This study establishes and validates a method for the precise quantification of aquatic microbial loads using microbial diversity absolute quantitative sequencing.By adding synthetic spike-in DNA to water samples from...This study establishes and validates a method for the precise quantification of aquatic microbial loads using microbial diversity absolute quantitative sequencing.By adding synthetic spike-in DNA to water samples from the Dahei River prior to DNA extraction and 16S rRNA gene sequencing,it generates standard curves to convert sequencing data into absolute microbial copy numbers.The method,which is proved highly accurate(R^(2)>0.99),reveals a clear contrast between the river sites:the upstream community has not only a significantly higher total microbial load but also a completely different makeup of species compared to the downstream site.This approach effectively overcomes the limitations of relative abundance analysis,providing a powerful tool for environmental monitoring,and proposes key steps for future standardization to ensure data comparability and integration.展开更多
Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features,making it difficult for traditional methods to ensure high accur...Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features,making it difficult for traditional methods to ensure high accuracy and robustness.Therefore,this study proposes a structural displacement identification and quantification method that integrates YOLOv8n with an improved edge-orientation gradient-based template matching algorithm.By combining deep learning techniques with traditional template matching methods,the accuracy and robustness of monitoring are enhanced under adverse conditions such as noise and extremely low illumination.Specifically,in the edge-orientation gradient matching stage,the Canny-Devernay sub-pixel edge detection technique and an improved ellipse-fitting method are employed for sub-pixel edge extraction,and a five-level Gaussian pyramid structure is introduced to accelerate the matching speed.Experimental results show that the proposed method achieves high-precision displacement monitoring under sufficient illumination,and it maintains stable target localization and displacement quantification performance under conditions of noise interference and extremely low illumination.Notably,under salt-and-pepper noise interference,although YOLOv8n maintains a high level of localization confidence,the accuracy of gradient matching deteriorates,resulting in a root-mean-square error(RMSE)of 0.035 mm.This finding reveals the differential impact of various noise types on different stages of the algorithm.The proposed method offers a novel technological approach for precise structural displacement monitoring in complex environments.展开更多
For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube samplin...For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube sampling,require a large number of samples,which entails huge computational costs.Therefore,how to construct a small-size sample space has been a hot issue of interest for researchers.To this end,this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification.First,the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis.Then,the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the"space filling"criterion.Finally,the sample selection function is established,and the next most informative sample is optimally selected to obtain the sequential test sample.Compared with the classical sampling method,the generated samples can retain more information on the basis of sparsity.A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme,which is a way to provide reliable uncertainty quantification results with small sample sizes.展开更多
Viral diseases are an important threat to crop yield,as they are responsible for losses greater than US$30 billion annually.Thus,understanding the dynamics of virus propagation within plant cells is essential for devi...Viral diseases are an important threat to crop yield,as they are responsible for losses greater than US$30 billion annually.Thus,understanding the dynamics of virus propagation within plant cells is essential for devising effective control strategies.However,viruses are complex to propagate and quantify.Existing methodologies for viral quantification tend to be expensive and time-consuming.Here,we present a rapid cost-effective approach to quantify viral propagation using an engineered virus expressing a fluorescent reporter.Using a microplate reader,we measured viral protein levels and we validated our findings through comparison by western blot analysis of viral coat protein,the most common approach to quantify viral titer.Our proposed methodology provides a practical and accessible approach to studying virus-host interactions and could contribute to enhancing our understanding of plant virology.展开更多
BACKGROUND The Streptococcus salivarius(S.salivarius)group,which produces the enzyme urease has been identified as a potential contributor to ammonia production in the gut.Researchers have reported that patients with ...BACKGROUND The Streptococcus salivarius(S.salivarius)group,which produces the enzyme urease has been identified as a potential contributor to ammonia production in the gut.Researchers have reported that patients with minimal HE had an increased abundance of the S.salivarius group,which is a specific change in the gut microbiota that distinguishes them from healthy individuals.The correlation between the aggregation of specific bacterial species and fibrosis progression in chronic liver disease(CLD)is yet to be fully elucidated.AIM To quantify S.salivarius using digital PCR(dPCR)as a liver fibrosis marker of CLD.METHODS This study retrospectively analysed 52 patients with CLD.To quantify S.salivarius in patients with CLD using dPCR,we evaluated the specificity and sensitivity of S.salivarius bacterial load using dPCR for a type strain.Next,we evaluated the clinical usefulness of dPCR for S.salivarius load quantification for detecting liver fibrosis in patients with CLD.The liver fibrosis stage was categorized into mild and advanced fibrosis based on pathological findings.RESULTS The dPCR assay revealed that S.salivarius was highly positive for the tnpA gene.The lower limit of quantification for dPCR using the tnpA gene with a 1μL template comprising 1.28×102 CFU/mL was 4.3 copies.After considering the detection range in dPCR,we adjusted the extracted DNA concentration to 5.0×10-4 ng/μL from 200 mg stool samples.The median bacterial loads of S.salivarius in stool sample from patients with mild and advanced fibrosis were 1.9 and 7.4 copies/μL,respectively.The quantification of S.salivarius load was observed more frequently in patients with advanced fibrosis than in those with mild fibrosis(P=0.032).CONCLUSION Quantifying of S.salivarius load using digital PCR is a useful biomarker for liver fibrosis in patients with CLD.展开更多
Excessive Fe^(3+) ion concentrations in wastewater pose a long-standing threat to human health.Achieving low-cost,high-efficiency quantification of Fe^(3+) ion concentration in unknown solutions can guide environmenta...Excessive Fe^(3+) ion concentrations in wastewater pose a long-standing threat to human health.Achieving low-cost,high-efficiency quantification of Fe^(3+) ion concentration in unknown solutions can guide environmental management decisions and optimize water treatment processes.In this study,by leveraging the rapid,real-time detection capabilities of nanopores and the specific chemical binding affinity of tannic acid to Fe^(3+),a linear relationship between the ion current and Fe^(3+) ion concentration was established.Utilizing this linear relationship,quantification of Fe^(3+) ion concentration in unknown solutions was achieved.Furthermore,ethylenediaminetetraacetic acid disodium salt was employed to displace Fe^(3+) from the nanopores,allowing them to be restored to their initial conditions and reused for Fe^(3+) ion quantification.The reusable bioinspired nanopores remain functional over 330 days of storage.This recycling capability and the long-term stability of the nanopores contribute to a significant reduction in costs.This study provides a strategy for the quantification of unknown Fe^(3+) concentration using nanopores,with potential applications in environmental assessment,health monitoring,and so forth.展开更多
During high-speed forward flight,helicopter rotor blades operate across a wide range of Reynolds and Mach numbers.Under such conditions,their aerodynamic performance is significantly influenced by dynamic stall—a com...During high-speed forward flight,helicopter rotor blades operate across a wide range of Reynolds and Mach numbers.Under such conditions,their aerodynamic performance is significantly influenced by dynamic stall—a complex,unsteady flow phenomenon highly sensitive to inlet conditions such asMach and Reynolds numbers.The key features of three-dimensional blade stall can be effectively represented by the dynamic stall behavior of a pitching airfoil.In this study,we conduct an uncertainty quantification analysis of dynamic stall aerodynamics in high-Mach-number flows over pitching airfoils,accounting for uncertainties in inlet parameters.A computational fluid dynamics(CFD)model based on the compressible unsteady Reynolds-averagedNavier–Stokes(URANS)equations,coupledwith sliding mesh techniques,is developed to simulate the unsteady aerodynamic behavior and associated flow fields.To efficiently capture the aerodynamic responses while maintaining high accuracy,a multi-fidelity Co-Kriging surrogate model is constructed.This model integrates the precision of high-fidelity wind tunnel experiments with the computational efficiency of lower-fidelity URANS simulations.Its accuracy is validated through direct comparison with experimental data.Building upon this surrogate model,we employ interval analysis and the Sobol sensitivity method to quantify the uncertainty and parameter sensitivity of the unsteady aerodynamic forces resulting frominlet condition variability.Both the inlet Mach number and Reynolds number are treated as uncertain inputs,modeled using interval representations.Our results demonstrate that variations inMach number contribute far more significantly to aerodynamic uncertainty than those in Reynolds number.Moreover,the presence of dynamic stall vortices markedly amplifies the aerodynamic sensitivity to Mach number fluctuations.展开更多
In the data transaction process within a data asset trading platform,quantifying the trustworthiness of data source nodes is challenging due to their numerous attributes and complex structures.To address this issue,a ...In the data transaction process within a data asset trading platform,quantifying the trustworthiness of data source nodes is challenging due to their numerous attributes and complex structures.To address this issue,a distributed data source trust assessment management framework,a trust quantification model,and a dynamic adjustment mechanism are proposed.Themodel integrates the Analytic Hierarchy Process(AHP)and Dempster-Shafer(D-S)evidence theory to determine attribute weights and calculate direct trust values,while the PageRank algorithm is employed to derive indirect trust values.Thedirect and indirect trust values are then combined to compute the comprehensive trust value of the data source.Furthermore,a dynamic adjustment mechanism is introduced to continuously update the comprehensive trust value based on historical assessment data.By leveraging the collaborative efforts of multiple nodes in the distributed network,the proposed framework enables a comprehensive,dynamic,and objective evaluation of data source trustworthiness.Extensive experimental analyses demonstrate that the trust quantification model effectively handles large-scale data source trust assessments,exhibiting both strong trust differentiation capability and high robustness.展开更多
Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular disease.However,the manual calculation of these parameters is challenging due to the high va...Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular disease.However,the manual calculation of these parameters is challenging due to the high variability among patients and the time-consuming nature of the process.In this study,the authors introduce a framework named MultiJSQ,comprising the feature presentation network(FRN)and the indicator prediction network(IEN),which is designed for simultaneous joint segmentation and quantification.The FRN is tailored for representing global image features,facilitating the direct acquisition of left ventricle(LV)contour images through pixel classification.Additionally,the IEN incorporates specifically designed modules to extract relevant clinical indices.The authors’method considers the interdependence of different tasks,demonstrating the validity of these relationships and yielding favourable results.Through extensive experiments on cardiac MR images from 145 patients,MultiJSQ achieves impressive outcomes,with low mean absolute errors of 124 mm^(2),1.72 mm,and 1.21 mm for areas,dimensions,and regional wall thicknesses,respectively,along with a Dice metric score of 0.908.The experimental findings underscore the excellent performance of our framework in LV segmentation and quantification,highlighting its promising clinical application prospects.展开更多
Generative adversarial network(GAN)models are widely used in mechanical designs.The aim in the airfoil shape design is to obtain shapes that exhibits the required aerodynamic performance,and conditional GAN is used fo...Generative adversarial network(GAN)models are widely used in mechanical designs.The aim in the airfoil shape design is to obtain shapes that exhibits the required aerodynamic performance,and conditional GAN is used for that aim.However,the output of GAN contains uncertainties.Additionally,the uncertainties of labels have not been quantified.This paper proposes an uncertainty quantification method to estimate the uncertainty of labels using Monte Carlo dropout.In addition,an uncertainty reduction method is proposed based on imbalanced training.The proposed method was evaluated for the airfoil generation task.The results indicated that the uncertainty was appropriately quantified and successfully reduced.展开更多
Wheat powdery mildew caused by Blumeria graminis f.sp.tritici(Bgt)is an important disease worldwide.Detection of latent infection of leaves by the pathogen in late autumn is valuable for estimating the inoculum potent...Wheat powdery mildew caused by Blumeria graminis f.sp.tritici(Bgt)is an important disease worldwide.Detection of latent infection of leaves by the pathogen in late autumn is valuable for estimating the inoculum potential to assess disease risks in the spring.We developed a new tool for rapid detection and quantification of latent infection of seedlings by the pathogen.The method was based on recombinase polymerase amplification(RPA)coupled with an end-point detection via lateral flow device(LFD).The limit of detection is 100 agμL^(-1)of Bgt DNA,without noticeable interference from either other common wheat pathogens or wheat material(Triticum aestivum).It was evaluated on wheat seedlings for this accuracy and sensitivity in detecting latent infection of Bgt.We further extended this RPALFD assay to estimate the level of latent infection by Bgt based on imaging analysis.There was a strong correlation between the image-based and real-time PCR assay estimates of Bgt DNA.The present results suggested that this new tool can provide rapid and accurate quantification of Bgt in latently infected leaves and can be further development as an on-site monitoring tool.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.32160172)the Key Science-Technology Project of Inner Mongolia(2023KYPT0010)+1 种基金the Natural Science Foundation of Inner Mongolia Autonomous Region of China(Grant No.2025QN03006)the 2023 Inner Mongolia Public Institution High-level Talent Introduction Scientific Research Support Project.
文摘This study establishes and validates a method for the precise quantification of aquatic microbial loads using microbial diversity absolute quantitative sequencing.By adding synthetic spike-in DNA to water samples from the Dahei River prior to DNA extraction and 16S rRNA gene sequencing,it generates standard curves to convert sequencing data into absolute microbial copy numbers.The method,which is proved highly accurate(R^(2)>0.99),reveals a clear contrast between the river sites:the upstream community has not only a significantly higher total microbial load but also a completely different makeup of species compared to the downstream site.This approach effectively overcomes the limitations of relative abundance analysis,providing a powerful tool for environmental monitoring,and proposes key steps for future standardization to ensure data comparability and integration.
基金supported by the National Natural Science Foundation of China(No.52408533)the Natural Science Foundation of Shandong Province(No.ZR2024QE408)+3 种基金the University of Jinan Disciplinary Cross-Convergence Construction Project 2023(XKJC202310)the Municipal and School Integration Development Strategic Project of Jinan City(JNSX2023023)Natural Science Foundation of Tianjin(24JCQNJC00870)Doctoral Fund Support Project of University of Jinan(XRC2563).
文摘Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features,making it difficult for traditional methods to ensure high accuracy and robustness.Therefore,this study proposes a structural displacement identification and quantification method that integrates YOLOv8n with an improved edge-orientation gradient-based template matching algorithm.By combining deep learning techniques with traditional template matching methods,the accuracy and robustness of monitoring are enhanced under adverse conditions such as noise and extremely low illumination.Specifically,in the edge-orientation gradient matching stage,the Canny-Devernay sub-pixel edge detection technique and an improved ellipse-fitting method are employed for sub-pixel edge extraction,and a five-level Gaussian pyramid structure is introduced to accelerate the matching speed.Experimental results show that the proposed method achieves high-precision displacement monitoring under sufficient illumination,and it maintains stable target localization and displacement quantification performance under conditions of noise interference and extremely low illumination.Notably,under salt-and-pepper noise interference,although YOLOv8n maintains a high level of localization confidence,the accuracy of gradient matching deteriorates,resulting in a root-mean-square error(RMSE)of 0.035 mm.This finding reveals the differential impact of various noise types on different stages of the algorithm.The proposed method offers a novel technological approach for precise structural displacement monitoring in complex environments.
基金co-supported by the National Natural Science Foundation of China(Nos.51875014,U2233212 and 51875015)the Natural Science Foundation of Beijing Municipality,China(No.L221008)+1 种基金Science,Technology Innovation 2025 Major Project of Ningbo of China(No.2022Z005)the Tianmushan Laboratory Project,China(No.TK2023-B-001)。
文摘For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube sampling,require a large number of samples,which entails huge computational costs.Therefore,how to construct a small-size sample space has been a hot issue of interest for researchers.To this end,this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification.First,the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis.Then,the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the"space filling"criterion.Finally,the sample selection function is established,and the next most informative sample is optimally selected to obtain the sequential test sample.Compared with the classical sampling method,the generated samples can retain more information on the basis of sparsity.A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme,which is a way to provide reliable uncertainty quantification results with small sample sizes.
基金Funding from Natural Sciences and Engineering Research Council of Canada award number RGPIN/4002-2020.
文摘Viral diseases are an important threat to crop yield,as they are responsible for losses greater than US$30 billion annually.Thus,understanding the dynamics of virus propagation within plant cells is essential for devising effective control strategies.However,viruses are complex to propagate and quantify.Existing methodologies for viral quantification tend to be expensive and time-consuming.Here,we present a rapid cost-effective approach to quantify viral propagation using an engineered virus expressing a fluorescent reporter.Using a microplate reader,we measured viral protein levels and we validated our findings through comparison by western blot analysis of viral coat protein,the most common approach to quantify viral titer.Our proposed methodology provides a practical and accessible approach to studying virus-host interactions and could contribute to enhancing our understanding of plant virology.
文摘BACKGROUND The Streptococcus salivarius(S.salivarius)group,which produces the enzyme urease has been identified as a potential contributor to ammonia production in the gut.Researchers have reported that patients with minimal HE had an increased abundance of the S.salivarius group,which is a specific change in the gut microbiota that distinguishes them from healthy individuals.The correlation between the aggregation of specific bacterial species and fibrosis progression in chronic liver disease(CLD)is yet to be fully elucidated.AIM To quantify S.salivarius using digital PCR(dPCR)as a liver fibrosis marker of CLD.METHODS This study retrospectively analysed 52 patients with CLD.To quantify S.salivarius in patients with CLD using dPCR,we evaluated the specificity and sensitivity of S.salivarius bacterial load using dPCR for a type strain.Next,we evaluated the clinical usefulness of dPCR for S.salivarius load quantification for detecting liver fibrosis in patients with CLD.The liver fibrosis stage was categorized into mild and advanced fibrosis based on pathological findings.RESULTS The dPCR assay revealed that S.salivarius was highly positive for the tnpA gene.The lower limit of quantification for dPCR using the tnpA gene with a 1μL template comprising 1.28×102 CFU/mL was 4.3 copies.After considering the detection range in dPCR,we adjusted the extracted DNA concentration to 5.0×10-4 ng/μL from 200 mg stool samples.The median bacterial loads of S.salivarius in stool sample from patients with mild and advanced fibrosis were 1.9 and 7.4 copies/μL,respectively.The quantification of S.salivarius load was observed more frequently in patients with advanced fibrosis than in those with mild fibrosis(P=0.032).CONCLUSION Quantifying of S.salivarius load using digital PCR is a useful biomarker for liver fibrosis in patients with CLD.
基金supported by the National Natural Science Foundation of China(Nos.52303380,52025132,52273305,22205185,21621091,22021001,and 22121001)Fundamental Research Funds for the Central Universities(No.20720240041)+3 种基金the 111 Project(Nos.B17027 and B16029)the National Science Foundation of Fujian Province of China(No.2022J02059)the Science and Technology Projects of Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(No.RD2022070601)the New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘Excessive Fe^(3+) ion concentrations in wastewater pose a long-standing threat to human health.Achieving low-cost,high-efficiency quantification of Fe^(3+) ion concentration in unknown solutions can guide environmental management decisions and optimize water treatment processes.In this study,by leveraging the rapid,real-time detection capabilities of nanopores and the specific chemical binding affinity of tannic acid to Fe^(3+),a linear relationship between the ion current and Fe^(3+) ion concentration was established.Utilizing this linear relationship,quantification of Fe^(3+) ion concentration in unknown solutions was achieved.Furthermore,ethylenediaminetetraacetic acid disodium salt was employed to displace Fe^(3+) from the nanopores,allowing them to be restored to their initial conditions and reused for Fe^(3+) ion quantification.The reusable bioinspired nanopores remain functional over 330 days of storage.This recycling capability and the long-term stability of the nanopores contribute to a significant reduction in costs.This study provides a strategy for the quantification of unknown Fe^(3+) concentration using nanopores,with potential applications in environmental assessment,health monitoring,and so forth.
文摘During high-speed forward flight,helicopter rotor blades operate across a wide range of Reynolds and Mach numbers.Under such conditions,their aerodynamic performance is significantly influenced by dynamic stall—a complex,unsteady flow phenomenon highly sensitive to inlet conditions such asMach and Reynolds numbers.The key features of three-dimensional blade stall can be effectively represented by the dynamic stall behavior of a pitching airfoil.In this study,we conduct an uncertainty quantification analysis of dynamic stall aerodynamics in high-Mach-number flows over pitching airfoils,accounting for uncertainties in inlet parameters.A computational fluid dynamics(CFD)model based on the compressible unsteady Reynolds-averagedNavier–Stokes(URANS)equations,coupledwith sliding mesh techniques,is developed to simulate the unsteady aerodynamic behavior and associated flow fields.To efficiently capture the aerodynamic responses while maintaining high accuracy,a multi-fidelity Co-Kriging surrogate model is constructed.This model integrates the precision of high-fidelity wind tunnel experiments with the computational efficiency of lower-fidelity URANS simulations.Its accuracy is validated through direct comparison with experimental data.Building upon this surrogate model,we employ interval analysis and the Sobol sensitivity method to quantify the uncertainty and parameter sensitivity of the unsteady aerodynamic forces resulting frominlet condition variability.Both the inlet Mach number and Reynolds number are treated as uncertain inputs,modeled using interval representations.Our results demonstrate that variations inMach number contribute far more significantly to aerodynamic uncertainty than those in Reynolds number.Moreover,the presence of dynamic stall vortices markedly amplifies the aerodynamic sensitivity to Mach number fluctuations.
基金funded by Haikou Science and Technology Plan Project(2022-007),in part by key Laboratory of PK System Technologies Research of Hainan,China.
文摘In the data transaction process within a data asset trading platform,quantifying the trustworthiness of data source nodes is challenging due to their numerous attributes and complex structures.To address this issue,a distributed data source trust assessment management framework,a trust quantification model,and a dynamic adjustment mechanism are proposed.Themodel integrates the Analytic Hierarchy Process(AHP)and Dempster-Shafer(D-S)evidence theory to determine attribute weights and calculate direct trust values,while the PageRank algorithm is employed to derive indirect trust values.Thedirect and indirect trust values are then combined to compute the comprehensive trust value of the data source.Furthermore,a dynamic adjustment mechanism is introduced to continuously update the comprehensive trust value based on historical assessment data.By leveraging the collaborative efforts of multiple nodes in the distributed network,the proposed framework enables a comprehensive,dynamic,and objective evaluation of data source trustworthiness.Extensive experimental analyses demonstrate that the trust quantification model effectively handles large-scale data source trust assessments,exhibiting both strong trust differentiation capability and high robustness.
基金Hefei Municipal Natural Science Foundation,Grant/Award Number:2022009Suqian Guiding Program Project,Grant/Award Number:Z202309Suqian Traditional Chinese Medicine Science and Technology Plan,Grant/Award Number:MS202301。
文摘Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular disease.However,the manual calculation of these parameters is challenging due to the high variability among patients and the time-consuming nature of the process.In this study,the authors introduce a framework named MultiJSQ,comprising the feature presentation network(FRN)and the indicator prediction network(IEN),which is designed for simultaneous joint segmentation and quantification.The FRN is tailored for representing global image features,facilitating the direct acquisition of left ventricle(LV)contour images through pixel classification.Additionally,the IEN incorporates specifically designed modules to extract relevant clinical indices.The authors’method considers the interdependence of different tasks,demonstrating the validity of these relationships and yielding favourable results.Through extensive experiments on cardiac MR images from 145 patients,MultiJSQ achieves impressive outcomes,with low mean absolute errors of 124 mm^(2),1.72 mm,and 1.21 mm for areas,dimensions,and regional wall thicknesses,respectively,along with a Dice metric score of 0.908.The experimental findings underscore the excellent performance of our framework in LV segmentation and quantification,highlighting its promising clinical application prospects.
基金supported by the Japan Society for the Promotion of Science and Grants-in-Aid for Scientific Research(Grant Nos.JP21K14064 and JP23K13239).
文摘Generative adversarial network(GAN)models are widely used in mechanical designs.The aim in the airfoil shape design is to obtain shapes that exhibits the required aerodynamic performance,and conditional GAN is used for that aim.However,the output of GAN contains uncertainties.Additionally,the uncertainties of labels have not been quantified.This paper proposes an uncertainty quantification method to estimate the uncertainty of labels using Monte Carlo dropout.In addition,an uncertainty reduction method is proposed based on imbalanced training.The proposed method was evaluated for the airfoil generation task.The results indicated that the uncertainty was appropriately quantified and successfully reduced.
基金supported by the funding from the National Natural Science Foundation of China(32072359)。
文摘Wheat powdery mildew caused by Blumeria graminis f.sp.tritici(Bgt)is an important disease worldwide.Detection of latent infection of leaves by the pathogen in late autumn is valuable for estimating the inoculum potential to assess disease risks in the spring.We developed a new tool for rapid detection and quantification of latent infection of seedlings by the pathogen.The method was based on recombinase polymerase amplification(RPA)coupled with an end-point detection via lateral flow device(LFD).The limit of detection is 100 agμL^(-1)of Bgt DNA,without noticeable interference from either other common wheat pathogens or wheat material(Triticum aestivum).It was evaluated on wheat seedlings for this accuracy and sensitivity in detecting latent infection of Bgt.We further extended this RPALFD assay to estimate the level of latent infection by Bgt based on imaging analysis.There was a strong correlation between the image-based and real-time PCR assay estimates of Bgt DNA.The present results suggested that this new tool can provide rapid and accurate quantification of Bgt in latently infected leaves and can be further development as an on-site monitoring tool.
