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 ...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.展开更多
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 id...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.展开更多
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 mult...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.展开更多
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 o...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.展开更多
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 ...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.展开更多
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 g...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.展开更多
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 co...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).展开更多
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 ach...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.展开更多
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 de...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.展开更多
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 ...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.展开更多
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 a...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.展开更多
[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 sy...[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).展开更多
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...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.展开更多
[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 ...[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.展开更多
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 Are...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.展开更多
The diversity of Samoa’s freshwater macroinvertebrates remains largely unexplored, with past studies focusing on specific species without comprehensive cataloguing. This research evaluated the health of Upolu Island...The diversity of Samoa’s freshwater macroinvertebrates remains largely unexplored, with past studies focusing on specific species without comprehensive cataloguing. This research evaluated the health of Upolu Island’s rural rivers through macroinvertebrate analysis, particularly in the Le Afe and Mulivaifagatoloa Rivers. Collaborating with Samoa’s Water Resources Division in the Ministry of Natural Resources and Environment (MNRE), three sites along each river were sampled, representing a gradient from pristine to anthropogenically impacted areas. A total of 2953 macroinvertebrates were collected and classified into five categories using established identification keys. The Macroinvertebrate Community Index (MCI) and Quantitative Macroinvertebrate Community Index (QMCI) were applied for analysis. The results showed no clear pattern of pollutant-sensitive species prevalence or decline in less disturbed rivers. High MCI scores with low QMCI values indicated numerous low-scoring species, while the opposite suggested a richness of high-scoring taxa. Although MCI and QMCI are tools for monitoring freshwater health, this study lays the groundwork for future research to categorize Samoan macroinvertebrates and assign tolerance scores based on their presence in varying river conditions. .展开更多
The combination of quantitative evaluation(QE)and non-quantitative evaluation(NQE)is an important evaluation tool in the fields of academic level evaluation(e.g.,EasyChair,Academic paper review form)and internet commo...The combination of quantitative evaluation(QE)and non-quantitative evaluation(NQE)is an important evaluation tool in the fields of academic level evaluation(e.g.,EasyChair,Academic paper review form)and internet commodity evaluation(e.g.,Amazon’s review,Feedback).But the inconsistency between QE and NQE greatly reduces the correctness and usability of the evaluation.Therefore,it is a necessary task to judge whether QE is consistent with NQE.In this paper,the predicate formula satisfiability problem is firstly reduced in polynomial time to the consistency problem of QE and NQE,and the uncertainty of the consistency problem is proved.Then the approximate solution to the problem is investigated by using a natural language processing method,which performs sentiment analysis on NQE and finally invokes a database query statement to determine whether QE is consistent with NQE.The results shed light on the feasibility of using the natural language processing method to solve undecidable problems.展开更多
BACKGROUND The level of Ki-67 expression has served as a prognostic factor in gastric cancer.The quantitative parameters based on the novel dual-layer spectral detector computed tomography(DLSDCT)in discriminating the...BACKGROUND The level of Ki-67 expression has served as a prognostic factor in gastric cancer.The quantitative parameters based on the novel dual-layer spectral detector computed tomography(DLSDCT)in discriminating the Ki-67 expression status are unclear.AIM To investigate the diagnostic ability of DLSDCT-derived parameters for Ki-67 expression status in gastric carcinoma(GC).METHODS Dual-phase enhanced abdominal DLSDCT was performed preoperatively in 108 patients with gastric adenocarcinoma.Primary tumor monoenergetic CT attenuation value at 40-100 kilo electron volt(kev),the slope of the spectral curve(λ_(HU)),iodine concentration(IC),normalized IC(nIC),effective atomic number(Z^(eff))and normalized Z^(eff)(nZ^(eff))in the arterial phase(AP)and venous phase(VP)were retrospectively compared between patients with low and high Ki-67 expression in gastric adenocarcinoma.Spearman’s correlation coefficient was used to analyze the association between the above parameters and Ki-67 expression status.Receiver operating characteristic(ROC)curve analysis was performed to compare the diagnostic efficacy of the statistically significant parameters between two groups.RESULTS Thirty-seven and 71 patients were classified as having low and high Ki-67 expression,respectively.CT_(40 kev-VP),CT_(70 kev-VP),CT_(100 kev-VP),and Z^(eff)-related parameters were significantly higher,but IC-related parameters were lower in the group with low Ki-67 expression status than the group with high Ki-67 expression status,and other analyzed parameters showed no statistical difference between the two groups.Spearman’s correlation analysis showed that CT_(40 kev-VP),CT_(70 kev-VP),CT_(100 kev-VP),Z^(eff),and n Z^(eff) exhibited a negative correlation with Ki-67 status,whereas IC and nIC had positive correlation with Ki-67 status.The ROC analysis demonstrated that the multi-variable model of spectral parameters performed well in identifying the Ki-67 status[area under the curve(AUC)=0.967;sensitivity 95.77%;specificity 91.89%)].Nevertheless,the differentiating capabilities of singlevariable model were moderate(AUC value 0.630-0.835).In addition,the nZ_(VP)^(eff) and nIC_(VP)(AUC 0.835 and 0.805)showed better performance than CT_(40 kev-VP),CT_(70 kev-VP) and CT_(100 kev-VP)(AUC 0.630,0.631 and 0.662)in discriminating the Ki-67 status.CONCLUSION Quantitative spectral parameters are feasible to distinguish low and high Ki-67 expression in gastric adenocarcinoma.Z^(eff) and IC may be useful parameters for evaluating the Ki-67 expression.展开更多
The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). I...The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.展开更多
To study the quantitative relationship between surface sedimentary diatoms and water depth,67 surface samples were collected for diatom analysis on eight profiles with water depth variation from the muddy intertidal z...To study the quantitative relationship between surface sedimentary diatoms and water depth,67 surface samples were collected for diatom analysis on eight profiles with water depth variation from the muddy intertidal zone to the shallow sea area in North-Central Bohai Bay,China.The results showed that the distribution of diatoms changed significantly in response to the change in water depth.Furthermore,the quantitative relationship between the distribution of dominant diatom species,their assemblages,and the water depth was established.The water depth optima for seven dominant species such as Cyclotella striata/stylorum,Paralia sulcata,and Coscinodiscus perforatus and the water depth indication range of seven diatom assemblages were obtained in the study area above the water depth(elevation)of-10 m.The quantitative relationship between surface sedimentary diatoms and water depth provides a proxy index for diatom-paleo-water depth reconstruction in the strata in Bohai Bay,China.展开更多
文摘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.
基金Projects(2021RC3007,2020RC3090)supported by the Science and Technology Innovation Program of Hunan Province,ChinaProjects(52374150,52174099)supported by the National Natural Science Foundation of China。
文摘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.
基金supported by National Key R&D Program of China(Grant No.2022YFC3003903)the S&T Program of Hebei(Grant No.19275408D),the Key-Area Research and Development Program of Guangdong Province(Grant No.2020B1111200001)+1 种基金the Key Project of Monitoring,Early Warning and Prevention of Major Natural Disasters of China(Grant No.2019YFC1510304)the Joint Fund of Key Laboratory of Atmosphere Sounding,CMA,and the Research Centre on Meteorological Observation Engineering Technology,CMA(Grant No.U2021Z05).
文摘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.
基金supported by the Major Science and Technology Project of Gansu Province(No.22ZD6FA021-5)the Industrial Support Project of Gansu Province(Nos.2023CYZC-19 and 2021CYZC-22)the Science and Technology Project of Gansu Province(Nos.23YFFA0074,22JR5RA137 and 22JR5RA151).
文摘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.
基金the financial support provided by the National Natural Science Foundation of China(Grant No.11872013).
文摘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.
基金supported by the National Natural Science Foundation of China (Grant Nos.92372101,52162036 and 21875155)the Fundamental Research Funds for the Central Universities (Grant Nos.20720220010)the National Key Research and Development Program of China (Grant Nos.2021YFA1201502)。
文摘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.
基金The National Natural Science Foundation of China under contract Nos 41875061 and 41775165.
文摘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).
基金supported by the Major Science and TechnologyTechnol-ogy Projects in Gansu Province(No.22ZD6FA021-5)Industrial Support Project of Gansu Province(Nos.2023CYZC-19 and 2021CYZC-22)+1 种基金Science and Technol-ogy Project of Gansu Province(Nos.23YFFA0074,22JR5RA137,and 22JR5RA151)Central Leading Local Science and Technology Development Fund Projects(No.23ZYQA293).
文摘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.
基金This research was funded by the Scientific Research Project of Leshan Normal University(No.2022SSDX002)the Scientific Plan Project of Leshan(No.22NZD012).
文摘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.
基金supported by the Nanjing Municipal Science and Technology Bureau(No.201803008)the Cardiocare Sponsored Optimized Antithrombotic Research Fund(No.BJUHFCSOARF201801-13).
文摘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.
基金financial supports from National Natural Science Foundation of China(No.62205172)Huaneng Group Science and Technology Research Project(No.HNKJ22-H105)Tsinghua University Initiative Scientific Research Program and the International Joint Mission on Climate Change and Carbon Neutrality。
文摘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.
基金Supported by Research Project of General Administration of Customs(2022HK126)Youth Science Foundation(2022D01B08).
文摘[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).
文摘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.
基金Supported by The National Project for the Prevention and Control of Major Exotic Animal Diseases(2022YFD1800500)National Mutton Sheep Industrial Technology System(CARS39).
文摘[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.
文摘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.
文摘The diversity of Samoa’s freshwater macroinvertebrates remains largely unexplored, with past studies focusing on specific species without comprehensive cataloguing. This research evaluated the health of Upolu Island’s rural rivers through macroinvertebrate analysis, particularly in the Le Afe and Mulivaifagatoloa Rivers. Collaborating with Samoa’s Water Resources Division in the Ministry of Natural Resources and Environment (MNRE), three sites along each river were sampled, representing a gradient from pristine to anthropogenically impacted areas. A total of 2953 macroinvertebrates were collected and classified into five categories using established identification keys. The Macroinvertebrate Community Index (MCI) and Quantitative Macroinvertebrate Community Index (QMCI) were applied for analysis. The results showed no clear pattern of pollutant-sensitive species prevalence or decline in less disturbed rivers. High MCI scores with low QMCI values indicated numerous low-scoring species, while the opposite suggested a richness of high-scoring taxa. Although MCI and QMCI are tools for monitoring freshwater health, this study lays the groundwork for future research to categorize Samoan macroinvertebrates and assign tolerance scores based on their presence in varying river conditions. .
基金Shanghai Foundation for Development of Industrial Internet Innovation,China(No.2019-GYHLW-004)。
文摘The combination of quantitative evaluation(QE)and non-quantitative evaluation(NQE)is an important evaluation tool in the fields of academic level evaluation(e.g.,EasyChair,Academic paper review form)and internet commodity evaluation(e.g.,Amazon’s review,Feedback).But the inconsistency between QE and NQE greatly reduces the correctness and usability of the evaluation.Therefore,it is a necessary task to judge whether QE is consistent with NQE.In this paper,the predicate formula satisfiability problem is firstly reduced in polynomial time to the consistency problem of QE and NQE,and the uncertainty of the consistency problem is proved.Then the approximate solution to the problem is investigated by using a natural language processing method,which performs sentiment analysis on NQE and finally invokes a database query statement to determine whether QE is consistent with NQE.The results shed light on the feasibility of using the natural language processing method to solve undecidable problems.
文摘BACKGROUND The level of Ki-67 expression has served as a prognostic factor in gastric cancer.The quantitative parameters based on the novel dual-layer spectral detector computed tomography(DLSDCT)in discriminating the Ki-67 expression status are unclear.AIM To investigate the diagnostic ability of DLSDCT-derived parameters for Ki-67 expression status in gastric carcinoma(GC).METHODS Dual-phase enhanced abdominal DLSDCT was performed preoperatively in 108 patients with gastric adenocarcinoma.Primary tumor monoenergetic CT attenuation value at 40-100 kilo electron volt(kev),the slope of the spectral curve(λ_(HU)),iodine concentration(IC),normalized IC(nIC),effective atomic number(Z^(eff))and normalized Z^(eff)(nZ^(eff))in the arterial phase(AP)and venous phase(VP)were retrospectively compared between patients with low and high Ki-67 expression in gastric adenocarcinoma.Spearman’s correlation coefficient was used to analyze the association between the above parameters and Ki-67 expression status.Receiver operating characteristic(ROC)curve analysis was performed to compare the diagnostic efficacy of the statistically significant parameters between two groups.RESULTS Thirty-seven and 71 patients were classified as having low and high Ki-67 expression,respectively.CT_(40 kev-VP),CT_(70 kev-VP),CT_(100 kev-VP),and Z^(eff)-related parameters were significantly higher,but IC-related parameters were lower in the group with low Ki-67 expression status than the group with high Ki-67 expression status,and other analyzed parameters showed no statistical difference between the two groups.Spearman’s correlation analysis showed that CT_(40 kev-VP),CT_(70 kev-VP),CT_(100 kev-VP),Z^(eff),and n Z^(eff) exhibited a negative correlation with Ki-67 status,whereas IC and nIC had positive correlation with Ki-67 status.The ROC analysis demonstrated that the multi-variable model of spectral parameters performed well in identifying the Ki-67 status[area under the curve(AUC)=0.967;sensitivity 95.77%;specificity 91.89%)].Nevertheless,the differentiating capabilities of singlevariable model were moderate(AUC value 0.630-0.835).In addition,the nZ_(VP)^(eff) and nIC_(VP)(AUC 0.835 and 0.805)showed better performance than CT_(40 kev-VP),CT_(70 kev-VP) and CT_(100 kev-VP)(AUC 0.630,0.631 and 0.662)in discriminating the Ki-67 status.CONCLUSION Quantitative spectral parameters are feasible to distinguish low and high Ki-67 expression in gastric adenocarcinoma.Z^(eff) and IC may be useful parameters for evaluating the Ki-67 expression.
基金jointly supported by the National Science Foundation of China (Grant Nos. 42275007 and 41865003)Jiangxi Provincial Department of science and technology project (Grant No. 20171BBG70004)。
文摘The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.
基金supported by the National Natural Science Foundation of China Youth Fund(41806109)the project of the China Geological Survey(DD20189506)。
文摘To study the quantitative relationship between surface sedimentary diatoms and water depth,67 surface samples were collected for diatom analysis on eight profiles with water depth variation from the muddy intertidal zone to the shallow sea area in North-Central Bohai Bay,China.The results showed that the distribution of diatoms changed significantly in response to the change in water depth.Furthermore,the quantitative relationship between the distribution of dominant diatom species,their assemblages,and the water depth was established.The water depth optima for seven dominant species such as Cyclotella striata/stylorum,Paralia sulcata,and Coscinodiscus perforatus and the water depth indication range of seven diatom assemblages were obtained in the study area above the water depth(elevation)of-10 m.The quantitative relationship between surface sedimentary diatoms and water depth provides a proxy index for diatom-paleo-water depth reconstruction in the strata in Bohai Bay,China.
