杂种优势利用是提高大豆产量的有效策略之一,细胞质雄性不育(Cytoplasmic-nuclear Male Sterility,CMS)在大豆杂种优势利用中具有重要作用,为了阐明大豆CMS发生的分子机制,利用大豆CMS系W931A及其保持系W931B的单核小孢子(Uninucleate M...杂种优势利用是提高大豆产量的有效策略之一,细胞质雄性不育(Cytoplasmic-nuclear Male Sterility,CMS)在大豆杂种优势利用中具有重要作用,为了阐明大豆CMS发生的分子机制,利用大豆CMS系W931A及其保持系W931B的单核小孢子(Uninucleate Microspore,UM)和二胞花粉期(Binucleate Pollen,BP)花蕾进行代谢组学研究,并与转录组数据联合分析,挖掘大豆CMS相关基因及代谢途径。代谢组分析结果显示,在CMS系W931A的UM和BP阶段分别鉴定到147和305个差异代谢物,主要包括脂质及类脂分子化合物、苯丙烷及聚酮化合物和有机杂环化合物等。转录组和代谢组联合分析揭示,差异表达基因(Differentially Expressed Genes,DEGs)和差异代谢物主要参与黄酮类化合物生物合成、苯丙氨酸代谢和苯丙烷类生物合成。在花粉发育过程中,黄酮类及衍生物的合成被显著抑制,F3H(Glyma.01G166200)和FLS(Glyma.06G110600)基因在CMS系W931A中显著下调表达,推测其在相应代谢途径中发挥关键调控作用,导致花药中相关代谢物的差异,进而引发不育系花粉的败育。研究结果有助于构建大豆CMS分子调控网络,并推动大豆CMS分子机制的研究进程。展开更多
An effective energy management strategy(EMS)is essential to optimize the energy efficiency of electric vehicles(EVs).With the advent of advanced machine learning techniques,the focus on developing sophisticated EMS fo...An effective energy management strategy(EMS)is essential to optimize the energy efficiency of electric vehicles(EVs).With the advent of advanced machine learning techniques,the focus on developing sophisticated EMS for EVs is increasing.Here,we introduce LearningEMS:a unified framework and open-source benchmark designed to facilitate rapid development and assessment of EMS.LearningEMS is distinguished by its ability to support a variety of EV configurations,including hybrid EVs,fuel cell EVs,and plug-in EVs,offering a general platform for the development of EMS.The framework enables detailed comparisons of several EMS algorithms,encompassing imitation learning,deep reinforcement learning(RL),offline RL,model predictive control,and dynamic programming.We rigorously evaluated these algorithms across multiple perspectives:energy efficiency,consistency,adaptability,and practicability.Furthermore,we discuss state,reward,and action settings for RL in EV energy management,introduce a policy extraction and reconstruction method for learning-based EMS deployment,and conduct hardware-in-the-loop experiments.In summary,we offer a unified and comprehensive framework that comes with three distinct EV platforms,over 10000 km of EMS policy data set,ten state-of-the-art algorithms,and over 160 benchmark tasks,along with three learning libraries.Its flexible design allows easy expansion for additional tasks and applications.The open-source algorithms,models,data sets,and deployment processes foster additional research and innovation in EV and broader engineering domains.展开更多
基金supported in part by the National Natural Science Foundation of China(52172377).
文摘An effective energy management strategy(EMS)is essential to optimize the energy efficiency of electric vehicles(EVs).With the advent of advanced machine learning techniques,the focus on developing sophisticated EMS for EVs is increasing.Here,we introduce LearningEMS:a unified framework and open-source benchmark designed to facilitate rapid development and assessment of EMS.LearningEMS is distinguished by its ability to support a variety of EV configurations,including hybrid EVs,fuel cell EVs,and plug-in EVs,offering a general platform for the development of EMS.The framework enables detailed comparisons of several EMS algorithms,encompassing imitation learning,deep reinforcement learning(RL),offline RL,model predictive control,and dynamic programming.We rigorously evaluated these algorithms across multiple perspectives:energy efficiency,consistency,adaptability,and practicability.Furthermore,we discuss state,reward,and action settings for RL in EV energy management,introduce a policy extraction and reconstruction method for learning-based EMS deployment,and conduct hardware-in-the-loop experiments.In summary,we offer a unified and comprehensive framework that comes with three distinct EV platforms,over 10000 km of EMS policy data set,ten state-of-the-art algorithms,and over 160 benchmark tasks,along with three learning libraries.Its flexible design allows easy expansion for additional tasks and applications.The open-source algorithms,models,data sets,and deployment processes foster additional research and innovation in EV and broader engineering domains.
