Establishment of Fluorescence Quantitative RT-PCR Assay for Detection of Equine Arteritis Virus

[Objective]The paper was to establish a fluorescence quantitative RT-PCR method for detection of equine arteritis virus(EAV).[Method]Primers and probes were developed for the EAV ORF7 gene sequence,and the reaction system was optimized.Standard curves were established,leading to the initial development of the EAV fluorescence quantitative RT-PCR assay.The accuracy,specificity,and sensitivity of this method were subsequently evaluated.[Result]The EAV fluorescence quantitative RT-PCR assay demonstrated optimal performance at an annealing temperature of 61 C,with a final concentration of primer and probe set at 0.6μmol/L.The plasmid standard demonstrated a strong linear correlation with Ct values within the range of 1.6×10^(7)-1.6×10^(2)copies/μL.The equation of the standard curve was determined to be y=-2.68x+32.88,with an R^(2) value of 0.9927.Consequently,the EAV fluorescence quantitative RT-PCR assay was successfully established.The methodology employed was effective in detecting EAV,Theileria equi,equine herpesvirus-1(EHV-1),equine herpesvirus-4(EHV-4),and equine influenza virus(EIV).The findings indicated that the method was specifically capable of detecting EAV,while the other pathogens tested yielded negative results.The method demonstrated a high degree of specificity.It was employed to detect the standard plasmid cRNA synthesized through in vitro transcription following a 10-fold dilution.The results indicated that the minimum detection limit of the method was 1.6×10^(2) copies/μL,and it exhibited high sensitivity.The coefficient of variation,both within and between groups,was maintained at 1.8%,indicating good reproducibility.In this study,the fluorescence quantitative RT-PCR assay developed was utilized alongside the EAV fluorescence quantitative RT-PCR assay established by previous researchers to analyze a total of 234 clinical samples.Both methods yielded a positive detection rate of 14.1%,and the coincidence rate between the two techniques was found to be 100%.[Conclusion]The fluorescence quantitative RT-PCR assay developed in this study offers a novel approach and concept for the prevention and control of equine viral arteritis(EVA).

Establishment and Modification of Ninety-seven Pneumococcal Serotyping Assays Based on Quantitative Real-time Polymerase Chain Reaction

Objective To establish and modify quantitative real-time polymerase chain reaction(qPCR)-based serotyping assays to distinguish 97 pneumococcal serotypes.Methods A database of capsular polysaccharide(cps)loci sequences was generated,covering 97 pneumococcal serotypes.Bioinformatics analyses were performed to identify the cps loci structure and target genes related to different pneumococcal serotypes with specific SNPs.A total of 27 novel qPCR serotyping assay primers and probes were established based on qPCR,while 27 recombinant plasmids containing serotype-specific DNA sequence fragments were constructed as reference target sequences to examine the specificity and sensitivity of the qPCR assay.A panel of pneumococcal reference strains was employed to evaluate the capability of pneumococcal serotyping.Results A total of 97 pneumococcal serotyping assays based on qPCR were established and modified,which included 64 serotypes previously reported as well as an additional 33 serotypes.Twenty-seven novel qPCR serotyping target sequences were implemented in the pneumococcal qPCR serotyping system.A total of 97 pneumococcal serotypes,which included 52 individual serotypes and 45 serotypes belonging to 20 serogroups,could not be identified as individual serotypes.The sensitivity of qPCR assays based on 27 target sequences was 1–100 copies/μL.The specificity of the qPCR assays was 100%,which were tested by a panel of 90 serotypes of the pneumococcal reference strains.Conclusion A total of 27 novel qPCR assays were established and modified to analyze 97pneumococcal serotypes.

Accurate method based on data filtering for quantitative multi-element analysis of soils using CF-LIBS

To obtain more stable spectral data for accurate quantitative analysis of multi-element,especially for the large-area in-situ elements detection of soils, we propose a method for a multielement quantitative analysis of soils using calibration-free laser-induced breakdown spectroscopy(CF-LIBS) based on data filtering. In this study, we analyze a standard soil sample doped with two heavy metal elements, Cu and Cd, with a specific focus on the line of Cu I324.75 nm for filtering the experimental data of multiple sample sets. Pre-and post-data filtering,the relative standard deviation for Cu decreased from 30% to 10%, The limits of detection(LOD)values for Cu and Cd decreased by 5% and 4%, respectively. Through CF-LIBS, a quantitative analysis was conducted to determine the relative content of elements in soils. Using Cu as a reference, the concentration of Cd was accurately calculated. The results show that post-data filtering, the average relative error of the Cd decreases from 11% to 5%, indicating the effectiveness of data filtering in improving the accuracy of quantitative analysis. Moreover, the content of Si, Fe and other elements can be accurately calculated using this method. To further correct the calculation, the results for Cd was used to provide a more precise calculation. This approach is of great importance for the large-area in-situ heavy metals and trace elements detection in soil, as well as for rapid and accurate quantitative analysis.

Research on batch multielement rapid quantitative analysis based on the standard curve-assisted calibration-free laserinduced breakdown spectroscopy method

This study proposes a batch rapid quantitative analysis method for multiple elements by combining the advantages of standard curve(SC)and calibration-free laser-induced breakdown spectroscopy(CF-LIBS)technology to achieve synchronous,rapid,and accurate measurement of elements in a large number of samples,namely,SC-assisted CF-LIBS.Al alloy standard samples,divided into calibration and test samples,were applied to validate the proposed method.SC was built based on the characteristic line of Pb and Cr in the calibration sample,and the contents of Pb and Cr in the test sample were calculated with relative errors of 6%and 4%,respectively.SC built using Cr with multiple characteristic lines yielded better calculation results.The relative contents of ten elements in the test sample were calculated using CF-LIBS.Subsequently,the SC-assisted CF-LIBS was executed,with the majority of the calculation relative errors falling within the range of 2%-5%.Finally,the Al and Na contents of the Al alloy were predicted.The results demonstrate that it effectively enables the rapid and accurate quantitative analysis of multiple elements after a single-element SC analysis of the tested samples.Furthermore,this quantitative analysis method was successfully applied to soil and Astragalus samples,realizing an accurate calculation of the contents of multiple elements.Thus,it is important to advance the LIBS quantitative analysis and its related applications.

Simplified quantitative analysis method and its application in the insitu synthesized copper-based azide chips

Copper-based azide(Cu(N_(3))2 or CuN_(3),CA)chips synthesized by in-situ azide reaction and utilized in miniaturized explosive systems has become a hot research topic in recent years.However,the advantages of in-situ synthesis method,including small size and low dosage,bring about difficulties in quantitative analysis and differences in ignition capabilities of CA chips.The aim of present work is to develop a simplified quantitative analysis method for accurate and safe analysis of components in CA chips to evaluate and investigate the corresponding ignition ability.In this work,Cu(N_(3))2 and CuN_(3)components in CA chips were separated through dissolution and distillation by utilizing the difference in solubility and corresponding content was obtained by measuring N_(3)-concentration through spectrophotometry.The spectrophotometry method was optimized by studying influencing factors and the recovery rate of different separation methods was studied,ensuring the accuracy and reproducibility of test results.The optimized method is linear in range from 1.0-25.0 mg/L,with a correlation coefficient R^(2)=0.9998,which meets the requirements of CA chips with a milligram-level content test.Compared with the existing ICP method,component analysis results of CA chips obtained by spectrophotometry are closer to real component content in samples and have satisfactory accuracy.Moreover,as its application in miniaturized explosive systems,the ignition ability of CA chips with different component contents for direct ink writing CL-20 and the corresponding mechanism was studied.This study provided a basis and idea for the design and performance evaluation of CA chips in miniaturized explosive systems.

Novel damage constitutive models and new quantitative identification method for stress thresholds of rocks under uniaxial compression

Four key stress thresholds exist in the compression process of rocks,i.e.,crack closure stress(σ_(cc)),crack initiation stress(σ_(ci)),crack damage stress(σ_(cd))and compressive strength(σ_(c)).The quantitative identifications of the first three stress thresholds are of great significance for characterizing the microcrack growth and damage evolution of rocks under compression.In this paper,a new method based on damage constitutive model is proposed to quantitatively measure the stress thresholds of rocks.Firstly,two different damage constitutive models were constructed based on acoustic emission(AE)counts and Weibull distribution function considering the compaction stages of the rock and the bearing capacity of the damage element.Then,the accumulative AE counts method(ACLM),AE count rate method(CRM)and constitutive model method(CMM)were introduced to determine the stress thresholds of rocks.Finally,the stress thresholds of 9 different rocks were identified by ACLM,CRM,and CMM.The results show that the theoretical stress−strain curves obtained from the two damage constitutive models are in good agreement with that of the experimental data,and the differences between the two damage constitutive models mainly come from the evolutionary differences of the damage variables.The results of the stress thresholds identified by the CMM are in good agreement with those identified by the AE methods,i.e.,ACLM and CRM.Therefore,the proposed CMM can be used to determine the stress thresholds of rocks.

Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

Quantitative analysis and prediction of the sound field convergence zone in mesoscale eddy environment based on data mining methods

The mesoscale eddy(ME)has a significant influence on the convergence effect in deep-sea acoustic propagation.This paper use statistical approaches to express quantitative relationships between the ME conditions and convergence zone(CZ)characteristics.Based on the Gaussian vortex model,we construct various sound propagation scenarios under different eddy conditions,and carry out sound propagation experiments to obtain simulation samples.With a large number of samples,we first adopt the unified regression to set up analytic relationships between eddy conditions and CZ parameters.The sensitivity of eddy indicators to the CZ is quantitatively analyzed.Then,we adopt the machine learning(ML)algorithms to establish prediction models of CZ parameters by exploring the nonlinear relationships between multiple ME indicators and CZ parameters.Through the research,we can express the influence of ME on the CZ quantitatively,and achieve the rapid prediction of CZ parameters in ocean eddies.The prediction accuracy(R)of the CZ distance(mean R:0.9815)is obviously better than that of the CZ width(mean R:0.8728).Among the three ML algorithms,Gradient Boosting Decision Tree has the best prediction ability(root mean square error(RMSE):0.136),followed by Random Forest(RMSE:0.441)and Extreme Learning Machine(RMSE:0.518).

Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

Clinical Value of the Quantitative Flow Ratio to Predict Long-term Target Vessel Failure in Patients with In-stent Restenosis after Drug-coated Balloon Angioplasty

Objective The study sought to investigate the clinical predictive value of quantitative flow ratio(QFR)for the long-term target vessel failure(TVF)outcome in patients with in-stent restenosis(ISR)by using drug-coated balloon(DCB)treatment after a long-term follow-up.Methods This was a retrospective study.A total of 186 patients who underwent DCB angioplasty for ISR in two hospitals from March 2014 to September 2019 were enrolled.The QFR of the entire target vessel was measured offline.The primary endpoint was TVF,including target vessel-cardiac death(TV-CD),target vessel-myocardial infarction(TV-MI),and clinically driven-target vessel revascularization(CD-TVR).Results The follow-up time was 3.09±1.53 years,and 50 patients had TVF.The QFR immediately after percutaneous coronary intervention(PCI)was significantly lower in the TVF group than in the no-TVF group.Multivariable Cox regression analysis indicated that the QFR immediately after PCI was an excellent predictor for TVF after the long-term follow-up[hazard ratio(HR):5.15×10−5(6.13×10−8−0.043);P<0.01].Receiver-operating characteristic(ROC)curve analysis demonstrated that the optimal cut-off value of the QFR immediately after PCI for predicting the long-term TVF was 0.925(area under the curve:0.886,95%confidence interval:0.834–0.938;sensitivity:83.40%,specificity:88.00;P<0.01).In addition,QFR≤0.925 post-PCI was strongly correlated with the TVF,including TV-MI and CD-TVR(P<0.01).Conclusion The QFR immediately after PCI showed a high predictive value of TVF after a long-term follow-up in ISR patients who underwent DCB angioplasty.A lower QFR immediately after PCI was associated with a worse TVF outcome.

Quantitative hepatitis B core antibody and quantitative hepatitis B surface antigen:Novel viral biomarkers for chronic hepatitis B management

The management of hepatitis B virus(HBV)infection now involves regular and appropriate monitoring of viral activity,disease progression,and treatment response.Traditional HBV infection biomarkers are limited in their ability to predict clinical outcomes or therapeutic effectiveness.Quantitation of HBV core antibodies(qAnti-HBc)is a novel non-invasive biomarker that may help with a variety of diagnostic issues.It was shown to correlate strongly with infection stages,hepatic inflammation and fibrosis,chronic infection exacerbations,and the presence of occult infection.Furthermore,qAnti-HBc levels were shown to be predictive of spontaneous or treatment-induced HBeAg and HBsAg seroclearance,relapse after medication termination,re-infection following liver transplantation,and viral reactivation in the presence of immunosuppression.qAnti-HBc,on the other hand,cannot be relied on as a single diagnostic test to address all problems,and its diagnostic and prognostic potential may be greatly increased when paired with qHBsAg.Commercial qAnti-HBc diagnostic kits are currently not widely available.Because many methodologies are only semi-quantitative,comparing data from various studies and defining universal cut-off values remains difficult.This review focuses on the clinical utility of qAnti-HBc and qHBsAg in chronic hepatitis B management.

Quantitative lithium substitution of carboxyl hydrogens in polyacrylic acid binder enables robust SiO electrodes with durable lithium storage stability

The design of advanced binders plays a critical role in stabilizing the cycling performance of large-volume-effect silicon monoxide(SiO)anodes.For the classic polyacrylic acid(PAA)binder,the self-association of-COOH groups in PAA leads to the formation of intramolecular and intermolecular hydrogen bonds,greatly weakening the bonding force of the binder to SiO surface.However,strengthening the binder-material interaction from the perspective of binder molecular regulation poses a significant challenge.Herein,a modified PAA-Li_(x)(0.25≤x≤1)binder with prominent mechanical properties and adhesion strength is specifically synthesized for SiO anodes by quantitatively substituting the carboxylic hydrogen with lithium.The appropriate lithium substitution(x=0.25)not only effectively increases the number of hydrogen bonds between the PAA binder and SiO surface owing to charge repulsion effect between ions,but also guarantees moderate entanglement between PAA-Li_x molecular chains through the ion-dipole interaction.As such,the PAA-Li_(0.25)/SiO electrode exhibits exceptional mechanical properties and the lowest volume change,as well as the optimum cycling(1237.3 mA h g^(-1)after 100cycles at 0.1 C)and rate performance(1000.6 mA h g^(-1)at 1 C),significantly outperforming the electrode using pristine PAA binder.This work paves the way for quantitative regulation of binders at the molecular level.

Quantitative identification and prediction of mixed lithology, Bohai Sea, China

The Paleogene Shahejie Formation in the KL16 oilfield, Bohai bay, is characterized by a thinly interbedded mixed sedimentary system, with complex sedimentary facies, lithologic types and distributions. It is hard for conventional logging methods to identify the lithology therein. In order to solve the difficulty in lithologic identification of mixed sedimentary system, analyses based on graph data base using elemental capture energy spectrum log have been proposed. Due to the different composition for the various minerals, we innovatively established the molar numbers of silicon, calcium, magnesium, and aluminum as characteristic parameters for sandstone, limestone, dolomite, and mudstone, and a graph clustering analysis method was applied to identify lithology. Considering the seismic waveforms corresponding to lithologic impedance of reservoir, three seismic phases were identified by neural network clustering analysis of seismic waveform, and the seismic attributes with high sensitivity to reservoir thickness were then selected to realize the fine description of the mixed carbonate-siliciclastic reservoir. Drilling results confirmed that the sedimentary facies were accurately identified, with reservoir prediction accuracy reaching up to 80%. Under the guidance of reservoir research, the oil-in-place discovered in the oilfield were estimated to be more than 5 million tonnes. This technology provides reference for the exploration and development of oilfields of mixed sedimentary system.

Reversed-Phase-HPLC Assay Method for Simultaneous Estimation of Sorbitol, Sodium Lactate, and Sodium Chlorides in Pharmaceutical Formulations and Drug Solution for Infusion

A rapid, straightforward, sensitive, efficient, and cost-effective reverse-phase high-performance liquid chromatographic method was employed for the simultaneous determination of Sorbitol, Sodium Lactate, and Chlorides in a drug solution for infusion. Sorbitol, Sodium lactate, and Chloride are all officially recognized in the USP monograph. Assay methods are provided through various techniques, with titrations being ineffective for trace-level quantification. Alternatively, IC, AAS, and ICP-MS, though highly accurate, are costly and often unavailable to most testing facilities. When considering methods, it’s important to prioritize both quality control requirements and user-friendly techniques. A simple HPLC simultaneous method was developed for the quantification of Chlorides, Sorbitol, and Sodium Lactate with a shorter run time. The separation utilized a Shimpack SCR-102(H) ion exclusion analytical column (7.9 mm × 300 mm, 7 μm), with a flow rate of 0.6 mL per min. The column compartment temperature was maintained at 40°C, and the injection volume was set at 10 μL, with detection at 200 nm. All measurements were conducted in a 0.1% solution of phosphoric acid. The analytical curves demonstrated linearity (r > 0.9999) in the concentration range of 0.79 to 3.8 mg per mL for Sodium Lactate (SL), 0.16 to 0.79 mg per mL for Sodium Chloride (SC), and 1.5 to 7.2 mg per mL for Sorbitol. Validation of the developed method followed the guidelines of the International Conference on Harmonization (ICH Q2B) and USP. The method exhibited precision, robustness, accuracy, and selectivity. In accelerated stability testing over 6 months, no significant variations were observed in organoleptic analysis and pH. Consequently, the developed method is deemed suitable for routine quality control analyses, enabling the simultaneous determination of Sodium Lactate, Sodium Chloride, and Sorbitol in pharmaceutical formulations and infusions.

Interferon-gamma release assays as a tool for differential diagnosis of gastrointestinal tuberculosis

In this editorial,we comment on an article published in a recent issue of the World Journal of Clinical Cases.There is a pressing need for reliable tools for diagnosing tuberculosis(TB)of the gastrointestinal tract.Despite advancements in the diagnosis and treatment,TB remains a global health challenge.Ali et al demon-strated that TB may mimic gastrointestinal conditions,such as gastric outlet obstruction,causing a delay in the diagnosis.Furthermore,the latter complication is frequently observed during infections,including Helicobacter pylori,and rarely is related to TB,as in the presented case.In line with this,we think that laboratory tests based on interferon-gamma release assays can be a helpful tool for diagnosing latent TB paced in the gastrointestinal tract.Innovative strategies and approaches for diagnosing latent/active extra pulmonary TB are crucial for establishing the diagnosis early and enhancing treatment strategies to mitigate the global burden of TB.

Quantitative magnetic resonance imaging in prostate cancer:A review of current technology

Prostate cancer(PCa)imaging forms an important part of PCa clinical management.Magnetic resonance imaging is the modality of choice for prostate imaging.Most of the current imaging assessment is qualitative i.e.,based on visual inspection and thus subjected to inter-observer disagreement.Quantitative imaging is better than qualitative assessment as it is more objective,and standardized,thus improving interobserver agreement.Apart from detecting PCa,few quantitative parameters may have potential to predict disease aggressiveness,and thus can be used for prognosis and deciding the course of management.There are various magnetic resonance imaging-based quantitative parameters and few of them are already part of PIRADS v.2.1.However,there are many other parameters that are under study and need further validation by rigorous multicenter studies before recommending them for routine clinical practice.This review intends to discuss the existing quantitative methods,recent developments,and novel techniques in detail.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Evaluation of Interferon-Gamma Release Assay Testing and Tuberculin Skin Test for Early Diagnosis of Tuberculosis in Children and Adolescents

Background: This study aimed to evaluate the diagnostic value of interferon-γ release assay (IGRA), a sensitive microbiological diagnostic method, in children and adolescents with suspected tuberculosis in a country with a high burden of tuberculosis. Method: This study included 581 children and adolescents aged 4 - 19 years who were suspected of having tuberculosis, were latently infected with Mycobacterium tuberculosis, and had received at least one dose of BCG vaccine between April 17, 2019, and February 24, 2021. The study evaluated the TST results of 106 patients who had a positive Quantiferon test and were suspected of having tuberculosis. Results: The study included 581 patients aged between 4 and 19 years. Of these, 106 patients tested positive for the Quantiferon test, while 19 were indeterminate and 456 were negative. The Quantiferon test positivity rate was 18.24%. Among the 106 QFT-Plus-positive cases, 23 patients also tested positive for TST. The difference in distribution was found to be statistically significant. Conclusion: The QFT-Plus test is considered an alternative to TST and other microbiological diagnostic methods for early tuberculosis diagnosis, particularly in children and adolescents.

Establishment of High-sensitivity Rapid Fluorescence Quantitative Detection Method for Antibody against Peste des Petits Ruminants Virus

[Objectives]This study was conducted to establish a rapid quantitative method for detecting antibody against Peste des Petits Ruminants Virus(PPR V)in sheep serum.[Methods]Soluble N protein and NH fusion protein were obtained in Escherichia coli prokaryotic expression system by optimizing codons and expression conditions of E.coli.Furthermore,based on the purified soluble N protein and NH fusion protein,a high-sensitivity fluorescence immunoassay kit for detecting the antibody against PPR V was established.[Results]The method could quickly and quantitatively detect PPR V antibody in sheep serum,with high sensitivity and specificity,without any cross reaction to other related sheep pathogens.The intra-batch and inter-batch coefficients of variation were less than 10%and 15%,respectively,and the method had good repeatability.Through detection on 292 clinical serum samples,it was compared with the French IDVET competitive ELISA kit,and the coincidence rate of the two methods reached 93.84%.Compared with the serum neutralization test,the detected titer value of the high-sensitivity rapid fluorescence quantitative detection method was basically consistent with the tilter value obtained by the neutralization test on the standard positive serum(provided by the WOAH Brucellosis Reference Laboratory of France).[Conclusions]This method can realize rapid quantitative detection of PPR V antibody on site,and has high practical value and popularization value.

Quantitative Precipitation Estimation Using X-band Phased Array Weather Radar

This study utilized data from an X-band phased array weather radar and ground-based rain gauge observations to conduct a quantitative precipitation estimation(QPE)analysis of a heavy rainfall event in Xiong an New Area from 20:00 on August 21 to 07:00 on August 22,2022.The analysis applied the Z-R relationship method for radar-based precipitation estimation and evaluated the QPE algorithm s performance using scatter density plots and binary classification scores.The results indicated that the QPE algorithm accurately estimates light to moderate rainfall but significantly underestimates heavy rainfall.The study identified disparities in the predictive accuracy of the QPE algorithm across various precipitation intensity ranges,offering essential insights for the further refinement of QPE techniques.