In our previous screening of the transcriptome of the causal agent of the devastating pine wilt disease,pine wood nematode(PWN,Bursaphelenchus xylophilus),after treatment with the nematicide fomepizole,Surfeit locus g...In our previous screening of the transcriptome of the causal agent of the devastating pine wilt disease,pine wood nematode(PWN,Bursaphelenchus xylophilus),after treatment with the nematicide fomepizole,Surfeit locus gene sft-4,which encodes a regulatory factor,was found to be downregulated.In situ hybridization results showed that the sft-4 was continuously expressed from egg to adult and was especially high in the reproductive system.Here in a study of the effect of RNA interference(RNAi)of sft-4 and recombinant SFT-4 on PWN activity,treatment with sft-4 dsRNA inhibited feeding,reproduction,oviposition and egg hatching of PWN with the greatest inhibition on reproduction and oviposition,whereas recombinant SFT-4 had the opposite effect.In addition,RNAi of sft-4 changed the female–male ratio and lifespan of PWN.In bioassays of PWNs,with RNAi of sft-4 on seedlings and 2-year-old Pinus thunbergii trees,none of the treated plants developed symp-toms during the monitoring period,indicating that virulence of PWNs was either significantly weakened.These results indicate that the influence of sft-4 on PWN pathogenicity may be mainly through regulating reproductive function of PWN and its lifespan.展开更多
Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and cou...Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.展开更多
基金supported by the Shandong Provincial Natural Science Foundation,China(ZR2020MC123)Qingdao Municipal People-benefitting Demonstration Project of Science and Technology,China(23-2-8-cspz-8-nsh).
文摘In our previous screening of the transcriptome of the causal agent of the devastating pine wilt disease,pine wood nematode(PWN,Bursaphelenchus xylophilus),after treatment with the nematicide fomepizole,Surfeit locus gene sft-4,which encodes a regulatory factor,was found to be downregulated.In situ hybridization results showed that the sft-4 was continuously expressed from egg to adult and was especially high in the reproductive system.Here in a study of the effect of RNA interference(RNAi)of sft-4 and recombinant SFT-4 on PWN activity,treatment with sft-4 dsRNA inhibited feeding,reproduction,oviposition and egg hatching of PWN with the greatest inhibition on reproduction and oviposition,whereas recombinant SFT-4 had the opposite effect.In addition,RNAi of sft-4 changed the female–male ratio and lifespan of PWN.In bioassays of PWNs,with RNAi of sft-4 on seedlings and 2-year-old Pinus thunbergii trees,none of the treated plants developed symp-toms during the monitoring period,indicating that virulence of PWNs was either significantly weakened.These results indicate that the influence of sft-4 on PWN pathogenicity may be mainly through regulating reproductive function of PWN and its lifespan.
基金supported by the National Key Research and Development Program (2022YFF0609504)the National Natural Science Foundation of China (61974126,51902273,62005230,62001405)the Natural Science Foundation of Fujian Province of China (No.2021J06009)
文摘Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.
