The complexity of river-tide interaction poses a significant challenge in predicting discharge in tidal rivers.Long short-term memory(LSTM)networks excel in processing and predicting crucial events with extended inter...The complexity of river-tide interaction poses a significant challenge in predicting discharge in tidal rivers.Long short-term memory(LSTM)networks excel in processing and predicting crucial events with extended intervals and time delays in time series data.Additionally,the sequence-to-sequence(Seq2Seq)model,known for handling temporal relationships,adapting to variable-length sequences,effectively capturing historical information,and accommodating various influencing factors,emerges as a robust and flexible tool in discharge forecasting.In this study,we introduce the application of LSTM-based Seq2Seq models for the first time in forecasting the discharge of a tidal reach of the Changjiang River(Yangtze River)Estuary.This study focuses on discharge forecasting using three key input characteristics:flow velocity,water level,and discharge,which means the structure of multiple input and single output is adopted.The experiment used the discharge data of the whole year of 2020,of which the first 80%is used as the training set,and the last 20%is used as the test set.This means that the data covers different tidal cycles,which helps to test the forecasting effect of different models in different tidal cycles and different runoff.The experimental results indicate that the proposed models demonstrate advantages in long-term,mid-term,and short-term discharge forecasting.The Seq2Seq models improved by 6%-60%and 5%-20%of the relative standard deviation compared to the harmonic analysis models and improved back propagation neural network models in discharge prediction,respectively.In addition,the relative accuracy of the Seq2Seq model is 1%to 3%higher than that of the LSTM model.Analytical assessment of the prediction errors shows that the Seq2Seq models are insensitive to the forecast lead time and they can capture characteristic values such as maximum flood tide flow and maximum ebb tide flow in the tidal cycle well.This indicates the significance of the Seq2Seq models.展开更多
Infection with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) causes diverse clinical manifestations and tissue injuries in multiple organs.However, cellular and molecular understanding of SARS-CoV-2 infe...Infection with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) causes diverse clinical manifestations and tissue injuries in multiple organs.However, cellular and molecular understanding of SARS-CoV-2 infection-associated pathology and immune defense features in different organs remains incomplete. Here, we profiled approximately 77 000single-nucleus transcriptomes of the lung, liver,kidney, and cerebral cortex in rhesus macaques(Macaca mulatta) infected with SARS-CoV-2 and healthy controls. Integrated analysis of the multiorgan dataset suggested that the liver harbored the strongest global transcriptional alterations. We observed prominent impairment in lung epithelial cells, especially in AT2 and ciliated cells, and evident signs of fibrosis in fibroblasts. These lung injury characteristics are similar to those reported in patients with coronavirus disease 2019(COVID-19).Furthermore, we found suppressed MHC class I/II molecular activity in the lung, inflammatory response in the liver, and activation of the kynurenine pathway,which induced the development of an immunosuppressive microenvironment. Analysis of the kidney dataset highlighted tropism of tubule cells to SARS-CoV-2, and we found membranous nephropathy(an autoimmune disease) caused by podocyte dysregulation. In addition, we identified the pathological states of astrocytes and oligodendrocytes in the cerebral cortex, providing molecular insights into COVID-19-related neurological implications. Overall, our multi-organ single-nucleus transcriptomic survey of SARS-CoV-2-infected rhesus macaques broadens our understanding of disease features and antiviral immune defects caused by SARS-CoV-2 infection,which may facilitate the development of therapeutic interventions for COVID-19.展开更多
Although vaccines have been developed,mutations of SARS-CoV-2,especially the dominant B.1.617.2(delta)and B.1.529(omicron)strains with more than 30 mutations on their spike protein,have caused a significant decline in...Although vaccines have been developed,mutations of SARS-CoV-2,especially the dominant B.1.617.2(delta)and B.1.529(omicron)strains with more than 30 mutations on their spike protein,have caused a significant decline in prophylaxis,calling for the need for drug improvement.Antibodies are drugs preferentially used in infectious diseases and are easy to get from immunized organisms.The current study combined molecular modeling and single memory B cell sequencing to assess candidate sequences before experiments,providing a strategy for the fabrication of SARS-CoV-2 neutralizing antibodies.A total of 128 sequences were obtained after sequencing 196 memory B cells,and 42 sequences were left after merging extremely similar ones and discarding incomplete ones,followed by homology modeling of the antibody variable region.Thirteen candidate sequences were expressed,of which three were tested positive for receptor binding domain recognition but only one was confirmed as having broad neutralization against several SARS-CoV-2 variants.The current study successfully obtained a SARS-CoV-2 antibody with broad neutralizing abilities and provided a strategy for antibody development in emerging infectious diseases using single memory B cell BCR sequencing and computer assistance in antibody fabrication.展开更多
Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects.However,most neural network models only use the da...Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects.However,most neural network models only use the data from a single monitoring point and neglect the spatial relationships between multiple monitoring points.Besides,most models lack flexibility in providing predictions for multiple days after monitoring activity.This study proposes a sequence-to-sequence(seq2seq)two-dimensional(2D)convolutional long short-term memory neural network(S2SCL2D)for predicting the spatiotemporal wall deflections induced by deep excavations.The model utilizes the data from all monitoring points on the entire wall and extracts spatiotemporal features from data by combining the 2D convolutional layers and long short-term memory(LSTM)layers.The S2SCL2D model achieves a long-term prediction of wall deflections through a recursive seq2seq structure.The excavation depth,which has a significant impact on wall deflections,is also considered using a feature fusion method.An excavation project in Hangzhou,China,is used to illustrate the proposed model.The results demonstrate that the S2SCL2D model has superior prediction accuracy and robustness than that of the LSTM and S2SCL1D(one-dimensional)models.The prediction model demonstrates a strong generalizability when applied to an adjacent excavation.Based on the long-term prediction results,practitioners can plan and allocate resources in advance to address the potential engineering issues.展开更多
Microsatellites are highly polymorphic markers which have been used in a wide range of genetic studies.In recent years, various sources of next-generation sequencing data have been used to develop new microsatellite l...Microsatellites are highly polymorphic markers which have been used in a wide range of genetic studies.In recent years, various sources of next-generation sequencing data have been used to develop new microsatellite loci, but compared with the more common shotgun genomic sequencing or transcriptome data, the potential utility of RAD-seq data for microsatellite ascertainment is comparatively under-used.In this study, we employed MiddRAD-seq data to develop polymorphic microsatellite loci for the endangered yew species Taxus florinii. Of 8,823,053 clean reads generated for ten individuals of a population, 94,851(~1%) contained microsatellite motifs. These corresponded to 2993 unique loci, of which 526(~18%) exhibited polymorphism. Of which, 237 were suitable for designing microsatellite primer pairs, and 128 loci were randomly selected for PCR validation and microsatellite screening. Out of the 128 primer pairs, 16 loci gave clear, reproducible patterns, and were then screened and characterized in 24 individuals from two populations. The total number of alleles per locus ranged from two to ten(mean=4.875), and within-population expected heterozygosity from zero to 0.789(mean = 0.530),indicating that these microsatellite loci will be useful for population genetics and speciation studies of T. florinii. This study represents one of few examples to mine polymorphic microsatellite loci from ddRAD data.展开更多
Saffron （Crocus sativus L.） always is grown for using its flowers in nutrient industry, color industry and healthy compounds due to its flowers and specially stigmas. Because of its expensive flowers, surveying and ...Saffron （Crocus sativus L.） always is grown for using its flowers in nutrient industry, color industry and healthy compounds due to its flowers and specially stigmas. Because of its expensive flowers, surveying and recognizing on effective genes for flowering is very important and its results can help us to control rate and timing of flowering at an early stage of flowering. The gene and gene state meant Pistillata like MADS box （PIC2） were surveyed for recognizing its molecular mechanism. The molecular sequence at the genes has high similarity to members of family MADS that is a factor for controls of protein at flowering stage. PIC2 gene was studied by bioinforrnatics resources. Primers were designed for replicating the gene and DNA and RNA were extracted from saffron＇s leaves. The gene＇s eDNA was built by recopying enzyme and used such a pattern for replicating gene PIC2 at polymerase chain reactions （PCR）. Segments were replicated such 900 eDNA pair-nucleotides and a segment such 2,100 of DNA＇s pair-nucleotides. The gene codes a protein that was composed of 210 amino acids that has MADS sequence box. Analysis of protein＇s molecular structure and homological modeling of the protein indicated that it has a regular structure.展开更多
基金The National Natural Science Foundation of China under contract Nos 42266006 and 41806114the Jiangxi Provincial Natural Science Foundation under contract Nos 20232BAB204089 and 20202ACBL214019.
文摘The complexity of river-tide interaction poses a significant challenge in predicting discharge in tidal rivers.Long short-term memory(LSTM)networks excel in processing and predicting crucial events with extended intervals and time delays in time series data.Additionally,the sequence-to-sequence(Seq2Seq)model,known for handling temporal relationships,adapting to variable-length sequences,effectively capturing historical information,and accommodating various influencing factors,emerges as a robust and flexible tool in discharge forecasting.In this study,we introduce the application of LSTM-based Seq2Seq models for the first time in forecasting the discharge of a tidal reach of the Changjiang River(Yangtze River)Estuary.This study focuses on discharge forecasting using three key input characteristics:flow velocity,water level,and discharge,which means the structure of multiple input and single output is adopted.The experiment used the discharge data of the whole year of 2020,of which the first 80%is used as the training set,and the last 20%is used as the test set.This means that the data covers different tidal cycles,which helps to test the forecasting effect of different models in different tidal cycles and different runoff.The experimental results indicate that the proposed models demonstrate advantages in long-term,mid-term,and short-term discharge forecasting.The Seq2Seq models improved by 6%-60%and 5%-20%of the relative standard deviation compared to the harmonic analysis models and improved back propagation neural network models in discharge prediction,respectively.In addition,the relative accuracy of the Seq2Seq model is 1%to 3%higher than that of the LSTM model.Analytical assessment of the prediction errors shows that the Seq2Seq models are insensitive to the forecast lead time and they can capture characteristic values such as maximum flood tide flow and maximum ebb tide flow in the tidal cycle well.This indicates the significance of the Seq2Seq models.
基金supported by the National Basic Research Program of China(2020YFA0804000,2020YFC0842000,2020YFA0112200,2021YFC2301703)Strategic Priority Research Program of the Chinese Academy of Sciences(XDB32010100)+6 种基金Special Associate Research Program of the Chinese Academy of Sciences(E1290601)National Natural Science Foundation of China(32122037,81891001,32192411,32100512,U1902215)Collaborative Research Fund of the Chinese Institute for Brain Research,Beijing(2020-NKX-PT-03)CAS Project for Young Scientists in Basic Research(YSBR-013)Young Elite Scientist Sponsorship Program by the China Association for Science and Technology(2020QNRC001)National Resource Center for Non-Human Primates。
文摘Infection with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) causes diverse clinical manifestations and tissue injuries in multiple organs.However, cellular and molecular understanding of SARS-CoV-2 infection-associated pathology and immune defense features in different organs remains incomplete. Here, we profiled approximately 77 000single-nucleus transcriptomes of the lung, liver,kidney, and cerebral cortex in rhesus macaques(Macaca mulatta) infected with SARS-CoV-2 and healthy controls. Integrated analysis of the multiorgan dataset suggested that the liver harbored the strongest global transcriptional alterations. We observed prominent impairment in lung epithelial cells, especially in AT2 and ciliated cells, and evident signs of fibrosis in fibroblasts. These lung injury characteristics are similar to those reported in patients with coronavirus disease 2019(COVID-19).Furthermore, we found suppressed MHC class I/II molecular activity in the lung, inflammatory response in the liver, and activation of the kynurenine pathway,which induced the development of an immunosuppressive microenvironment. Analysis of the kidney dataset highlighted tropism of tubule cells to SARS-CoV-2, and we found membranous nephropathy(an autoimmune disease) caused by podocyte dysregulation. In addition, we identified the pathological states of astrocytes and oligodendrocytes in the cerebral cortex, providing molecular insights into COVID-19-related neurological implications. Overall, our multi-organ single-nucleus transcriptomic survey of SARS-CoV-2-infected rhesus macaques broadens our understanding of disease features and antiviral immune defects caused by SARS-CoV-2 infection,which may facilitate the development of therapeutic interventions for COVID-19.
基金supported by the Jiangsu Provincial Key Research and Development Program (Grant No.BE2020616)the National Key R&D Program of China (Grant No.2018YFC1200603)+1 种基金the National Science and Technology Major Project (Grant No.2019SWAQ05-5-4)Jiangsu Key Lab of Cancer Biomarkers,Prevention and Treatment,Collaborative Innovation Center for Cancer Personalized Medicine,Nanjing Medical University.
文摘Although vaccines have been developed,mutations of SARS-CoV-2,especially the dominant B.1.617.2(delta)and B.1.529(omicron)strains with more than 30 mutations on their spike protein,have caused a significant decline in prophylaxis,calling for the need for drug improvement.Antibodies are drugs preferentially used in infectious diseases and are easy to get from immunized organisms.The current study combined molecular modeling and single memory B cell sequencing to assess candidate sequences before experiments,providing a strategy for the fabrication of SARS-CoV-2 neutralizing antibodies.A total of 128 sequences were obtained after sequencing 196 memory B cells,and 42 sequences were left after merging extremely similar ones and discarding incomplete ones,followed by homology modeling of the antibody variable region.Thirteen candidate sequences were expressed,of which three were tested positive for receptor binding domain recognition but only one was confirmed as having broad neutralization against several SARS-CoV-2 variants.The current study successfully obtained a SARS-CoV-2 antibody with broad neutralizing abilities and provided a strategy for antibody development in emerging infectious diseases using single memory B cell BCR sequencing and computer assistance in antibody fabrication.
基金supported by the National Natural Science Foundation of China(Grant No.42307218)the Foundation of Key Laboratory of Soft Soils and Geoenvironmental Engineering(Zhejiang University),Ministry of Education(Grant No.2022P08)the Natural Science Foundation of Zhejiang Province(Grant No.LTZ21E080001).
文摘Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects.However,most neural network models only use the data from a single monitoring point and neglect the spatial relationships between multiple monitoring points.Besides,most models lack flexibility in providing predictions for multiple days after monitoring activity.This study proposes a sequence-to-sequence(seq2seq)two-dimensional(2D)convolutional long short-term memory neural network(S2SCL2D)for predicting the spatiotemporal wall deflections induced by deep excavations.The model utilizes the data from all monitoring points on the entire wall and extracts spatiotemporal features from data by combining the 2D convolutional layers and long short-term memory(LSTM)layers.The S2SCL2D model achieves a long-term prediction of wall deflections through a recursive seq2seq structure.The excavation depth,which has a significant impact on wall deflections,is also considered using a feature fusion method.An excavation project in Hangzhou,China,is used to illustrate the proposed model.The results demonstrate that the S2SCL2D model has superior prediction accuracy and robustness than that of the LSTM and S2SCL1D(one-dimensional)models.The prediction model demonstrates a strong generalizability when applied to an adjacent excavation.Based on the long-term prediction results,practitioners can plan and allocate resources in advance to address the potential engineering issues.
基金funded by the National Natural Science Foundations of China (31370252, 41571059)the National Key Basic Research Program of China (2014CB954100)supported by the China Scholarship Council for one-year study at the Aberystwyth University,UK
文摘Microsatellites are highly polymorphic markers which have been used in a wide range of genetic studies.In recent years, various sources of next-generation sequencing data have been used to develop new microsatellite loci, but compared with the more common shotgun genomic sequencing or transcriptome data, the potential utility of RAD-seq data for microsatellite ascertainment is comparatively under-used.In this study, we employed MiddRAD-seq data to develop polymorphic microsatellite loci for the endangered yew species Taxus florinii. Of 8,823,053 clean reads generated for ten individuals of a population, 94,851(~1%) contained microsatellite motifs. These corresponded to 2993 unique loci, of which 526(~18%) exhibited polymorphism. Of which, 237 were suitable for designing microsatellite primer pairs, and 128 loci were randomly selected for PCR validation and microsatellite screening. Out of the 128 primer pairs, 16 loci gave clear, reproducible patterns, and were then screened and characterized in 24 individuals from two populations. The total number of alleles per locus ranged from two to ten(mean=4.875), and within-population expected heterozygosity from zero to 0.789(mean = 0.530),indicating that these microsatellite loci will be useful for population genetics and speciation studies of T. florinii. This study represents one of few examples to mine polymorphic microsatellite loci from ddRAD data.
文摘Saffron （Crocus sativus L.） always is grown for using its flowers in nutrient industry, color industry and healthy compounds due to its flowers and specially stigmas. Because of its expensive flowers, surveying and recognizing on effective genes for flowering is very important and its results can help us to control rate and timing of flowering at an early stage of flowering. The gene and gene state meant Pistillata like MADS box （PIC2） were surveyed for recognizing its molecular mechanism. The molecular sequence at the genes has high similarity to members of family MADS that is a factor for controls of protein at flowering stage. PIC2 gene was studied by bioinforrnatics resources. Primers were designed for replicating the gene and DNA and RNA were extracted from saffron＇s leaves. The gene＇s eDNA was built by recopying enzyme and used such a pattern for replicating gene PIC2 at polymerase chain reactions （PCR）. Segments were replicated such 900 eDNA pair-nucleotides and a segment such 2,100 of DNA＇s pair-nucleotides. The gene codes a protein that was composed of 210 amino acids that has MADS sequence box. Analysis of protein＇s molecular structure and homological modeling of the protein indicated that it has a regular structure.
