Twenty five serotypes of Bluetongue virus (BTV) have been identified worldwide. Rapid and reliable methods of virus universal detection are essential for fighting against bluetongue (BT). We have therefore developed a...Twenty five serotypes of Bluetongue virus (BTV) have been identified worldwide. Rapid and reliable methods of virus universal detection are essential for fighting against bluetongue (BT). We have therefore developed and evaluated a pair of primers which can detect various serotypes of BTV by RT-PCR. Analysis of the viral protein 7 (VP7) and the non-structural protein (NS1) gene from different serotypes of BTV by DNAstar showed that the 5' end of the NS1 gene is the most conserved region. The primer pairs (P1 and P2) were designed based on the highly conserved region of NS1. The novel primers were evaluated by detecting BTV serotypes 1, 3, 5, 8, 10, 11, 21 and 22. The specificity of the primers was estimated by comparing to gene sequences of viruses published in GenBank, and further assessed by detecting BTV serotype 1-12 and Epizootic hemorrhagic disease virus (EHDV) serotype 1-4. The sensitivity and repeatability of PCR with the novel primers were evaluated by successfully detecting the recombinant plasmid pGEM-T121 containing the diagnosed nucleotide sequence. Our results suggest that these unique primers can be used in high throughout and universal detection of the NS1 gene from various BTV serotypes.展开更多
At inference time,deep neural networks are susceptible to backdoor attacks,which can produce attackercontrolled outputs when inputs contain carefully crafted triggers.Existing defense methods often focus on specific a...At inference time,deep neural networks are susceptible to backdoor attacks,which can produce attackercontrolled outputs when inputs contain carefully crafted triggers.Existing defense methods often focus on specific attack types or incur high costs,such as data cleaning or model fine-tuning.In contrast,we argue that it is possible to achieve effective and generalizable defense without removing triggers or incurring high model-cleaning costs.Fromthe attacker’s perspective and based on characteristics of vulnerable neuron activation anomalies,we propose an Adaptive Feature Injection(AFI)method for black-box backdoor detection.AFI employs a pre-trained image encoder to extract multi-level deep features and constructs a dynamic weight fusionmechanism for precise identification and interception of poisoned samples.Specifically,we select the control samples with the largest feature differences fromthe clean dataset via feature-space analysis,and generate blended sample pairs with the test sample using dynamic linear interpolation.The detection statistic is computed by measuring the divergence G(x)in model output responses.We systematically evaluate the effectiveness of AFI against representative backdoor attacks,including BadNets,Blend,WaNet,and IAB,on three benchmark datasets:MNIST,CIFAR-10,and ImageNet.Experimental results show that AFI can effectively detect poisoned samples,achieving average detection rates of 95.20%,94.15%,and 86.49%on these datasets,respectively.Compared with existing methods,AFI demonstrates strong cross-domain generalization ability and robustness to unknown attacks.展开更多
Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal cha...Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.展开更多
Cervical cancer is a prevalent gynecological malignancy,with approximately 90%of cases attributed to human papillomavirus(HPV)infection.Rapid and accurate nucleic acid detection is one of the leading methods to improv...Cervical cancer is a prevalent gynecological malignancy,with approximately 90%of cases attributed to human papillomavirus(HPV)infection.Rapid and accurate nucleic acid detection is one of the leading methods to improving screening coverage for early cervical cancer diagnosis.However,most existing techniques are usually complex and require expensive instrumentation.Clustered regularly interspaced short palindromic repeats(CRISPR)and CRISPR-associated systems have great advantages in nucleic acid detection.We herein combined the CRISPR-Cas12a with a universal dual-mode fluorescent nanoparticles(FNPs)platform to construct a highly sensitive signal-offassay for HPV high-risk subtypes detection.The signal readout module uses a single-stranded DNA linker,which forms a sandwich structure with DNA-functionalized magnetic beads and DNA-functionalized FNPs to generate signals.If trans-cleavage activity was activated by the targets,the linker was consumed and therefore could not form the sandwich structure to produce signals.HPV16 and HPV18 as model targets,the limits of detection as low as 5 pmol/L were successfully achieved without amplification.We validated the feasibility of the real-sample detection using HPV16 and HPV18 pseudo viruses.The proposed method can be easily adapted for other virus or bacterial assays by modifying the CRISPR-derived RNA(crRNA),which shows great potential for clinical diagnosis.展开更多
Rapid on-site screening of small-molecule pollutants in complex samples is essential but remains unachieved.In this study,we introduce a universal fluorescence color gradient immunochromatographic assay(FCGICA)utilizi...Rapid on-site screening of small-molecule pollutants in complex samples is essential but remains unachieved.In this study,we introduce a universal fluorescence color gradient immunochromatographic assay(FCGICA)utilizing dual-signal superposition to enable ultra-sensitive,wide-range,and simultaneous quantitative detection of multiple small molecules.A red fluorescent nanomembrane(GTQD@Si)is synthesized by the continuous self-assembly of multilayer quantum dots and a SiO_(2)shell on a graphene oxide surface.This nanomembrane exhibits high stability in complex environments and provides superior fluorescence along with a larger reactive interface for sensing.The integration of GTQD@Si with green fluorescent microspheres embedded in the test line generates a broad fluorescence color gradient based on variations in target molecule concentrations,thereby significantly enhancing the sensitivity,stability,and quantitative range of the immunochromatographic assay(ICA).By directly reading the ratio of red and green image signals,the proposed FCGICA enables simultaneous,high-sensitivity,and quantitative detection of three different types of small-molecule pollutants including fumonisin B1,imidacloprid,and clenbuterol within 15 min,with a detection range improved by 2–3 orders of magnitude compared with traditional methods.Moreover,the powerful practicality of FCGICA has been verified through comprehensive testing on various real samples,demonstrating its great potential in on-site detection of small molecules.展开更多
The universality of detecting organophosphorus pesticides in vegetables and fruits is crucial for ensuring food safety;however,this remains a challenge.In this study,we successfully developed an efficient nanozyme for...The universality of detecting organophosphorus pesticides in vegetables and fruits is crucial for ensuring food safety;however,this remains a challenge.In this study,we successfully developed an efficient nanozyme for organophosphorus detection with easy-to-interpret visual results,using Zr-metal-organic framework(MOF)to anchor Pr_(6)O_(11).The detection capability of Pr_(6)O_(11),which arises from the polyvalent states of metal atomic orbitals,was significantly enhanced by Zr-MOF.This combination prevented Pr_(6)O_(11)aggregation and enriched organophosphorus compounds in the environment through coordination,allowing the colorimetric method to achieve a limit of detection(LOD,signal-to-noise ratio(S/N)=3)as low as 1.47μg·mL^(-1).Additionally,the method provides a visual and rapid result interpretation via red-green-blue(RGB)color analysis using a smartphone app,making it accessible for non-experts.To demonstrate its accessibility,organophosphorus residues in commercially available cucumbers,lettuce,and rapeseed were investigated,validating the method with a spiked recovery test.The recovery rates ranged from 95%to 105%,with a relative error of less than 5%.The reaction time of the method was as short as 40 min,confirming its reliability and accessibility.Therefore,the Pr_(6)O_(11)/Zr-MOF nanozyme,coupled with the smartphone app,offers a reliable organophosphorus detection method with significant implications for food safety.展开更多
基金Hi-Tech Research and Development Program of China (2006AA10Z446)
文摘Twenty five serotypes of Bluetongue virus (BTV) have been identified worldwide. Rapid and reliable methods of virus universal detection are essential for fighting against bluetongue (BT). We have therefore developed and evaluated a pair of primers which can detect various serotypes of BTV by RT-PCR. Analysis of the viral protein 7 (VP7) and the non-structural protein (NS1) gene from different serotypes of BTV by DNAstar showed that the 5' end of the NS1 gene is the most conserved region. The primer pairs (P1 and P2) were designed based on the highly conserved region of NS1. The novel primers were evaluated by detecting BTV serotypes 1, 3, 5, 8, 10, 11, 21 and 22. The specificity of the primers was estimated by comparing to gene sequences of viruses published in GenBank, and further assessed by detecting BTV serotype 1-12 and Epizootic hemorrhagic disease virus (EHDV) serotype 1-4. The sensitivity and repeatability of PCR with the novel primers were evaluated by successfully detecting the recombinant plasmid pGEM-T121 containing the diagnosed nucleotide sequence. Our results suggest that these unique primers can be used in high throughout and universal detection of the NS1 gene from various BTV serotypes.
基金supported by the National Natural Science Foundation of China Grant(No.61972133)Project of Leading Talents in Science and Technology Innovation for Thousands of People Plan in Henan Province Grant(No.204200510021)the Key Research and Development Plan Special Project of Henan Province Grant(No.241111211400).
文摘At inference time,deep neural networks are susceptible to backdoor attacks,which can produce attackercontrolled outputs when inputs contain carefully crafted triggers.Existing defense methods often focus on specific attack types or incur high costs,such as data cleaning or model fine-tuning.In contrast,we argue that it is possible to achieve effective and generalizable defense without removing triggers or incurring high model-cleaning costs.Fromthe attacker’s perspective and based on characteristics of vulnerable neuron activation anomalies,we propose an Adaptive Feature Injection(AFI)method for black-box backdoor detection.AFI employs a pre-trained image encoder to extract multi-level deep features and constructs a dynamic weight fusionmechanism for precise identification and interception of poisoned samples.Specifically,we select the control samples with the largest feature differences fromthe clean dataset via feature-space analysis,and generate blended sample pairs with the test sample using dynamic linear interpolation.The detection statistic is computed by measuring the divergence G(x)in model output responses.We systematically evaluate the effectiveness of AFI against representative backdoor attacks,including BadNets,Blend,WaNet,and IAB,on three benchmark datasets:MNIST,CIFAR-10,and ImageNet.Experimental results show that AFI can effectively detect poisoned samples,achieving average detection rates of 95.20%,94.15%,and 86.49%on these datasets,respectively.Compared with existing methods,AFI demonstrates strong cross-domain generalization ability and robustness to unknown attacks.
基金Taishan Young Scholars Program of Shandong Province,Key Development Program for Basic Research of Shandong Province(ZR2020ZD44).
文摘Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.
基金financially supported by the National Natural Science Foundation of China(22004005)Special Project on Biomedical Innovation(223777118D)。
文摘Cervical cancer is a prevalent gynecological malignancy,with approximately 90%of cases attributed to human papillomavirus(HPV)infection.Rapid and accurate nucleic acid detection is one of the leading methods to improving screening coverage for early cervical cancer diagnosis.However,most existing techniques are usually complex and require expensive instrumentation.Clustered regularly interspaced short palindromic repeats(CRISPR)and CRISPR-associated systems have great advantages in nucleic acid detection.We herein combined the CRISPR-Cas12a with a universal dual-mode fluorescent nanoparticles(FNPs)platform to construct a highly sensitive signal-offassay for HPV high-risk subtypes detection.The signal readout module uses a single-stranded DNA linker,which forms a sandwich structure with DNA-functionalized magnetic beads and DNA-functionalized FNPs to generate signals.If trans-cleavage activity was activated by the targets,the linker was consumed and therefore could not form the sandwich structure to produce signals.HPV16 and HPV18 as model targets,the limits of detection as low as 5 pmol/L were successfully achieved without amplification.We validated the feasibility of the real-sample detection using HPV16 and HPV18 pseudo viruses.The proposed method can be easily adapted for other virus or bacterial assays by modifying the CRISPR-derived RNA(crRNA),which shows great potential for clinical diagnosis.
基金supported by the National Key Research and Development Program of China(2023YFC2606200)National Natural Science Foundation of China(Grant nos.82302638,82372348,32200076 and 82272423)+1 种基金Guangdong Basic and Applied Basic Research Foundation(2022B1515230005)Research foundation for advanced talents of Guangdong Provincial People’s Hospital(KJ012021097).
文摘Rapid on-site screening of small-molecule pollutants in complex samples is essential but remains unachieved.In this study,we introduce a universal fluorescence color gradient immunochromatographic assay(FCGICA)utilizing dual-signal superposition to enable ultra-sensitive,wide-range,and simultaneous quantitative detection of multiple small molecules.A red fluorescent nanomembrane(GTQD@Si)is synthesized by the continuous self-assembly of multilayer quantum dots and a SiO_(2)shell on a graphene oxide surface.This nanomembrane exhibits high stability in complex environments and provides superior fluorescence along with a larger reactive interface for sensing.The integration of GTQD@Si with green fluorescent microspheres embedded in the test line generates a broad fluorescence color gradient based on variations in target molecule concentrations,thereby significantly enhancing the sensitivity,stability,and quantitative range of the immunochromatographic assay(ICA).By directly reading the ratio of red and green image signals,the proposed FCGICA enables simultaneous,high-sensitivity,and quantitative detection of three different types of small-molecule pollutants including fumonisin B1,imidacloprid,and clenbuterol within 15 min,with a detection range improved by 2–3 orders of magnitude compared with traditional methods.Moreover,the powerful practicality of FCGICA has been verified through comprehensive testing on various real samples,demonstrating its great potential in on-site detection of small molecules.
基金supported by Central Government Guided Local Science and Technology Development Fund Project(No.guikeZY22096017)Natural Science Foundation of Guangxi Province(Nos.2025GXNSFAA069589 and 2024GXNSFDA010036)+2 种基金National Natural Science Foundation of China(Nos.22164014,22463008,and U23A2089)Innovation Project of Guangxi Graduate Education(No.YCSW2024465)Innovation and Entrepreneurship Training Program for College Students(No.202410603015).
文摘The universality of detecting organophosphorus pesticides in vegetables and fruits is crucial for ensuring food safety;however,this remains a challenge.In this study,we successfully developed an efficient nanozyme for organophosphorus detection with easy-to-interpret visual results,using Zr-metal-organic framework(MOF)to anchor Pr_(6)O_(11).The detection capability of Pr_(6)O_(11),which arises from the polyvalent states of metal atomic orbitals,was significantly enhanced by Zr-MOF.This combination prevented Pr_(6)O_(11)aggregation and enriched organophosphorus compounds in the environment through coordination,allowing the colorimetric method to achieve a limit of detection(LOD,signal-to-noise ratio(S/N)=3)as low as 1.47μg·mL^(-1).Additionally,the method provides a visual and rapid result interpretation via red-green-blue(RGB)color analysis using a smartphone app,making it accessible for non-experts.To demonstrate its accessibility,organophosphorus residues in commercially available cucumbers,lettuce,and rapeseed were investigated,validating the method with a spiked recovery test.The recovery rates ranged from 95%to 105%,with a relative error of less than 5%.The reaction time of the method was as short as 40 min,confirming its reliability and accessibility.Therefore,the Pr_(6)O_(11)/Zr-MOF nanozyme,coupled with the smartphone app,offers a reliable organophosphorus detection method with significant implications for food safety.
