Draft Genome of White-blotched River Stingray Provides Novel Clues for Niche Adaptation and Skeleton Formation

The white-blotched river stingray(Potamotrygon leopoldi)is a cartilaginous fish native to the Xingu River,a tributary of the Amazon River system.As a rare freshwater-dwelling cartilaginous fish in the Potamotrygonidae family in which no member has the genome sequencing information available,P.leopoldi provides the evolutionary details in fish phylogeny,niche adaptation,and skeleton formation.In this study,we present its draft genome of 4.11 Gb comprising 16,227 contigs and 13,238 scaffolds,with contig N50 of 3937 kb and scaffold N50 of 5675 kb in size.Our analysis shows that P.leopoldi is a slow-evolving fish that diverged from elephant sharks about 96 million years ago.Moreover,two gene families related to the immune system(immunoglobulin heavy constant delta genes and T-cell receptor alpha/delta variable genes)exhibit expansion in P.leopoldi only.We also identified the Hox gene clusters in P.leopoldi and discovered that seven Hox genes shared by five representative fish species are missing in P.leopoldi.The RNA sequencing data from P.leopoldi and other three fish species demonstrate that fishes have a more diversified tissue expression spectrum when compared to mammals.Our functional studies suggest that lack of the gc gene encoding vitamin D-binding protein in cartilaginous fishes(both P.leopoldi and Callorhinchus milii)could partly explain the absence of hard bone in their endoskeleton.Overall,this genome resource provides new insights into the niche adaptation,body plan,and skeleton formation of P.leopoldi,as well as the genome evolution in cartilaginous fishes.

A Reference Vector-Assisted Many-Objective Optimization Algorithm with Adaptive Niche Dominance Relation

It is still a huge challenge for traditional Pareto-dominatedmany-objective optimization algorithms to solve manyobjective optimization problems because these algorithms hardly maintain the balance between convergence and diversity and can only find a group of solutions focused on a small area on the Pareto front,resulting in poor performance of those algorithms.For this reason,we propose a reference vector-assisted algorithmwith an adaptive niche dominance relation,for short MaOEA-AR.The new dominance relation forms a niche based on the angle between candidate solutions.By comparing these solutions,the solutionwith the best convergence is found to be the non-dominated solution to improve the selection pressure.In reproduction,a mutation strategy of k-bit crossover and hybrid mutation is used to generate high-quality offspring.On 23 test problems with up to 15-objective,we compared the proposed algorithm with five state-of-the-art algorithms.The experimental results verified that the proposed algorithm is competitive.