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.

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.

Method for rapid warning and activity concentration estimates in online waterγ-spectrometry systems

Onlineγ-spectrometry systems for inland waters,most of which extract samples in situ and in real time,are able to produce reliable activity concentration measurements for waterborne radionuclides only when they are distributed relatively uniformly and enter into a steady-state diffusion regime in the measurement chamber.To protect residents’health and ensure the safety of the living environment,better timeliness is required for this measurement method.To address this issue,this study established a mathematical model of the online waterγ-spectrometry system so that rapid warning and activity estimates can be obtained for water under non-steady-state(NSS)conditions.In addition,the detection efficiency of the detector for radionuclides during the NSS diffusion process was determined by applying the computational fluid dynamics technique in conjunction with Monte Carlo simulations.On this basis,a method was developed that allowed the online waterγ-spectrometry system to provide rapid warning and activity concentration estimates for radionuclides in water.Subsequent analysis of the NSS-mode measurements of^(40)K radioactive solutions with different activity concentrations determined the optimum warning threshold and measurement time for producing accurate activity concentration estimates for radionuclides.The experimental results show that the proposed NSS measurement method is able to give warning and yield accurate activity concentration estimates for radionuclides 55.42 and 69.42 min after the entry of a 10 Bq/L^(40)K radioactive solution into the measurement chamber,respectively.These times are much shorter than the 90 min required by the conventional measurement method.Furthermore,the NSS measurement method allows the measurement system to give rapid(within approximately 15 min)warning when the activity concentrations of some radionuclides reach their respective limits stipulated in the Guidelines for Drinking-water Quality of the WHO,suggesting that this method considerably enhances the warning capacity of in situ online waterγ-spectrometry systems.

A dual-RPA based lateral flow strip for sensitive,on-site detection of CP4-EPSPS and Cry1Ab/Ac genes in genetically modified crops

Traditional transgenic detection methods require high test conditions and struggle to be both sensitive and efficient.In this study,a one-tube dual recombinase polymerase amplification(RPA)reaction system for CP4-EPSPS and Cry1Ab/Ac was proposed and combined with a lateral flow immunochromatographic assay,named“Dual-RPA-LFD”,to visualize the dual detection of genetically modified(GM)crops.In which,the herbicide tolerance gene CP4-EPSPS and the insect resistance gene Cry1Ab/Ac were selected as targets taking into account the current status of the most widespread application of insect resistance and herbicide tolerance traits and their stacked traits.Gradient diluted plasmids,transgenic standards,and actual samples were used as templates to conduct sensitivity,specificity,and practicality assays,respectively.The constructed method achieved the visual detection of plasmid at levels as low as 100 copies,demonstrating its high sensitivity.In addition,good applicability to transgenic samples was observed,with no cross-interference between two test lines and no influence from other genes.In conclusion,this strategy achieved the expected purpose of simultaneous detection of the two popular targets in GM crops within 20 min at 37°C in a rapid,equipmentfree field manner,providing a new alternative for rapid screening for transgenic assays in the field.

Rapid Detection of Accelerants in Fire Debris Using a Field Portable Mid-Infrared Quantum Cascade Laser Based Analyzer

Arson presents a challenging crime scene for fire investigators worldwide. Key to the investigation of suspected arson cases is the analysis of fire debris for the presence of accelerants or ignitable liquids. This study has investigated the application and method development of vapor phase mid-Infrared (mid-IR) spectroscopy using a field portable quantum cascade laser (QCL) based system for the detection and identification of accelerant residues such as gasoline, diesel, and ethanol in fire debris. A searchable spectral library of various ignitable fluids and fuel components measured in the vapor phase was constructed that allowed for real-time identification of accelerants present in samples using software developed in-house. Measurement of vapors collected from paper material that had been doused with an accelerant followed by controlled burning and then extinguished with water showed that positive identification could be achieved for gasoline, diesel, and ethanol. This vapor phase mid-IR QCL method is rapid, easy to use, and has the sensitivity and discrimination capability that make it well suited for non-destructive crime scene sample analysis. Sampling and measurement can be performed in minutes with this 7.5 kg instrument. This vibrational spectroscopic method required no time-consuming sample pretreatment or complicated solvent extraction procedure. The results of this initial feasibility study demonstrate that this portable fire debris analyzer would greatly benefit arson investigators performing analysis on-site.

Target Detection Algorithm in Foggy Scenes Based on Dual Subnets

Under the influence of air humidity,dust,aerosols,etc.,in real scenes,haze presents an uneven state.In this way,the image quality and contrast will decrease.In this case,It is difficult to detect the target in the image by the universal detection network.Thus,a dual subnet based on multi-task collaborative training(DSMCT)is proposed in this paper.Firstly,in the training phase,the Gated Context Aggregation Network(GCANet)is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes.In the test phase,only the YOLOX branch needs to be activated to ensure the detection speed of the model.Secondly,the deformable convolution module is used to improve GCANet to enhance the model’s ability to capture details of non-homogeneous fog.Finally,the Coordinate Attention mechanism is introduced into the Vision Transformer and the backbone network of YOLOX is redesigned.In this way,the feature extraction ability of the network for deep-level information can be enhanced.The experimental results on artificial fog data set FOG_VOC and real fog data set RTTS show that the map value of DSMCT reached 86.56%and 62.39%,respectively,which was 2.27%and 4.41%higher than the current most advanced detection model.The DSMCT network has high practicality and effectiveness for target detection in real foggy scenes.

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

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

SDH-FCOS:An Efficient Neural Network for Defect Detection in Urban Underground Pipelines

Urban underground pipelines are an important infrastructure in cities,and timely investigation of problems in underground pipelines can help ensure the normal operation of cities.Owing to the growing demand for defect detection in urban underground pipelines,this study developed an improved defect detection method for urban underground pipelines based on fully convolutional one-stage object detector(FCOS),called spatial pyramid pooling-fast(SPPF)feature fusion and dual detection heads based on FCOS(SDH-FCOS)model.This study improved the feature fusion component of the model network based on FCOS,introduced an SPPF network structure behind the last output feature layer of the backbone network,fused the local and global features,added a top-down path to accelerate the circulation of shallowinformation,and enriched the semantic information acquired by shallow features.The ability of the model to detect objects with multiple morphologies was strengthened by introducing dual detection heads.The experimental results using an open dataset of underground pipes show that the proposed SDH-FCOS model can recognize underground pipe defects more accurately;the average accuracy was improved by 2.7% compared with the original FCOS model,reducing the leakage rate to a large extent and achieving real-time detection.Also,our model achieved a good trade-off between accuracy and speed compared with other mainstream methods.This proved the effectiveness of the proposed model.

A self-organization formation configuration based assignment probability and collision detection

The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.

Alignment of Track Oscillations during Tropical Cyclone Rapid Intensification

Recent studies on tropical cyclone(TC)intensity change indicate that the development of a vertically aligned TC circulation is a key feature of its rapid intensification(RI),however,understanding how vortex alignment occurs remains a challenging topic in TC intensity change research.Based on the simulation outputs of North Atlantic Hurricane Wilma(2005)and western North Pacific Typhoon Rammasun(2014),vortex track oscillations at different vertical levels and their associated role in vortex alignment are examined to improve our understanding of the vortex alignment during RI of TCs with initial hurricane intensity.It is found that vortex tracks at different vertical levels oscillate consistently in speed and direction during the RI of the two simulated TCs.While the consistent track oscillation reduces the oscillation tilt during RI,the reduction of vortex tilt results mainly from the mean track before RI.It is also found that the vortex tilt is primarily due to the mean vortex track before and after RI.The track oscillations are closely associated with wavenumber-1 vortex Rossby waves that are dominant wavenumber-1 circulations in the TC inner-core region.This study suggests that the dynamics of the wavenumber-1 vortex Rossby waves play an important role in the regulation of the physical processes associated with the track oscillation and vertical alignment of TCs.

Phenotypic Detection of Enterobacterales Strains Susceptible of Producing OXA-48 Carbapenemase

Background: Nowadays, emergence of Carbapenemase-Producing Enterobacterales (CPE) throughout the world has become a public health problem, especially in countries with limited resources. In recent years, CPE of type OXA-48 (Ambler class D) have been identified in Dakar. The aim of this study was to evaluate the phenotypic detection of OXA-48 CPE using a temocillin disc (30 μg). Methodology: A retrospective study was carried out at Medical Biology Laboratory of Pasteur Institute in Dakar on Ertapenem-Resistant Enterobacterales (ERE) strains isolated from 2015 to 2017. These strains were then tested with a 30 μg temocillin disc. Any strain resistant to temocillin (inhibition diameter Results: Forty-one ERE isolated during the study period were tested, of which 34 (82.9%) were OXA-48 based on phenotypic detection using temocillin disc and confirmed by PCR (100%). OXA-48 CPE strains detected were composed of Klebsiella pneumoniae (n = 14;41.2%), Enterobacter cloacae (n = 8;23.5%), Escherichia coli (n = 7, 20.5%), Citrobacter freundii (n = 3;8.8%), Cronobacter sakazakii (n = 1;3%) and Morganella morganii (n = 1;3%). Conclusion: Temocillin resistance has a good positive predictive value for detecting OXA-48 CPE by phenotypic method, confirmed by PCR. Temocillin is therefore a good marker for detection of OXA-48 CPE except Hafnia alvei.

Strengthening Network Security: Deep Learning Models for Intrusion Detectionwith Optimized Feature Subset and Effective Imbalance Handling

In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security.This paper presents a novel intrusion detection system consisting of a data prepro-cessing stage and a deep learning model for accurately identifying network attacks.We have proposed four deep neural network models,which are constructed using architectures such as Convolutional Neural Networks(CNN),Bi-directional Long Short-Term Memory(BiLSTM),Bidirectional Gate Recurrent Unit(BiGRU),and Attention mechanism.These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models,we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset,resulting in an optimized feature subset.Moreover,we address class imbalance in the dataset using focal loss.Finally,we employ the BO-TPE algorithm to optimize the hyperparameters of the four models,maximizing their detection performance.The test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data effectively.In binary and multiclass experiments,it achieved accuracy rates of 0.999158 and 0.999091,respectively,surpassing other state-of-the-art methods.

A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting

The data analysis of blasting sites has always been the research goal of relevant researchers.The rise of mobile blasting robots has aroused many researchers’interest in machine learning methods for target detection in the field of blasting.Serverless Computing can provide a variety of computing services for people without hardware foundations and rich software development experience,which has aroused people’s interest in how to use it in the field ofmachine learning.In this paper,we design a distributedmachine learning training application based on the AWS Lambda platform.Based on data parallelism,the data aggregation and training synchronization in Function as a Service(FaaS)are effectively realized.It also encrypts the data set,effectively reducing the risk of data leakage.We rent a cloud server and a Lambda,and then we conduct experiments to evaluate our applications.Our results indicate the effectiveness,rapidity,and economy of distributed training on FaaS.

Analysis of the joint detection capability of the SMILE satellite and EISCAT-3D radar

The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research.

Finite element model updating for structural damage detection using transmissibility data

This paper presents a new finite element model updating method for estimating structural parameters and detecting structural damage location and severity based on the structural responses(output-only data).The method uses the sensitivity relation of transmissibility data through a least-squares algorithm and appropriate normalization of the extracted equations.The proposed transmissibility-based sensitivity equation produces a more significant number of equations than the sensitivity equations based on the frequency response function(FRF),which can estimate the structural parameters with higher accuracy.The abilities of the proposed method are assessed by using numerical data of a two-story two-bay frame model and a plate structure model.In evaluating different damage cases,the number,location,and stiffness reduction of the damaged elements and the severity of the simulated damage have been accurately identified.The reliability and stability of the presented method against measurement and modeling errors are examined using error-contaminated data.The parameter estimation results prove the method’s capabilities as an accurate model updating algorithm.

Observation of the Effectiveness of Rapid Recovery Nursing on Lung Cancer Surgery Patients and Its Impact on Sleep Quality

Objective: To investigate the nursing effects of rapid recovery care measures on lung cancer surgery patients. Methods: 42 cases of lung cancer surgery patients were divided into control group and study group, with 21 cases in each group. The sleep quality and postoperative recovery indicators were compared between the two groups. Results: The study group showed better results than the control group in terms of PSQI scores, venting time, extubation time, time to getting out of bed, and duration of antibiotic use, with P Conclusion: Rapid recovery nursing has a positive impact on improving sleep quality and promoting postoperative recovery in lung cancer surgery patients.

Microstrip Patch Antenna with an Inverted T-Type Notch in the Partial Ground for Breast Cancer Detections

This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumorcells inside the human breast.The size of the current antenna is small enough(18mm×21mm×1.6mm)todistribute around the breast phantom.The operating frequency has been observed from6–14GHzwith a minimumreturn loss of−61.18 dB and themaximumgain of current proposed antenna is 5.8 dBiwhich is flexiblewith respectto the size of antenna.After the distribution of eight antennas around the breast phantom,the return loss curveswere observed in the presence and absence of tumor cells inside the breast phantom,and these observations showa sharp difference between the presence and absence of tumor cells.The simulated results show that this proposedantenna is suitable for early detection of cancerous cells inside the breast.