Predicting the grades of Astragali radix using mass spectrometrybased metabolomics and machine learning

Astragali radix(AR,the dried root of Astragalus)is a popular herbal remedy in both China and the United States.The commercially available AR is commonly classified into premium graded(PG)and ungraded(UG)ones only according to the appearance.To uncover novel sensitive and specific markers for AR grading,we took the integrated mass spectrometry-based untargeted and targeted metabolomics approaches to characterize chemical features of PG and UG samples in a discovery set(n=16 batches).A series of five differential compounds were screened out by univariate statistical analysis,including arginine,calycosin,ononin,formononetin,and astragalosideⅣ,most of which were observed to be accumulated in PG samples except for astragalosideⅣ.Then,we performed machine learning on the quantification data of five compounds and constructed a logistic regression prediction model.Finally,the external validation in an independent validation set of AR(n=20 batches)verified that the five compounds,as well as the model,had strong capability to distinguish the two grades of AR,with the prediction accuracy>90%.Our findings present a panel of meaningful candidate markers that would significantly catalyze the innovation in AR grading.

Rapid Identification of a Candidate Gene Related to Fiber Strength Using a Superior Chromosome Segment Substitution Line from Gossypium hirsutum × Gossypium barbadense via Bulked Segregant RNA-Sequencing

Cotton is the most widely cultivated commercial crop producing natural fiber around the world.As a critical trait for fiber quality,fiber strength principally determined during the secondary wall thickening period.Based on the developed BC5F3:5 CSSLs(chromosome segment substitution lines)from Gossypium hirsutum CCRI36×G.barbadense Hai 1,the superior MBI9915 was chosen to construct the secondary segregated population BC7F2 with its recurrent parent CCRI36,which was subsequently subjected to Bulk segregant RNA-sequencing(BSR-seq)for rapid identification of candidate genes related to fiber strength.A total of 4 fiber-transcriptome libraries were separately constructed and sequenced,including two parents(CCRI36 and MBI9915)and two extreme pools at 20 DPA(days post anathesis).Through multiple comparisons,536 DEGs(differentially expressed genes)were overlapped at 20 DPA.Allelic-polymorphism comparison in mRNA sequences revealed 831 highly probable SNPs between two extreme pools related to fiber strength.Linkage analysis was performed between two extreme pools with SNP-index method.Eighteen correlated regions with 1981 annotation genes were obtained between two pools at 20 DPA,of which 12 common DEGs were similarly identified both between two parents and two pools.One gene(Gh_A07G0837)in the candidate region related to fiber strength was differentially expressed in both parents and extreme pools and involved in fiber strength development through reactive oxygen species(ROS)activity.Co-expression analysis of Gh_A07G0837 showed that Gh_A07G0837 may cooperate with other genes to regulate fiber strength.The reliability of BSR-seq results was validated by the quantitative real-time PCR(qRT-PCR)experiments on 5 common DGEs 20 DPA.Co-expressed analysis results indicated that there were some genes expressed especially low in MBI9915,resulting in good fiber strength.Focusing on bulked segregant analysis on the extreme pools derived from superior CSSL population,this study indicates that BSR-seq can be efficiently applied on rapid identification of candidate genes related to fiber strength,which make contributions to our understanding of fiber quality formation in cotton.

A Weighted Runge-Kutta Method with Weak Numerical Dispersion for Solving Wave Equations

In this paper, we propose a weighted Runge-Kutta (WRK) method to solvethe 2D acoustic and elastic wave equations. This method successfully suppresses thenumerical dispersion resulted from discretizing the wave equations. In this method,the partial differential wave equation is first transformed into a system of ordinarydifferential equations (ODEs), then a third-order Runge-Kutta method is proposedto solve the ODEs. Like the conventional third-order RK scheme, this new methodincludes three stages. By introducing a weight to estimate the displacement and itsgradients in every stage, we obtain a weighted RK (WRK) method. In this paper, weinvestigate the theoretical properties of the WRK method, including the stability criteria, numerical error, and the numerical dispersion in solving the 1D and 2D scalarwave equations. We also compare it against other methods such as the high-ordercompact or so-called Lax-Wendroff correction (LWC) and the staggered-grid schemes.To validate the efficiency and accuracy of the method, we simulate wave fields in the2D homogeneous transversely isotropic and heterogeneous isotropic media. We conclude that the WRK method can effectively suppress numerical dispersions and sourcenoises caused in using coarse grids and can further improve the original RK methodin terms of the numerical dispersion and stability condition.