On-site rapid detection of multiple pesticide residues in tea leaves by lateral flow immunoassay

The application of pesticides (mostly insecticides and fungicides) during the tea-planting process will undoubtedly increase the dietary risk associated with drinking tea. Thus, it is necessary to ascertain whether pesticide residues in tea products exceed the maximum residue limits. However, the complex matrices present in tea samples comprise a major challenge in the analytical detection of pesticide residues. In this study, nine types of lateral flow immunochromatographic strips (LFICSs) were developed to detect the pesticides of interest (fenpropathrin, chlorpyrifos, imidacloprid, thiamethoxam, acetamiprid, carbendazim, chlorothalonil, pyraclostrobin, and iprodione). To reduce the interference of tea substrates on the assay sensitivity, the pretreatment conditions for tea samples, including the extraction solvent, extraction time, and purification agent, were optimized for the simultaneous detection of these pesticides. The entire testing procedure (including pretreatment and detection) could be completed within 30 min. The detected results of authentic tea samples were confirmed by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), which suggest that the LFICS coupled with sample rapid pretreatment can be used for on-site rapid screening of the target pesticide in tea products prior to their market release.

Rapid Detection of Somatic Cell Count Based on Hybrid Variable Selection Method

Somatic cell count detection is the daily work of dairy farms to monitor the health of cows.The feasibility of applying near-infrared spectroscopy to somatic cell count detection was researched in this paper.Milk samples with different somatic cell counts were collected and preprocessing methods were studied.Variable selection algorithm based on hybrid strategy and modelling method based on ensemble learning were explored for somatic cell count detection.Detection model was used to diagnose subclinical mastitis and the results showed that near-infrared spectroscopy could be a tool to realize rapid detection of somatic cell count in milk.

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

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

A Rapid Crack Detection Technique Based on Attention for Intelligent M&O of Cross-Sea Bridge

Rapid and accurate segmentation of structural cracks is essential for ensuring the quality and safety of engineering projects.In practice,however,this task faces the challenge of finding a balance between detection accuracy and efficiency.To alleviate this problem,a lightweight and efficient real-time crack segmentation framework was developed.Specifically,in the network model system based on an encoding-decoding structure,the encoding network is equipped with packet convolution and attention mechanisms to capture features of different visual scales in layers,and in the decoding process,we also introduce a fusion module based on spatial attention to effectively aggregate these hierarchical features.Codecs are connected by pyramid pooling model(PPM)filtering.The results show that the crack segmentation accuracy and real-time operation capability larger than 76%and 15 fps,respectively,are validated by three publicly available datasets.These wide-ranging results highlight the potential of the model for the intelligent O&M for cross-sea bridge.

Evaluation of a Rapid Diagnostic Test, Boson Biotech SARS CoV-2 Ag, for the Detection of SARS-CoV-2 in Gabon

1) Background: Rapid and acurate diagnostic testing for case identification, quarantine, and contact tracing is essential for managing the COVID 19 pandemic. Rapid antigen detection tests are available, however, it is important to evaluate their performances before use. We tested a rapid antigen detection of SARS-CoV-2, based on the immunochromatography (Boson Biotech SARS-CoV-2 Ag Test (Xiamen Boson Biotech Co., Ltd., China)) and the results were compared with the real time reverse transcriptase-Polymerase chain reaction (RT-PCR) (Gold standard) results;2) Methods: From November 2021 to December 2021, samples were collected from symptomatic patients and asymptomatic individuals referred for testing in a hospital during the second pandemic wave in Gabon. All these participants attending “CTA Angondjé”, a field hospital set up as part of the management of COVID-19 in Gabon. Two nasopharyngeal swabs were collected in all the patients, one for Ag test and the other for RT-PCR;3) Results: A total of 300 samples were collected from 189 symptomatic and 111 asymptomatic individuals. The sensitivity and specificity of the antigen test were 82.5% [95%CI 73.8 - 89.3] and 97.9 % [95%CI 92.2 - 98.2] respectively, and the diagnostic accuracy was 84.4% (95% CI: 79.8 - 88.3%). The antigen test was more likely to be positive for samples with RT-PCR Ct values ≤ 32, with a sensitivity of 89.8%;4) Conclusions: The Boson Biotech SARS-CoV-2 Ag Test has good sensitivity and can detect SARS-CoV-2 infection, especially among symptomatic individuals with low viral load. This test could be incorporated into efficient testing algorithms as an alternative to PCR to decrease diagnostic delays and curb viral transmission.

Development of RPA-Cas12a-fluorescence assay for rapid and reliable detection of human bocavirus 1

Human bocavirus(HBoV)1 is considered an important pathogen that mainly affects infants aged 6–24 months,but preventing viral transmission in resource-limited regions through rapid and affordable on-site diagnosis of individuals with early infection of HBoV1 remains somewhat challenging.Herein,we present a novel faster,lower cost,reliable method for the detection of HBoV1,which integrates a recombinase polymerase amplification(RPA)assay with the CRISPR/Cas12a system,designated the RPA-Cas12a-fluorescence assay.The RPA-Cas12a-fluorescence system can specifically detect target gene levels as low as 0.5 copies of HBoV1 plasmid DNA per microliter within 40 min at 37℃without the need for sophisticated instruments.The method also demonstrates excellent specificity without cross-reactivity to non-target pathogens.Furthermore,the method was appraised using 28 clinical samples,and displayed high accuracy with positive and negative predictive agreement of 90.9%and 100%,respectively.Therefore,our proposed rapid and sensitive HBoV1 detection method,the RPA-Cas12a-fluorescence assay,shows promising potential for early on-site diagnosis of HBoV1 infection in the fields of public health and health care.The established RPA-Cas12a-fluorescence assay is rapid and reliable method for human bocavirus 1 detection.The RPA-Cas12a-fluorescence assay can be completed within 40 min with robust specificity and sensitivity of 0.5 copies/μl.

A LFD-RAA-Based Rapid Detection Method for Pseudomonas syringae pv. tabaci

This study aimed to achieve rapid detection of Pseudomonas syringae pv.tabaci,the pathogen of tobacco wildfire disease.The specific primers and probes for recombinase-aided amplification(RAA)were designed with HrpZ as the target gene.RAA was then combined with the lateral flow dipstick(LFD)to establish a LFD-RAA-based rapid detection system for the pathogen.Furthermore,the detection performance of the established method was tested.The results showed that the LFDRAA method had high specificity.The amplification could be completed after 25 min of reaction at 39℃.The sensitivity of the established method reached 0.0001 ng/μL,which was superior to that of PCR detection.Moreover,the LFD-RAA method could quickly detect P.syringae pv.tabaci from tobacco leaves,demonstrating field applicability.To sum up,the LFD-RAA method established in this study can be applied in the rapid detection and early diagnosis of tobacco wildfire disease.

基于改进Detection Transformer的棉花幼苗与杂草检测模型研究

基于深度学习的目标检测技术在棉花幼苗与杂草检测领域已取得一定进展。论文提出了基于改进Detection Transformer的棉花幼苗与杂草检测模型,以提高杂草目标检测的准确率和效率。首先,引入了可变形注意力模块替代原始模型中的Transformer注意力模块,提高模型对特征图目标形变的处理能力。提出新的降噪训练机制,解决了二分图匹配不稳定问题。提出混合查询选择策略,提高解码器对目标类别和位置信息的利用效率。使用Swin Transformer作为网络主干,提高模型特征提取能力。通过对比原网络,论文提出的模型方法在训练过程中表现出更快的收敛速度,并且在准确率方面提高了6.7%。

Filtration assisted pretreatment for rapid enrichment and accurate detection of Salmonella in vegetables

Rapid detection of target foodborne pathogens plays more and more significant roles in food safety,which requires the efficiency,sensitivity,and accuracy.In this research,we proposed a new st rategy of isothermal-molecular-amplification integrated with lateral-flow-strip for rapid detection of Salmonella without traditional enrichment-culture.Th e designed syringe-assisted-filtration can contribute to simultaneous collection and concentration of target bacterium from vegetable samples in just 3 min,resolving the drawbacks of traditional random sampling protocols.After simple and convenient ultrasonication,samples can be directly amplified at 39℃ in 25 min and the amplicons are qualitatively and quantitatively analyzed with the designed lateral-flow-strip in 5 min.Finally,satisfied results have been achieved within 40 min,which greatly improve the efficiency while the accuracy is also guaranteed.Furthermore,all detection steps can be completed under instrument-free conditions.This method will hold great promise for target pathogen detection in the resource-limited district,or for emergency on-site identification.

Recent Advances in the Rapid Detection and Performance Evaluation Methods of Detergent Additives for Gasoline

Although detergent additives for gasoline have been widely commercialized,their formulas are often kept confidential and there is still no standardized method for quickly detecting the main active ingredients and evaluating their effectiveness,which makes their regulation difficult.An overview of the current state of the development and application of detergent additives for gasoline in China and other regions,as well as a review of the rapid detection and performance evaluation methods available for analyzing detergent additives are given herein.The review focuses on the convenience,cost,efficiency,and feasibility of on-site detection and the evaluation of various methods,and also looks into future research directions,such as detecting and evaluating detergent additives in ethanol gasoline and with advanced engine technologies.

Intraprocedural gastric juice analysis as compared to rapid urease test for real-time detection of Helicobacter pylori

BACKGROUND Endofaster is an innovative technology that can be combined with upper gastrointestinal endoscopy(UGE)to perform gastric juice analysis and real-time detection of Helicobacter pylori(H.pylori).AIM To assess the diagnostic performance of this technology and its impact on the management of H.pylori in the real-life clinical setting.METHODS Patients undergoing routine UGE were prospectively recruited.Biopsies were taken to assess gastric histology according to the updated Sydney system and for rapid urease test(RUT).Gastric juice sampling and analysis was performed using the Endofaster,and the diagnosis of H.pylori was based on real-time ammonium measurements.Histological detection of H.pylori served as the diagnostic gold standard for comparing Endofaster-based H.pylori diagnosis with RUT-based H.pylori detection.RESULTS A total of 198 patients were prospectively enrolled in an H.pylori diagnostic study by Endofasterbased gastric juice analysis(EGJA)during the UGE.Biopsies for RUT and histological assessment were performed on 161 patients(82 men and 79 women,mean age 54.8±19.2 years).H.pylori infection was detected by histology in 47(29.2%)patients.Overall,the sensitivity,specificity,accuracy,positive predictive value,and negative predictive value(NPV)for H.pylori diagnosis by EGJA were 91.5%,93.0%,92.6%,84.3%,and 96.4%,respectively.In patients on treatment with proton pump inhibitors,diagnostic sensitivity was reduced by 27.3%,while specificity and NPV were unaffected.EGJA and RUT were comparable in diagnostic performance and highly concordant in H.pylori detection(κ-value=0.85).CONCLUSION Endofaster allows for rapid and highly accurate detection of H.pylori during gastroscopy.This may guide taking additional biopsies for antibiotic susceptibility testing during the same procedure and then selecting an individually tailored eradication regimen.

Establishment of a rapid detection method of Ureaplasma urealyticum based on recombinant polymerase amplification

Ureaplasma urealyticum(UU),is one of the most vital pathogens causing genitourinary tract infections of the body,and it can result in poor maternal and perinatal outcomes.The aim of this study was to establish a method to detect Ureaplasma urealyticum based on recombinant polymerase amplification(RPA)technique.Specific primers and probes were designed according to the 16sRNA gene sequence of Ureaplasma urealyticum.Six pathogens were detected for real-time fluorescence RPA specificity verification,including Mycoplasma hominis(MH),Chlamydia trachomatis(CT),Neisseria gonorrhoeae(NG),Staphylococcus aureus,Escherichia coli,and Lactobacillus vaginalis.The sensitivity of the method was performed by gradient dilution of the extracted template.A total of 60 clinical samples were detected by the established real-time fluorescence RPA.Detection of Ureaplasma urealyticum can be completed within 20 minutes at 39°C using established RPA method.The minimum detection limit of Ureaplasma urealyticum by real-time fluorescence RPA was 3 pg.The evaluation of 60 clinical samples proved that RPA method was feasible.A high specificity,sensitivity,simplicity and rapidity method for Ureaplasma urealyticum detection was successfully established based on the real-time fluorescence RPA method.

A Hybrid Intrusion Detection Method Based on Convolutional Neural Network and AdaBoost

To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

基于形状因子优化的RAPID算法对CFRP通孔损伤的研究

在传统RAPID算法损伤成像中,形状因子β的设定通常是人为取值,其取值不同,成像的效果也不同,往往存在成像结果分散、分辨率较低的问题,因此提出了一种计算β值的新方法。通过ABAQUS有限元仿真,以CFRP层合板的通孔缺陷为检测对象,将形状因子优化后的RAPID算法与RAPID算法的损伤成像结果进行对比分析。结果表明,形状因子优化后的RAPID算法对缺陷定位结果的集中度更高,更能实现损伤位置的可视化。

Esophageal cancer screening,early detection and treatment:Current insights and future directions

Esophageal cancer ranks among the most prevalent malignant tumors globally,primarily due to its highly aggressive nature and poor survival rates.According to the 2020 global cancer statistics,there were approximately 604000 new cases of esophageal cancer,resulting in 544000 deaths.The 5-year survival rate hovers around a mere 15%-25%.Notably,distinct variations exist in the risk factors associated with the two primary histological types,influencing their worldwide incidence and distribution.Squamous cell carcinoma displays a high incidence in specific regions,such as certain areas in China,where it meets the cost-effect-iveness criteria for widespread endoscopy-based early diagnosis within the local population.Conversely,adenocarcinoma(EAC)represents the most common histological subtype of esophageal cancer in Europe and the United States.The role of early diagnosis in cases of EAC originating from Barrett's esophagus(BE)remains a subject of controversy.The effectiveness of early detection for EAC,particularly those arising from BE,continues to be a debated topic.The variations in how early-stage esophageal carcinoma is treated in different regions are largely due to the differing rates of early-stage cancer diagnoses.In areas with higher incidences,such as China and Japan,early diagnosis is more common,which has led to the advancement of endoscopic methods as definitive treatments.These techniques have demonstrated remarkable efficacy with minimal complications while preserving esophageal functionality.Early screening,prompt diagnosis,and timely treatment are key strategies that can significantly lower both the occurrence and death rates associated with esophageal cancer.

Feature extraction for machine learning-based intrusion detection in IoT networks

A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems(NIDSs).Consequently,network interruptions and loss of sensitive data have occurred,which led to an active research area for improving NIDS technologies.In an analysis of related works,it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction(FR)and Machine Learning(ML)techniques on NIDS datasets.However,these datasets are different in feature sets,attack types,and network design.Therefore,this paper aims to discover whether these techniques can be generalised across various datasets.Six ML models are utilised:a Deep Feed Forward(DFF),Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),Decision Tree(DT),Logistic Regression(LR),and Naive Bayes(NB).The accuracy of three Feature Extraction(FE)algorithms is detected;Principal Component Analysis(PCA),Auto-encoder(AE),and Linear Discriminant Analysis(LDA),are evaluated using three benchmark datasets:UNSW-NB15,ToN-IoT and CSE-CIC-IDS2018.Although PCA and AE algorithms have been widely used,the determination of their optimal number of extracted dimensions has been overlooked.The results indicate that no clear FE method or ML model can achieve the best scores for all datasets.The optimal number of extracted dimensions has been identified for each dataset,and LDA degrades the performance of the ML models on two datasets.The variance is used to analyse the extracted dimensions of LDA and PCA.Finally,this paper concludes that the choice of datasets significantly alters the performance of the applied techniques.We believe that a universal(benchmark)feature set is needed to facilitate further advancement and progress of research in this field.

YOLO-DD:Improved YOLOv5 for Defect Detection

As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes.To address this issue,this paper proposes YOLO-DD,a defect detectionmodel based on YOLOv5 that is effective and robust.To improve the feature extraction process and better capture global information,the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer(RDAT).Additionally,an Information Gap Filling Strategy(IGFS)is proposed to improve the fusion of features at different scales.The classic lightweight attention mechanism Squeeze-and-Excitation(SE)module is also incorporated into the neck section to enhance feature expression and improve the model’s performance.Experimental results on the NEU-DET dataset demonstrate that YOLO-DDachieves competitive results compared to state-of-the-art methods,with a 2.0% increase in accuracy compared to the original YOLOv5,achieving 82.41% accuracy and38.25FPS(framesper second).Themodel is also testedon a self-constructed fabric defect dataset,and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5,demonstrating its stability and generalization ability.The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection.

Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.