SPA-LSSVM, with root mean squares error of prediction (RMSEP) of 1.994 6, coefficient of determination (R 2 ) of 0.949 3, and the residual predictive deviation (RPD) of 4.370 5, respectively. Conclusions The MCUVE-SPA-LSSVM model is accuracy enough to measure the Mn content in cottonseed meal, which can be used as an alternative way to substitute for traditional analytical method.

Background Utilization of heterosis has greatly improved the productivity of many crops worldwide. Understanding the potential molecular mechanism about how hybridization produces superior yield in upland cotton is critical for efficient breeding programs. Results In this study, high, medium, and low hybrids varying in the level of yield heterosis were screened based on field experimentation of different years and locations. Phenotypically, high hybrid produced a mean of 14% more seed cotton yield than its better parent. Whole-genome RNA sequencing of these hybrids and their four inbred parents was performed using different tissues of the squaring stage. Comparative transcriptomic differences in each hybrid parent triad revealed a higher percentage of differentially expressed genes (DEGs) in each tissue. Expression level dominance analysis identified majority of hybrids DEGs were biased towards parent like expressions. An array of DEGs involved in ATP and protein binding, membrane, cell wall, mitochondrion, and protein phosphorylation had more functional annotations in hybrids. Sugar metabolic and plant hormone signal transduction pathways were most enriched in each hybrid. Further, these two pathways had most mapped DEGs on known seed cotton yield QTLs. Integration of transcriptome, QTLs, and gene co-expression network analysis discovered genes Gh_A03G1024, Gh_D08G1440, Gh_A08G2210, Gh_A12G2183, Gh_D07G1312, Gh_D08G1467, Gh_A03G0889, Gh_A08G2199, and Gh_D05G0202 displayed a complex regulatory network of many interconnected genes. qRT-PCR of these DEGs was performed to ensure the accuracy of RNA-Seq data. Conclusions Through genome-wide comparative transcriptome analysis, the current study identified nine key genes and pathways associated with biological process of yield heterosis in upland cotton. Our results and data resources provide novel insights and will be useful for dissecting the molecular mechanism of yield heterosis in cotton.

Additional file 19: Table S6. Number of co-expressed modules and genes for hybrids.

Additional file 21: Table S8. List of gene primers used for qRT-PCR.

Figure S1. Variation of prediction residual error sum of squares value (PRESS value) with different latent variables (LVs) for Mn full-spectra PLS model. (DOCX 46 kb)