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-EPSP...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.展开更多
The aim of this work is to detect electrogenerated hydroxyl radicals and chlorine by simple and less expensive methods. Preparative electrolyses of perchloric acid (HClO4) and sodium chloride (NaCl) were performed on ...The aim of this work is to detect electrogenerated hydroxyl radicals and chlorine by simple and less expensive methods. Preparative electrolyses of perchloric acid (HClO4) and sodium chloride (NaCl) were performed on a boron-doped diamond (BDD) electrode. The hydroxyl radicals were quantified indirectly by assaying the samples from the HClO4 (0.1 M) electrolysis with a 10−4 M potassium permanganate solution. The investigations showed that the amount of hydroxyl radicals depends on the concentration of HClO4 and the current density. As for chlorine, a qualitative determination was carried out. A mixture of the electrolyte solution of HClO4 (0.1 M) + NaI (0.2 M) + 2 mL of hexane, taken in this order, leads to a purplish-pink coloration attesting to the presence of Cl2. The same test was carried out with NaBr and NaI giving pale and very pale pink colourations, respectively, showing that the intensity of the colouration depends on the strength of the oxidant present. In addition, oxidants were detected during the electrooxidation of metronidazole (MNZ). The results showed the participation of electrogenerated hydroxyl radicals. The generation of chlorine has also been proven. Furthermore, the degradation leads to a chemical oxygen demand (COD) removal rate of 83.48% and the process is diffusion-controlled.展开更多
Introduction: Coagulase-negative staphylococci (CoNS) are currently recognized as genuine pathogens. However, little is known about the resistance and virulence genes that explain their pathogenicity in hospitals in C...Introduction: Coagulase-negative staphylococci (CoNS) are currently recognized as genuine pathogens. However, little is known about the resistance and virulence genes that explain their pathogenicity in hospitals in Cte d'Ivoire. The aim of this study was to contribute to the genotypic identification of resistance and virulence genes in CoNS isolated from blood cultures at the University Teaching Hospital (CHU) of Bouak, in order to improve patient management. Material and Methods: This was a descriptive study conducted from September to December 2023. The CoNS isolates studied came from the collection of strains isolated from blood cultures of febrile patients hospitalized or attending consultations at the CHU of Bouak. The strains were analyzed using conventional simplex PCR. Results: Of the 45 isolates analyzed, 46.7% carried both the aacA-aphD and tetK genes and 40% carried the mecA gene. With regard to virulence genes, only the LukS-PV gene was observed in S. epidermidis and S. haemolyticus isolates. Conclusion: The high prevalence of CoNS isolates carrying the mecA gene and the presence of virulence genes observed in this study give cause for concern in hospitals. It is important to develop comprehensive surveillance strategies against nosocomial and multi-resistant infections at the CHU of Bouak.展开更多
The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems(IDSs).IDSs have become a research hotspot and have seen remarkable performance improvements.Gener...The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems(IDSs).IDSs have become a research hotspot and have seen remarkable performance improvements.Generative adversarial networks(GANs)have also garnered increasing research interest recently due to their remarkable ability to generate data.This paper investigates the application of(GANs)in(IDS)and explores their current use within this research field.We delve into the adoption of GANs within signature-based,anomaly-based,and hybrid IDSs,focusing on their objectives,methodologies,and advantages.Overall,GANs have been widely employed,mainly focused on solving the class imbalance issue by generating realistic attack samples.While GANs have shown significant potential in addressing the class imbalance issue,there are still open opportunities and challenges to be addressed.Little attention has been paid to their applicability in distributed and decentralized domains,such as IoT networks.Efficiency and scalability have been mostly overlooked,and thus,future works must aim at addressing these gaps.展开更多
Electronic waste(e-waste)and diabetes are global challenges to modern societies.However,solving these two challenges together has been challenging until now.Herein,we propose a laser-induced transfer method to fabrica...Electronic waste(e-waste)and diabetes are global challenges to modern societies.However,solving these two challenges together has been challenging until now.Herein,we propose a laser-induced transfer method to fabricate portable glucose sensors by recycling copper from e-waste.We bring up a laser-induced full-automatic fabrication method for synthesizing continuous heterogeneous Cu_(x)O(h-Cu_(x)O)nano-skeletons electrode for glucose sensing,offering rapid(<1 min),clean,air-compatible,and continuous fabrication,applicable to a wide range of Cu-containing substrates.Leveraging this approach,h-Cu_(x)O nanoskeletons,with an inner core predominantly composed of Cu_(2)O with lower oxygen content,juxtaposed with an outer layer rich in amorphous Cu_(x)O(a-Cu_(x)O)with higher oxygen content,are derived from discarded printed circuit boards.When employed in glucose detection,the h-Cu_(x)O nano-skeletons undergo a structural evolution process,transitioning into rigid Cu_(2)O@CuO nano-skeletons prompted by electrochemical activation.This transformation yields exceptional glucose-sensing performance(sensitivity:9.893 mA mM^(-1) cm^(-2);detection limit:0.34μM),outperforming most previously reported glucose sensors.Density functional theory analysis elucidates that the heterogeneous structure facilitates gluconolactone desorption.This glucose detection device has also been downsized to optimize its scalability and portability for convenient integration into people’s everyday lives.展开更多
The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.H...The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.However,the efficacy of deep learning models hinges upon a substantial abundance of flaw samples.The existing research on X-ray image augmentation for flaw detection suffers from shortcomings such as poor diversity of flaw samples and low reliability of quality evaluation.To this end,a novel approach was put forward,which involves the creation of the Interpolation-Deep Convolutional Generative Adversarial Network(I-DCGAN)for flaw detection image generation and a comprehensive evaluation algorithm named TOPSIS-IFP.I-DCGAN enables the generation of high-resolution,diverse simulated images with multiple appearances,achieving an improvement in sample diversity and quality while maintaining a relatively lower computational complexity.TOPSIS-IFP facilitates multi-dimensional quality evaluation,including aspects such as diversity,authenticity,image distribution difference,and image distortion degree.The results indicate that the X-ray radiographic images of magnesium and aluminum alloy castings achieve optimal performance when trained up to the 800th and 600th epochs,respectively.The TOPSIS-IFP value reaches 78.7%and 73.8%similarity to the ideal solution,respectively.Compared to single index evaluation,the TOPSIS-IFP algorithm achieves higher-quality simulated images at the optimal training epoch.This approach successfully mitigates the issue of unreliable quality associated with single index evaluation.The image generation and comprehensive quality evaluation method developed in this paper provides a novel approach for image augmentation in flaw recognition,holding significant importance for enhancing the robustness of subsequent flaw recognition networks.展开更多
Objective:Autosomal recessive bestrophinopathy(ARB),a retinal degenerative disease,is characterized by central visual loss,yellowish multifocal diffuse subretinal deposits,and a dramatic decrease in the light peak on ...Objective:Autosomal recessive bestrophinopathy(ARB),a retinal degenerative disease,is characterized by central visual loss,yellowish multifocal diffuse subretinal deposits,and a dramatic decrease in the light peak on electrooculogram.The potential pathogenic mechanism involves mutations in the BEST1 gene,which encodes Ca2+-activated Cl−channels in the retinal pigment epithelium(RPE),resulting in degeneration of RPE and photoreceptor.In this study,the complete clinical characteristics of two Chinese ARB families were summarized.Methods:Pacific Biosciences(PacBio)single-molecule real-time(SMRT)sequencing was performed on the probands to screen for disease-causing gene mutations,and Sanger sequencing was applied to validate variants in the patients and their family members.Results:Two novel mutations,c.202T>C(chr11:61722628,p.Y68H)and c.867+97G>A,in the BEST1 gene were identified in the two Chinese ARB families.The novel missense mutation BEST1 c.202T>C(p.Y68H)resulted in the substitution of tyrosine with histidine in the N-terminal region of transmembrane domain 2 of bestrophin-1.Another novel variant,BEST1 c.867+97G>A(chr11:61725867),located in intron 7,might be considered a regulatory variant that changes allele-specific binding affinity based on motifs of important transcriptional regulators.Conclusion:Our findings represent the first use of third-generation sequencing(TGS)to identify novel BEST1 mutations in patients with ARB,indicating that TGS can be a more accurate and efficient tool for identifying mutations in specific genes.The novel variants identified further broaden the mutation spectrum of BEST1 in the Chinese population.展开更多
Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two line...Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates.Relying on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,respectively.Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level.Numerical results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.展开更多
In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,t...In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.展开更多
Fault diagnosis of traction systems is important for the safety operation of high-speed trains.Long-term operation of the trains will degrade the performance of systems,which decreases the fault detection accuracy.To ...Fault diagnosis of traction systems is important for the safety operation of high-speed trains.Long-term operation of the trains will degrade the performance of systems,which decreases the fault detection accuracy.To solve this problem,this paper proposes a fault detection method developed by a Generalized Autoencoder(GAE)for systems with performance degradation.The advantage of this method is that it can accurately detect faults when the traction system of high-speed trains is affected by performance degradation.Regardless of the probability distribution,it can handle any data,and the GAE has extremely high sensitivity in anomaly detection.Finally,the effectiveness of this method is verified through the Traction Drive Control System(TDCS)platform.At different performance degradation levels,our method’s experimental results are superior to traditional methods.展开更多
Overlooking the issue of false alarm suppression in heterogeneous change detection leads to inferior detection per-formance.This paper proposes a method to handle false alarms in heterogeneous change detection.A light...Overlooking the issue of false alarm suppression in heterogeneous change detection leads to inferior detection per-formance.This paper proposes a method to handle false alarms in heterogeneous change detection.A lightweight network of two channels is bulit based on the combination of convolutional neural network(CNN)and graph convolutional network(GCN).CNNs learn feature difference maps of multitemporal images,and attention modules adaptively fuse CNN-based and graph-based features for different scales.GCNs with a new kernel filter adaptively distinguish between nodes with the same and those with different labels,generating change maps.Experimental evaluation on two datasets validates the efficacy of the pro-posed method in addressing false alarms.展开更多
In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial earl...In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.展开更多
Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence i...Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence is to identify illustrations that deviate significantly from the main distribution of data or that differ from known cases. Anomalous nodes in node-attributed networks can be identified with greater precision if both graph and node attributes are taken into account. Almost all of the studies in this area focus on supervised techniques for spotting outliers. While supervised algorithms for anomaly detection work well in theory, they cannot be applied to real-world applications owing to a lack of labelled data. Considering the possible data distribution, our model employs a dual variational autoencoder (VAE), while a generative adversarial network (GAN) assures that the model is robust to adversarial training. The dual VAEs are used in another capacity: as a fake-node generator. Adversarial training is used to ensure that our latent codes have a Gaussian or uniform distribution. To provide a fair presentation of the graph, the discriminator instructs the generator to generate latent variables with distributions that are more consistent with the actual distribution of the data. Once the model has been learned, the discriminator is used for anomaly detection via reconstruction loss which has been trained to distinguish between the normal and artificial distributions of data. First, using a dual VAE, our model simultaneously captures cross-modality interactions between topological structure and node characteristics and overcomes the problem of unlabeled anomalies, allowing us to better understand the network sparsity and nonlinearity. Second, the proposed model considers the regularization of the latent codes while solving the issue of unregularized embedding techniques that can quickly lead to unsatisfactory representation. Finally, we use the discriminator reconstruction loss for anomaly detection as the discriminator is well-trained to separate the normal and generated data distributions because reconstruction-based loss does not include the adversarial component. Experiments conducted on attributed networks demonstrate the effectiveness of the proposed model and show that it greatly surpasses the previous methods. The area under the curve scores of our proposed model for the BlogCatalog, Flickr, and Enron datasets are 0.83680, 0.82020, and 0.71180, respectively, proving the effectiveness of the proposed model. The result of the proposed model on the Enron dataset is slightly worse than other models;we attribute this to the dataset’s low dimensionality as the most probable explanation.展开更多
Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis,treatment,and tracking of complex conditions,including neurodegenerative disorders such as Alzheimer’s and ...Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis,treatment,and tracking of complex conditions,including neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases.While no definitive methods of diagnosis or treatment exist for either disease,researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers.Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment.However,such techniques require further development aimed at improving transparency,adaptability,and reproducibility.In this review,we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer’s and Parkinson’s diseases.展开更多
Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to cr...Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.展开更多
Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,maki...Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.展开更多
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...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.展开更多
BACKGROUND Validation of the reference gene(RG)stability during experimental analyses is essential for correct quantitative real-time polymerase chain reaction(RT-qPCR)data normalisation.Commonly,in an unreliable way,...BACKGROUND Validation of the reference gene(RG)stability during experimental analyses is essential for correct quantitative real-time polymerase chain reaction(RT-qPCR)data normalisation.Commonly,in an unreliable way,several studies use genes involved in essential cellular functions[glyceraldehyde-3-phosphate dehydro-genase(GAPDH),18S rRNA,andβ-actin]without paying attention to whether they are suitable for such experimental conditions or the reason for choosing such genes.Furthermore,such studies use only one gene when Minimum Information for Publication of Quantitative Real-Time PCR Experiments guidelines recom-mend two or more genes.It impacts the credibility of these studies and causes dis-tortions in the gene expression findings.For tissue engineering,the accuracy of gene expression drives the best experimental or therapeutical approaches.We cultivated DPSCs under two conditions:Undifferentiated and osteogenic dif-ferentiation,both for 35 d.We evaluated the gene expression of 10 candidates for RGs[ribosomal protein,large,P0(RPLP0),TATA-binding protein(TBP),GAPDH,actin beta(ACTB),tubulin(TUB),aminolevulinic acid synthase 1(ALAS1),tyro-sine 3-monooxygenase/tryptophan 5-monooxygenase activation protein,zeta(YWHAZ),eukaryotic translational elongation factor 1 alpha(EF1a),succinate dehydrogenase complex,subunit A,flavoprotein(SDHA),and beta-2-micro-globulin(B2M)]every 7 d(1,7,14,21,28,and 35 d)by RT-qPCR.The data were analysed by the four main algorithms,ΔCt method,geNorm,NormFinder,and BestKeeper and ranked by the RefFinder method.We subdivided the samples into eight subgroups.RESULTS All of the data sets from clonogenic and osteogenic samples were analysed using the RefFinder algorithm.The final ranking showed RPLP0/TBP as the two most stable RGs and TUB/B2M as the two least stable RGs.Either theΔCt method or NormFinder analysis showed TBP/RPLP0 as the two most stable genes.However,geNorm analysis showed RPLP0/EF1αin the first place.These algorithms’two least stable RGs were B2M/GAPDH.For BestKeeper,ALAS1 was ranked as the most stable RG,and SDHA as the least stable RG.The pair RPLP0/TBP was detected in most subgroups as the most stable RGs,following the RefFinfer ranking.CONCLUSION For the first time,we show that RPLP0/TBP are the most stable RGs,whereas TUB/B2M are unstable RGs for long-term osteogenic differentiation of human DPSCs in traditional monolayers.展开更多
Grain weight is one of the key components of wheat(Triticum aestivum L.)yield.Genetic manipulation of grain weight is an efficient approach for improving yield potential in breeding programs.A recombinant inbred line(...Grain weight is one of the key components of wheat(Triticum aestivum L.)yield.Genetic manipulation of grain weight is an efficient approach for improving yield potential in breeding programs.A recombinant inbred line(RIL)population derived from a cross between W7268 and Chuanyu 12(CY12)was employed to detect quantitative trait loci(QTLs)for thousand-grain weight(TGW),grain length(GL),grain width(GW),and the ratio of grain length to width(GLW)in six environments.Seven major QTLs,QGl.cib-2D,QGw.cib-2D,QGw.cib-3B,QGw.cib-4B.1,QGlw.cib-2D.1,QTgw.cib-2D.1 and QTgw.cib-3B.1,were consistently identified in at least four environments and the best linear unbiased estimation(BLUE)datasets,and they explained 2.61 to 34.85%of the phenotypic variance.Significant interactions were detected between the two major TGW QTLs and three major GW loci.In addition,QTgw.cib-3B.1 and QGw.cib-3B were co-located,and the improved TGW at this locus was contributed by GW.Unlike other loci,QTgw.cib-3B.1/QGw.cib-3B had no effect on grain number per spike(GNS).They were further validated in advanced lines using Kompetitive Allele Specific PCR(KASP)markers,and a comparison analysis indicated that QTgw.cib-3B.1/QGw.cib-3B is likely a novel locus.Six haplotypes were identified in the region of this QTL and their distribution frequencies varied between the landraces and cultivars.According to gene annotation,spatial expression patterns,ortholog analysis and sequence variation,the candidate gene of QTgw.cib-3B.1/QGw.cib-3B was predicted.Collectively,the major QTLs and KASP markers reported here provide valuable information for elucidating the genetic architecture of grain weight and for molecular marker-assisted breeding in grain yield improvement.展开更多
基金supported by the Scientific and Innovative Action Plan of Shanghai(21N31900800)Shanghai Rising-Star Program(23QB1403500)+4 种基金the Shanghai Sailing Program(20YF1443000)Shanghai Science and Technology Commission,the Belt and Road Project(20310750500)Talent Project of SAAS(2023-2025)Runup Plan of SAAS(ZP22211)the SAAS Program for Excellent Research Team(2022(B-16))。
文摘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.
文摘The aim of this work is to detect electrogenerated hydroxyl radicals and chlorine by simple and less expensive methods. Preparative electrolyses of perchloric acid (HClO4) and sodium chloride (NaCl) were performed on a boron-doped diamond (BDD) electrode. The hydroxyl radicals were quantified indirectly by assaying the samples from the HClO4 (0.1 M) electrolysis with a 10−4 M potassium permanganate solution. The investigations showed that the amount of hydroxyl radicals depends on the concentration of HClO4 and the current density. As for chlorine, a qualitative determination was carried out. A mixture of the electrolyte solution of HClO4 (0.1 M) + NaI (0.2 M) + 2 mL of hexane, taken in this order, leads to a purplish-pink coloration attesting to the presence of Cl2. The same test was carried out with NaBr and NaI giving pale and very pale pink colourations, respectively, showing that the intensity of the colouration depends on the strength of the oxidant present. In addition, oxidants were detected during the electrooxidation of metronidazole (MNZ). The results showed the participation of electrogenerated hydroxyl radicals. The generation of chlorine has also been proven. Furthermore, the degradation leads to a chemical oxygen demand (COD) removal rate of 83.48% and the process is diffusion-controlled.
文摘Introduction: Coagulase-negative staphylococci (CoNS) are currently recognized as genuine pathogens. However, little is known about the resistance and virulence genes that explain their pathogenicity in hospitals in Cte d'Ivoire. The aim of this study was to contribute to the genotypic identification of resistance and virulence genes in CoNS isolated from blood cultures at the University Teaching Hospital (CHU) of Bouak, in order to improve patient management. Material and Methods: This was a descriptive study conducted from September to December 2023. The CoNS isolates studied came from the collection of strains isolated from blood cultures of febrile patients hospitalized or attending consultations at the CHU of Bouak. The strains were analyzed using conventional simplex PCR. Results: Of the 45 isolates analyzed, 46.7% carried both the aacA-aphD and tetK genes and 40% carried the mecA gene. With regard to virulence genes, only the LukS-PV gene was observed in S. epidermidis and S. haemolyticus isolates. Conclusion: The high prevalence of CoNS isolates carrying the mecA gene and the presence of virulence genes observed in this study give cause for concern in hospitals. It is important to develop comprehensive surveillance strategies against nosocomial and multi-resistant infections at the CHU of Bouak.
文摘The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems(IDSs).IDSs have become a research hotspot and have seen remarkable performance improvements.Generative adversarial networks(GANs)have also garnered increasing research interest recently due to their remarkable ability to generate data.This paper investigates the application of(GANs)in(IDS)and explores their current use within this research field.We delve into the adoption of GANs within signature-based,anomaly-based,and hybrid IDSs,focusing on their objectives,methodologies,and advantages.Overall,GANs have been widely employed,mainly focused on solving the class imbalance issue by generating realistic attack samples.While GANs have shown significant potential in addressing the class imbalance issue,there are still open opportunities and challenges to be addressed.Little attention has been paid to their applicability in distributed and decentralized domains,such as IoT networks.Efficiency and scalability have been mostly overlooked,and thus,future works must aim at addressing these gaps.
基金funded by the Hong Kong Research Grants Council(25201620/C6001-22Y)the Hong Kong Innovation Technology Commission(ITC)under project No.MHP/060/21support of the State Key Laboratory of Advanced Displays and Optoelectronics Technologies at HKUST.
文摘Electronic waste(e-waste)and diabetes are global challenges to modern societies.However,solving these two challenges together has been challenging until now.Herein,we propose a laser-induced transfer method to fabricate portable glucose sensors by recycling copper from e-waste.We bring up a laser-induced full-automatic fabrication method for synthesizing continuous heterogeneous Cu_(x)O(h-Cu_(x)O)nano-skeletons electrode for glucose sensing,offering rapid(<1 min),clean,air-compatible,and continuous fabrication,applicable to a wide range of Cu-containing substrates.Leveraging this approach,h-Cu_(x)O nanoskeletons,with an inner core predominantly composed of Cu_(2)O with lower oxygen content,juxtaposed with an outer layer rich in amorphous Cu_(x)O(a-Cu_(x)O)with higher oxygen content,are derived from discarded printed circuit boards.When employed in glucose detection,the h-Cu_(x)O nano-skeletons undergo a structural evolution process,transitioning into rigid Cu_(2)O@CuO nano-skeletons prompted by electrochemical activation.This transformation yields exceptional glucose-sensing performance(sensitivity:9.893 mA mM^(-1) cm^(-2);detection limit:0.34μM),outperforming most previously reported glucose sensors.Density functional theory analysis elucidates that the heterogeneous structure facilitates gluconolactone desorption.This glucose detection device has also been downsized to optimize its scalability and portability for convenient integration into people’s everyday lives.
基金funded by the National Key R&D Program of China(2020YFB1710100)the National Natural Science Foundation of China(Nos.52275337,52090042,51905188).
文摘The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.However,the efficacy of deep learning models hinges upon a substantial abundance of flaw samples.The existing research on X-ray image augmentation for flaw detection suffers from shortcomings such as poor diversity of flaw samples and low reliability of quality evaluation.To this end,a novel approach was put forward,which involves the creation of the Interpolation-Deep Convolutional Generative Adversarial Network(I-DCGAN)for flaw detection image generation and a comprehensive evaluation algorithm named TOPSIS-IFP.I-DCGAN enables the generation of high-resolution,diverse simulated images with multiple appearances,achieving an improvement in sample diversity and quality while maintaining a relatively lower computational complexity.TOPSIS-IFP facilitates multi-dimensional quality evaluation,including aspects such as diversity,authenticity,image distribution difference,and image distortion degree.The results indicate that the X-ray radiographic images of magnesium and aluminum alloy castings achieve optimal performance when trained up to the 800th and 600th epochs,respectively.The TOPSIS-IFP value reaches 78.7%and 73.8%similarity to the ideal solution,respectively.Compared to single index evaluation,the TOPSIS-IFP algorithm achieves higher-quality simulated images at the optimal training epoch.This approach successfully mitigates the issue of unreliable quality associated with single index evaluation.The image generation and comprehensive quality evaluation method developed in this paper provides a novel approach for image augmentation in flaw recognition,holding significant importance for enhancing the robustness of subsequent flaw recognition networks.
文摘Objective:Autosomal recessive bestrophinopathy(ARB),a retinal degenerative disease,is characterized by central visual loss,yellowish multifocal diffuse subretinal deposits,and a dramatic decrease in the light peak on electrooculogram.The potential pathogenic mechanism involves mutations in the BEST1 gene,which encodes Ca2+-activated Cl−channels in the retinal pigment epithelium(RPE),resulting in degeneration of RPE and photoreceptor.In this study,the complete clinical characteristics of two Chinese ARB families were summarized.Methods:Pacific Biosciences(PacBio)single-molecule real-time(SMRT)sequencing was performed on the probands to screen for disease-causing gene mutations,and Sanger sequencing was applied to validate variants in the patients and their family members.Results:Two novel mutations,c.202T>C(chr11:61722628,p.Y68H)and c.867+97G>A,in the BEST1 gene were identified in the two Chinese ARB families.The novel missense mutation BEST1 c.202T>C(p.Y68H)resulted in the substitution of tyrosine with histidine in the N-terminal region of transmembrane domain 2 of bestrophin-1.Another novel variant,BEST1 c.867+97G>A(chr11:61725867),located in intron 7,might be considered a regulatory variant that changes allele-specific binding affinity based on motifs of important transcriptional regulators.Conclusion:Our findings represent the first use of third-generation sequencing(TGS)to identify novel BEST1 mutations in patients with ARB,indicating that TGS can be a more accurate and efficient tool for identifying mutations in specific genes.The novel variants identified further broaden the mutation spectrum of BEST1 in the Chinese population.
基金supported by the National Natural Science Foundation of China(61971432)Taishan Scholar Project of Shandong Province(tsqn201909156)the Outstanding Youth Innovation Team Program of University in Shandong Province(2019KJN031)。
文摘Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates.Relying on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,respectively.Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level.Numerical results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.
基金Supported by the National Natural Science Foundation of China(No.62203390)the Science and Technology Project of China TobaccoZhejiang Industrial Co.,Ltd(No.ZJZY2022E004)。
文摘In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.
基金supported by the National Natural Science Foundation of China(Grant Nos.U20A20186 and 62372063).
文摘Fault diagnosis of traction systems is important for the safety operation of high-speed trains.Long-term operation of the trains will degrade the performance of systems,which decreases the fault detection accuracy.To solve this problem,this paper proposes a fault detection method developed by a Generalized Autoencoder(GAE)for systems with performance degradation.The advantage of this method is that it can accurately detect faults when the traction system of high-speed trains is affected by performance degradation.Regardless of the probability distribution,it can handle any data,and the GAE has extremely high sensitivity in anomaly detection.Finally,the effectiveness of this method is verified through the Traction Drive Control System(TDCS)platform.At different performance degradation levels,our method’s experimental results are superior to traditional methods.
基金This work was supported by the Natural Science Foundation of Heilongjiang Province(LH2022F049).
文摘Overlooking the issue of false alarm suppression in heterogeneous change detection leads to inferior detection per-formance.This paper proposes a method to handle false alarms in heterogeneous change detection.A lightweight network of two channels is bulit based on the combination of convolutional neural network(CNN)and graph convolutional network(GCN).CNNs learn feature difference maps of multitemporal images,and attention modules adaptively fuse CNN-based and graph-based features for different scales.GCNs with a new kernel filter adaptively distinguish between nodes with the same and those with different labels,generating change maps.Experimental evaluation on two datasets validates the efficacy of the pro-posed method in addressing false alarms.
文摘In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.
文摘Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence is to identify illustrations that deviate significantly from the main distribution of data or that differ from known cases. Anomalous nodes in node-attributed networks can be identified with greater precision if both graph and node attributes are taken into account. Almost all of the studies in this area focus on supervised techniques for spotting outliers. While supervised algorithms for anomaly detection work well in theory, they cannot be applied to real-world applications owing to a lack of labelled data. Considering the possible data distribution, our model employs a dual variational autoencoder (VAE), while a generative adversarial network (GAN) assures that the model is robust to adversarial training. The dual VAEs are used in another capacity: as a fake-node generator. Adversarial training is used to ensure that our latent codes have a Gaussian or uniform distribution. To provide a fair presentation of the graph, the discriminator instructs the generator to generate latent variables with distributions that are more consistent with the actual distribution of the data. Once the model has been learned, the discriminator is used for anomaly detection via reconstruction loss which has been trained to distinguish between the normal and artificial distributions of data. First, using a dual VAE, our model simultaneously captures cross-modality interactions between topological structure and node characteristics and overcomes the problem of unlabeled anomalies, allowing us to better understand the network sparsity and nonlinearity. Second, the proposed model considers the regularization of the latent codes while solving the issue of unregularized embedding techniques that can quickly lead to unsatisfactory representation. Finally, we use the discriminator reconstruction loss for anomaly detection as the discriminator is well-trained to separate the normal and generated data distributions because reconstruction-based loss does not include the adversarial component. Experiments conducted on attributed networks demonstrate the effectiveness of the proposed model and show that it greatly surpasses the previous methods. The area under the curve scores of our proposed model for the BlogCatalog, Flickr, and Enron datasets are 0.83680, 0.82020, and 0.71180, respectively, proving the effectiveness of the proposed model. The result of the proposed model on the Enron dataset is slightly worse than other models;we attribute this to the dataset’s low dimensionality as the most probable explanation.
文摘Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis,treatment,and tracking of complex conditions,including neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases.While no definitive methods of diagnosis or treatment exist for either disease,researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers.Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment.However,such techniques require further development aimed at improving transparency,adaptability,and reproducibility.In this review,we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer’s and Parkinson’s diseases.
基金supported by the National Key R&D Program of China(Nos.2022YFB3104103,and 2019QY1406)the National Natural Science Foundation of China(Nos.61732022,61732004,61672020,and 62072131).
文摘Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.
基金National Key Research and Development Program of China(Nos.2022YFB4700600 and 2022YFB4700605)National Natural Science Foundation of China(Nos.61771123 and 62171116)+1 种基金Fundamental Research Funds for the Central UniversitiesGraduate Student Innovation Fund of Donghua University,China(No.CUSF-DH-D-2022044)。
文摘Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.
基金supported in part by the National Key R&D Program of China(No.2022YFB3904503)National Natural Science Foundation of China(No.62172418)。
文摘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.
基金Supported by São Paulo Research Foundation(FAPESP),No.2010/08918-9 and 2020/11564-6the KBSP Young Investigator Fellowship,No.2011/00204-0+2 种基金the DBF Fellowship,No.2019/27492-7the LMG Fellowship,No.2014/01395-1the CFB Fellowship,No.2014/14278-3.
文摘BACKGROUND Validation of the reference gene(RG)stability during experimental analyses is essential for correct quantitative real-time polymerase chain reaction(RT-qPCR)data normalisation.Commonly,in an unreliable way,several studies use genes involved in essential cellular functions[glyceraldehyde-3-phosphate dehydro-genase(GAPDH),18S rRNA,andβ-actin]without paying attention to whether they are suitable for such experimental conditions or the reason for choosing such genes.Furthermore,such studies use only one gene when Minimum Information for Publication of Quantitative Real-Time PCR Experiments guidelines recom-mend two or more genes.It impacts the credibility of these studies and causes dis-tortions in the gene expression findings.For tissue engineering,the accuracy of gene expression drives the best experimental or therapeutical approaches.We cultivated DPSCs under two conditions:Undifferentiated and osteogenic dif-ferentiation,both for 35 d.We evaluated the gene expression of 10 candidates for RGs[ribosomal protein,large,P0(RPLP0),TATA-binding protein(TBP),GAPDH,actin beta(ACTB),tubulin(TUB),aminolevulinic acid synthase 1(ALAS1),tyro-sine 3-monooxygenase/tryptophan 5-monooxygenase activation protein,zeta(YWHAZ),eukaryotic translational elongation factor 1 alpha(EF1a),succinate dehydrogenase complex,subunit A,flavoprotein(SDHA),and beta-2-micro-globulin(B2M)]every 7 d(1,7,14,21,28,and 35 d)by RT-qPCR.The data were analysed by the four main algorithms,ΔCt method,geNorm,NormFinder,and BestKeeper and ranked by the RefFinder method.We subdivided the samples into eight subgroups.RESULTS All of the data sets from clonogenic and osteogenic samples were analysed using the RefFinder algorithm.The final ranking showed RPLP0/TBP as the two most stable RGs and TUB/B2M as the two least stable RGs.Either theΔCt method or NormFinder analysis showed TBP/RPLP0 as the two most stable genes.However,geNorm analysis showed RPLP0/EF1αin the first place.These algorithms’two least stable RGs were B2M/GAPDH.For BestKeeper,ALAS1 was ranked as the most stable RG,and SDHA as the least stable RG.The pair RPLP0/TBP was detected in most subgroups as the most stable RGs,following the RefFinfer ranking.CONCLUSION For the first time,we show that RPLP0/TBP are the most stable RGs,whereas TUB/B2M are unstable RGs for long-term osteogenic differentiation of human DPSCs in traditional monolayers.
基金supported by the Major Program of National Agricultural Science and Technology of China(NK20220607)the West Light Foundation of the Chinese Academy of Sciences(2022XBZG_XBQNXZ_A_001)the Sichuan Science and Technology Program,China(2022ZDZX0014)。
文摘Grain weight is one of the key components of wheat(Triticum aestivum L.)yield.Genetic manipulation of grain weight is an efficient approach for improving yield potential in breeding programs.A recombinant inbred line(RIL)population derived from a cross between W7268 and Chuanyu 12(CY12)was employed to detect quantitative trait loci(QTLs)for thousand-grain weight(TGW),grain length(GL),grain width(GW),and the ratio of grain length to width(GLW)in six environments.Seven major QTLs,QGl.cib-2D,QGw.cib-2D,QGw.cib-3B,QGw.cib-4B.1,QGlw.cib-2D.1,QTgw.cib-2D.1 and QTgw.cib-3B.1,were consistently identified in at least four environments and the best linear unbiased estimation(BLUE)datasets,and they explained 2.61 to 34.85%of the phenotypic variance.Significant interactions were detected between the two major TGW QTLs and three major GW loci.In addition,QTgw.cib-3B.1 and QGw.cib-3B were co-located,and the improved TGW at this locus was contributed by GW.Unlike other loci,QTgw.cib-3B.1/QGw.cib-3B had no effect on grain number per spike(GNS).They were further validated in advanced lines using Kompetitive Allele Specific PCR(KASP)markers,and a comparison analysis indicated that QTgw.cib-3B.1/QGw.cib-3B is likely a novel locus.Six haplotypes were identified in the region of this QTL and their distribution frequencies varied between the landraces and cultivars.According to gene annotation,spatial expression patterns,ortholog analysis and sequence variation,the candidate gene of QTgw.cib-3B.1/QGw.cib-3B was predicted.Collectively,the major QTLs and KASP markers reported here provide valuable information for elucidating the genetic architecture of grain weight and for molecular marker-assisted breeding in grain yield improvement.
