Gap-free genome assembly and CYP450 gene family analysis reveal the biosynthesis of anthocyanins in Scutellaria baicalensis

Scutellaria baicalensis Georgi,a member of the Lamiaceae family,is a widely utilized medicinal plant.The flavones extracted from S.baicalensis contribute to numerous health benefits,including anti-inflammatory,antiviral,and anti-tumor activities.However,the incomplete genome assembly hinders biological studies on S.baicalensis.This study presents the first telomere-to-telomere(T2T)gap-free genome assembly of S.baicalensis through the integration of Pacbio HiFi,Nanopore ultra-long and Hi-C technologies.A total of 384.59 Mb of genome size with a contig N50 of 42.44 Mb was obtained,and all sequences were anchored into nine pseudochromosomes without any gap or mismatch.In addition,we analysed the major cyanidin-and delphinidin-based anthocyanins involved in the determination of blue-purple flower using a widely-targeted metabolome approach.Based on the genome-wide identification of Cytochrome P450(CYP450)gene family,three genes(SbFBH1,2,and 5)encoding flavonoid 3′-hydroxylases(F3′Hs)and one gene(SbFBH7)encoding flavonoid 3′5′-hydroxylase(F3′5′H)were found to hydroxylate the B-ring of flavonoids.Our studies enrich the genomic information available for the Lamiaceae family and provide a toolkit for discovering CYP450 genes involved in the flavonoid decoration.

A Spider Monkey Optimization Algorithm Combining Opposition-Based Learning and Orthogonal Experimental Design

As a new bionic algorithm,Spider Monkey Optimization(SMO)has been widely used in various complex optimization problems in recent years.However,the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant.Thus,this paper focuses on how to reconstruct SMO to improve its performance,and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design(SMO^(3))is developed.A position updatingmethod based on the historical optimal domain and particle swarmfor Local Leader Phase(LLP)andGlobal Leader Phase(GLP)is presented to improve the diversity of the population of SMO.Moreover,an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal values.Also,a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in time.Furthermore,an extended SMO^(3)named CSMO^(3)is investigated to deal with constrained optimization problems.The proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering problems.Experimental results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems.

On the analysis of ant colony optimization for the maximum independent set problem

1 Introduction Inspired by natural evolution and biological behavior,researchers have developed many successful bio-inspired algorithms.Ant colony optimization(ACO)is one of the most successful bio-inspired computing methods for complex optimization problems.In contrast to the wide range of applications,the theoretical understanding of this kind of algorithms lagged far behind[1].Therefore,it is desirable and necessary to improve the theoretical foundation of the algorithm in order to have a better understanding of the execution mechanism of the algorithm and guide the algorithm design.Many researches are devoted to understanding the working principles of bio-inspired algorithms,and try to bridge the gap between theoretical research and practical applications of the algorithms.Many encouraging results have been obtained[2].