C-type lectins(CTLs)are a superfamily of Ca^(2+)-dependent carbohydrate-recognition proteins,and an important pattern recognition receptor(PRR)in insect innate immunity which can mediate humoral and cellular immunity ...C-type lectins(CTLs)are a superfamily of Ca^(2+)-dependent carbohydrate-recognition proteins,and an important pattern recognition receptor(PRR)in insect innate immunity which can mediate humoral and cellular immunity in insects.In this study,we report a novel dual carbohydrate-recognition domain(CRD)CTL from Plutella xylostella which we designate PxIML.PxIML is a protein with a 969 bp open reading frame(ORF)encoding 322 amino acids,containing a signal peptide and a dual-CRD with EPN(Glu_(124)-Pro_(125)-Asn_(126))and QPD(Gln_(274)-Pro_(275)-Asp_(276))motifs.The expression of PxIML mRNA in the fat body was significantly higher than in hemocytes and midgut.The relative expression levels of PxIML in the whole insect and the fat body were significantly inhibited after infection with Bacillus thuringiensis 8010(Bt8010)at 18 h,while they were significantly upregulated after infection with Serratia marcescens IAE6 or Pichia pastoris.The recombinant PxIML(rPxIML)protein could bind to the tested pathogen-associated molecular patterns(PAMPs),and the bacteria of Enterobacter sp.IAE5,S.marcescens IAE6,Staphylococcus aureus,Escherichia coli BL21,and Bt8010 in a Ca^(2+)-dependent manner,however,it showed limited binding to the fungus,P.pastoris.The rPxIML exhibited strong activity in the presence of Ca^(2+) to agglutinate Bt8010,Enterobacter sp.IAE5 and S.aureus,but it only weakly agglutinated with E.coli BL21,and could not agglutinate with S.marcescens IAE6 or P.pastoris.Furthermore,the rPxIML could bind to hemocytes,promote the adsorption of hemocytes to beads,and enhance the phenoloxidase(PO)activity and melanization of P.xylostella.Our results suggest that PxIML plays an important role in pathogen recognition and in mediating subsequent humoral and cellular immunity of P.xylostella.展开更多
Wheat leaf rust,caused by Puccinia triticina(Pt),is one of the most devastating diseases in common wheat(Triticum aestivum L.)and can lead to heavy yield loss(Chai et al.2020).Leaf rust can result in 50%yield loss dur...Wheat leaf rust,caused by Puccinia triticina(Pt),is one of the most devastating diseases in common wheat(Triticum aestivum L.)and can lead to heavy yield loss(Chai et al.2020).Leaf rust can result in 50%yield loss during epidemic years(Huerta-Espino et al.2011;Gebrewahid et al.2020;Kolomiets et al.2021).Breeding varieties resistant to leaf rust have been recognized as the most effective and economical method to mitigate wheat losses caused by Pt.The narrow genetic basis of wheat constrains the number of cultivars resistant to leaf rust(Jin et al.2021).展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewa...Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.展开更多
[Objectives] To investigate the systematic evaluation of pharmacognostic identification of Polygonum capitatum . [Methods] 10 batches of P. capitatum cultivated in Guizhou were chosen for plant samples. Macroscopical ...[Objectives] To investigate the systematic evaluation of pharmacognostic identification of Polygonum capitatum . [Methods] 10 batches of P. capitatum cultivated in Guizhou were chosen for plant samples. Macroscopical identification was conducted on plant roots, stems, leaves, flowers and fruits. The P. capitatum powder was processed for physical and chemical distinction by FeCl 3 chromogenic reaction, hydrochloric acid magnesium powder reaction, AlCl 3 color development reaction and thin-layer chromatography.Microscope identification was carried out on the powder. Plant genome DNeasy Plant Kit was adopted for DNA molecular marker identification. [Results] The results showed that the stem of P. capitatum was tufted, the leaves were oval, 2 to 5 cm long, and 1 to 2 cm wide;the leaf apex was acute and cuneate at the base, the inflorescence was capitate, paired or solitary;the raceme was erect and nearly spherical, and the perianth was light red. Furthermore, for the chromogenic reaction of FeCl 3 ethanol extract of P. capitatum , appeared blue and turned to dark blue after long time storing at room temperature. For the reaction of hydrochloric acid magnesium powder, the alcohol extract of P. capitatum , exhibited deep red. In the color reaction of AlCl 3, the alcohol extract revealed yellow fluorescence under 360 nm UV lamp. Microscope identification of the powder displayed pollen grains, crystal sheath fibers, cellulose, vessels, starch grains, cork cells, and other characteristic fragments. In addition, DNA barcoding electrophoresis results showed that P. capitatum showed a clear and bright single band near 500 bp, and further sequencing results showed that the sequence differences were mainly concentrated in ITS1 and ITS2 region. [Conclusions] Systematic evaluation for the identification of P. capitatum is established, which combines with macroscopic identification, physicochemical identification, powder microscope identification, and DNA molecular identification. Finally, the original medicinal material is identified as P. capitatum Buch.-Ham. ex D. Don.展开更多
Microbes play a critical role in shaping immune development,with growing interest in how rhinovirus(RV)interacts with the host immune system,particularly in individuals with asthma and chronic obstructive pul-monary d...Microbes play a critical role in shaping immune development,with growing interest in how rhinovirus(RV)interacts with the host immune system,particularly in individuals with asthma and chronic obstructive pul-monary disease(COPD).Disruptions in microbial balance during RV infections can impair immune homeostasis and worsen disease outcomes.Recent studies emphasize RV-induced regulation of antiviral defenses,cytokine production,and immune tolerance.This review explores the interplay between RV,the immune system,and microbiota,highlighting the importance of these interactions in guiding effective therapies for respiratory in-fections.It advances existing literature by considering microbiota-mediated therapies as a novel approach to managing RV exacerbations in respiratory diseases like asthma and COPD.展开更多
The identification of the traction acting on a portion of the surface of an anisotropic solid is very important in structural health monitoring and optimal design of structures. The traction can be determined using in...The identification of the traction acting on a portion of the surface of an anisotropic solid is very important in structural health monitoring and optimal design of structures. The traction can be determined using inverse methods in which displacement or strain measurements are taken at several points on the body. This paper presents an inverse method based on the method of fundamental solutions for the traction identification problem in two-dimensional anisotropic elasticity. The method of fundamental solutions is an efficient boundary-type meshless method widely used for analyzing various problems. Since the problem is linear, the sensitivity analysis is simply performed by solving the corresponding direct problem several times with different loads. The effects of important parameters such as the number of measurement data, the position of the measurement points, the amount of measurement error, and the type of measurement, i.e., displacement or strain, on the results are also investigated. The results obtained show that the presented inverse method is suitable for the problem of traction identification. It can be concluded from the results that the use of strain measurements in the inverse analysis leads to more accurate results than the use of displacement measurements. It is also found that measurement points closer to the boundary with unknown traction provide more reliable solutions. Additionally, it is found that increasing the number of measurement points increases the accuracy of the inverse solution. However, in cases with a large number of measurement points, further increasing the number of measurement data has little effect on the results.展开更多
Accurate kinematic calibration is the very foundation for robots'application in industry demanding high precision such as machining.Considering the complex error characteristic and severe ill-posed identification ...Accurate kinematic calibration is the very foundation for robots'application in industry demanding high precision such as machining.Considering the complex error characteristic and severe ill-posed identification issues of a 5-DoF parallel machining robot,this paper proposes an adaptive and weighted identification method to achieve high-precision kinematic calibration while maintaining reliable stability.First,a kinematic error propagation mechanism model considering the non-ideal constraints and the screw self-rotation is formulated by incorporating the intricate structure of multiple chains and a unique driven screw arrangement of the robot.To address the challenge of accurately identifying such a sophisticated error model,a novel adaptive and weighted identification method based on generalized cross validation(GCV)is proposed.Specifically,this approach innovatively introduces Gauss-Markov estimation into the GCV algorithm and utilizes prior physical information to construct both a weighted identification model and a weighted cross-validation function,thus eliminating the inaccuracy caused by significant differences in dimensional magnitudes of pose errors and achieving accurate identification with flexible numerical stability.Finally,the kinematic calibration experiment is conducted.The comparative experimental results demonstrate that the presented approach is effective and has enhanced accuracy performance over typical least squares methods,with maximum position and orientation errors reduced from 2.279 mm to 0.028 mm and from 0.206°to 0.017°,respectively.展开更多
[Objectives] To identify Pyrostegia venusta (Ker-Gawler.) Miers by microscope and ultraviolet spectrum. [Methods] The paraffin section, slide section and freehand section were used to make the cross section of the ste...[Objectives] To identify Pyrostegia venusta (Ker-Gawler.) Miers by microscope and ultraviolet spectrum. [Methods] The paraffin section, slide section and freehand section were used to make the cross section of the stem and leaf, and the surface of the leaf and the powder of the root, stem and leaf were made by the conventional method, which were observed under the optical microscope. Ultraviolet-visible spectrum identification was carried out according to a conventional method. [Results] The microscopic identification and ultraviolet-visible absorption characteristics of P. venusta (Ker-Gawler) Miers were described in detail. [Conclusions] This study is expected to provide a reference for the identification of P. venusta(KerGawler)Miers and the establishment of the related quality standard.展开更多
GS1 is an international standards organization,which focuses on product identification and product data,helping businesses and governments to improve commerce and supply chain.Why trusted data is essential to high-qua...GS1 is an international standards organization,which focuses on product identification and product data,helping businesses and governments to improve commerce and supply chain.Why trusted data is essential to high-quality development?More than 50 years ago,GS1 was initiated with the bar code,a profound transformation of the way we work and live.From then on,a simple scan connected a physical product to its digital identity.It transformed commerce,improving supply chains and enabling safer healthcare.Collaboration between industry and governments,and a strong partnership with ISO and IEC laid the foundations for the global adoption of a common product identification over the past 50 years and all around the world.展开更多
Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model...Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model approximating the actual system is obtained online.The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error,which is then utilized in the design of a tube-based robust model predictive controller.The effectiveness of the proposed approach is validated by numerical simulation.展开更多
To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is deve...To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is developed to identify the geometric parameters.The study utilizes a common precast element for highway bridges as the research subject.First,edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology.Subsequently,a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output.A dataset is generated by varying the control parameters and noise levels for model training.Finally,field measurements are conducted to validate the accuracy of the developed method.The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components,with an error rate maintained within 5%.展开更多
As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and...As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.展开更多
The RING-type E3 ligase OsBBI1 regulates rice resistance against Magnaporthe oryzae through modifying cell wall defenses.In this study,we report the function of an OsBBI1 substrate,eukaryotic translation initiation fa...The RING-type E3 ligase OsBBI1 regulates rice resistance against Magnaporthe oryzae through modifying cell wall defenses.In this study,we report the function of an OsBBI1 substrate,eukaryotic translation initiation factor OseIF5A4,in rice immunity.OsBBI1 interacts with OseIF5A4 and other four members of the OseIF5A family.The RING domain in OsBBI1 and the eIF-5a domain in OseIF5A4 are critical for the OsBBI1-OseIF5A4 interaction.OsBBI1 ubiquitinates OseIF5A4 and mediates its degradation in vitro and in vivo.Moreover,the expression of OseIF5A4 was upregulated during early stage of compatible interaction but downregulated in incompatible interaction between rice and M.oryzae.Knockout of OseIF5A4 enhances rice immunity against M.oryzae and Xanthomonas oryzae pv.oryzae,boosts pattern-triggered immune responses,and strengthens pathogen-induced defense responses(e.g.,expression of defense genes,accumulation of reactive oxygen species and reinforcement of cell wall).However,overexpression of OseIF5A4 attenuates rice immunity and immune responses.These results demonstrate that OseIF5A4,a substrate of the immunity-associated E3 ligase OsBBI1,negatively regulates rice immunity against M.oryzae and X.oryzae pv.oryzae through modulating pathogen-induced defense responses,highlighting the importance of the protein translational machinery in rice immunity.展开更多
Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on ...Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.展开更多
It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in...It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in logging curves,this study establishes a grayscale-phase model based on high-resolution grayscale curves using clustering analysis algorithms for shale lithofacies identification,working with the Shahejie For-mation,Bohai Bay Basin,China.The grayscale phase is defined as the sum of absolute grayscale and relative amplitude as well as their features.The absolute grayscale is the absolute magnitude of the gray values and is utilized for evaluating the material composition(mineral composition+total organic carbon)of shale,while the relative amplitude is the difference between adjacent gray values and is used to identify the shale structure type.The research results show that the grayscale phase model can identify shale lithofacies well,and the accuracy and applicability of this model were verified by the fitting relationship between absolute grayscale and shale mineral composition,as well as corresponding re-lationships between relative amplitudes and laminae development in shales.Four lithofacies are iden-tified in the target layer of the study area:massive mixed shale,laminated mixed shale,massive calcareous shale and laminated calcareous shale.This method can not only effectively characterize the material composition of shale,but also numerically characterize the development degree of shale laminae,and solve the problem that difficult to identify millimeter-scale laminae based on logging curves,which can provide technical support for shale lithofacies identification,sweet spot evaluation and prediction of complex continental lacustrine basins.展开更多
Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from b...Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.展开更多
The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor...The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise,often complicated by the coexistence of other minerals.This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals.The utilizied four advanced deep learning models—PSPNet,U-Net,FPN,and LinkNet—has significant advancements in efficiency and accuracy.Among these models,PSPNet exhibited superior performance,achieving the highest intersection over union(IoU)scores and demonstrating exceptional reliability in segmenting quartz minerals,even in complex scenarios.The study involved a comprehensive dataset of 120 thin sections,encompassing 2470 hyperspectral images prepared from 20 rock samples.Expert-reviewed masks were used for model training,ensuring robust segmentation results.This automated approach not only expedites the recognition process but also enhances reliability,providing a valuable tool for geologists and advancing the field of mineralogical analysis.展开更多
In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has...In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.展开更多
In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology bas...In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.展开更多
基金the project of the National Key R&D Program of China(2017YFE0122000)the National Natural Science Foundation of China(31871968)the Natural Science Foundation of Fujian Province,China(2018J01614).
文摘C-type lectins(CTLs)are a superfamily of Ca^(2+)-dependent carbohydrate-recognition proteins,and an important pattern recognition receptor(PRR)in insect innate immunity which can mediate humoral and cellular immunity in insects.In this study,we report a novel dual carbohydrate-recognition domain(CRD)CTL from Plutella xylostella which we designate PxIML.PxIML is a protein with a 969 bp open reading frame(ORF)encoding 322 amino acids,containing a signal peptide and a dual-CRD with EPN(Glu_(124)-Pro_(125)-Asn_(126))and QPD(Gln_(274)-Pro_(275)-Asp_(276))motifs.The expression of PxIML mRNA in the fat body was significantly higher than in hemocytes and midgut.The relative expression levels of PxIML in the whole insect and the fat body were significantly inhibited after infection with Bacillus thuringiensis 8010(Bt8010)at 18 h,while they were significantly upregulated after infection with Serratia marcescens IAE6 or Pichia pastoris.The recombinant PxIML(rPxIML)protein could bind to the tested pathogen-associated molecular patterns(PAMPs),and the bacteria of Enterobacter sp.IAE5,S.marcescens IAE6,Staphylococcus aureus,Escherichia coli BL21,and Bt8010 in a Ca^(2+)-dependent manner,however,it showed limited binding to the fungus,P.pastoris.The rPxIML exhibited strong activity in the presence of Ca^(2+) to agglutinate Bt8010,Enterobacter sp.IAE5 and S.aureus,but it only weakly agglutinated with E.coli BL21,and could not agglutinate with S.marcescens IAE6 or P.pastoris.Furthermore,the rPxIML could bind to hemocytes,promote the adsorption of hemocytes to beads,and enhance the phenoloxidase(PO)activity and melanization of P.xylostella.Our results suggest that PxIML plays an important role in pathogen recognition and in mediating subsequent humoral and cellular immunity of P.xylostella.
基金funded by the National Natural Science Foundation of China(32272083)。
文摘Wheat leaf rust,caused by Puccinia triticina(Pt),is one of the most devastating diseases in common wheat(Triticum aestivum L.)and can lead to heavy yield loss(Chai et al.2020).Leaf rust can result in 50%yield loss during epidemic years(Huerta-Espino et al.2011;Gebrewahid et al.2020;Kolomiets et al.2021).Breeding varieties resistant to leaf rust have been recognized as the most effective and economical method to mitigate wheat losses caused by Pt.The narrow genetic basis of wheat constrains the number of cultivars resistant to leaf rust(Jin et al.2021).
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金supported by the Scientific and Technological Innovation Project of the China Academy of Chinese Medical Sciences(CI2021A04013)the National Natural Science Foundation of China(82204610)+1 种基金the Qihang Talent Program(L2022046)the Fundamental Research Funds for the Central Public Welfare Research Institutes(ZZ15-YQ-041 and L2021029).
文摘Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.
基金Supported by Guizhou Provincial Science and Technology Project[ZK(2022)-362](2022)4028+5 种基金ZK(2021)-554ZK(2023)-378]Science Foundation of Guizhou Health Commission(gzwkj2021-449)Innovation and Entrepreneurship Training Program for Undergraduates from China(202210660131)Science Foundation of Guizhou Education Technology(2022-064)Rural Economic Revitalization Research Project of Guizhou Medical University(GZYKDX-2022-002).
文摘[Objectives] To investigate the systematic evaluation of pharmacognostic identification of Polygonum capitatum . [Methods] 10 batches of P. capitatum cultivated in Guizhou were chosen for plant samples. Macroscopical identification was conducted on plant roots, stems, leaves, flowers and fruits. The P. capitatum powder was processed for physical and chemical distinction by FeCl 3 chromogenic reaction, hydrochloric acid magnesium powder reaction, AlCl 3 color development reaction and thin-layer chromatography.Microscope identification was carried out on the powder. Plant genome DNeasy Plant Kit was adopted for DNA molecular marker identification. [Results] The results showed that the stem of P. capitatum was tufted, the leaves were oval, 2 to 5 cm long, and 1 to 2 cm wide;the leaf apex was acute and cuneate at the base, the inflorescence was capitate, paired or solitary;the raceme was erect and nearly spherical, and the perianth was light red. Furthermore, for the chromogenic reaction of FeCl 3 ethanol extract of P. capitatum , appeared blue and turned to dark blue after long time storing at room temperature. For the reaction of hydrochloric acid magnesium powder, the alcohol extract of P. capitatum , exhibited deep red. In the color reaction of AlCl 3, the alcohol extract revealed yellow fluorescence under 360 nm UV lamp. Microscope identification of the powder displayed pollen grains, crystal sheath fibers, cellulose, vessels, starch grains, cork cells, and other characteristic fragments. In addition, DNA barcoding electrophoresis results showed that P. capitatum showed a clear and bright single band near 500 bp, and further sequencing results showed that the sequence differences were mainly concentrated in ITS1 and ITS2 region. [Conclusions] Systematic evaluation for the identification of P. capitatum is established, which combines with macroscopic identification, physicochemical identification, powder microscope identification, and DNA molecular identification. Finally, the original medicinal material is identified as P. capitatum Buch.-Ham. ex D. Don.
文摘Microbes play a critical role in shaping immune development,with growing interest in how rhinovirus(RV)interacts with the host immune system,particularly in individuals with asthma and chronic obstructive pul-monary disease(COPD).Disruptions in microbial balance during RV infections can impair immune homeostasis and worsen disease outcomes.Recent studies emphasize RV-induced regulation of antiviral defenses,cytokine production,and immune tolerance.This review explores the interplay between RV,the immune system,and microbiota,highlighting the importance of these interactions in guiding effective therapies for respiratory in-fections.It advances existing literature by considering microbiota-mediated therapies as a novel approach to managing RV exacerbations in respiratory diseases like asthma and COPD.
基金funded by Vice Chancellor of Research at Shiraz University(grant 3GFU2M1820).
文摘The identification of the traction acting on a portion of the surface of an anisotropic solid is very important in structural health monitoring and optimal design of structures. The traction can be determined using inverse methods in which displacement or strain measurements are taken at several points on the body. This paper presents an inverse method based on the method of fundamental solutions for the traction identification problem in two-dimensional anisotropic elasticity. The method of fundamental solutions is an efficient boundary-type meshless method widely used for analyzing various problems. Since the problem is linear, the sensitivity analysis is simply performed by solving the corresponding direct problem several times with different loads. The effects of important parameters such as the number of measurement data, the position of the measurement points, the amount of measurement error, and the type of measurement, i.e., displacement or strain, on the results are also investigated. The results obtained show that the presented inverse method is suitable for the problem of traction identification. It can be concluded from the results that the use of strain measurements in the inverse analysis leads to more accurate results than the use of displacement measurements. It is also found that measurement points closer to the boundary with unknown traction provide more reliable solutions. Additionally, it is found that increasing the number of measurement points increases the accuracy of the inverse solution. However, in cases with a large number of measurement points, further increasing the number of measurement data has little effect on the results.
基金Supported by National Key R&D Program of China(Grant No.2022YFB3404101)National Natural Science Foundation of China(Grant Nos.52375018,92148301)。
文摘Accurate kinematic calibration is the very foundation for robots'application in industry demanding high precision such as machining.Considering the complex error characteristic and severe ill-posed identification issues of a 5-DoF parallel machining robot,this paper proposes an adaptive and weighted identification method to achieve high-precision kinematic calibration while maintaining reliable stability.First,a kinematic error propagation mechanism model considering the non-ideal constraints and the screw self-rotation is formulated by incorporating the intricate structure of multiple chains and a unique driven screw arrangement of the robot.To address the challenge of accurately identifying such a sophisticated error model,a novel adaptive and weighted identification method based on generalized cross validation(GCV)is proposed.Specifically,this approach innovatively introduces Gauss-Markov estimation into the GCV algorithm and utilizes prior physical information to construct both a weighted identification model and a weighted cross-validation function,thus eliminating the inaccuracy caused by significant differences in dimensional magnitudes of pose errors and achieving accurate identification with flexible numerical stability.Finally,the kinematic calibration experiment is conducted.The comparative experimental results demonstrate that the presented approach is effective and has enhanced accuracy performance over typical least squares methods,with maximum position and orientation errors reduced from 2.279 mm to 0.028 mm and from 0.206°to 0.017°,respectively.
基金Supported by Scientific Research Program of Guangxi University of Chinese Medicine(P200246).
文摘[Objectives] To identify Pyrostegia venusta (Ker-Gawler.) Miers by microscope and ultraviolet spectrum. [Methods] The paraffin section, slide section and freehand section were used to make the cross section of the stem and leaf, and the surface of the leaf and the powder of the root, stem and leaf were made by the conventional method, which were observed under the optical microscope. Ultraviolet-visible spectrum identification was carried out according to a conventional method. [Results] The microscopic identification and ultraviolet-visible absorption characteristics of P. venusta (Ker-Gawler) Miers were described in detail. [Conclusions] This study is expected to provide a reference for the identification of P. venusta(KerGawler)Miers and the establishment of the related quality standard.
文摘GS1 is an international standards organization,which focuses on product identification and product data,helping businesses and governments to improve commerce and supply chain.Why trusted data is essential to high-quality development?More than 50 years ago,GS1 was initiated with the bar code,a profound transformation of the way we work and live.From then on,a simple scan connected a physical product to its digital identity.It transformed commerce,improving supply chains and enabling safer healthcare.Collaboration between industry and governments,and a strong partnership with ISO and IEC laid the foundations for the global adoption of a common product identification over the past 50 years and all around the world.
基金supported by the National Natural Science Foundation of China(62473020).
文摘Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model approximating the actual system is obtained online.The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error,which is then utilized in the design of a tube-based robust model predictive controller.The effectiveness of the proposed approach is validated by numerical simulation.
基金The National Natural Science Foundation of China(No.52338011,52378291)Young Elite Scientists Sponsorship Program by CAST(No.2022-2024QNRC0101).
文摘To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is developed to identify the geometric parameters.The study utilizes a common precast element for highway bridges as the research subject.First,edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology.Subsequently,a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output.A dataset is generated by varying the control parameters and noise levels for model training.Finally,field measurements are conducted to validate the accuracy of the developed method.The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components,with an error rate maintained within 5%.
基金supported by the National Key R&D Program of China(2021YFC2203502 and 2022YFF0711502)the National Natural Science Foundation of China(NSFC)(12173077)+4 种基金the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095 and 2023TSYCCX0112)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(PTYQ2022YZZD01)China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360)。
文摘As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.
基金supported by grants from the National Natural Science Foundation of China(32072403 and 31871945)the National Key Research and Development Program of China(2016YFD0100600).
文摘The RING-type E3 ligase OsBBI1 regulates rice resistance against Magnaporthe oryzae through modifying cell wall defenses.In this study,we report the function of an OsBBI1 substrate,eukaryotic translation initiation factor OseIF5A4,in rice immunity.OsBBI1 interacts with OseIF5A4 and other four members of the OseIF5A family.The RING domain in OsBBI1 and the eIF-5a domain in OseIF5A4 are critical for the OsBBI1-OseIF5A4 interaction.OsBBI1 ubiquitinates OseIF5A4 and mediates its degradation in vitro and in vivo.Moreover,the expression of OseIF5A4 was upregulated during early stage of compatible interaction but downregulated in incompatible interaction between rice and M.oryzae.Knockout of OseIF5A4 enhances rice immunity against M.oryzae and Xanthomonas oryzae pv.oryzae,boosts pattern-triggered immune responses,and strengthens pathogen-induced defense responses(e.g.,expression of defense genes,accumulation of reactive oxygen species and reinforcement of cell wall).However,overexpression of OseIF5A4 attenuates rice immunity and immune responses.These results demonstrate that OseIF5A4,a substrate of the immunity-associated E3 ligase OsBBI1,negatively regulates rice immunity against M.oryzae and X.oryzae pv.oryzae through modulating pathogen-induced defense responses,highlighting the importance of the protein translational machinery in rice immunity.
基金support from the National Natural Science Foundation of China(Grant Nos:52379103 and 52279103)the Natural Science Foundation of Shandong Province(Grant No:ZR2023YQ049).
文摘Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.
基金supported by the National Natural Science Foundation of China(42122017,41821002)the Independent Innovation Research Program of China University of Petroleum(East China)(21CX06001A).
文摘It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in logging curves,this study establishes a grayscale-phase model based on high-resolution grayscale curves using clustering analysis algorithms for shale lithofacies identification,working with the Shahejie For-mation,Bohai Bay Basin,China.The grayscale phase is defined as the sum of absolute grayscale and relative amplitude as well as their features.The absolute grayscale is the absolute magnitude of the gray values and is utilized for evaluating the material composition(mineral composition+total organic carbon)of shale,while the relative amplitude is the difference between adjacent gray values and is used to identify the shale structure type.The research results show that the grayscale phase model can identify shale lithofacies well,and the accuracy and applicability of this model were verified by the fitting relationship between absolute grayscale and shale mineral composition,as well as corresponding re-lationships between relative amplitudes and laminae development in shales.Four lithofacies are iden-tified in the target layer of the study area:massive mixed shale,laminated mixed shale,massive calcareous shale and laminated calcareous shale.This method can not only effectively characterize the material composition of shale,but also numerically characterize the development degree of shale laminae,and solve the problem that difficult to identify millimeter-scale laminae based on logging curves,which can provide technical support for shale lithofacies identification,sweet spot evaluation and prediction of complex continental lacustrine basins.
基金support by the National Natural Science Foundation of China(Grant No.52402520)。
文摘Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.
文摘The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise,often complicated by the coexistence of other minerals.This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals.The utilizied four advanced deep learning models—PSPNet,U-Net,FPN,and LinkNet—has significant advancements in efficiency and accuracy.Among these models,PSPNet exhibited superior performance,achieving the highest intersection over union(IoU)scores and demonstrating exceptional reliability in segmenting quartz minerals,even in complex scenarios.The study involved a comprehensive dataset of 120 thin sections,encompassing 2470 hyperspectral images prepared from 20 rock samples.Expert-reviewed masks were used for model training,ensuring robust segmentation results.This automated approach not only expedites the recognition process but also enhances reliability,providing a valuable tool for geologists and advancing the field of mineralogical analysis.
文摘In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.
基金financially supported by National Key R&D Program(2021YFF0701905)。
文摘In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.
