Biological and molecular characterization of tomato brown rugose fruit virus and development of quadruplex RT-PCR detection

Tomato brown rugose fruit virus(ToBRFV) is a novel tobamovirus firstly reported in 2015 and poses a severe threat to the tomato industry. So far, it has spread to 10 countries in America, Asia, and Europe. In 2019, ToBRFV was identified in Shandong Province(ToBRFV-SD), China. In this study, it was shown that ToBRFV-SD induced mild to severe mosaic and blistering on leaves, necrosis on sepals and pedicles, and deformation, yellow spots, and brown rugose necrotic lesions on fruits. ToBRFV-SD induced distinct symptoms on plants of tomato, Capsicum annumm, and Nicotiana benthamiana, and caused latent infection on plants of Solanum tuberosum, Solanum melongena, and N. tabacum cv. Zhongyan 102. All the 50 tomato cultivars tested were highly sensitive to ToBRFV-SD. The complete genomic sequence of ToBRFV-SD shared the highest nucleotide and amino acid identities with isolate IL from Israel. In the phylogenetic tree constructed with the complete genomic sequence, all the ToBRFV isolates were clustered together and formed a sister branch with tobacco mosaic virus(TMV). Furthermore, a quadruplex RT-PCR system was developed that could differentiate ToBRFV from other economically important viruses affecting tomatoes, such as TMV, tomato mosaic virus, and tomato spotted wilt virus. The findings of this study enhance our understanding of the biological and molecular characteristics of ToBRFV and provide an efficient and effective detection method for multiple infections, which is helpful in the management of ToBRFV.

Quantitative real-time RT-PCR detection for CEA, CK20 and CK19 mRNA in peripheral blood of colorectal cancer patients

This study is aimed at establishing a sensitive approach to detect disseminated tumor cells in peripheral blood and evaluate its clinical significance. A total of 198 blood samples including 168 from colorectal carcinoma (CRC) patients and 30 from healthy volunteers were examined by quantitative real-time reverse transcription-polymerase chain reaction (RT-PCR) to evaluate the expression of carcinoembryonic antigen (CEA), cytokeratin 20 (CK20) and cytokeratin 19 (CK19) mRNA. CEA mRNA was detected in 35.8% of patients and 3.3% of controls, CK20 mRNA in 28.3% of patients and 6.7% of controls, and CK19 mRNA in 41.9% of patients and 3.3% of controls. CEA and CK20 mRNA positive ratio increased with the advancing Dukes stages, but there was no significant difference in positive ratio between any two stages (P>0.05). Also, relatively high positive ratio of CEA, CK20 and CK19 mRNA expression was observed in some CRC patients with earlier Dukes stages. A higher positive ratio was obtained when two or three detection markers were combined compared to a single marker. Our study indicates that quanti-tative real-time RT-PCR detection for CEA, CK20 and CK19 mRNA in peripheral blood is a valuable tool for monitoring early stage dissemination of CRC cells in blood circulation.

Establishment of Universal RT-PCR Detection Method for Duck Reovirus

This study was conducted to rapidly detect clinical infection condition of duck reovirus. A pair of specific primers was designed according to gene se- quence of σC protein of duck reovirus, and a specific RT-PCR detection method of duck reovirus was established with genome of duck reovirus as template. Differ- ent samples were collected from ducks infected by suspected reovirus in Jiangsu Province and subjected to PCR detection. The results showed that the established RT-PCR method could specifically amplify the 438 bp sequence of the conservative region of σC gene, and detec the DNA of duck rcovirus as low as 1gf, with a detection rate of 100%. The RT-PCR method could be used for rapid clinical diagnosis of duck reovirus.

Expression Levels of RFP in Normal and Cancer Human Tissues via Real-time RT-PCR Detection

Ret finger protein（RFP） is a member of the tripartite motif family, which is characterized by a conserved RING finger of motif, a B-box, and a coiled-coil domain（they are called RBCC generally）. Although RFP was known to be an oncogene when its RBCC moiety was connected with a tyrosine kinase domain by DNA rearrangement, its biological function was not well defined. In this study, by using real-time RT-PCR, the RFP expressions in human and mouse normal tissues, and in the cervical squamous cell carcinoma, endometrial adenocarcinoma, gastric adenocarcinoma, esophageal squamous cell carcinoma, and brain cancer tissues were analyzed. The result of the study proved that the highest level of mRNA reverse transcription appeared in the normal testical tissue, whereas that in other normal tissues of human and mice were low. The mRNA reverse transcription level of RFP was higher in the endometrial adenocarcinoma tissue than in the cervical squamous cell carcinoma tissue; the mRNA reverse transcription level of RFP in the gastric adenocarcinoma tissue was significantly higher than that in the esophageal squamous cell carcinoma tissue. It was also found that the mRNA reverse transcription level of RFP in the brain cancer tissue was higher than that in the normal brain tissue. These results suggested that RFP could possibly be a useful molecular target for the development of new therapeutics for malignant tumors.

An internal control applied to RT-PCR detection of HCV and HIV-1 in human pooled plasma and plasma-derived medicinal products

A competitive internal control(IC)adapted to RT-PCR in-house assay was developed for HCV RNA detection in human pooled plasma.Also,it was applied in a multiplex RT-PCR for the HIV-1 and HCV RNA screening in human pooled plasma and plasma-derived products.A 258-bp PCR product from the 5´non-coding region of HCV genome was obtained.A competitive IC template was constructed by inserting a 52-bp double strand sequence into the NheI site of the 258-bp amplicon.This sequence was cloned and the obtained plasmid was used to generate a synthetic RNA.The IC/RNA was incorporated in in-house HCV and/or HIV PCR technique to monitor the efficiency of extraction,reverse transcription,and PCR amplification steps.IC was also used to detect all major genotypes of HCV and HIV-1 strains with similar sensitivity.The detection limit of the assay for HCV and HIV-1 was 52.7 IU/mL and 164.2 IU/mL,respectively.These techniques have been evaluated in international programs of external quality assurance with highly satisfactory results.This IC is an essential reagent in PCR techniques to detect and identify HCV and HIV-1 in pooled plasma samples involved in the manufacture of plasma-derived products as well as in the field of clinical microbiology with limited resources.

Tomato mottle mosaic virus: Characterization, resistance gene effectiveness, and quintuplex RT-PCR detection system

Tomato mottle mosaic virus(ToMMV), an economically important species of the genus Tobamovirus, causes significant loss in yield and quality of tomato fruits. Here, we identified the Shandong isolate of ToMMV(ToMMV-SD) collected from symptomatic tomato fruits in Weifang, Shandong Province of China. ToMMV-SD caused symptoms such as severe mosaic, mottling, and necrosis of tomato leaves, yellow spot and necrotic lesions on tomato fruits. The obtained full genome of ToMMV-SD was 6 399 nucleotides(accession number MW373515) and had the highest identity of 99.5% with that of isolate SC13-051 from the United States of America at the genomic level. The infectious clone of ToMMV-SD was constructed and induced clear mosaic and necrotic symptoms onto Nicotiana benthamiana leaves. Several commercial tomato cultivars, harboring Tm-2~2 resistance gene, and pepper cultivars, containing L resistance gene, were susceptible to ToMMV-SD. Plants of Solanum melongena(eggplant) and Brassica pekinensis(napa cabbage) showed mottling symptoms, while N. tabacum cv. Zhongyan 100 displayed latent infection. ToMMV-SD did not infect plants of N. tabacum cv. Xanthi NN, Brassica rapa ssp. chinensis(bok choy), Raphanus sativus(radish), Vigna unguiculata cv. Yuanzhong 28-2(cowpea), or Tm-2~2 transgenic N. benthamiana. A quintuplex RT-PCR system differentiated ToMMV from tomato mosaic virus, tomato brown rugose fruit virus, tobacco mosaic virus, and tomato spotted wilt virus, with the threshold amount of 0.02 pg. These results highlight the threat posed by ToMMV to tomato and pepper cultivation and offer an efficient detection system for the simultaneous detection of four tobamoviruses and tomato spotted wilt virus infecting tomato plants in the field.

Multiple RT-PCR Detection of H5,H7,and H9 Subtype Avian Influenza Viruses and Newcastle Disease Virus

Objective:This paper focuses on the multiple detection RT-PCR technology of H5,H7,AND H9 subtype avian influenza viruses and Newcastle disease virus,and points out the specific detection methods and detection procedures of avian influenza and Newcastle disease virus.Methods:The genes of Newcastle disease virus carrying out the HA gene sequence of H5,H7 and H9 subtype AIV in GenBank were used to establish a strategy for simultaneous detection of three subtypes of avian influenza virus and Newcastle disease virus.Results:The results showed that the program can detect and distinguish H5,H7 and H9 subtype avian influenza viruses and Newcastle disease virus at one time.Conclusion:Multiple RT-PCR detection method has high detection sensitivity and can detect and determine different subtypes of avian influenza virus and Newcastle disease virus quickly and accurately,therefore,it has a crucial role in the detection and control of avian influenza H5,H7 and H9 subtypes and Newcastle disease.

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.

Automatic detection of small bowel lesions with different bleeding risks based on deep learning models

BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some unresolved challenges.AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks,and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.METHODS The proposed model represents a two-stage method that combined image classification with object detection.First,we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images,normal SB mucosa images,and invalid images.Then,the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding,and the location of the lesion was marked.We constructed training and testing sets and compared model-assisted reading with physician reading.RESULTS The accuracy of the model constructed in this study reached 98.96%,which was higher than the accuracy of other systems using only a single module.The sensitivity,specificity,and accuracy of the model-assisted reading detection of all images were 99.17%,99.92%,and 99.86%,which were significantly higher than those of the endoscopists’diagnoses.The image processing time of the model was 48 ms/image,and the image processing time of the physicians was 0.40±0.24 s/image(P<0.001).CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images,which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.

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.

YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.

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.

An Optimized Approach to Deep Learning for Botnet Detection and Classification for Cybersecurity in Internet of Things Environment

The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS attacks.The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices.Anomaly detection models evaluate transmission patterns,network traffic,and device behaviour to detect deviations from usual activities.Machine learning(ML)techniques detect patterns signalling botnet activity,namely sudden traffic increase,unusual command and control patterns,or irregular device behaviour.In addition,intrusion detection systems(IDSs)and signature-based techniques are applied to recognize known malware signatures related to botnets.Various ML and deep learning(DL)techniques have been developed to detect botnet attacks in IoT systems.To overcome security issues in an IoT environment,this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classification(GTODL-BADC)technique.The GTODL-BADC technique follows feature selection(FS)with optimal DL-based classification for accomplishing security in an IoT environment.For data preprocessing,the min-max data normalization approach is primarily used.The GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature subsets.Moreover,the multi-head attention-based long short-term memory(MHA-LSTM)technique was applied for botnet detection.Finally,the tree seed algorithm(TSA)was used to select the optimum hyperparameter for the MHA-LSTM method.The experimental validation of the GTODL-BADC technique can be tested on a benchmark dataset.The simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.

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.

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.

Automated Vulnerability Detection of Blockchain Smart Contacts Based on BERT Artificial Intelligent Model

The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.Despite the benefits of virtual currency,vulnerabilities in smart contracts have resulted in substantial losses to users.While researchers have identified these vulnerabilities and developed tools for detecting them,the accuracy of these tools is still far from satisfactory,with high false positive and false negative rates.In this paper,we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model,which can quickly and effectively process and detect smart contracts.More specifically,we preprocess and make symbol substitution in the contract,which can make the pre-training model better obtain contract features.We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools,demonstrating its superior accuracy.

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.