Heading date is a key trait in rice domestication and adaption, and a number of quantitative trait loci（QTLs）have been identified. The rice（Oryza sativa L.） cultivars in the Heilongjiang Province, the northernmost...Heading date is a key trait in rice domestication and adaption, and a number of quantitative trait loci（QTLs）have been identified. The rice（Oryza sativa L.） cultivars in the Heilongjiang Province, the northernmost region of China,have to flower extremely early to fulfill their life cycle.However, the critical genes or different gene combinations controlling early flowering in this region have not been determined. QTL and candidate gene analysis revealed that Hd2/Ghd7.1/Os PRR37 plays a major role in controlling rice distribution in Heilongjiang. Further association analysis with a collection of rice cultivars demonstrated that another three major QTL genes（Hd4/Ghd7, Hd5/DTH8/Ghd8, and Hd1）also participate in regulating heading date under natural long day（LD） conditions. Hd2/Ghd7.1/Os PRR37 and Hd4/Ghd7 are two major QTLs and function additively. With the northward rice cultivation, the Hd2/Ghd7.1/Os PRR37 and Hd4/Ghd7 haplotypes became non-functional alleles. Hd1 might be non-functional in most Heilongjiang rice varieties,implying that recessive hd1 were selected during local rice breeding. Non-functional Hd5/DTH8/Ghd8 is very rare, but constitutes a potential target for breeding extremely early flowering cultivars. Our results indicated that diverse genetic combinations of Hd1, Hd2, Hd4, and Hd5 determined the different distribution of rice varieties in this northernmost province of China.展开更多
Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading...Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading to poor performance and privacy breaches.Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business development.To ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)model.However,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the cloud.HD2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of Authority.The“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to poisoning.FL with Blockchain allows quick consensus through smart member selection and verification.The HD2C paradigm significantly improves the computational processing efficiency of intelligent manufacturing.Extensive analysis results derived from IIoT datasets confirm HD2C superiority.When compared to other consensus algorithms,the Blockchain PoA’s foundational cost is significant.The accuracy and memory utilization evaluation results predict the total benefits of the system.In comparison to the values 0.004 and 0.04,the value of 0.4 achieves good accuracy.According to the experiment results,the number of transactions per second has minimal impact on memory requirements.The findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.展开更多
基金supported by the Hundred-Talent Program of the Chinese Academy of SciencesNational Natural Science Foundation of China(31070255,31371588)+1 种基金Excellent Academic Leaders of Harbin(RC2014XK002003)the High Tech Program of Ministry of Science and Technology of China(2014AA10A602-5)
文摘Heading date is a key trait in rice domestication and adaption, and a number of quantitative trait loci（QTLs）have been identified. The rice（Oryza sativa L.） cultivars in the Heilongjiang Province, the northernmost region of China,have to flower extremely early to fulfill their life cycle.However, the critical genes or different gene combinations controlling early flowering in this region have not been determined. QTL and candidate gene analysis revealed that Hd2/Ghd7.1/Os PRR37 plays a major role in controlling rice distribution in Heilongjiang. Further association analysis with a collection of rice cultivars demonstrated that another three major QTL genes（Hd4/Ghd7, Hd5/DTH8/Ghd8, and Hd1）also participate in regulating heading date under natural long day（LD） conditions. Hd2/Ghd7.1/Os PRR37 and Hd4/Ghd7 are two major QTLs and function additively. With the northward rice cultivation, the Hd2/Ghd7.1/Os PRR37 and Hd4/Ghd7 haplotypes became non-functional alleles. Hd1 might be non-functional in most Heilongjiang rice varieties,implying that recessive hd1 were selected during local rice breeding. Non-functional Hd5/DTH8/Ghd8 is very rare, but constitutes a potential target for breeding extremely early flowering cultivars. Our results indicated that diverse genetic combinations of Hd1, Hd2, Hd4, and Hd5 determined the different distribution of rice varieties in this northernmost province of China.
文摘Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading to poor performance and privacy breaches.Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business development.To ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)model.However,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the cloud.HD2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of Authority.The“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to poisoning.FL with Blockchain allows quick consensus through smart member selection and verification.The HD2C paradigm significantly improves the computational processing efficiency of intelligent manufacturing.Extensive analysis results derived from IIoT datasets confirm HD2C superiority.When compared to other consensus algorithms,the Blockchain PoA’s foundational cost is significant.The accuracy and memory utilization evaluation results predict the total benefits of the system.In comparison to the values 0.004 and 0.04,the value of 0.4 achieves good accuracy.According to the experiment results,the number of transactions per second has minimal impact on memory requirements.The findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.
