Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the ...Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on Mat Elab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI.展开更多
Alfalfa(Medicago sativa.L.)is a globally significant autotetraploid legume forage crop.However,despite its importance,establishing efficient gene editing systems for cultivated alfalfa remains a formidable challenge.I...Alfalfa(Medicago sativa.L.)is a globally significant autotetraploid legume forage crop.However,despite its importance,establishing efficient gene editing systems for cultivated alfalfa remains a formidable challenge.In this study,we pioneered the development of a highly effective ultrasonic-assisted leaf disc transformation system for Gongnong 1 alfalfa,a variety widely cultivated in Northeast China.Subsequently,we created a single transcript CRISPR/Cas9(CRISPR_2.0)toolkit,incorporating multiplex gRNAs,designed for gene editing in Gongnong 1.Both Cas9 and gRNA scaffolds were under the control of the Arabidopsis ubiquitin-10 promoter,a widely employed polymeraseⅡconstitutive promoter known for strong transgene expression in dicots.To assess the toolkit’s efficiency,we targeted PALM1,a gene associated with a recognizable multifoliate phenotype.Utilizing the CRISPR_2.0 toolkit,we directed PALM1 editing at two sites in the wild-type Gongnong 1.Results indicated a 35.1%occurrence of editing events all in target 2 alleles,while no mutations were detected at target 1 in the transgenic-positive lines.To explore more efficient sgRNAs,we developed a rapid,reliable screening system based on Agrobacterium rhizogenes-mediated hairy root transformation,incorporating the visible reporter MtLAP1.This screening system demonstrated that most purple visible hairy roots underwent gene editing.Notably,sgRNA3,with an 83.0%editing efficiency,was selected using the visible hairy root system.As anticipated,tetra-allelic homozygous palm1 mutations exhibited a clear multifoliate phenotype.These palm1 lines demonstrated an average crude protein yield increase of 21.5%compared to trifoliolate alfalfa.Our findings highlight the modified CRISPR_2.0 system as a highly efficient and robust gene editing tool for autotetraploid alfalfa.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 52022106 and 52172258)the Informatization Plan of Chinese Academy of Sciences (Grant No. CASWX2021SF-0102)。
文摘Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on Mat Elab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA26030301)Hohhot Key R&D Project(2023-JBGSS-1),the National Natural Science Foundation of China(U23A200206,32071864,32325035)+1 种基金the Taishan Scholar Program of Shandong(to Chunxiang Fu)the Shandong Provincial Natural Science Foundation(ZR202210270038)。
文摘Alfalfa(Medicago sativa.L.)is a globally significant autotetraploid legume forage crop.However,despite its importance,establishing efficient gene editing systems for cultivated alfalfa remains a formidable challenge.In this study,we pioneered the development of a highly effective ultrasonic-assisted leaf disc transformation system for Gongnong 1 alfalfa,a variety widely cultivated in Northeast China.Subsequently,we created a single transcript CRISPR/Cas9(CRISPR_2.0)toolkit,incorporating multiplex gRNAs,designed for gene editing in Gongnong 1.Both Cas9 and gRNA scaffolds were under the control of the Arabidopsis ubiquitin-10 promoter,a widely employed polymeraseⅡconstitutive promoter known for strong transgene expression in dicots.To assess the toolkit’s efficiency,we targeted PALM1,a gene associated with a recognizable multifoliate phenotype.Utilizing the CRISPR_2.0 toolkit,we directed PALM1 editing at two sites in the wild-type Gongnong 1.Results indicated a 35.1%occurrence of editing events all in target 2 alleles,while no mutations were detected at target 1 in the transgenic-positive lines.To explore more efficient sgRNAs,we developed a rapid,reliable screening system based on Agrobacterium rhizogenes-mediated hairy root transformation,incorporating the visible reporter MtLAP1.This screening system demonstrated that most purple visible hairy roots underwent gene editing.Notably,sgRNA3,with an 83.0%editing efficiency,was selected using the visible hairy root system.As anticipated,tetra-allelic homozygous palm1 mutations exhibited a clear multifoliate phenotype.These palm1 lines demonstrated an average crude protein yield increase of 21.5%compared to trifoliolate alfalfa.Our findings highlight the modified CRISPR_2.0 system as a highly efficient and robust gene editing tool for autotetraploid alfalfa.
