In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,...In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.展开更多
To achieve the enormous potential of gene-editing technology in clinical therapies,one needs to evaluate both the on-target efficiency and unintended editing consequences comprehensively.However,there is a lack of a p...To achieve the enormous potential of gene-editing technology in clinical therapies,one needs to evaluate both the on-target efficiency and unintended editing consequences comprehensively.However,there is a lack of a pipelined,large-scale,and economical workflow for detecting genome editing outcomes,in particular insertion or deletion of a large fragment.Here,we describe an approach for efficient and accurate detection of multiple genetic changes after CRISPR/Cas9 editing by pooled nanopore sequencing of barcoded long-range PCR products.Recognizing the high error rates of Oxford nanopore sequencing,we developed a novel pipeline to capture the barcoded sequences by grepping reads of nanopore amplicon sequencing(GREPore-seq).GREPore-seq can assess nonhomologous end-joining(NHEJ)-mediated double-stranded oligodeoxynucleotide(dsODN)insertions with comparable accuracy to Illumina next-generation sequencing(NGS).GREPore-seq also reveals a full spectrum of homology-directed repair(HDR)-mediated large gene knock-in,correlating well with the fluorescence-activated cell sorting(FACS)analysis results.Of note,we discovered low-level fragmented and full-length plasmid backbone insertion at the CRISPR cutting site.Therefore,we have established a practical workflow to evaluate various genetic changes,including quantifying insertions of short dsODNs,knock-ins of long pieces,plasmid insertions,and large fragment deletions after CRISPR/Cas9-mediated editing.GREPore-seq is freely available at GitHub(https://github.com/lisiang/GREPore-seq)and the National Genomics Data Center(NGDC)BioCode(https://ngdc.cncb.ac.cn/biocode/tools/BT007293).展开更多
猫疱疹病毒I型(FHV-1)是威胁猫科动物健康的重要传染性疾病,本研究旨在研发一种灵敏、高效的FHV-1检测技术。依据GenBank上FHV-1 US6保守区域的基因序列,设计合成1对针对FHV-1 gD基因的引物,建立了一种SYBR Green I荧光定量PCR的检测方...猫疱疹病毒I型(FHV-1)是威胁猫科动物健康的重要传染性疾病,本研究旨在研发一种灵敏、高效的FHV-1检测技术。依据GenBank上FHV-1 US6保守区域的基因序列,设计合成1对针对FHV-1 gD基因的引物,建立了一种SYBR Green I荧光定量PCR的检测方法,构建标准曲线后分别验证该方法的特异性、敏感性、重复性,并将其进一步应用于人工感染猫产生的临床样本检测。结果:该特异性引物与猫杯状病毒(FCV)、猫细小病毒(FPV)和猫冠状病毒(FCoV)均未出现交叉反应,检测下限为14.78 copies/μL,组内和组间重复试验的变异系数均低于2%;该方法对临床样本的检出率比常规PCR高出25.46%;通过该方法检测人工感染FHV-1强毒后猫的每日排毒量,结果呈现上升趋势,与临床发病程度相符,猫的脏器病毒载量存在个体差异,但集中在心脏、肺脏、肠道和膀胱中检出。综上,该研究建立的SYBR Green I荧光定量PCR方法对FHV-1具有较好的特异性、灵敏度和重复性,为FHV-1感染的快速诊断以及疾病的防控提供方法支持。展开更多
文摘In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.
基金supported by the National Key R&D Program of China(Grant Nos.2016YFA0100600,2019YFA0110800,and 2019YFA0110204)the National Natural Science Foundation of China(Grant Nos.81890990,81730006,81770198,81870149,and 82070115)the Chinese Academy of Medical Sciences(CAMS)Innovation Fund for Medical Sciences(CIFMS)(Grant Nos.2019-I2M-1-006 and 2021-I2M-1-041).
文摘To achieve the enormous potential of gene-editing technology in clinical therapies,one needs to evaluate both the on-target efficiency and unintended editing consequences comprehensively.However,there is a lack of a pipelined,large-scale,and economical workflow for detecting genome editing outcomes,in particular insertion or deletion of a large fragment.Here,we describe an approach for efficient and accurate detection of multiple genetic changes after CRISPR/Cas9 editing by pooled nanopore sequencing of barcoded long-range PCR products.Recognizing the high error rates of Oxford nanopore sequencing,we developed a novel pipeline to capture the barcoded sequences by grepping reads of nanopore amplicon sequencing(GREPore-seq).GREPore-seq can assess nonhomologous end-joining(NHEJ)-mediated double-stranded oligodeoxynucleotide(dsODN)insertions with comparable accuracy to Illumina next-generation sequencing(NGS).GREPore-seq also reveals a full spectrum of homology-directed repair(HDR)-mediated large gene knock-in,correlating well with the fluorescence-activated cell sorting(FACS)analysis results.Of note,we discovered low-level fragmented and full-length plasmid backbone insertion at the CRISPR cutting site.Therefore,we have established a practical workflow to evaluate various genetic changes,including quantifying insertions of short dsODNs,knock-ins of long pieces,plasmid insertions,and large fragment deletions after CRISPR/Cas9-mediated editing.GREPore-seq is freely available at GitHub(https://github.com/lisiang/GREPore-seq)and the National Genomics Data Center(NGDC)BioCode(https://ngdc.cncb.ac.cn/biocode/tools/BT007293).
文摘猫疱疹病毒I型(FHV-1)是威胁猫科动物健康的重要传染性疾病,本研究旨在研发一种灵敏、高效的FHV-1检测技术。依据GenBank上FHV-1 US6保守区域的基因序列,设计合成1对针对FHV-1 gD基因的引物,建立了一种SYBR Green I荧光定量PCR的检测方法,构建标准曲线后分别验证该方法的特异性、敏感性、重复性,并将其进一步应用于人工感染猫产生的临床样本检测。结果:该特异性引物与猫杯状病毒(FCV)、猫细小病毒(FPV)和猫冠状病毒(FCoV)均未出现交叉反应,检测下限为14.78 copies/μL,组内和组间重复试验的变异系数均低于2%;该方法对临床样本的检出率比常规PCR高出25.46%;通过该方法检测人工感染FHV-1强毒后猫的每日排毒量,结果呈现上升趋势,与临床发病程度相符,猫的脏器病毒载量存在个体差异,但集中在心脏、肺脏、肠道和膀胱中检出。综上,该研究建立的SYBR Green I荧光定量PCR方法对FHV-1具有较好的特异性、灵敏度和重复性,为FHV-1感染的快速诊断以及疾病的防控提供方法支持。
