N6-methyladenosine(m6A)is an important RNA methylation modification involved in regulating diverse biological processes across multiple species.Hence,the identification of m6A modification sites provides valuable insi...N6-methyladenosine(m6A)is an important RNA methylation modification involved in regulating diverse biological processes across multiple species.Hence,the identification of m6A modification sites provides valuable insight into the biological mechanisms of complex diseases at the post-transcriptional level.Although a variety of identification algorithms have been proposed recently,most of them capture the features of m6A modification sites by focusing on the sequential dependencies of nucleotides at different positions in RNA sequences,while ignoring the structural dependencies of nucleotides in their threedimensional structures.To overcome this issue,we propose a cross-species end-to-end deep learning model,namely CR-NSSD,which conduct a cross-domain representation learning process integrating nucleotide structural and sequential dependencies for RNA m6A site identification.Specifically,CR-NSSD first obtains the pre-coded representations of RNA sequences by incorporating the position information into single-nucleotide states with chaos game representation theory.It then constructs a crossdomain reconstruction encoder to learn the sequential and structural dependencies between nucleotides.By minimizing the reconstruction and binary cross-entropy losses,CR-NSSD is trained to complete the task of m6A site identification.Extensive experiments have demonstrated the promising performance of CR-NSSD by comparing it with several state-of-the-art m6A identification algorithms.Moreover,the results of cross-species prediction indicate that the integration of sequential and structural dependencies allows CR-NSSD to capture general features of m6A modification sites among different species,thus improving the accuracy of cross-species identification.展开更多
In this study,microbial fuel cells(MFCs)were explored to promote the nitrogen removal performance of combined anaerobic ammonium oxidation(anammox)and Fe-C micro-electrolysis(CAE)systems.The average total nitrogen(TN)...In this study,microbial fuel cells(MFCs)were explored to promote the nitrogen removal performance of combined anaerobic ammonium oxidation(anammox)and Fe-C micro-electrolysis(CAE)systems.The average total nitrogen(TN)removal efficiency of the modified MFC system was 85.00%,while that of the anammox system was 62.16%.Additionally,the effective operation time of this system increased from six(CAE system alone)to over 50 days,significantly promoting TN removal.The enhanced performance could be attributed to the electron transferred from the anode to the cathode,which aided in reducing nitrate/nitrite in denitrification.The H+released through the proton exchange membrane caused a decrease in the pH,facilitating Fe corrosion.The pyrolyzed waste tire used as the cathode could immobilize microorganisms,enhance electron transport,and produce a natural Fe-C micro-electrolysis system.According to the microbial community analysis,Candidatus kuenenia was the major genus involved in the anammox process.Furthermore,the SM1A02 genus exhibited the highest abundance and was enriched the fastest,and could be a novel potential strain that aids the anammox process.展开更多
基金supported in part by the National Natural Science Foundation of China(62373348)the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2021D01D05)+1 种基金the Tianshan Talent Training Program(2023TSYCLJ0021)the Pioneer Hundred Talents Program of Chinese Academy of Sciences.
文摘N6-methyladenosine(m6A)is an important RNA methylation modification involved in regulating diverse biological processes across multiple species.Hence,the identification of m6A modification sites provides valuable insight into the biological mechanisms of complex diseases at the post-transcriptional level.Although a variety of identification algorithms have been proposed recently,most of them capture the features of m6A modification sites by focusing on the sequential dependencies of nucleotides at different positions in RNA sequences,while ignoring the structural dependencies of nucleotides in their threedimensional structures.To overcome this issue,we propose a cross-species end-to-end deep learning model,namely CR-NSSD,which conduct a cross-domain representation learning process integrating nucleotide structural and sequential dependencies for RNA m6A site identification.Specifically,CR-NSSD first obtains the pre-coded representations of RNA sequences by incorporating the position information into single-nucleotide states with chaos game representation theory.It then constructs a crossdomain reconstruction encoder to learn the sequential and structural dependencies between nucleotides.By minimizing the reconstruction and binary cross-entropy losses,CR-NSSD is trained to complete the task of m6A site identification.Extensive experiments have demonstrated the promising performance of CR-NSSD by comparing it with several state-of-the-art m6A identification algorithms.Moreover,the results of cross-species prediction indicate that the integration of sequential and structural dependencies allows CR-NSSD to capture general features of m6A modification sites among different species,thus improving the accuracy of cross-species identification.
基金supported by the Scientific and Technological Project of Shanxi Province(Nos.201903D321057 and 201903D321055)by the National Natural Science Foundation of China(Grant Nos.51708386 and 21501129)+1 种基金by the China Postdoctoral Science Foundation(No.2016M601290)the Ministry of Environmental Protection of China(Major Science and Technology Program,Nos.2019YFC0408601 and 2019YFC0408602)。
文摘In this study,microbial fuel cells(MFCs)were explored to promote the nitrogen removal performance of combined anaerobic ammonium oxidation(anammox)and Fe-C micro-electrolysis(CAE)systems.The average total nitrogen(TN)removal efficiency of the modified MFC system was 85.00%,while that of the anammox system was 62.16%.Additionally,the effective operation time of this system increased from six(CAE system alone)to over 50 days,significantly promoting TN removal.The enhanced performance could be attributed to the electron transferred from the anode to the cathode,which aided in reducing nitrate/nitrite in denitrification.The H+released through the proton exchange membrane caused a decrease in the pH,facilitating Fe corrosion.The pyrolyzed waste tire used as the cathode could immobilize microorganisms,enhance electron transport,and produce a natural Fe-C micro-electrolysis system.According to the microbial community analysis,Candidatus kuenenia was the major genus involved in the anammox process.Furthermore,the SM1A02 genus exhibited the highest abundance and was enriched the fastest,and could be a novel potential strain that aids the anammox process.
