基于Real-time PCR法检测乳粉中牛源性成分定量研究

基于Real-timePCR建立了乳粉中牛源性成分相对定量检测方法,并对牛的特异性引物与探针进行了特异性、灵敏度和稳定性测试。通过模拟不同浓度牛乳粉与马乳粉混合样本,根据其△Ct值的函数关系进行线性拟合进而绘制标准曲线,建立乳粉中牛源性成分的相对定量检测。结果显示,该方法的最低检测限为0.00001 mg/mL,回收率为91.11%~119.2%,组间变异系数≤0.58%、组内变异系数≤1.44%。说明该方法在特异性与稳定性上适用于乳粉中牛源性成分及含量的掺假检测。

一种基于real-time PCR技术的TTV检测方法的建立及应用

目的:本研究旨在开发一种具有更高灵敏度和特异性的TTV检测技术,为揭示TTV在多种疾病过程中的作用提供重要的技术支持。方法:为了更精确、灵敏的检测TTV,本研究分析了目前公布的所有亚型的TTV基因序列,在此基础上建立了一种基于UTR区域的real-time PCR检测方法,并与文献报道应用较为广泛的PCR检测方法进行了对比。结果:本研究建立的方法在1×10^(7)~1×10^(1) copies/μL标准品浓度范围内具有良好的线性关系,相关系数为1.000,斜率为-3.446,检测下限为1×10^(1) copies/μL。重复性试验结果显示,组内变异系数为7.22%,表明本方法重复性、稳定性较强。针对30份临床样本,使用本研究建立的real-time PCR检测方法及目前被多个研究所使用的4套引物进行对比。结果表明,本研究所建立的方法灵敏度显著高于文献中报道的4种方法(P<0.01);Sanger测序结果表明,本方法检测出的30份阳性样本均为TTV,检测特异性为100%。结论:本研究采用基于TaqMan探针的real-time PCR检测方法,检测灵敏性高、覆盖基因型范围广,尤其对于TTV病毒载量较低的情况下能够进行定量检测,对于TTV病毒的致病性及作为免疫标志物的应用提供重要的技术支持。

Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

To address the impact of wind-power fluctuations on the stability of power systems,we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system.First,we employ a strategy that restricts long-and short-term power output deviations to smoothen wind power fluctuations in real time.Second,we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise(SW-ICEEMDAN)strategy to achieve real-time decomposition of the energy storage power,facilitating internal power distribution within the hybrid energy storage system.Finally,we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components.Through simulation validation,we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power.Compared with traditional empirical mode decomposition(EMD),ensemble empirical mode decomposition(EEMD),and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)decomposition strategies,the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal.Additionally,the state-of-charge of energy storage components fluctuates within a reasonable range,enhancing the stability of the power system and ensuring the secure operation of the energy storage system.

Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.

Models to Simulate Effective Coverage of Fire Station Based on Real-Time Travel Times

In recent years,frequent fire disasters have led to enormous damage in China.Effective firefighting rescues can minimize the losses caused by fires.During the rescue processes,the travel time of fire trucks can be severely affected by traffic conditions,changing the effective coverage of fire stations.However,it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions.This paper addresses this issue by combining the traveling time calculationmodelwith the effective coverage simulationmodel.In addition,it proposes a new index of total effective coverage area(TECA)based on the time-weighted average of the effective coverage area(ECA)to evaluate the urban fire services.It also selects China as the case study to validate the feasibility of the models,a fire station(FS-JX)in Changsha.FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points,respectively.A total of 196 simulation scenarios throughout a consecutiveweek are analyzed.Eventually,1,933,815 sets of valid sample data are obtained.The results showed that the TECA of FS-JX is 3.27 km^(2),which is far below the standard requirement of 7.00 km^(2) due to the traffic conditions.The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage.The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations.

Pore-pressure and stress-coupled creep behavior in deep coal:Insights from real-time NMR analysis

Understanding the variations in microscopic pore-fracture structures(MPFS) during coal creep under pore pressure and stress coupling is crucial for coal mining and effective gas treatment. In this manuscript, a triaxial creep test on deep coal at various pore pressures using a test system that combines in-situ mechanical loading with real-time nuclear magnetic resonance(NMR) detection was conducted.Full-scale quantitative characterization, online real-time detection, and visualization of MPFS during coal creep influenced by pore pressure and stress coupling were performed using NMR and NMR imaging(NMRI) techniques. The results revealed that seepage pores and microfractures(SPM) undergo the most significant changes during coal creep, with creep failure gradually expanding from dense primary pore fractures. Pore pressure presence promotes MPFS development primarily by inhibiting SPM compression and encouraging adsorption pores(AP) to evolve into SPM. Coal enters the accelerated creep stage earlier at lower stress levels, resulting in more pronounced creep deformation. The connection between the micro and macro values was established, demonstrating that increased porosity at different pore pressures leads to a negative exponential decay of the viscosity coefficient. The Newton dashpot in the ideal viscoplastic body and the Burgers model was improved using NMR experimental results, and a creep model that considers pore pressure and stress coupling using variable-order fractional operators was developed. The model’s reasonableness was confirmed using creep experimental data. The damagestate adjustment factors ω and β were identified through a parameter sensitivity analysis to characterize the effect of pore pressure and stress coupling on the creep damage characteristics(size and degree of difficulty) of coal.

A Real-time Lithological Identification Method based on SMOTE-Tomek and ICSA Optimization

In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.

Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

24重荧光real-time PCR技术在食物中毒快速检测中的应用

目的:应用24重荧光real-time PCR检测技术快速筛检食物中毒病原菌,结合国家标准中的培养法探讨24重荧光real-time PCR检测技术符合性和应用价值。方法:采用高灵敏度、高特异性的24重荧光real-time PCR检测技术作为中毒病原菌的初筛方法,国标方法进行细菌分离培养,并对分离出的病原菌进行生化鉴定。结果:5份食物中毒患者肛拭子在增菌前检出4份霍乱弧菌核酸(非O1/非O139群,24重荧光real-time PCR),9份患者肛拭子在增菌后检出5株霍乱弧菌(非O1/非O139群,国标培养法),其中包含4份PCR技术初筛阳性样品,两个方法的符合率为80%。所有样本均未检出沙门氏菌、志贺菌、副溶血性弧菌、致泻性大肠埃希菌以及金黄色葡萄球菌。结论:应用24重荧光real-time PCR检测技术同时检测24种常见致病病原菌,能高效锁定中毒病原菌。将其与国标培养法相结合,对临床治疗和食物中毒快速处置能起到积极作用,值得应用和推广。

A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method

Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.

肉中猪源性成分Real-time PCR定量检测技术

【目的】建立一种快速、准确的肉中猪源性成分定量检测方法。【方法】首先从GenBank数据库中筛选猪特异性的微卫星DNA,根据微卫星DNA核酸序列设计引物,对常见10种动物基因组DNA进行PCR扩增,通过有无扩增产物判断筛选的微卫星DNA对猪源性成分的特异性。然后根据微卫星DNA核酸序列,设计特异性引物和探针,建立猪源性成分Real-time PCR检测方法,采用双标准曲线分别对猪源性成分和总动物源性成分进行定量,计算猪源性成分的百分含量。【结果】筛选到猪特异性微卫星DNA(Accession EF172428),根据其序列设计的引物SEQ-sus2-F/R只能从猪基因组DNA中扩增出目的条带,其他动物的基因组均无目的条带扩增。建立的Real-time PCR检测方法灵敏度为0.02 ng/25μL反应体系。该方法能够准确检测出混合DNA样品中猪源性成分和混合肉样品中猪源性成分,百分误差分别约为1.32%和1.06%-7.12%。【结论】本研究利用Real-time PCR技术建立的定量猪源性成分的检测方法可以用来检测猪源性成分在混合样品中的百分含量。

基于Simulink Real-Time的汽车空调半实物仿真试验系统研发

针对多样化汽车空调电子零部件的高效、低成本总成级测试需求,研究基于一汽大众速腾1.6 L车型原型,设计了基于Simulink Real-Time的汽车空调半实物仿真试验系统。该系统集成了真实的汽车空调总成、制冷循环以及包含宿主机和目标机的双机实时仿真系统,用于开发和运行台架监控和空调控制模型,为汽车空调电子零部件提供半实物仿真测试环境。通过系统集成及功能验证,结果表明,汽车空调半实物仿真试验系统能够实现汽车空调基本控制功能,同时在不同工况下实现乘员舱温度控制的稳定性,稳态误差控制在±1℃以内。此外,该系统还具备灵活调整空调运行工况和状态的能力,为汽车空调电子零部件提供了高效、低成本的半实物仿真测试环境。

多主棒孢SdhB-H278Y突变位点real-time PCR检测体系的建立与应用

根据GenBank已登录序列中黄瓜多主棒孢琥珀酸脱氢酶B亚基(SdhB)基因序列差异,针对SdhB-H278Y突变设计特异性引物,建立SdhB-H278Y突变实时荧光定量PCR(real-time PCR)检测体系。结果表明:供试多主棒孢携带SdhBH278Y、SdhB-I280V突变;SdhB-H278Y突变株对啶酰菌胺抗性较强,EC50值为21.47μg·mL^(-1)或>30μg·mL^(-1);建立的real-time PCR检测体系具有良好的线性关系,相关系数R2=0.9929,可特异性检测SdhB-H278Y突变,灵敏度为3.6×10^(-4) ng·μL^(-1),为AS-PCR的10倍。利用携带SdhB-H278Y突变不同比例的基因组DNA对检测体系进行验证,预期值与检测值具有很高的相关性,R^(2)=0.9997;利用该检测体系对山东地区黄瓜棒孢叶斑病病斑中多主棒孢SdhB-H278Y突变株所占比例进行检测,检测结果为0.12%~2.69%。综上,本试验建立的real-time PCR检测体系高效、灵敏、定量,可用于多主棒孢SdhB-H278Y突变的检测,为黄瓜棒孢叶斑病抗性治理提供技术支持。

羊肉中貉源成分Real-time PCR检测方法的建立

[目的]检测羊肉中是否含有貉肉成分。[方法]通过实时荧光定量PCR方法,以cytB为靶基因设计特异性检测引物,选取8个不同物种的肌肉组织样本为研究对象,根据其ΔCt值函数关系进行线性拟合建立标准曲线。[结果]该检测方法所用引物能将貉与其他物种区分,特异性较强,且最低检测限可达到3.2 pg/μL,回收率在97.71%~104.36%,组内变异系数≤0.28%,组间变异系数≤1.08%。[结论]该研究建立的羊肉中貉肉源成分实时荧光定量检测方法具有良好的特异性和敏感性,可用该方法检测实际羊肉样品中是否有貉肉源成分,为羊肉制品掺假的检测提供简单快捷准确的技术手段和执法依据。

Real-time prediction of mechanical behaviors of underwater shield tunnel structure using machine learning method based on structural health monitoring data

Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure.

Evolutionary-assisted reinforcement learning for reservoir real-time production optimization under uncertainty

Production optimization has gained increasing attention from the smart oilfield community because it can increase economic benefits and oil recovery substantially.While existing methods could produce high-optimality results,they cannot be applied to real-time optimization for large-scale reservoirs due to high computational demands.In addition,most methods generally assume that the reservoir model is deterministic and ignore the uncertainty of the subsurface environment,making the obtained scheme unreliable for practical deployment.In this work,an efficient and robust method,namely evolutionaryassisted reinforcement learning(EARL),is proposed to achieve real-time production optimization under uncertainty.Specifically,the production optimization problem is modeled as a Markov decision process in which a reinforcement learning agent interacts with the reservoir simulator to train a control policy that maximizes the specified goals.To deal with the problems of brittle convergence properties and lack of efficient exploration strategies of reinforcement learning approaches,a population-based evolutionary algorithm is introduced to assist the training of agents,which provides diverse exploration experiences and promotes stability and robustness due to its inherent redundancy.Compared with prior methods that only optimize a solution for a particular scenario,the proposed approach trains a policy that can adapt to uncertain environments and make real-time decisions to cope with unknown changes.The trained policy,represented by a deep convolutional neural network,can adaptively adjust the well controls based on different reservoir states.Simulation results on two reservoir models show that the proposed approach not only outperforms the RL and EA methods in terms of optimization efficiency but also has strong robustness and real-time decision capacity.

A multiplex real-time PCR assay for simultaneous detection of classical swine fever virus,African swine fever virus,and atypical porcine pestivirus

With the implementation of the C-strain vaccine,classical swine fever(CSF) has been under control in China,which is currently in a chronic atypical epidemic situation.African swine fever(ASF) emerged in China in 2018 and spread quickly across the country.It is presently occurring sporadically due to the lack of commercial vaccines and farmers’ increased awareness of biosafety.Atypical porcine pestivirus(APPV) was first detected in Guangdong Province,China,in 2016,which mainly harms piglets and has a local epidemic situation in southern China.These three diseases have similar clinical symptoms in pig herds,which cause considerable losses to the pig industry.They are difficult to be distinguished only by clinical diagnosis.Therefore,developing an early and accurate simultaneous detection and differential diagnosis of the diseases induced by these viruses is essential.In this study,three pairs of specific primers and Taq-man probes were designed from highly conserved genomic regions of CSFV(5’ UTR),African swine fever virus(ASFV)(B646L),and APPV(5’ UTR),followed by the optimization of reaction conditions to establish a multiplex real-time PCR detection assay.The results showed that the method did not cross-react with other swine pathogens(porcine circovirus type 2(PCV2),porcine reproductive and respiratory syndrome virus(PRRSV),foot-and-mouth disease virus(FMDV),pseudorabies virus(PRV),porcine parvovirus(PPV),and bovine viral diarrhea virus BVDV).The sensitivity results showed that CSFV,ASFV,and APPV could be detected as low as 1 copy μL–1;the repeatability results showed that the intra-assay and interassay coefficient of variation of ASFV,CSFV,and APPV was less than 1%.Twenty-two virus samples were detected by the multiplex real-time PCR,compared with national standard diagnostic and patented method assay for CSF(GB/T 27540–2011),ASF(GB/T 18648–2020),and APPV(CN108611442A),respectively.The sensitivity of this triple real-time PCR for CSFV,ASFV,and APPV was almost the same,and the compliance results were the same(100%).A total of 451 clinical samples were detected,and the results showed that the positive rates of CSFV,ASFV,and APPV were 0.22% (1/451),1.3%(6/451),and 0%(0/451),respectively.This assay provides a valuale tool for rapid detection and accurate diagnosis of CSFV,ASFV,and APPV.

ROS2 Real-time Performance Optimization and Evaluation

Real-time interaction with uncertain and dynamic environments is essential for robotic systems to achieve functions such as visual perception,force interaction,spatial obstacle avoidance,and motion planning.To ensure the reliability and determinism of system execution,a flexible real-time control system architecture and interaction algorithm are required.The ROS framework was designed to improve the reusability of robotic software development by providing a distributed structure,hardware abstraction,message-passing mechanism,and application prototypes.Rich ecosystems for robotic development have been built around ROS1 and ROS2 architectures based on the Linux system.However,because of the fairness scheduling principle of the default Linux system design and the complexity of the kernel,the system does not have real-time computing.To achieve a balance between real-time and non-real-time computing,this paper uses the transmission mechanism of ROS2,combines it with the scheduling mechanism of the Linux operating system,and uses Preempt_RT to enhance the real-time computing of ROS1 and ROS2.The real-time performance evaluation of ROS1 and ROS2 is conducted from multiple perspectives,including throughput,transmission mode,QoS service quality,frequency,number of subscription nodes and EtherCAT master.This paper makes two significant contributions:firstly,it employs Preempt_RT to optimize the native ROS2 system,effectively enhancing the real-time performance of native ROS2 message transmission;secondly,it conducts a comprehensive evaluation of the real-time performance of both native and optimized ROS2 systems.This comparison elucidates the benefits of the optimized ROS2 architecture regarding real-time performance,with results vividly demonstrated through illustrative figures.

Evaluation of Reference Genes for Quantitative Real-Time PCR Analysis in Manila Clam Ruditapes philippinarum Under Hypoxic Stress

Quantitative real-time PCR(qRT-PCR)has been widely used for gene expression analysis,and selection of reference genes is a key point to obtain accurate results.To find out optimal reference genes for qRT-PCR in Manila clam Ruditapes philippinarum in response to hypoxia,different tissues were used and compared to evaluate the stability of candidate reference genes under low oxygen stress(DO 0.5mgL^(−1) and DO 2.0mgL^(−1))and normal condition(DO 7.5mgL^(−1)).Seven candidate reference genes were selected to evaluate the stability of their expression levels.The reference genes were evaluated by Delta Ct,BestKeeper,NormFinder and geNorm,and then screened by RefFinder calculation.Under hypoxic stress of 0.5mgL^(−1),the most suitable reference gene for gill and hepatopancreas was RPL31,and the optimal reference genes for axe foot and adductor muscle were TUB and HIS,respectively.For hypoxic stress of 2.0mgL^(−1),the most stable reference gene for gill and hepatopancreas was RPL31,and the optimal reference genes for axe foot and adductor muscle were RPS23 and EF1A,respectively.At the normal condition,HIS and EF1A were identified as the optimal internal reference genes in gill and hepatopancreas respectively,and GFRP2 was the best internal reference gene for axe foot and adductor muscle.The present findings will provide important basis for the selection of reference genes for qRT-PCR analysis of gene expression level in bivalves under hypoxic stress,which might be helpful for the analysis of other molluscs too.

A multi-sensor-based distributed real-time measurement system for glacier deformation

Glacier disasters occur frequently in alpine regions around the world,but the current conventional geological disaster measurement technology cannot be directly used for glacier disaster measurement.Hence,in this study,a distributed multi-sensor measurement system for glacier deformation was established by integrating piezoelectric sensing,coded sensing,attitude sensing technology and wireless communication technology.The traditional Modbus protocol was optimized to solve the problem of data identification confusion of different acquisition nodes.Through indoor wireless transmission,adaptive performance analysis,error measurement experiment and landslide simulation experiment,the performance of the measurement system was analyzed and evaluated.Using unmanned aerial vehicle technology,the reliability and effectiveness of the measurement system were verified on the site of Galongla glacier in southeastern Tibet,China.The results show that the mean absolute percentage errors were only 1.13%and 2.09%for the displacement and temperature,respectively.The distributed glacier deformation real-time measurement system provides a new means for the assessment of the development process of glacier disasters and disaster prevention and mitigation.