With the rapid development of high-throughput sequencing technologies and the accumulation of large-scale multi-omics data,deep learning(DL)has emerged as a powerful tool to solve complex biological problems,with part...With the rapid development of high-throughput sequencing technologies and the accumulation of large-scale multi-omics data,deep learning(DL)has emerged as a powerful tool to solve complex biological problems,with particular promise in plant genomics.This review systematically examines the progress of DL applications in DNA,RNA,and protein sequence analysis,covering key tasks such as gene regulatory element identification,gene function annotation,and protein structure prediction,and highlighting how these DL applications illuminate research of plants,including horticultural plants.We evaluate the advantages of different neural network architectures and their applications in different biology studies,as well as the development of large language models(LLMs)in genomic modelling,such as the plantspecific models PDLLMs and AgroNT.We also briefly introduce the general workflow of the basic DL model for plant genomics study.While DL has significantly improved prediction accuracy in plant genomics,its broader application remains constrained by several challenges,including the limited availability of well-annotated data,computational capacity,innovative model architectures adapted to plant genomes,and model interpretability.Future advances will require interdisciplinary collaborations to develop DL applications for intelligent plant genomic research frameworks with broader applicability.展开更多
BACKGROUND:The mechanisms underlying hypothermic liver injury necessitate investigation for the development of effective diagnostic and therapeutic approaches.We aim to establish a model of hypothermic liver injury to...BACKGROUND:The mechanisms underlying hypothermic liver injury necessitate investigation for the development of effective diagnostic and therapeutic approaches.We aim to establish a model of hypothermic liver injury to explore the hepatic alterations,thereby facilitating the prevention and treatment of the liver injury associated with hypothermia.METHODS:The mice were placed in a−20℃ environment,to establish a hypothermic injury model.The liver function,metabolites,and proteins expression were measured by thromboelastography,histopathology,metabolomics and western blotting,respectively.Furthermore,apoptosis and pathway changes in the liver cells conducted with target metabolites were examined and verified.RESULTS:According to the prolonged righting reflex recovery time and death occurrence,the mice with the anal temperature(AT)dropping to 20℃ or 15℃ were used to establish a model of hypothermia.The model mice showed changes in alanine aminotransferase(ALT),aspartate transaminase(AST),and coagulation indicators.HE staining results indicated that liver tissue in the AT 20℃ mice had large hemorrhagic patches,while the AT 15℃ mice displayed significant congestion,along with extensive infiltration of inflammatory cells around the central vein.Metabolomic and Kyoto Encyclopedia of Genes and Genomes(KEGG)analyses of target metabolites revealed a significant increase in 3-hydroxybutyric acid and changes in the cyclic adenosine monophosphate(cAMP)signaling pathway in the liver tissue of hypothermic mice.The hypothermic mice showed decreases in levels of cAMP,protein kinase A C-α(PKA C-α),and phosphorylated BCL-2/BCL-XL-associated death promoter(p-Bad)and an increase in BCL-2/BCLXL-associated death promoter(Bad)level in the liver.These protein changes and apoptosis were intensified by 3-hydroxybutyric acid in liver cells.CONCLUSION:Hypothermia may induce apoptosis in the liver cell which may be related to the changes of the cAMP-PKA pathway proteins expression.These findings provide a basis for the treatment of hypothermic injury.展开更多
The acquisition,tracking,and pointing(ATP)system is widely used in target tracking,counter-UAV operations,and other related fields.As UAV technology develops,there is a growing demand to enhance the tracking capabilit...The acquisition,tracking,and pointing(ATP)system is widely used in target tracking,counter-UAV operations,and other related fields.As UAV technology develops,there is a growing demand to enhance the tracking capabilities of ATP systems.However,in practical applications,ATP systems face various design constraints and functional limitations,making it infeasible to indefinitely improve hardware performance to meet tracking requirements.As a result,tracking algorithms are required to execute increasingly complex tasks.This study introduces a multi-rate feedforward predictive controller to address issues such as low image feedback frequency and significant delays in ATP systems,which lead to tracking jitter,poor tracking performance,low precision,and target loss.At the same time,the pro-posed approach aims to improve the tracking capabilities of ATP systems for high-speed and highly maneuverable targets under conditions of low sampling feedback rates and high feedback delays.The method suggested is also characterized by its low order,fast response,and robustness to model parameter variations.In this study,an actual ATP system is built for target tracking test,and the proposed algorithm is fully validated in terms of simulation and actual system application verification.Results from both simulations and experiments demonstrate that the method effectively compensates for delays and low sampling rates.For targets with relative angular velocities ranging from 0 to 90°/s and angular accelerations between 0 and 470°/s^(2),the system improved tracking accuracy by 70.0%-89.9%at a sampling frequency of 50 Hz and a delay of 30 m s.Moreover,the compensation algorithm demonstrated consistent performance across actuators with varying characteristics,further confirming its robustness to model insensitivity.In summary,the proposed algorithm considerably enhances the tracking accuracy and capability of ATP systems for high-speed and highly maneuverable targets,reducing the probability of target loss from high speed.This approach offers a practical solution for future multi-target tracking across diverse operational scenarios.展开更多
In current neural network algorithms for nuclide identification in high-background,poor-resolution detectors,traditional network paradigms including back-propagation networks,convolutional neural networks,recurrent ne...In current neural network algorithms for nuclide identification in high-background,poor-resolution detectors,traditional network paradigms including back-propagation networks,convolutional neural networks,recurrent neural networks,etc.,have been limited in research on γ spectrum analysis because of their inherent mathematical mechanisms.It is difficult to make progress in terms of training data requirements and prediction accuracy.In contrast to traditional network paradigms,network models based on the transformer structure have the characteristics of parallel computing,position encoding,and deep stacking,which have enabled good performance in natural language processing tasks in recent years.Therefore,in this paper,a transformer-based neural network (TBNN) model is proposed to achieve nuclide identification for the first time.First,the Geant4 program was used to generate the basic single-nuclide energy spectrum through Monte Carlo simulations.A multi-nuclide energy spectrum database was established for neural network training using random matrices of γ-ray energy,activity,and noise.Based on the encoder–decoder structure,a network topology based on the transformer was built,transforming the 1024-channel energy spectrum data into a 32×32 energy spectrum sequence as the model input.Through experiments and adjustments of model parameters,including the learning rate of the TBNN model,number of attention heads,and number of network stacking layers,the overall recognition rate reached 98.7%.Additionally,this database was used for training AI models such as back-propagation networks,convolutional neural networks,residual networks,and long shortterm memory neural networks,with overall recognition rates of 92.8%,95.3%,96.3%,and 96.6%,respectively.This indicates that the TBNN model exhibited better nuclide identification among these AI models,providing an important reference and theoretical basis for the practical application of transformers in the qualitative and quantitative analysis of the γ spectrum.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.U23A20210,31722048,and 32102382)Central Public-interest Scientific Institution Basal Research Fund,The Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural SciencesKey Laboratory of Biology and Genetic Improvement of Horticultural Crops,Ministry of Agriculture and Rural Affairs,China.
文摘With the rapid development of high-throughput sequencing technologies and the accumulation of large-scale multi-omics data,deep learning(DL)has emerged as a powerful tool to solve complex biological problems,with particular promise in plant genomics.This review systematically examines the progress of DL applications in DNA,RNA,and protein sequence analysis,covering key tasks such as gene regulatory element identification,gene function annotation,and protein structure prediction,and highlighting how these DL applications illuminate research of plants,including horticultural plants.We evaluate the advantages of different neural network architectures and their applications in different biology studies,as well as the development of large language models(LLMs)in genomic modelling,such as the plantspecific models PDLLMs and AgroNT.We also briefly introduce the general workflow of the basic DL model for plant genomics study.While DL has significantly improved prediction accuracy in plant genomics,its broader application remains constrained by several challenges,including the limited availability of well-annotated data,computational capacity,innovative model architectures adapted to plant genomes,and model interpretability.Future advances will require interdisciplinary collaborations to develop DL applications for intelligent plant genomic research frameworks with broader applicability.
基金supported by a grant from the Liaoning Provincial Science&Technology Committee(2023-BS-032,2021JH2/10300024).
文摘BACKGROUND:The mechanisms underlying hypothermic liver injury necessitate investigation for the development of effective diagnostic and therapeutic approaches.We aim to establish a model of hypothermic liver injury to explore the hepatic alterations,thereby facilitating the prevention and treatment of the liver injury associated with hypothermia.METHODS:The mice were placed in a−20℃ environment,to establish a hypothermic injury model.The liver function,metabolites,and proteins expression were measured by thromboelastography,histopathology,metabolomics and western blotting,respectively.Furthermore,apoptosis and pathway changes in the liver cells conducted with target metabolites were examined and verified.RESULTS:According to the prolonged righting reflex recovery time and death occurrence,the mice with the anal temperature(AT)dropping to 20℃ or 15℃ were used to establish a model of hypothermia.The model mice showed changes in alanine aminotransferase(ALT),aspartate transaminase(AST),and coagulation indicators.HE staining results indicated that liver tissue in the AT 20℃ mice had large hemorrhagic patches,while the AT 15℃ mice displayed significant congestion,along with extensive infiltration of inflammatory cells around the central vein.Metabolomic and Kyoto Encyclopedia of Genes and Genomes(KEGG)analyses of target metabolites revealed a significant increase in 3-hydroxybutyric acid and changes in the cyclic adenosine monophosphate(cAMP)signaling pathway in the liver tissue of hypothermic mice.The hypothermic mice showed decreases in levels of cAMP,protein kinase A C-α(PKA C-α),and phosphorylated BCL-2/BCL-XL-associated death promoter(p-Bad)and an increase in BCL-2/BCLXL-associated death promoter(Bad)level in the liver.These protein changes and apoptosis were intensified by 3-hydroxybutyric acid in liver cells.CONCLUSION:Hypothermia may induce apoptosis in the liver cell which may be related to the changes of the cAMP-PKA pathway proteins expression.These findings provide a basis for the treatment of hypothermic injury.
基金supported by the National Natural Science Foun-dation of China(Grant No.52275099).
文摘The acquisition,tracking,and pointing(ATP)system is widely used in target tracking,counter-UAV operations,and other related fields.As UAV technology develops,there is a growing demand to enhance the tracking capabilities of ATP systems.However,in practical applications,ATP systems face various design constraints and functional limitations,making it infeasible to indefinitely improve hardware performance to meet tracking requirements.As a result,tracking algorithms are required to execute increasingly complex tasks.This study introduces a multi-rate feedforward predictive controller to address issues such as low image feedback frequency and significant delays in ATP systems,which lead to tracking jitter,poor tracking performance,low precision,and target loss.At the same time,the pro-posed approach aims to improve the tracking capabilities of ATP systems for high-speed and highly maneuverable targets under conditions of low sampling feedback rates and high feedback delays.The method suggested is also characterized by its low order,fast response,and robustness to model parameter variations.In this study,an actual ATP system is built for target tracking test,and the proposed algorithm is fully validated in terms of simulation and actual system application verification.Results from both simulations and experiments demonstrate that the method effectively compensates for delays and low sampling rates.For targets with relative angular velocities ranging from 0 to 90°/s and angular accelerations between 0 and 470°/s^(2),the system improved tracking accuracy by 70.0%-89.9%at a sampling frequency of 50 Hz and a delay of 30 m s.Moreover,the compensation algorithm demonstrated consistent performance across actuators with varying characteristics,further confirming its robustness to model insensitivity.In summary,the proposed algorithm considerably enhances the tracking accuracy and capability of ATP systems for high-speed and highly maneuverable targets,reducing the probability of target loss from high speed.This approach offers a practical solution for future multi-target tracking across diverse operational scenarios.
基金supported by the National Natural Science Foundation of China(No.42127807)Natural Science Foundation of Sichuan Province(Nos.2024NSFSC0422,23NSFSCC0116)Nuclear Energy Development Project(No.[2021]-88).
文摘In current neural network algorithms for nuclide identification in high-background,poor-resolution detectors,traditional network paradigms including back-propagation networks,convolutional neural networks,recurrent neural networks,etc.,have been limited in research on γ spectrum analysis because of their inherent mathematical mechanisms.It is difficult to make progress in terms of training data requirements and prediction accuracy.In contrast to traditional network paradigms,network models based on the transformer structure have the characteristics of parallel computing,position encoding,and deep stacking,which have enabled good performance in natural language processing tasks in recent years.Therefore,in this paper,a transformer-based neural network (TBNN) model is proposed to achieve nuclide identification for the first time.First,the Geant4 program was used to generate the basic single-nuclide energy spectrum through Monte Carlo simulations.A multi-nuclide energy spectrum database was established for neural network training using random matrices of γ-ray energy,activity,and noise.Based on the encoder–decoder structure,a network topology based on the transformer was built,transforming the 1024-channel energy spectrum data into a 32×32 energy spectrum sequence as the model input.Through experiments and adjustments of model parameters,including the learning rate of the TBNN model,number of attention heads,and number of network stacking layers,the overall recognition rate reached 98.7%.Additionally,this database was used for training AI models such as back-propagation networks,convolutional neural networks,residual networks,and long shortterm memory neural networks,with overall recognition rates of 92.8%,95.3%,96.3%,and 96.6%,respectively.This indicates that the TBNN model exhibited better nuclide identification among these AI models,providing an important reference and theoretical basis for the practical application of transformers in the qualitative and quantitative analysis of the γ spectrum.
