Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have b...Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.展开更多
Doubled haploid(DH)technology is an important tool in crop breeding because it can significantly accelerate the breeding process.ZmPLA1/MATL/NLD and ZmDMP are two key genes controlling haploid induction(HI)in maize,ex...Doubled haploid(DH)technology is an important tool in crop breeding because it can significantly accelerate the breeding process.ZmPLA1/MATL/NLD and ZmDMP are two key genes controlling haploid induction(HI)in maize,exhibiting a synergistic effect.However,it is unknown whether knock out of ZmDMP orthologs can stimulate HI in rice.In this study,a ZmPLA1 ortholog(OsPLA1)and two ZmDMP orthologs(OsDMP3 and OsDMP6)were identified in rice.All three genes encode plasma membrane-localized proteins and were highly expressed in mature anthers.Knockout of OsPLA1 in both Minghui 63 and Nipponbare resulted in reduced seed setting rate(SSR)and caused HI.The osdmp3,osdmp6 and the double mutant failed to trigger HI independently,nor increased the haploid induction rate(HIR)when combined with ospla1.Repeated pollinations operations of QX654A with the ospla1 mutant significantly improve SSR,while reducing HIR.RNA-seq profiling of mature ospla1 mutant anthers indicated that a large number of differentially expressed genes(DEGs)were enriched in redox homeostasis and lipid metabolic GO terms,plant hormone signal transduction,and MAPK signaling pathways.These findings provide important insights towards construction of an efficient DH breeding technology and study of the molecular mechanism of HI in rice.展开更多
The practical applications of zinc metal batteries are plagued by the dendritic propagation of its metal anodes due to the limited transfer rate of charge and mass at the electrode/electrolyte interphase.To enhance th...The practical applications of zinc metal batteries are plagued by the dendritic propagation of its metal anodes due to the limited transfer rate of charge and mass at the electrode/electrolyte interphase.To enhance the reversibility of Zn metal,a quasi-solid interphase composed by defective metal-organic framework(MOF)nanoparticles(D-UiO-66)and two kinds of zinc salts electrolytes is fabricated on the Zn surface served as a zinc ions reservoir.Particularly,anions in the aqueous electrolytes could be spontaneously anchored onto the Lewis acidic sites in defective MOF channels.With the synergistic effect between the MOF channels and the anchored anions,Zn^(2+)transport is prompted significantly.Simultaneously,such quasi-solid interphase boost charge and mass transfer of Zn^(2+),leading to a high zinc transference number,good ionic conductivity,and high Zn^(2+)concentration near the anode,which mitigates Zn dendrite growth obviously.Encouragingly,unprecedented average coulombic efficiency of 99.8%is achieved in the Zn||Cu cell with the proposed quasi-solid interphase.The cycling performance of D-UiO-66@Zn||MnO_(2)(~92.9%capacity retention after 2000 cycles)and D-UiO-66@Zn||NH_(4)V_(4)O_(10)(~84.0%capacity retention after 800 cycles)prove the feasibility of the quasi-solid interphase.展开更多
Predicting disruptions across different tokamaks is necessary for next generation device.Future large-scale tokamaks can hardly tolerate disruptions at high performance discharge,which makes it difficult for current d...Predicting disruptions across different tokamaks is necessary for next generation device.Future large-scale tokamaks can hardly tolerate disruptions at high performance discharge,which makes it difficult for current data-driven methods to obtain an acceptable result.A machine learning method capable of transferring a disruption prediction model trained on one tokamak to another is required to solve the problem.The key is a feature extractor which is able to extract common disruption precursor traces in tokamak diagnostic data,and can be easily transferred to other tokamaks.Based on the concerns above,this paper presents a deep feature extractor,namely,the fusion feature extractor(FFE),which is designed specifically for extracting disruption precursor features from common diagnostics on tokamaks.Furthermore,an FFE-based disruption predictor on J-TEXT is demonstrated.The feature extractor is aimed to extracting disruption-related precursors and is designed according to the precursors of disruption and their representations in common tokamak diagnostics.Strong inductive bias on tokamak diagnostics data is introduced.The paper presents the evolution of the neural network feature extractor and its comparison against general deep neural networks,as well as a physics-based feature extraction with a traditional machine learning method.Results demonstrate that the FFE may reach a similar effect with physics-guided manual feature extraction,and obtain a better result compared with other deep learning methods.展开更多
基金Project supported by the Gansu Province Industrial Support Plan (Grant No.2023CYZC-25)the Natural Science Foundation of Gansu Province (Grant No.23JRRA770)the National Natural Science Foundation of China (Grant No.62162040)。
文摘Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.
基金This work was supported by the National Key Research and Development Program of China(2022YFD1200800)the China Agriculture Research System(CARS-02-05)+1 种基金Beijing Nova Program(2023067)Yunnan Province Science and Technology Department(202305AF150026).
文摘Doubled haploid(DH)technology is an important tool in crop breeding because it can significantly accelerate the breeding process.ZmPLA1/MATL/NLD and ZmDMP are two key genes controlling haploid induction(HI)in maize,exhibiting a synergistic effect.However,it is unknown whether knock out of ZmDMP orthologs can stimulate HI in rice.In this study,a ZmPLA1 ortholog(OsPLA1)and two ZmDMP orthologs(OsDMP3 and OsDMP6)were identified in rice.All three genes encode plasma membrane-localized proteins and were highly expressed in mature anthers.Knockout of OsPLA1 in both Minghui 63 and Nipponbare resulted in reduced seed setting rate(SSR)and caused HI.The osdmp3,osdmp6 and the double mutant failed to trigger HI independently,nor increased the haploid induction rate(HIR)when combined with ospla1.Repeated pollinations operations of QX654A with the ospla1 mutant significantly improve SSR,while reducing HIR.RNA-seq profiling of mature ospla1 mutant anthers indicated that a large number of differentially expressed genes(DEGs)were enriched in redox homeostasis and lipid metabolic GO terms,plant hormone signal transduction,and MAPK signaling pathways.These findings provide important insights towards construction of an efficient DH breeding technology and study of the molecular mechanism of HI in rice.
基金supported by Zhejiang University K.P.Chao’s High Technology Development Foundation.
文摘The practical applications of zinc metal batteries are plagued by the dendritic propagation of its metal anodes due to the limited transfer rate of charge and mass at the electrode/electrolyte interphase.To enhance the reversibility of Zn metal,a quasi-solid interphase composed by defective metal-organic framework(MOF)nanoparticles(D-UiO-66)and two kinds of zinc salts electrolytes is fabricated on the Zn surface served as a zinc ions reservoir.Particularly,anions in the aqueous electrolytes could be spontaneously anchored onto the Lewis acidic sites in defective MOF channels.With the synergistic effect between the MOF channels and the anchored anions,Zn^(2+)transport is prompted significantly.Simultaneously,such quasi-solid interphase boost charge and mass transfer of Zn^(2+),leading to a high zinc transference number,good ionic conductivity,and high Zn^(2+)concentration near the anode,which mitigates Zn dendrite growth obviously.Encouragingly,unprecedented average coulombic efficiency of 99.8%is achieved in the Zn||Cu cell with the proposed quasi-solid interphase.The cycling performance of D-UiO-66@Zn||MnO_(2)(~92.9%capacity retention after 2000 cycles)and D-UiO-66@Zn||NH_(4)V_(4)O_(10)(~84.0%capacity retention after 800 cycles)prove the feasibility of the quasi-solid interphase.
基金Project supported by the National Key R&D Program of China (Grant No. 2022YFE03040004)the National Natural Science Foundation of China (Grant No. 51821005)
文摘Predicting disruptions across different tokamaks is necessary for next generation device.Future large-scale tokamaks can hardly tolerate disruptions at high performance discharge,which makes it difficult for current data-driven methods to obtain an acceptable result.A machine learning method capable of transferring a disruption prediction model trained on one tokamak to another is required to solve the problem.The key is a feature extractor which is able to extract common disruption precursor traces in tokamak diagnostic data,and can be easily transferred to other tokamaks.Based on the concerns above,this paper presents a deep feature extractor,namely,the fusion feature extractor(FFE),which is designed specifically for extracting disruption precursor features from common diagnostics on tokamaks.Furthermore,an FFE-based disruption predictor on J-TEXT is demonstrated.The feature extractor is aimed to extracting disruption-related precursors and is designed according to the precursors of disruption and their representations in common tokamak diagnostics.Strong inductive bias on tokamak diagnostics data is introduced.The paper presents the evolution of the neural network feature extractor and its comparison against general deep neural networks,as well as a physics-based feature extraction with a traditional machine learning method.Results demonstrate that the FFE may reach a similar effect with physics-guided manual feature extraction,and obtain a better result compared with other deep learning methods.
