Creation of targeted inversion mutations in plants using an RNA-guided endonuclease

Inversions are DNA rearrangements that are essential for plant gene evolution and adaptation to environmental changes. We demonstrate the creation of targeted inversions and previously reported targeted deletion mutations via delivery of a pair of RNA-guided endonucleases(RGENs) of CRISPR/Cas9. The efficiencies of the targeted inversions were2.6% and 2.2% in the Arabidopsis FLOWERING TIME(At FT) and TERMINAL FLOWER 1(At TFL1)loci, respectively. Thus, we successfully established an approach that can potentially be used to introduce targeted DNA inversions of interest for functional studies and crop improvement.

GOPS:A general optimal control problem solver for autonomous driving and industrial control applications

Solving optimal control problems serves as the basic demand of industrial control tasks.Existing methods like model predictive control often suffer from heavy online computational burdens.Reinforcement learning has shown promise in computer and board games but has yet to be widely adopted in industrial applications due to a lack of accessible,high-accuracy solvers.Current Reinforcement learning(RL)solvers are often developed for academic research and require a significant amount of theoretical knowledge and programming skills.Besides,many of them only support Python-based environments and limit to model-free algorithms.To address this gap,this paper develops General Optimal control Problems Solver(GOPS),an easy-to-use RL solver package that aims to build real-time and high-performance controllers in industrial fields.GOPS is built with a highly modular structure that retains a flexible framework for secondary development.Considering the diversity of industrial control tasks,GOPS also includes a conversion tool that allows for the use of Matlab/Simulink to support environment construction,controller design,and performance validation.To handle large-scale problems,GOPS can automatically create various serial and parallel trainers by flexibly combining embedded buffers and samplers.It offers a variety of common approximate functions for policy and value functions,including polynomial,multilayer perceptron,convolutional neural network,etc.Additionally,constrained and robust algorithms for special industrial control systems with state constraints and model uncertainties are also integrated into GOPS.Several examples,including linear quadratic control,inverted double pendulum,vehicle tracking,humanoid robot,obstacle avoidance,and active suspension control,are tested to verify the performances of GOPS.

Genome Editing Enables Next-Generation Hybrid Seed Production Technology

The next-generation hybrid seed technology enables the successful production of sortable hybrid seeds from genic male sterile(GMS)lines and maintainers;however,it requires multiple laborious and complicated steps.Here,we designed a simple next-generation hybrid seed production strategy that takes advantage of the CRISPR/Cas9 technology to create a Manipulated GMS Maintainer(MGM)system via a single transformation.Under this schema,the maize male fertility gene ZmMS26 was nullified by removal of its fifth exon using the CRISPR/Cas9 system on a vector,and a second vector carrying a functional ZmMS26 cDNA was co-transformed to restore fertility.The second vector also contains a male gametophyte inactivation gene(ZmAA1)encoding maizeα-amylase driven by the pollen-specific promoter PG47 and an endosperm fluorescent marker(DsRED)driven by the barley endosperm aleurone-specific promoter Ltp2.The derived single-copy hemizygous MGM lines bore a mutated MS26 gene,leading to complete male sterility but normal vegetative growth and grain yield.The MGM system could prevent genetic transmission of the MGM elements via male gametophytes,providing an efficient method for sorting maintainer seeds labeled by DsRED.This strategy can be extended to any GMS gene and to hybrid crops other than maize.