Objective To develop a highly sensitive and rapid nucleic acid detection method for the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Methods We designed,developed,and manufactured an integrated disposab...Objective To develop a highly sensitive and rapid nucleic acid detection method for the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Methods We designed,developed,and manufactured an integrated disposable device for SARS-CoV-2 nucleic acid extraction and detection.The precision of the liquid transfer and temperature control was tested.A comparison between our device and a commercial kit for SARS-Cov-2 nucleic acid extraction was performed using real-time fluorescence reverse transcription polymerase chain reaction(RT-PCR).The entire process,from SARS-CoV-2 nucleic acid extraction to amplification,was evaluated.Results The precision of the syringe transfer volume was 19.2±1.9μL(set value was 20),32.2±1.6(set value was 30),and 57.2±3.5(set value was 60).Temperature control in the amplification tube was measured at 60.0±0.0℃(set value was 60)and 95.1±0.2℃(set value was 95)respectively.SARS-Cov-2 nucleic acid extraction yield through the device was 7.10×10^(6) copies/mL,while a commercial kit yielded 2.98×10^(6) copies/mL.The mean time to complete the entire assay,from SARS-CoV-2 nucleic acid extraction to amplification detection,was 36 min and 45 s.The detection limit for SARS-CoV-2 nucleic acid was 250 copies/mL.Conclusion The integrated disposable devices may be used for SARS-CoV-2 Point-of-Care test(POCT).展开更多
With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(...With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.展开更多
The current COVID-19 pandemic urges the extremely sensitive and prompt detection of SARS-CoV-2 virus.Here,we present a Human Angiotensin-converting-enzyme 2(ACE2)-functionalized gold“virus traps”nanostructure as an ...The current COVID-19 pandemic urges the extremely sensitive and prompt detection of SARS-CoV-2 virus.Here,we present a Human Angiotensin-converting-enzyme 2(ACE2)-functionalized gold“virus traps”nanostructure as an extremely sensitive SERS biosensor,to selectively capture and rapidly detect S-protein expressed coronavirus,such as the current SARS-CoV-2 in the contaminated water,down to the single-virus level.Such a SERS sensor features extraordinary 106-fold virus enrichment originating from high-affinity of ACE2 with S protein as well as“virus-traps”composed of oblique gold nanoneedles,and 109-fold enhancement of Raman signals originating from multi-component SERS effects.Furthermore,the identification standard of virus signals is established by machine-learning and identification techniques,resulting in an especially low detection limit of 80 copies mL^(−1) for the simulated contaminated water by SARS-CoV-2 virus with complex circumstance as short as 5 min,which is of great significance for achieving real-time monitoring and early warning of coronavirus.Moreover,here-developed method can be used to establish the identification standard for future unknown coronavirus,and immediately enable extremely sensitive and rapid detection of novel virus.展开更多
Postweaning multisystemic wasting syndrome (PMWS) is an important swine disease that is closely associated with porcine circovirus type 2 (PCV2). The capsid protein (Cap protein) is a major structural protein that has...Postweaning multisystemic wasting syndrome (PMWS) is an important swine disease that is closely associated with porcine circovirus type 2 (PCV2). The capsid protein (Cap protein) is a major structural protein that has at least three immunoreactive regions, and it can be a suitable candidate antigen for detecting the specific antibodies of a PCV2 infection. In the present study, an indirect enzyme-linked immunosorbent assay (TcELISA) based on a truncated soluble Cap protein produced in Escherichia coli (E.coli) was established and validated for the diagnostic PCV2 antibodies in swine. The TcELISA was validated by comparison with an indirect immunofluorescence assay (IIFA). The diagnostic sensitivity (DSN), specificity (DSP), and accuracy of the TcELISA were 88.6%, 90.7% and 89.4%, respectively. The agreement rate was 89.38% between results obtained with TcELISA and IIFA on 113 field sera. A cross-reactivity assay showed that the method was PCV2-specific by comparison with other sera of viral disease. Therefore ,the TcELISA will be helpful for the development of a reliable serology diagnostic test for large scale detection of PCV2 antibodies and for the evaluation of vaccine against PCV2 in swine.展开更多
Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely dis...Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases.Therefore,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes.The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction ability.In addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection accuracy.The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes.Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians.Experimental results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset.Besides,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.展开更多
基金supported by National Key R&D Program of China[2021YFC2301103 and 2022YFE0202600]Shenzhen Science and Technology Program[JSGG20220606142605011].
文摘Objective To develop a highly sensitive and rapid nucleic acid detection method for the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Methods We designed,developed,and manufactured an integrated disposable device for SARS-CoV-2 nucleic acid extraction and detection.The precision of the liquid transfer and temperature control was tested.A comparison between our device and a commercial kit for SARS-Cov-2 nucleic acid extraction was performed using real-time fluorescence reverse transcription polymerase chain reaction(RT-PCR).The entire process,from SARS-CoV-2 nucleic acid extraction to amplification,was evaluated.Results The precision of the syringe transfer volume was 19.2±1.9μL(set value was 20),32.2±1.6(set value was 30),and 57.2±3.5(set value was 60).Temperature control in the amplification tube was measured at 60.0±0.0℃(set value was 60)and 95.1±0.2℃(set value was 95)respectively.SARS-Cov-2 nucleic acid extraction yield through the device was 7.10×10^(6) copies/mL,while a commercial kit yielded 2.98×10^(6) copies/mL.The mean time to complete the entire assay,from SARS-CoV-2 nucleic acid extraction to amplification detection,was 36 min and 45 s.The detection limit for SARS-CoV-2 nucleic acid was 250 copies/mL.Conclusion The integrated disposable devices may be used for SARS-CoV-2 Point-of-Care test(POCT).
基金supported by Tianshan Talent Training Project-Xinjiang Science and Technology Innovation Team Program(2023TSYCTD).
文摘With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.
基金the National Natural Science Foundation of China(No.51471182)this work is also supported by Shanghai international science and Technology Cooperation Fund(No.17520711700)the National Key Research and Development Project(No.2017YFB0310600).
文摘The current COVID-19 pandemic urges the extremely sensitive and prompt detection of SARS-CoV-2 virus.Here,we present a Human Angiotensin-converting-enzyme 2(ACE2)-functionalized gold“virus traps”nanostructure as an extremely sensitive SERS biosensor,to selectively capture and rapidly detect S-protein expressed coronavirus,such as the current SARS-CoV-2 in the contaminated water,down to the single-virus level.Such a SERS sensor features extraordinary 106-fold virus enrichment originating from high-affinity of ACE2 with S protein as well as“virus-traps”composed of oblique gold nanoneedles,and 109-fold enhancement of Raman signals originating from multi-component SERS effects.Furthermore,the identification standard of virus signals is established by machine-learning and identification techniques,resulting in an especially low detection limit of 80 copies mL^(−1) for the simulated contaminated water by SARS-CoV-2 virus with complex circumstance as short as 5 min,which is of great significance for achieving real-time monitoring and early warning of coronavirus.Moreover,here-developed method can be used to establish the identification standard for future unknown coronavirus,and immediately enable extremely sensitive and rapid detection of novel virus.
基金supported by a project from National Key Technology R&D Program in the 11th Fiveyear Plan of China (2006BAD06A12)
文摘Postweaning multisystemic wasting syndrome (PMWS) is an important swine disease that is closely associated with porcine circovirus type 2 (PCV2). The capsid protein (Cap protein) is a major structural protein that has at least three immunoreactive regions, and it can be a suitable candidate antigen for detecting the specific antibodies of a PCV2 infection. In the present study, an indirect enzyme-linked immunosorbent assay (TcELISA) based on a truncated soluble Cap protein produced in Escherichia coli (E.coli) was established and validated for the diagnostic PCV2 antibodies in swine. The TcELISA was validated by comparison with an indirect immunofluorescence assay (IIFA). The diagnostic sensitivity (DSN), specificity (DSP), and accuracy of the TcELISA were 88.6%, 90.7% and 89.4%, respectively. The agreement rate was 89.38% between results obtained with TcELISA and IIFA on 113 field sera. A cross-reactivity assay showed that the method was PCV2-specific by comparison with other sera of viral disease. Therefore ,the TcELISA will be helpful for the development of a reliable serology diagnostic test for large scale detection of PCV2 antibodies and for the evaluation of vaccine against PCV2 in swine.
基金The authors are grateful to the Deanship of Scientific Research,King Saud University,Riyadh,Saudi Arabia,for funding this work through the Vice Deanship of Scientific Research Chairs:Research Chair of Pervasive and Mobile Computing.
文摘Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases.Therefore,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes.The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction ability.In addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection accuracy.The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes.Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians.Experimental results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset.Besides,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.
