Reducing phosphorylation of nitrate reductase improves nitrate assimilation in rice

Nitrate reductase(NR) is an important enzyme for nitrate assimilation in plants, and post-translational phosphorylation regulates NR activity. To evaluate the impact of the dephosphorylation of nitrate reductase 1(NIA1) protein on NR activity,nitrogen metabolism and plant growth, NIA1 phosphorylation site directed mutant lines(S532 D and S532 A) and an OsNia1 over-expression line(OE) were constructed, and the phenotype, NIA1 protein and its phosphorylation level, NR activity,nitrate metabolism and reactive oxygen metabolism of the transgenic lines were analysed. Exogenous NIA1 protein was not phosphorylated in S532 D and S532 A mutant lines, and their NR activities, activity states of NR and assimilation efficiencies of NO3–-N were higher than those in Kitaake(WT) and OE. The changes in these physiological and biochemical indexes in the OE line were less than in S532 D and S532 A compared to WT. These results suggest that the removal of transcriptional level control had little effect on nitrogen metabolism, but the removal of post-translational modification had a profound effect on it. With the removal of NIA1 phosphorylation and the improvement in the nitrate assimilation efficiency, the plant height and chlorophyll content of S532 D and S532 A decreased and the hydrogen peroxide and malondialdehyde contents of rice seedlings increased, which may be related to the excessive accumulation of nitrite as an intermediate metabolite. These results indicated that the phosphorylation of NR may be a self-protection mechanism of rice. The reduced phosphorylation level of nitrate reductase improved the assimilation of nitrate, and the increased phosphorylation level reduced the accumulation of nitrite and prevented the toxic effects of reactive oxygen species in rice.

Robust multi-objective optimization of rolling schedule for tandem cold rolling based on evolutionary direction differential evolution algorithm

According to the actual requirements,profile and rolling energy consumption are selected as objective functions of rolling schedule optimization for tandem cold rolling.Because of mechanical wear,roll diameter has some uncertainty during the rolling process,ignoring which will cause poor robustness of rolling schedule.In order to solve this problem,a robust multi-objective optimization model of rolling schedule for tandem cold rolling was established.A differential evolution algorithm based on the evolutionary direction was proposed.The algorithm calculated the horizontal angle of the vector,which was used to choose mutation vector.The chosen vector contained converging direction and it changed the random mutation operation in differential evolution algorithm.Efficiency of the proposed algorithm was verified by two benchmarks.Meanwhile,in order to ensure that delivery thicknesses have descending order like actual rolling schedule during evolution,a modified Latin Hypercube Sampling process was proposed.Finally,the proposed algorithm was applied to the model above.Results showed that profile was improved and rolling energy consumption was reduced compared with the actual rolling schedule.Meanwhile,robustness of solutions was ensured.

Hybrid gray wolf optimization-cuckoo search algorithm for RFID network planning

In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network planning(RNP)has become the primary concern.Compared with the traditional methods,meta-heuristic method is widely used in RNP.Aiming at the target requirements of RFID,such as fewer readers,covering more tags,reducing the interference between readers and saving costs,this paper proposes a hybrid gray wolf optimization-cuckoo search(GWO-CS)algorithm.This method uses the input representation based on random gray wolf search and evaluates the tag density and location to determine the combination performance of the reader's propagation area.Compared with particle swarm optimization(PSO)algorithm,cuckoo search(CS)algorithm and gray wolf optimization(GWO)algorithm under the same experimental conditions,the coverage of GWO-CS is 9.306%higher than that of PSO algorithm,6.963%higher than that of CS algorithm,and 3.488%higher than that of GWO algorithm.The results show that the GWO-CS algorithm cannot only improve the global search range,but also improve the local search depth.