Herbivorous insects and pathogens cause severe damage to rice tissues,affecting yield and grain quality.Damaged cells trigger downstream defense responses through various signals.Extracellular ATP(eATP),a signaling mo...Herbivorous insects and pathogens cause severe damage to rice tissues,affecting yield and grain quality.Damaged cells trigger downstream defense responses through various signals.Extracellular ATP(eATP),a signaling molecule released during mechanical cell damage,is considered a constitutive damage-associated molecular pattern(DAMP),which is crucial for initiating plant defense responses.Thus,understanding how rice plants cope with DAMPs such as eATP is essential.Here,we found that exogenous ATP affected rice growth and development,cell wall composition,chloroplast development,and cell death.Subsequent global transcriptome analysis revealed that several pathways were involved in the eATP response,including genes related to cell surface receptors,cell wall organization,chlorophyll biosynthesis,heat and temperature stimulation,epigenetic regulation,and reactive oxygen species metabolism.Cell surface receptors,including members of the lectin receptor-like kinases(LecRKs),were found to participate in the eATP response.We further investigated ATP-induced genes in T-DNA activation mutants of OsLecRKs,demonstrating their involvement in eATP signaling in rice.This study confirms a DAMP-mediated transcriptional response in plants and provides novel candidates for advancing resistant rice breeding against insect herbivores and pathogens.展开更多
As the global electric vehicle market continues to grow,the recycling of Li-ion battery (LIB) becomes more important worldwide and the resynthesis of cathode materials would be the most value-added recycling approach ...As the global electric vehicle market continues to grow,the recycling of Li-ion battery (LIB) becomes more important worldwide and the resynthesis of cathode materials would be the most value-added recycling approach taking into account limited metal resources.Although resynthesized homogenous LiNi_(x)Co_(y)Mn_(z)O_(2)(NCM) from spent LIB leachate shows comparable battery performance to pristine NCM from virgin materials,there is general concern in its cycling performance.Here,we synthesize core–shell(CS) Ni-rich NCM,which consists of Ni-rich NCM as the core and NCM derived from the original or purified leachate of spent LIBs as the shell.Resynthesized CS Ni-rich NCM exhibits improved rate capability resulting from expanded interslab thickness in the NCM structure.CS Ni-rich NCM from purified LIB leachate shows improvement in cycling performance and thermal stability.It specifically delivers a capacity retention of 86.6%at a high temperature after 80 cycles compared to that (75.0%) of pristine CS Ni-rich NCM.These improvements are caused by a relatively high Mg content on the shell and the widespread distribution of Al through the CS structure.CS Ni-rich NCM derived from spent LIB leachate provides a new alternative approach to conventional LIB recycling methods,which would utilize efficiently limited metal resources for the sustainable LIB production.展开更多
Developing a universal model for predicting condensation heat transfer coefficients remains challenging,particularly for steam–non-condensable gas mixtures,owing to the intricate nonlinear interactions between multip...Developing a universal model for predicting condensation heat transfer coefficients remains challenging,particularly for steam–non-condensable gas mixtures,owing to the intricate nonlinear interactions between multiphase flow,heat,and mass transfer phenomena.Data-driven machine learning(ML)shows promise in efficiently and accurately predicting condensation heat transfer coefficients.Research has employed various ML methods—multilayer perceptron neural networks,convolutional-neural-network–based DenseNet,backpropagation neural networks,etc.—to investigate steam condensation with non-condensable gases.However,these exhibit limited extrapolation ability and heavily rely on data quantity owing to their black-box nature.This study proposes a physics-informed ML model that combines physical constraints derived from the modified Nusselt model with conventional data-driven ML techniques.The model's predictive performance is evaluated using a comprehensive database(879 datapoints from 13 studies).A physics-constrained and eight data-driven ML methods are assessed.The results reveal that the physics-constrained approach combined with XGBoost significantly outperforms conventional ML methods on extrapolation datasets(199 datapoints from 3 studies),achieving a mean absolute percentage error of 11.22%,which is approximately half that of the best-performing fully data-driven model at 21.63%.The model demonstrates consistent and reliable performance across diverse datasets,making it an effective tool for predicting heat transfer coefficients in steam–non-condensable gas mixtures.By deepening the understanding of the underlying physical processes,the proposed model supports the development of precise and efficient engineering solutions for condensation heat transfer.展开更多
基金supported by the Brain Pool Program funded by the Ministry of Science and Information and Communication Technology through the National Research Foundation of Korea(Grant Nos.2022H1D3A2A01096185 and RS-2024-00410063)the Basic Science Research Program through the National Research Foundation of Korea(Grant No.RS-2023-00247376)+4 种基金the Cooperative Research Program for Agriculture Science and Technology Development,Korea(Grant No.RS-2022-RD010386)US National Science Foundation Plant Genome Program(Grant No.IOS-2048410)the US National Institute of General Medical Sciences of the National Institutes of Health(Grant No.R01GM121445)US Department of Agriculture’s National Institute of Food and Agriculture(Grant No.USDA-AFRI-2023-67013-39896)the National Science Foundation(Grant No.IOS-PGRP-2348319).
文摘Herbivorous insects and pathogens cause severe damage to rice tissues,affecting yield and grain quality.Damaged cells trigger downstream defense responses through various signals.Extracellular ATP(eATP),a signaling molecule released during mechanical cell damage,is considered a constitutive damage-associated molecular pattern(DAMP),which is crucial for initiating plant defense responses.Thus,understanding how rice plants cope with DAMPs such as eATP is essential.Here,we found that exogenous ATP affected rice growth and development,cell wall composition,chloroplast development,and cell death.Subsequent global transcriptome analysis revealed that several pathways were involved in the eATP response,including genes related to cell surface receptors,cell wall organization,chlorophyll biosynthesis,heat and temperature stimulation,epigenetic regulation,and reactive oxygen species metabolism.Cell surface receptors,including members of the lectin receptor-like kinases(LecRKs),were found to participate in the eATP response.We further investigated ATP-induced genes in T-DNA activation mutants of OsLecRKs,demonstrating their involvement in eATP signaling in rice.This study confirms a DAMP-mediated transcriptional response in plants and provides novel candidates for advancing resistant rice breeding against insect herbivores and pathogens.
基金supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2023R1A2C100571511,RS-2023-00254424)the Ministry of Education(2020R1A6A1A03038540)。
文摘As the global electric vehicle market continues to grow,the recycling of Li-ion battery (LIB) becomes more important worldwide and the resynthesis of cathode materials would be the most value-added recycling approach taking into account limited metal resources.Although resynthesized homogenous LiNi_(x)Co_(y)Mn_(z)O_(2)(NCM) from spent LIB leachate shows comparable battery performance to pristine NCM from virgin materials,there is general concern in its cycling performance.Here,we synthesize core–shell(CS) Ni-rich NCM,which consists of Ni-rich NCM as the core and NCM derived from the original or purified leachate of spent LIBs as the shell.Resynthesized CS Ni-rich NCM exhibits improved rate capability resulting from expanded interslab thickness in the NCM structure.CS Ni-rich NCM from purified LIB leachate shows improvement in cycling performance and thermal stability.It specifically delivers a capacity retention of 86.6%at a high temperature after 80 cycles compared to that (75.0%) of pristine CS Ni-rich NCM.These improvements are caused by a relatively high Mg content on the shell and the widespread distribution of Al through the CS structure.CS Ni-rich NCM derived from spent LIB leachate provides a new alternative approach to conventional LIB recycling methods,which would utilize efficiently limited metal resources for the sustainable LIB production.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024–00353227 and RS-2024–00411577).
文摘Developing a universal model for predicting condensation heat transfer coefficients remains challenging,particularly for steam–non-condensable gas mixtures,owing to the intricate nonlinear interactions between multiphase flow,heat,and mass transfer phenomena.Data-driven machine learning(ML)shows promise in efficiently and accurately predicting condensation heat transfer coefficients.Research has employed various ML methods—multilayer perceptron neural networks,convolutional-neural-network–based DenseNet,backpropagation neural networks,etc.—to investigate steam condensation with non-condensable gases.However,these exhibit limited extrapolation ability and heavily rely on data quantity owing to their black-box nature.This study proposes a physics-informed ML model that combines physical constraints derived from the modified Nusselt model with conventional data-driven ML techniques.The model's predictive performance is evaluated using a comprehensive database(879 datapoints from 13 studies).A physics-constrained and eight data-driven ML methods are assessed.The results reveal that the physics-constrained approach combined with XGBoost significantly outperforms conventional ML methods on extrapolation datasets(199 datapoints from 3 studies),achieving a mean absolute percentage error of 11.22%,which is approximately half that of the best-performing fully data-driven model at 21.63%.The model demonstrates consistent and reliable performance across diverse datasets,making it an effective tool for predicting heat transfer coefficients in steam–non-condensable gas mixtures.By deepening the understanding of the underlying physical processes,the proposed model supports the development of precise and efficient engineering solutions for condensation heat transfer.
