Making time-series forecasting in a robust way is a difficult task only based on the observed data of a non-linear system.In this work,a neural network computing framework,the spatiotemporal information conver-sion ma...Making time-series forecasting in a robust way is a difficult task only based on the observed data of a non-linear system.In this work,a neural network computing framework,the spatiotemporal information conver-sion machine(STICM),was developed to efficiently and accurately render a forecasting of a time series by employing a spatial-temporal information(STI)transformation.STICM combines the advantages of both the STI equation and the temporal convolutional network,which maps the high-dimensional/spatial data to the future temporal values of a target variable,thus naturally providing the forecasting of the target variable.From the observed variables,the STICM also infers the causal factors of the target variable in the sense of Granger causality,which are in turn selected as effective spatial information to improve the robustness of time-series forecasting.The STICM was successfully applied to both benchmark systems and real-world datasets,all of which show superior and robust performance in timeseries forecasting,even when the data were perturbed by noise.From both theoretical and computational viewpoints,the STICM has great potential in practical applications in artificial intelligence or as a model-free method based only on the observed data,and also opens a new way to explore the observed high-dimensional data in a dynamical manner for machine learning.展开更多
All kinds of sensing organs in humans are able to reflect only the formal factors of objects,named formal information.It is believed,however,that not only the formal information but also the content information and va...All kinds of sensing organs in humans are able to reflect only the formal factors of objects,named formal information.It is believed,however,that not only the formal information but also the content information and value information of objects could play fundamental roles in the process of information understanding and decisionmaking in human thinking.Therefore,the questions of where and how the content information and the value information be produced from the formal information become critical in the theory of information understanding and decision-making.A conjectural theory that may reasonably answer the question is presented here in the paper.展开更多
Dear Editor,Transfer RNA(tRNA)is an indispensable adaptor molecule in the messenger RNA(mRNA)translation machinery,facilitating the conversion of genetic information encoded in mRNA into functional proteins.Numerous p...Dear Editor,Transfer RNA(tRNA)is an indispensable adaptor molecule in the messenger RNA(mRNA)translation machinery,facilitating the conversion of genetic information encoded in mRNA into functional proteins.Numerous posttranscriptional modifications in tRNA have been identified,which play significantroles in modulating tRNA folding,biochemical stability,amino-acylation,and codon–anticodon interaction(Suzuki,2021).TRMT10A,the mammalian homolog of Trm10,incorporates N1-methylguanosine modification at position 9(m1G9)of various cytoplasmic tRNAs,including tRNAGln and tRNAIniMeth(Vilardo et al.,2020).Mutations in human TRMT10A,which is enriched in pancreatic islets and brain(Igoillo-Esteve et al.,2013),are often associated with microcephaly,intellectual disability,early-onset diabetes,and short stature(Igoillo-Esteve et al.,2013;Uçan Tokuçet al.,2024).展开更多
基金supported by the National Natural Science Foundation of China(T2341022,T2350003,T2341007,12322119,62172164,12271180,12131020,and 31930022)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB38040400)+3 种基金Guangdong Basic and Applied Basic Research Foundation(2024A1515011797)the Special Fund for Science and Technology Innovation Strategy of Guangdong Province(2021B0909050004,2021B0909060002)the Major Key Project of Peng Cheng Laboratory(PCL2021A12)JST Moonshot R&D(JPMJMS2021).
文摘Making time-series forecasting in a robust way is a difficult task only based on the observed data of a non-linear system.In this work,a neural network computing framework,the spatiotemporal information conver-sion machine(STICM),was developed to efficiently and accurately render a forecasting of a time series by employing a spatial-temporal information(STI)transformation.STICM combines the advantages of both the STI equation and the temporal convolutional network,which maps the high-dimensional/spatial data to the future temporal values of a target variable,thus naturally providing the forecasting of the target variable.From the observed variables,the STICM also infers the causal factors of the target variable in the sense of Granger causality,which are in turn selected as effective spatial information to improve the robustness of time-series forecasting.The STICM was successfully applied to both benchmark systems and real-world datasets,all of which show superior and robust performance in timeseries forecasting,even when the data were perturbed by noise.From both theoretical and computational viewpoints,the STICM has great potential in practical applications in artificial intelligence or as a model-free method based only on the observed data,and also opens a new way to explore the observed high-dimensional data in a dynamical manner for machine learning.
基金The work was supported in part by the National Natural Science Foundation of China(Grant Nos.60575034 and 60873001)。
文摘All kinds of sensing organs in humans are able to reflect only the formal factors of objects,named formal information.It is believed,however,that not only the formal information but also the content information and value information of objects could play fundamental roles in the process of information understanding and decisionmaking in human thinking.Therefore,the questions of where and how the content information and the value information be produced from the formal information become critical in the theory of information understanding and decision-making.A conjectural theory that may reasonably answer the question is presented here in the paper.
基金Supplementary material is available at Journal of Molecular Cell Biology online.This study was supported by grants from the National Natural Science Foundation of China(82230075 to D.G.32270159 to J.W.)+2 种基金Guangdong Basic and Applied Basic Research Foundation(2023A1515012613 to J.W.)Shenzhen Science and Technology Program(JCYJ20200109142201695 and KQTD20180411143323605 to D.G.,JCYJ20220530145608018 to J.W.)Shenzhen Key Laboratory of Systems Medicine for Inflammatory Diseases(ZDSYS20220606100803007 to J.W.).
文摘Dear Editor,Transfer RNA(tRNA)is an indispensable adaptor molecule in the messenger RNA(mRNA)translation machinery,facilitating the conversion of genetic information encoded in mRNA into functional proteins.Numerous posttranscriptional modifications in tRNA have been identified,which play significantroles in modulating tRNA folding,biochemical stability,amino-acylation,and codon–anticodon interaction(Suzuki,2021).TRMT10A,the mammalian homolog of Trm10,incorporates N1-methylguanosine modification at position 9(m1G9)of various cytoplasmic tRNAs,including tRNAGln and tRNAIniMeth(Vilardo et al.,2020).Mutations in human TRMT10A,which is enriched in pancreatic islets and brain(Igoillo-Esteve et al.,2013),are often associated with microcephaly,intellectual disability,early-onset diabetes,and short stature(Igoillo-Esteve et al.,2013;Uçan Tokuçet al.,2024).
