Genome-wide identification,evolutionary selection,and genetic variation of DNA methylation-related genes in Brassica rapa and Brassica oleracea

DNA methylation plays an important role in plant growth and development,and in regulating the activity of transposable elements(TEs).Research on DNA methylation-related(DMR)genes has been reported in Arabidopsis,but little research on DMR genes has been reported in Brassica rapa and Brassica oleracea,the genomes of which exhibit significant differences in TE content.In this study,we identified 78 and 77 DMR genes in Brassica rapa and Brassica oleracea,respectively.Detailed analysis revealed that the numbers of DMR genes in different DMR pathways varied in B.rapa and B.oleracea.The evolutionary selection pressure of DMR genes in B.rapa and B.oleracea was compared,and the DMR genes showed differential evolution between these two species.The nucleotide diversity(π)and selective sweep(Tajima’s D)revealed footprints of selection in the B.rapa and B.oleracea populations.Transcriptome analysis showed that most DMR genes exhibited similar expression characteristics in B.rapa and B.oleracea.This study dissects the evolutionary differences and genetic variations of the DMR genes in B.rapa and B.oleracea,and will provide valuable resources for future research on the divergent evolution of DNA methylation between B.rapa and B.oleracea.

Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

Growth mechanism of a preferentially oriented mordenite membrane

A preferentially oriented mordenite membrane was successfully prepared on a seeded porous α-alumina support. Characterization results of XRD (X-ray diffractometer) and FE-SEM (field emission scanning electron microscope) revealed that evolutionary selection might predominantly contribute to the formation of the sharply oriented mordenite membrane. The nec- essary conditions under which evolutionary selection occurs are: (a) the number density of nuclei on the support surface should be high enough at the early stage; (b) the crystals should grow fastest along one direction; and (c) the zeolite layer should proceed via the successive growth of the crystals nucleated on the support surface instead of the accumulation of the crystals formed in the bulk solution.