Objective:To explore a method to solve the issue of interference in fluorescence quantitative PCR non-specific amplification for gene detection.Method:A three-step method was used for amplification,and the quantitativ...Objective:To explore a method to solve the issue of interference in fluorescence quantitative PCR non-specific amplification for gene detection.Method:A three-step method was used for amplification,and the quantitative fluorescence signal collection process was set in the extension stage.Results:Three-step amplification has the advantages of wide application range;improved accuracy;and reduced primer design requirements.Conclusion:The interference of non-specific amplification signals was effectively avoided,the melting curve plotting process was omitted,the reaction time was shortened,and the detection accuracy was improved.展开更多
Objective:To study the refractory factors associated with schizophrenia.Methods: 200 patients with refractory schizophrenia and 200 patients with non-refractory schizophrenia were selected. The CYP series of genes CYP...Objective:To study the refractory factors associated with schizophrenia.Methods: 200 patients with refractory schizophrenia and 200 patients with non-refractory schizophrenia were selected. The CYP series of genes CYP1A2, CYP3A4 and CYP2D6 were detected by drug gene, and the rapid metabolic probabilities of the three genes were compared and analyzed. 200 patients with refractory schizophrenia were randomly divided into two groups: the combined drug treatment group and the single drug treatment group. The results were compared between the treatment of 0W and 4W for drug gene detection, 3 genes fast metabolizing type and BPRS scale. analysis.Results: The rapid metabolizing probability and non-refractory difference of CYP1A2, CYP3A4 and CYP2D6 genes in patients with refractory schizophrenia were significant. The comparison of fast metabotropic probabilities of CYP1A2, CYP3A4 and CYP2D6 genes in patients treated with 4W after treatment There was no significant difference in the single drug treatment group. The BPRS scale score was significantly higher in the drug-treated group than in the single-drug group. After logistic regression analysis, the refractory characteristics of schizophrenia and The CYP series of genes CYP1A2, CYP3A4, and CYP2D6 are rapidly metabolized.Conclusion: CYP series of genes CYP1A2, CYP3A4, CYP2D6 fast metabolites are related factors of refractory schizophrenia, antipsychotic drugs combined with CYP enzyme inhibitor treatment can improve the efficacy.展开更多
BACKGROUND Colorectal cancer(CRC)is one of the most common malignancies worldwide.Given its insidious onset,the condition often already progresses to advanced stage when symptoms occur.Thus,early diagnosis is of great...BACKGROUND Colorectal cancer(CRC)is one of the most common malignancies worldwide.Given its insidious onset,the condition often already progresses to advanced stage when symptoms occur.Thus,early diagnosis is of great significance for timely clinical intervention,efficacy enhancement,and prognostic improvement.Featuring high throughput,fastness,and rich information,next generation sequencing(NGS)can greatly shorten the detection time,which is a widely used detection technique at present.AIM To screen specific genes or gene combinations in fecal DNA that are suitable for diagnosis and prognostic prediction of CRC,and to establish a technological platform for CRC screening,diagnosis,and efficacy monitoring through fecal DNA detection.METHODS NGS was used to sequence the stool DNA of patients with CRC,which were then compared with the genetic testing results of the stool samples of normal controls and patients with benign intestinal disease,as well as the tumor tissues of CRC patients.Specific genes or gene combinations in fecal DNA suitable for diagnosis and prognostic prediction of CRC were screened,and their significances in diagnosing CRC and predicting patients'prognosis were comprehensively evaluated.RESULTS High mutation frequencies of TP53,APC,and KRAS were detected in the stools and tumor tissues of CRC patients prior to surgery.Contrastively,no pathogenic mutations of the above three genes were noted in the postoperative stools,the normal controls,or the benign intestinal disease group.This indicates that tumor-specific DNA was detectable in the preoperative stools of CRC patients.The preoperative fecal expression of tumor-associated genes can reflect the gene mutations in tumor tissues to some extent.Compared to the postoperative stools and the stools in the two control groups,the pathogenic mutation frequencies of TP53 and KRAS were significantly higher for the preoperative stools(χ^(2)=7.328,P<0.05;χ^(2)=4.219,P<0.05),suggesting that fecal TP53 and KRAS genes can be used for CRC screening,diagnosis,and prognostic prediction.No significant difference in the pathogenic mutation frequency of the APC gene was found from the postoperative stools or the two control groups(χ^(2)=0.878,P>0.05),so further analysis with larger sample size is required.Among CRC patients,the pathogenic mutation sites of TP53 occurred in 16 of 27 preoperative stools,with a true positive rate of 59.26%,while the pathogenic mutation sites of KRAS occurred in 10 stools,with a true positive rate of 37.04%.The sensitivity and negative predictive values of the combined genetic testing of TP53 and KRAS were 66.67%(18/27)and 68.97%,respectively,both of which were higher than those of TP53 or KRAS mutation detection alone,suggesting that the combined genetic testing can improve the CRC detection rate.The mutation sites TP53 exon 4 A84G and EGFR exon 20 I821T(mutation start and stop positions were both 7579436 for the former,while 55249164 for the latter)were found in the preoperative stools and tumor tissues.These"undetected"mutation sites may be new types of mutations occurring during the CRC carcinogenesis and progression,which needs to be confirmed through further research.Some mutations of"unknown clinical significance"were found in such genes as TP53,PTEN,KRAS,BRAF,AKT1,and PIK3CA,whose clinical values is worthy of further exploration.CONCLUSION NGS-based fecal genetic testing can be used as a complementary technique for the CRC diagnosis.Fecal TP53 and KRAS can be used as specific genes for the screening,diagnosis,prognostic prediction,and recurrence monitoring of CRC.Moreover,the combined testing of TP53 and KRAS genes can improve the CRC detection rate.展开更多
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
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
Single-cell RNA-seq (scRNA-seq) allows the analysis of gene expression in each cell, which enables the detection of highly variable genes (HVG) that contribute to cell-to-cell variation within a homogeneous cell popul...Single-cell RNA-seq (scRNA-seq) allows the analysis of gene expression in each cell, which enables the detection of highly variable genes (HVG) that contribute to cell-to-cell variation within a homogeneous cell population. HVG detection is necessary for clustering analysis to improve the clustering result. scRNA-seq includes some genes that are expressed with a certain probability in all cells which make the cells indistinguishable. These genes are referred to as background noise. To remove the background noise and select the informative genes for clustering analysis, in this paper, we propose an effective HVG detection method based on principal component analysis (PCA). The proposed method utilizes PCA to evaluate the genes (features) on the sample space. The distortion-free principal components are selected to calculate the distance from the origin to gene as the weight of each gene. The genes that have the greatest distances to the origin are selected for clustering analysis. Experimental results on both synthetic and gene expression datasets show that the proposed method not only removes the background noise to select the informative genes for clustering analysis, but also outperforms the existing HVG detection methods.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
Seed weight is a component of seed yield in rapeseed(Brassica napus L.).Although quantitative trait loci(QTL)for seed weight have been reported in rapeseed,only a few causal quantitative trait genes(QTGs)have been ide...Seed weight is a component of seed yield in rapeseed(Brassica napus L.).Although quantitative trait loci(QTL)for seed weight have been reported in rapeseed,only a few causal quantitative trait genes(QTGs)have been identified,resulting in a limitation in understanding of seed weight regulation.We constructed a gene coexpression network at the early seed developmental stage using transcripts of 20,408 genes in QTL intervals and 1017 rapeseed homologs of known genes from other species.Among the 10 modules in this gene coexpression network,modules 1 and 2 were core modules and contained genes involved in source–flow–sink processes such as synthesis and transportation of fatty acid and protein,and photosynthesis.A hub gene SERINE CARBOXYPEPTIDASE-LIKE 19(SCPL19)was identified by candidate gene association analysis in rapeseed and functionally investigated using Arabidopsis T-DNA mutant and overexpression lines.Our study demonstrates the power of gene coexpression analysis to prioritize candidate genes from large candidate QTG sets and enhances the understanding of molecular mechanism for seed weight at the early developmental stage in rapeseed.展开更多
Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the co...Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge effortlessly.As such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems.To address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.We collectively refer to the convergence of different technology sectors as the internet of blended environment.The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder.An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection accuracy.Furthermore,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational autoencoder.The proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.展开更多
Background: COVID-19 is a disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Epidemiological data indicated that bacterial complications in COVID-19 would decrease clearance rate of the in...Background: COVID-19 is a disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Epidemiological data indicated that bacterial complications in COVID-19 would decrease clearance rate of the infecting agent and increase mortality rate. Macrolides such as Azithromycin are usually administered to COVID-19 patients as palliative treatments. Currently, a considerable number of bacterial strains have developed resistance to various antibiotics, especially macrolides. Resistance is reported to be due to possession of mefA, ermB, and mphA genes by Gram positive and Gram negative bacteria. Therefore, this study determined antibiotic resistance patterns and identify mefA, ermB and mphA macrolide-resistant genes in bacterial pathogens isolated from COVID-19 cases in Ibadan, Nigeria. Methods: 400 Nasopharyngeal samples were collected from symptomatic cases before antibiotic medication;structured questionnaires were administered to collect socio-demographic data of participants. Samples were cultured on Blood, Chocolate, MacConkey and Mannitol salt agar at 37°C for 48 hrs. Bacterial identification was performed using VITEK 2.0 ID cards and API 20E for Gram positive and negative bacteria respectively. Antibiotic Susceptibility Testing was performed using Kirby Bauer disc diffusion methods and VITEK 2.0 AST card kits. DNA of multidrug resistant bacterial isolates was extracted;resistant genes were determined using a polymerase chain reaction with specific primers. Amplified genes were detected using agarose gel electrophoresis. Results: 240 (60%) had bacterial growth and 97 (22.2%) yielded no growth. From the 240 bacterial isolates, 38 (15.83%) were multi-drug resistant including resistance to macrolides (Azithromycin) 20 (52.63%) of which were positive for either mefA or ermB, and none (0.0%) possess mphA gene;14 (36.8%) isolates had mefA gene, 10 (26.3%) isolates carried ermB gene. Conclusion: Multi-drug bacterial resistance including macrolides and quinolones was detected. Only mefA and ermB genes were detected in the bacterial isolates, especially in Gram positive organisms. The detection of mefA and ermB genes in the MDR bacterial isolates raised concern on the use of azithromycin as palliative treatment for COVID-19 symptomatic patients.展开更多
Due to over industrialisation, the environmental pollution problem is becoming increasingly serious, especially in aquatic ecosystems. Compared with traditional physical and chemical detection methods, the use of biol...Due to over industrialisation, the environmental pollution problem is becoming increasingly serious, especially in aquatic ecosystems. Compared with traditional physical and chemical detection methods, the use of biological indicators has become popular. The freshwater planarian Dugesia japonica is distributed extensively in aquatic ecosystems and has been applied to the area of environmental toxicology for its high chemical sensitivity. Moreover, D. japonica also has a powerful regenerative capability in which the injured planarian can regenerate a new brain in 5 days and complete an adult individual remodelling in 14 days. Therefore, it has been used as a new model organism in the field of neuro-regeneration toxicology. In our past study, D. japonica can be used as a biological indicator to detect water pollution. This can provide basic data for the detection of water pollution and provide a warning system in regard to aquatic ecosystems.展开更多
The wireless ad-hoc networks are decentralized networks with a dynamic topology that allows for end-to-end communications via multi-hop routing operations with several nodes collaborating themselves,when the destinati...The wireless ad-hoc networks are decentralized networks with a dynamic topology that allows for end-to-end communications via multi-hop routing operations with several nodes collaborating themselves,when the destination and source nodes are not in range of coverage.Because of its wireless type,it has lot of security concerns than an infrastructure networks.Wormhole attacks are one of the most serious security vulnerabilities in the network layers.It is simple to launch,even if there is no prior network experience.Signatures are the sole thing that preventive measures rely on.Intrusion detection systems(IDS)and other reactive measures detect all types of threats.The majority of IDS employ features from various network layers.One issue is calculating a huge layered features set from an ad-hoc network.This research implements genetic algorithm(GA)-based feature reduction intrusion detection approaches to minimize the quantity of wireless feature sets required to identify worm hole attacks.For attack detection,the reduced feature set was put to a fuzzy logic system(FLS).The performance of proposed model was compared with principal component analysis(PCA)and statistical parametric mapping(SPM).Network performance analysis like delay,packet dropping ratio,normalized overhead,packet delivery ratio,average energy consumption,throughput,and control overhead are evaluated and the IDS performance parameters like detection ratio,accuracy,and false alarm rate are evaluated for validation of the proposed model.The proposed model achieves 95.5%in detection ratio with 96.8%accuracy and produces very less false alarm rate(FAR)of 14%when compared with existing techniques.展开更多
Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial perturbations.The remaining perturbations tend to amplify as they ...Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial perturbations.The remaining perturbations tend to amplify as they propagate through deeper layers of the network,leading to misclassifications.Moreover,image denoising compromises the classification accuracy of original examples.To address these challenges in AE defense through image denoising,this paper proposes a novel AE detection technique.The proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network(CNN)network structures.The used detector model integrates the classification results of different models as the input to the detector and calculates the final output of the detector based on a machine-learning voting algorithm.By analyzing the discrepancy between predictions made by the model on original examples and denoised examples,AEs are detected effectively.This technique reduces computational overhead without modifying the model structure or parameters,effectively avoiding the error amplification caused by denoising.The proposed approach demonstrates excellent detection performance against mainstream AE attacks.Experimental results show outstanding detection performance in well-known AE attacks,including Fast Gradient Sign Method(FGSM),Basic Iteration Method(BIM),DeepFool,and Carlini&Wagner(C&W),achieving a 94%success rate in FGSM detection,while only reducing the accuracy of clean examples by 4%.展开更多
Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication.This study addresses challenges associated with small datasets and class imba...Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication.This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network(GAN)based augmentation on diverse datasets,including iSarcasm,SemEval-18,and Ghosh.This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network(RGAN).The proposed RGAN method works by inverting labels between original and synthetic data during the training process.This inversion of labels provides feedback to the generator for generating high-quality data closely resembling the original distribution.Notably,the proposed RGAN model exhibits performance on par with standard GAN,showcasing its robust efficacy in augmenting text data.The exploration of various datasets highlights the nuanced impact of augmentation on model performance,with cautionary insights into maintaining a delicate balance between synthetic and original data.The methodological framework encompasses comprehensive data pre-processing and GAN-based augmentation,with a meticulous comparison against Natural Language Processing Augmentation(NLPAug)as an alternative augmentation technique.Overall,the F1-score of our proposed technique outperforms that of the synonym replacement augmentation technique using NLPAug.The increase in F1-score in experiments using RGAN ranged from 0.066%to 1.054%,and the use of standard GAN resulted in a 2.88%increase in F1-score.The proposed RGAN model outperformed the NLPAug method and demonstrated comparable performance to standard GAN,emphasizing its efficacy in text data augmentation.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
Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph ...Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.展开更多
Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an...Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an image.However,there is still room for improvement in terms of detection effi-ciency.This study aims to develop a technique for detecting objects in images.To enhance overall detection performance,we considered object detection a two-fold problem,including localization and classification.The proposed method generates class-independent,high-quality,and precise proposals using an agglomerative clustering technique.We then combine these proposals with the relevant input image to train our network on convolutional features.Next,a network refinement module decreases the quantity of generated proposals to produce fewer high-quality candidate proposals.Finally,revised candidate proposals are sent into the network’s detection process to determine the object type.The algorithm’s performance is evaluated using publicly available the PASCAL Visual Object Classes Challenge 2007(VOC2007),VOC2012,and Microsoft Common Objects in Context(MS-COCO)datasets.Using only 100 proposals per image at intersection over union((IoU)=0.5 and 0.7),the proposed method attains Detection Recall(DR)rates of(93.17%and 79.35%)and(69.4%and 58.35%),and Mean Average Best Overlap(MABO)values of(79.25%and 62.65%),for the VOC2007 and MS-COCO datasets,respectively.Besides,it achieves a Mean Average Precision(mAP)of(84.7%and 81.5%)on both VOC datasets.The experiment findings reveal that our method exceeds previous approaches in terms of overall detection performance,proving its effectiveness.展开更多
文摘Objective:To explore a method to solve the issue of interference in fluorescence quantitative PCR non-specific amplification for gene detection.Method:A three-step method was used for amplification,and the quantitative fluorescence signal collection process was set in the extension stage.Results:Three-step amplification has the advantages of wide application range;improved accuracy;and reduced primer design requirements.Conclusion:The interference of non-specific amplification signals was effectively avoided,the melting curve plotting process was omitted,the reaction time was shortened,and the detection accuracy was improved.
基金Hainan Natural Science Foundation(Item Number 20168325).
文摘Objective:To study the refractory factors associated with schizophrenia.Methods: 200 patients with refractory schizophrenia and 200 patients with non-refractory schizophrenia were selected. The CYP series of genes CYP1A2, CYP3A4 and CYP2D6 were detected by drug gene, and the rapid metabolic probabilities of the three genes were compared and analyzed. 200 patients with refractory schizophrenia were randomly divided into two groups: the combined drug treatment group and the single drug treatment group. The results were compared between the treatment of 0W and 4W for drug gene detection, 3 genes fast metabolizing type and BPRS scale. analysis.Results: The rapid metabolizing probability and non-refractory difference of CYP1A2, CYP3A4 and CYP2D6 genes in patients with refractory schizophrenia were significant. The comparison of fast metabotropic probabilities of CYP1A2, CYP3A4 and CYP2D6 genes in patients treated with 4W after treatment There was no significant difference in the single drug treatment group. The BPRS scale score was significantly higher in the drug-treated group than in the single-drug group. After logistic regression analysis, the refractory characteristics of schizophrenia and The CYP series of genes CYP1A2, CYP3A4, and CYP2D6 are rapidly metabolized.Conclusion: CYP series of genes CYP1A2, CYP3A4, CYP2D6 fast metabolites are related factors of refractory schizophrenia, antipsychotic drugs combined with CYP enzyme inhibitor treatment can improve the efficacy.
基金Supported by Taizhou Social Development Plan,No.TS202004Natural Science Foundation of Nanjing University of Chinese Medicine China,No.XZR2020093Taizhou People's Hospital Medical Innovation Team Foundation,No.CXTDA201901.
文摘BACKGROUND Colorectal cancer(CRC)is one of the most common malignancies worldwide.Given its insidious onset,the condition often already progresses to advanced stage when symptoms occur.Thus,early diagnosis is of great significance for timely clinical intervention,efficacy enhancement,and prognostic improvement.Featuring high throughput,fastness,and rich information,next generation sequencing(NGS)can greatly shorten the detection time,which is a widely used detection technique at present.AIM To screen specific genes or gene combinations in fecal DNA that are suitable for diagnosis and prognostic prediction of CRC,and to establish a technological platform for CRC screening,diagnosis,and efficacy monitoring through fecal DNA detection.METHODS NGS was used to sequence the stool DNA of patients with CRC,which were then compared with the genetic testing results of the stool samples of normal controls and patients with benign intestinal disease,as well as the tumor tissues of CRC patients.Specific genes or gene combinations in fecal DNA suitable for diagnosis and prognostic prediction of CRC were screened,and their significances in diagnosing CRC and predicting patients'prognosis were comprehensively evaluated.RESULTS High mutation frequencies of TP53,APC,and KRAS were detected in the stools and tumor tissues of CRC patients prior to surgery.Contrastively,no pathogenic mutations of the above three genes were noted in the postoperative stools,the normal controls,or the benign intestinal disease group.This indicates that tumor-specific DNA was detectable in the preoperative stools of CRC patients.The preoperative fecal expression of tumor-associated genes can reflect the gene mutations in tumor tissues to some extent.Compared to the postoperative stools and the stools in the two control groups,the pathogenic mutation frequencies of TP53 and KRAS were significantly higher for the preoperative stools(χ^(2)=7.328,P<0.05;χ^(2)=4.219,P<0.05),suggesting that fecal TP53 and KRAS genes can be used for CRC screening,diagnosis,and prognostic prediction.No significant difference in the pathogenic mutation frequency of the APC gene was found from the postoperative stools or the two control groups(χ^(2)=0.878,P>0.05),so further analysis with larger sample size is required.Among CRC patients,the pathogenic mutation sites of TP53 occurred in 16 of 27 preoperative stools,with a true positive rate of 59.26%,while the pathogenic mutation sites of KRAS occurred in 10 stools,with a true positive rate of 37.04%.The sensitivity and negative predictive values of the combined genetic testing of TP53 and KRAS were 66.67%(18/27)and 68.97%,respectively,both of which were higher than those of TP53 or KRAS mutation detection alone,suggesting that the combined genetic testing can improve the CRC detection rate.The mutation sites TP53 exon 4 A84G and EGFR exon 20 I821T(mutation start and stop positions were both 7579436 for the former,while 55249164 for the latter)were found in the preoperative stools and tumor tissues.These"undetected"mutation sites may be new types of mutations occurring during the CRC carcinogenesis and progression,which needs to be confirmed through further research.Some mutations of"unknown clinical significance"were found in such genes as TP53,PTEN,KRAS,BRAF,AKT1,and PIK3CA,whose clinical values is worthy of further exploration.CONCLUSION NGS-based fecal genetic testing can be used as a complementary technique for the CRC diagnosis.Fecal TP53 and KRAS can be used as specific genes for the screening,diagnosis,prognostic prediction,and recurrence monitoring of CRC.Moreover,the combined testing of TP53 and KRAS genes can improve the CRC detection rate.
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.
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 in part by the New Energy and Industrial Technology Development Organization (AJD30064) and JST COI-NEXT.
文摘Single-cell RNA-seq (scRNA-seq) allows the analysis of gene expression in each cell, which enables the detection of highly variable genes (HVG) that contribute to cell-to-cell variation within a homogeneous cell population. HVG detection is necessary for clustering analysis to improve the clustering result. scRNA-seq includes some genes that are expressed with a certain probability in all cells which make the cells indistinguishable. These genes are referred to as background noise. To remove the background noise and select the informative genes for clustering analysis, in this paper, we propose an effective HVG detection method based on principal component analysis (PCA). The proposed method utilizes PCA to evaluate the genes (features) on the sample space. The distortion-free principal components are selected to calculate the distance from the origin to gene as the weight of each gene. The genes that have the greatest distances to the origin are selected for clustering analysis. Experimental results on both synthetic and gene expression datasets show that the proposed method not only removes the background noise to select the informative genes for clustering analysis, but also outperforms the existing HVG detection methods.
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provided by the National Natural Science Foundation of China(32201776)the Natural Science Foundation of Chongqing(cstc2019jcyj-bsh X0055,cstc2019jcyj-zdxm X0012)。
文摘Seed weight is a component of seed yield in rapeseed(Brassica napus L.).Although quantitative trait loci(QTL)for seed weight have been reported in rapeseed,only a few causal quantitative trait genes(QTGs)have been identified,resulting in a limitation in understanding of seed weight regulation.We constructed a gene coexpression network at the early seed developmental stage using transcripts of 20,408 genes in QTL intervals and 1017 rapeseed homologs of known genes from other species.Among the 10 modules in this gene coexpression network,modules 1 and 2 were core modules and contained genes involved in source–flow–sink processes such as synthesis and transportation of fatty acid and protein,and photosynthesis.A hub gene SERINE CARBOXYPEPTIDASE-LIKE 19(SCPL19)was identified by candidate gene association analysis in rapeseed and functionally investigated using Arabidopsis T-DNA mutant and overexpression lines.Our study demonstrates the power of gene coexpression analysis to prioritize candidate genes from large candidate QTG sets and enhances the understanding of molecular mechanism for seed weight at the early developmental stage in rapeseed.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2021R1A2C2011391)was supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2021-0-01806Development of security by design and security management technology in smart factory).
文摘Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge effortlessly.As such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems.To address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.We collectively refer to the convergence of different technology sectors as the internet of blended environment.The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder.An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection accuracy.Furthermore,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational autoencoder.The proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.
文摘Background: COVID-19 is a disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Epidemiological data indicated that bacterial complications in COVID-19 would decrease clearance rate of the infecting agent and increase mortality rate. Macrolides such as Azithromycin are usually administered to COVID-19 patients as palliative treatments. Currently, a considerable number of bacterial strains have developed resistance to various antibiotics, especially macrolides. Resistance is reported to be due to possession of mefA, ermB, and mphA genes by Gram positive and Gram negative bacteria. Therefore, this study determined antibiotic resistance patterns and identify mefA, ermB and mphA macrolide-resistant genes in bacterial pathogens isolated from COVID-19 cases in Ibadan, Nigeria. Methods: 400 Nasopharyngeal samples were collected from symptomatic cases before antibiotic medication;structured questionnaires were administered to collect socio-demographic data of participants. Samples were cultured on Blood, Chocolate, MacConkey and Mannitol salt agar at 37°C for 48 hrs. Bacterial identification was performed using VITEK 2.0 ID cards and API 20E for Gram positive and negative bacteria respectively. Antibiotic Susceptibility Testing was performed using Kirby Bauer disc diffusion methods and VITEK 2.0 AST card kits. DNA of multidrug resistant bacterial isolates was extracted;resistant genes were determined using a polymerase chain reaction with specific primers. Amplified genes were detected using agarose gel electrophoresis. Results: 240 (60%) had bacterial growth and 97 (22.2%) yielded no growth. From the 240 bacterial isolates, 38 (15.83%) were multi-drug resistant including resistance to macrolides (Azithromycin) 20 (52.63%) of which were positive for either mefA or ermB, and none (0.0%) possess mphA gene;14 (36.8%) isolates had mefA gene, 10 (26.3%) isolates carried ermB gene. Conclusion: Multi-drug bacterial resistance including macrolides and quinolones was detected. Only mefA and ermB genes were detected in the bacterial isolates, especially in Gram positive organisms. The detection of mefA and ermB genes in the MDR bacterial isolates raised concern on the use of azithromycin as palliative treatment for COVID-19 symptomatic patients.
文摘Due to over industrialisation, the environmental pollution problem is becoming increasingly serious, especially in aquatic ecosystems. Compared with traditional physical and chemical detection methods, the use of biological indicators has become popular. The freshwater planarian Dugesia japonica is distributed extensively in aquatic ecosystems and has been applied to the area of environmental toxicology for its high chemical sensitivity. Moreover, D. japonica also has a powerful regenerative capability in which the injured planarian can regenerate a new brain in 5 days and complete an adult individual remodelling in 14 days. Therefore, it has been used as a new model organism in the field of neuro-regeneration toxicology. In our past study, D. japonica can be used as a biological indicator to detect water pollution. This can provide basic data for the detection of water pollution and provide a warning system in regard to aquatic ecosystems.
文摘The wireless ad-hoc networks are decentralized networks with a dynamic topology that allows for end-to-end communications via multi-hop routing operations with several nodes collaborating themselves,when the destination and source nodes are not in range of coverage.Because of its wireless type,it has lot of security concerns than an infrastructure networks.Wormhole attacks are one of the most serious security vulnerabilities in the network layers.It is simple to launch,even if there is no prior network experience.Signatures are the sole thing that preventive measures rely on.Intrusion detection systems(IDS)and other reactive measures detect all types of threats.The majority of IDS employ features from various network layers.One issue is calculating a huge layered features set from an ad-hoc network.This research implements genetic algorithm(GA)-based feature reduction intrusion detection approaches to minimize the quantity of wireless feature sets required to identify worm hole attacks.For attack detection,the reduced feature set was put to a fuzzy logic system(FLS).The performance of proposed model was compared with principal component analysis(PCA)and statistical parametric mapping(SPM).Network performance analysis like delay,packet dropping ratio,normalized overhead,packet delivery ratio,average energy consumption,throughput,and control overhead are evaluated and the IDS performance parameters like detection ratio,accuracy,and false alarm rate are evaluated for validation of the proposed model.The proposed model achieves 95.5%in detection ratio with 96.8%accuracy and produces very less false alarm rate(FAR)of 14%when compared with existing techniques.
基金supported in part by the Natural Science Foundation of Hunan Province under Grant Nos.2023JJ30316 and 2022JJ2029in part by a project supported by Scientific Research Fund of Hunan Provincial Education Department under Grant No.22A0686+1 种基金in part by the National Natural Science Foundation of China under Grant No.62172058Researchers Supporting Project(No.RSP2023R102)King Saud University,Riyadh,Saudi Arabia.
文摘Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial perturbations.The remaining perturbations tend to amplify as they propagate through deeper layers of the network,leading to misclassifications.Moreover,image denoising compromises the classification accuracy of original examples.To address these challenges in AE defense through image denoising,this paper proposes a novel AE detection technique.The proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network(CNN)network structures.The used detector model integrates the classification results of different models as the input to the detector and calculates the final output of the detector based on a machine-learning voting algorithm.By analyzing the discrepancy between predictions made by the model on original examples and denoised examples,AEs are detected effectively.This technique reduces computational overhead without modifying the model structure or parameters,effectively avoiding the error amplification caused by denoising.The proposed approach demonstrates excellent detection performance against mainstream AE attacks.Experimental results show outstanding detection performance in well-known AE attacks,including Fast Gradient Sign Method(FGSM),Basic Iteration Method(BIM),DeepFool,and Carlini&Wagner(C&W),achieving a 94%success rate in FGSM detection,while only reducing the accuracy of clean examples by 4%.
文摘Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication.This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network(GAN)based augmentation on diverse datasets,including iSarcasm,SemEval-18,and Ghosh.This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network(RGAN).The proposed RGAN method works by inverting labels between original and synthetic data during the training process.This inversion of labels provides feedback to the generator for generating high-quality data closely resembling the original distribution.Notably,the proposed RGAN model exhibits performance on par with standard GAN,showcasing its robust efficacy in augmenting text data.The exploration of various datasets highlights the nuanced impact of augmentation on model performance,with cautionary insights into maintaining a delicate balance between synthetic and original data.The methodological framework encompasses comprehensive data pre-processing and GAN-based augmentation,with a meticulous comparison against Natural Language Processing Augmentation(NLPAug)as an alternative augmentation technique.Overall,the F1-score of our proposed technique outperforms that of the synonym replacement augmentation technique using NLPAug.The increase in F1-score in experiments using RGAN ranged from 0.066%to 1.054%,and the use of standard GAN resulted in a 2.88%increase in F1-score.The proposed RGAN model outperformed the NLPAug method and demonstrated comparable performance to standard GAN,emphasizing its efficacy in text data augmentation.
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This work was supported in part by the National Natural Science Foundation of China under Grants 62273272,62303375 and 61873277in part by the Key Research and Development Program of Shaanxi Province under Grant 2023-YBGY-243+2 种基金in part by the Natural Science Foundation of Shaanxi Province under Grants 2022JQ-606 and 2020-JQ758in part by the Research Plan of Department of Education of Shaanxi Province under Grant 21JK0752in part by the Youth Innovation Team of Shaanxi Universities.
文摘Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.
基金funded by Huanggang Normal University,China,Self-type Project of 2021(No.30120210103)and 2022(No.2042021008).
文摘Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an image.However,there is still room for improvement in terms of detection effi-ciency.This study aims to develop a technique for detecting objects in images.To enhance overall detection performance,we considered object detection a two-fold problem,including localization and classification.The proposed method generates class-independent,high-quality,and precise proposals using an agglomerative clustering technique.We then combine these proposals with the relevant input image to train our network on convolutional features.Next,a network refinement module decreases the quantity of generated proposals to produce fewer high-quality candidate proposals.Finally,revised candidate proposals are sent into the network’s detection process to determine the object type.The algorithm’s performance is evaluated using publicly available the PASCAL Visual Object Classes Challenge 2007(VOC2007),VOC2012,and Microsoft Common Objects in Context(MS-COCO)datasets.Using only 100 proposals per image at intersection over union((IoU)=0.5 and 0.7),the proposed method attains Detection Recall(DR)rates of(93.17%and 79.35%)and(69.4%and 58.35%),and Mean Average Best Overlap(MABO)values of(79.25%and 62.65%),for the VOC2007 and MS-COCO datasets,respectively.Besides,it achieves a Mean Average Precision(mAP)of(84.7%and 81.5%)on both VOC datasets.The experiment findings reveal that our method exceeds previous approaches in terms of overall detection performance,proving its effectiveness.
