49 results about "Virus identification" patented technology

The invention discloses an identification and analysis method for unknown pathogenic microorganisms, which comprises the following steps: filtering out genome sequences with low integrity, pollution and tag errors, and establishing a high-quality virus identification database; constructing a virus host prediction model through a machine learning algorithm; unknown pathogenic microorganisms are identified and analyzed, potential hosts or pathogenicity of the unknown microorganisms are identified, and whether the unknown microorganisms are unknown pathogenic viruses or bacteria or not is further judged. On the basis of metagenome data analysis, potential unknown pathogenic microorganisms in samples of human bodies, environments and the like can be identified more accurately.

The invention discloses a Teensy virus identification and protection method for a mobile hard disk, and relates to the technical field of virus identification and protection.The method effectively identifies high-simulation attack equipment through composite equipment fingerprint construction and combination of hardware ripple features and dynamic protocol response, and breaks through the limitation of traditional single feature detection; meanwhile, the protocol state transition probability model adopts hidden Markov chain real-time analysis, microsecond-level protocol switching abnormity is accurately captured, and the problem of missing detection caused by rough time granularity of an existing scheme is solved; in addition, a window context instruction association mechanism binds a system focus state with an HID operation, and blocks a hidden attack chain formed by legal instruction combination; the hierarchical fusing strategy fuses protocol endpoint control and physical layer isolation, so that the availability of a storage function is ensured, meanwhile, attack blocking is realized, and the situation that normal use is influenced due to full-port forbidding in a traditional scheme is avoided.

The invention relates to the technical field of industrial Internet of Things security, and discloses an industrial information adaptive anti-intrusion supervision system and method based on Internet of Things, and the system comprises an industrial information network intrusion information identification module, an industrial information network intrusion firewall matching module, and an industrial information network intrusion processing feedback module. Comprehensive and accurate matching of industrial information anti-intrusion security processing firewall types is carried out based on big data storage industrial information security processing firewall type parameters in combination with an intelligent identification algorithm and network intrusion virus identification parameters and security level analysis parameters of industrial information; the industrial firewall type is adaptively and scientifically selected based on the industrial information network intrusion virus information and the data security level information, and the industrial Internet of Things industrial information anti-intrusion supervision effect is improved.

The embodiment of the invention provides a virus interception method and device, electronic equipment and a storage medium. The method comprises the steps of starting a target drive program in an operating system in response to a starting instruction of the operating system; acquiring a rule set by the target driver, wherein the rule set comprises a plurality of virus rules; in the process of loading the starting drive program, obtaining first program information of the starting drive program by the target drive program; the target driving program performs virus identification on the starting driving program based on the first program information of the starting driving program and the rule set to obtain a virus identification result; and if the virus identification result indicates that the starting drive program is the virus, intercepting the starting drive program. By the adoption of the method, virus detection can be carried out before the starting drive program is started, interception is carried out when it is detected and confirmed that the starting drive program is the virus program, and therefore the safety of an operating system is improved.

The invention discloses a detection kit for identifying muscovy duck gosling plague virus and duck short beak and dwarf syndrome virus, and belongs to the technical field of virus identification. The invention aims to identify the muscovy duck gosling plague virus and the duck short beak and dwarf syndrome virus. The invention provides a kit for identifying muscovy duck gosling plague virus and duck short beak and dwarf syndrome virus. The detection kit comprises an RPA (recombinase polymerase amplification) isothermal amplification system and a CRISPR (clustered regularly interspaced short palindromic repeats) / Cas12a detection system, the RPA isothermal amplification system comprises an RPA primer pair, and the CRISPR / Cas12a detection system comprises crRNA (Complementary Ribonucleic Acid) and a probe. The invention establishes a detection technology for rapidly distinguishing the muscovy duck gosling plague virus and the duck short beak and dwarf syndrome virus, and provides technical support for taking corresponding prevention and control measures in a targeted manner.

The invention belongs to the technical field of biological detection, and particularly relates to a monoclonal antibody 1E12 for specific detection of AAV6 and application of the monoclonal antibody 1E12. According to the monoclonal antibody 1E12 provided by the invention, a heavy chain variable region and a light chain variable region respectively comprise three complementary determining regions (CDR) corresponding to SEQ ID NO.1-3 and SEQ ID NO.4-6. The CDRs endow the antibody with high specificity and affinity to AAV6, the antibody can accurately recognize AAV6 virus without cross reaction with other serotypes, and the detection sensitivity reaches 4 * 10 < 6 > vg / ml level. The antibody is suitable for development of multiple detection tools such as a double-antibody sandwich ELISA kit, a test strip and an antibody chip, especially has excellent performance in a biotin-avidin amplification system, can be used for virus identification, titer determination and clinical dose evaluation of AAV6, and provides an effective means for standardized and high-throughput detection.

The invention relates to the field of separation and culture of bovine coronavirus, in particular to a separation and identification method of bovine coronavirus. The method comprises the following steps: collecting fresh excrement of the diarrhea calf by utilizing a rectum stimulation method, obtaining excrement sample supernate through PBS dilution, vortex oscillation and centrifugation, and screening positive sample supernate through RT-PCR. Carrying out pancreatin treatment on the positive sample solution, inoculating HRT-18 cells, carrying out infection, adding a pancreatin-containing DMEM maintenance solution, and carrying out freeze thawing treatment, centrifugation and virus collection; carrying out subculture on the collected virus liquid, detecting each generation of virus culture by utilizing RT-PCR (Reverse Transcription-Polymerase Chain Reaction), and taking a positive sample for continuous subculture; virus identification is carried out through cytopathic effect observation, RT-PCR (Reverse Transcription-Polymerase Chain Reaction) detection, transmission electron microscope observation, indirect immunofluorescence detection and genetic genetic evolution analysis. According to the method, a BCoV positive sample is treated and then inoculated with an HRT-18 cell line, and the bovine coronavirus is successfully separated according to verification.

The invention discloses a different-length genome sequence coding method based on improved self-attention and Mamba double modules, and relates to a different-length genome sequence coding method. The invention aims to solve the problem that a genome sequence cannot be reasonably input into a neural network model in the prior art. The method comprises the following steps: in a data processing sequence feature extraction process, extracting kmer position feature information, kmer centrality feature information and inter-kmer relevance feature information of genome sequences with different lengths; in the pre-training process, a model based on improved self-attention and Mamba double modules is constructed, a sequence coding model can capture universal features of genome sequences through mask pre-training, and universal representation vectors of the same dimension of the genome sequences of different lengths are obtained; in the fine tuning process, by using specific biological task data for fine tuning training, the coding model can be better matched with various virus-based genomes such as virus recognition, virus host and virus lifestyle recognition. The invention belongs to the technical field of deep learning and gene engineering.

The application discloses a macrovirus group analysis method based on second-generation and third-generation sequencing technologies, which comprises the following steps: performing quality control and filtering on second-generation sequencing raw data to obtain clean reads, performing single-sample and multi-sample assembly on the quality-controlled data to obtain contigs sequences; performing correction and quality control on third-generation sequencing raw data to obtain clean long sequences, performing assembly on the quality-controlled third-generation data to obtain contigs sequences; performing mixed assembly on the quality-controlled data of the second-generation and third-generation sequencing to obtain contigs sequences; merging all contigs to construct a non-redundant contigs set; and finally performing virus identification and determination, virus species annotation and functional annotation. The application provides a reliable macrovirus group analysis method based on second-generation and third-generation sequencing technologies, and the implementation method is simple and the application range is wide.

The invention discloses a primer design method and a primer group. According to the application, through deep analysis of influenza monitoring network data nationwide, an important contribution is made to improvement of the identification accuracy and sensitivity of influenza viruses. The revision of the primer not only achieves a remarkable effect in an experiment, but also provides beneficial enlightenment for future flu monitoring and research. In-depth understanding of hereditary characteristics and epidemic rules of influenza viruses is of great significance in formulating scientific and effective prevention and control strategies. In disease prevention and public health management, continuous improvement of monitoring methods and strategies will bring greater benefits to timely coping with infectious diseases such as influenza.

The application belongs to the cross field of virology and bioinformatics, and particularly relates to a virus identification model construction method and system based on capsid proteins. The method collects capsid protein sequence data and non-capsid protein sequence data, and trains a capsid protein identification model; obtains capsid protein sequences with known structures, structure information and hierarchical classification information, and identifies a few-sample category with a sample quantity lower than a preset threshold; generates supplementary data of the few-sample category through a protein sequence design model and screens the supplementary data from the collected capsid protein sequence data, predicts the three-dimensional structure of the sequence in the supplementary data, and together with the corresponding classification information, forms a capsid protein supplementary data set for training a capsid protein classification model. The application alleviates the problems of data imbalance caused by insufficient data of the few-sample category and high classification difficulty of remote homologous proteins caused by single feature extraction, and is helpful to realize efficient preliminary identification of viruses.

The invention discloses an information system operation and maintenance service system, which comprises a data acquisition module, a data processing module and an operation and maintenance module, relates to the technical field of information systems, and solves the technical problem that an information system maintenance system in the prior art is not high in stability and reliability in the operation process. The method comprises the steps of calculating a state evaluation coefficient of an information system based on operation data; inputting the state evaluation coefficient of the latest period into a state coefficient prediction model to obtain a state evaluation coefficient prediction value; determining a risk level of the information system based on the state evaluation coefficient prediction value; and taking corresponding maintenance measures for the information system based on the risk level. According to the method, the state evaluation coefficient of the information system is calculated by substituting the average response duration, the response timeout times and the virus identification times into the formula, so that the state evaluation coefficient can accurately reflect the actual operation condition of the information system, and the stability and the reliability of the information system in the operation process are improved.

The invention provides a physical AI virus identification method and system based on a Bayesian neural network, and belongs to the technical field of network security. According to the method, aiming at the fact that a target recognition object is a physical AI virus (including a physical confrontation sample, a physical backdoor, an unknown physical domain AI virus and the like), the physical confrontation sample is taken as an example, and the confrontation sample and a clean sample collected in a physical real world are creatively adopted to construct a training data set. According to the recognition framework provided by the invention, a multi-modal data fusion mechanism under simulation of real environment disturbance is utilized, higher robustness and generalization ability are shown in a complex physical scene, and AI viruses such as physical adversarial samples in the real world can be better recognized.

The present invention relates to the technical field of industrial Internet of Things security, and discloses an industrial information adaptive anti-intrusion supervision system and method based on the Internet of Things. The system comprises an industrial information network intrusion information identification module, an industrial information network intrusion firewall matching module, and an industrial information network intrusion processing feedback module. The industrial information anti-intrusion security processing firewall type is comprehensively and accurately matched based on big data storage industrial information security processing firewall type parameters combined with intelligent recognition algorithms and industrial information network intrusion virus identification parameters and security level analysis parameters, so as to realize adaptive and scientific selection of industrial firewall types based on industrial information network intrusion virus information and data security level information, thereby improving the industrial information anti-intrusion supervision effect of the industrial Internet of Things.

The invention belongs to the technical field of virus analysis, and discloses an industrial internet ransomware virus analysis method, device and equipment and a storage medium. The method comprises the following steps: acquiring information of a to-be-analyzed virus during running in a preset sandbox to obtain an interface behavior sequence corresponding to the to-be-analyzed virus; performing type identification on the interface behavior sequence through a preset virus identification model, and determining whether the to-be-analyzed virus is a ransomware virus or not; and if the to-be-analyzed virus is the ransomware virus, determining a virus family classification to which the to-be-analyzed virus belongs according to the interface behavior sequence and each virus fingerprint sequence in a preset virus fingerprint database. Whether the to-be-analyzed virus is the ransomware virus or not is identified according to the interface behavior sequence of the to-be-analyzed virus through the pre-trained preset virus identification model, the overall identification process is rapid, and after the to-be-analyzed virus is determined to be the ransomware virus, the virus family classification of the to-be-analyzed virus can be determined, so that targeted processing can be ensured, infection and transmission of the ransomware virus can be prevented, and the accuracy of the identification process is improved. And the security and defense capability are improved.

The application relates to the technical field of data processing, and discloses a multi-modal attention deep learning method for enhancing virus identification in metagenomic data, which comprises five main steps of data preprocessing, sequence embedding, multi-modal feature extraction, dynamic feature fusion and prediction, and hyperparameter optimization and training; through four model paths of GAT, a self-encoder, a convolutional long short-term memory network and a Transformer, the graph structure features, the latent features, the space-time features and the global features of sequences are respectively extracted, the self-attention mechanism is introduced, the contribution weights of the paths are adaptively adjusted according to the features of the input sequences, the sequence embedding is carried out by using the GAT and the self-encoder, the long-distance dependence relationship and the latent features are captured, the space-time and global features are extracted by using the ConvLSTM and the Transformer model paths, and the hyperparameter optimization and the training strategy optimization are carried out; the application improves the virus sequence identification capability, improves the feature fusion accuracy, and further improves the performance and the robustness of the model.

The application discloses a monoclonal antibody specifically binding to coxsackievirus A6 and application thereof. The monoclonal antibody comprises a light chain and a heavy chain, the amino acid sequence of the complementarity determining region HCDR1 of the heavy chain is shown as SEQ ID No:1, the amino acid sequence of the complementarity determining region HCDR2 of the heavy chain is shown as SEQ ID No:4, the amino acid sequence of the complementarity determining region HCDR3 of the heavy chain is shown as SEQ ID No:7, the amino acid sequence of the complementarity determining region LCDR1 of the light chain is shown as SEQ ID No:10, the amino acid sequence of the complementarity determining region LCDR2 of the light chain is shown as GTS, and the amino acid sequence of the complementarity determining region LCDR3 of the light chain is shown as SEQ ID No:13. The monoclonal antibody has good specificity and sensitivity and has a virus neutralization capacity, and shows great potential in virus identification, diagnosis, treatment and development of multivalent vaccines.

A method and system for virus detection in tumor RNA sequencing data are disclosed. The method includes preprocessing the raw tumor RNA sequencing data; inputting the processed tumor RNA sequencing data into sequence-information-based channels and codon-based channels for feature extraction to generate a feature matrix; constructing a sequence information prediction model and a codon information prediction model, and inputting the feature matrices generated from the sequence information-based channels and codon-based channels into the sequence information prediction model and codon information prediction model, respectively, for training and optimization; predicting the virus probability of each sequencing read to obtain a model score; and selecting viral sequencing reads based on the model scores to assemble viral contigs. This invention improves the accuracy and robustness of virus monitoring by introducing a multimodal deep learning method, and can adaptively process sequencing data from different sources and of different lengths, thereby better meeting the needs of medical and research fields for virus identification in tumor sequencing data.

The application discloses a macrovirus group data analysis method, and belongs to the technical field of macrovirology. The method comprises the following steps: sequence quality control, sequence assembly, sequence clustering, virus sequence identification, virus sequence checking, virus abundance calculation, species annotation, virus lifestyle judgment, virus host prediction and virus auxiliary metabolism gene analysis. The application uses Trimmomatic software, BWA-MEN, Megahit software, CD-HIT and other tools to execute the analysis process of macrovirus group data. Practice proves that the application can accurately identify and annotate virus species, comprehensively and systematically deeply analyze and mine macrovirus group data, the steps are simple and clear, the analysis time is short, and the effect of macrovirus identification research is greatly improved.

The application provides a novel 2019-nCoV nucleic acid detection kit, in particular, provides a kit and a method for multiplex detection of 2019-nCoV nucleic acid, which can simultaneously detect three nucleic acid targets of 2019-nCoV, can be verified by different targets, can prevent false positives, can confirm the missed detection caused by mutation, and significantly improves the accuracy of virus identification.

The embodiment of the present disclosure provides a computer network security server virtualization processing method and related equipment. The processing method includes: obtaining target input data through a data perception module and detecting the type of the target input data; the data perception module inputs the target input data into the target virtual server application layer according to the type of the target input data, so that the target virtual server application layer calls the target input data and generates dynamic operation data; filtering the dynamic operation data and the target input data through a data disguise module to obtain redundant disguise data, and storing the redundant disguise data in a file library; detecting abnormal files in the redundant disguise data in the file library through a data capture module, and storing the abnormal files in a separately divided virtualization system in the cloud virtual platform; and performing virus identification on the abnormal files through a data identification module. The technical solution disclosed in the present disclosure can isolate viruses from spreading in the virtual server application layer.

The invention discloses a micropterus salmoides rhabdovirus gene engineering vaccine preparation method, which comprises: S1, recombinant plasmid construction: carrying out PCR amplification on a micropterus salmoides rhabdovirus G protein gene with a 6 * His tag, cloning the amplified gene between BamH I and Hind III restriction enzyme cutting sites of a pVL1393 vector to obtain a pVL1393-G-His recombinant plasmid, S2, preparing a transfection system, and S3, carrying out purification on the transfection system to obtain the micropterus salmoides rhabdovirus gene engineering vaccine. S3, recombinant baculovirus preparation and identification, S31, cell transfection, S32, P1-generation virus harvesting, and S33, virus identification, the G protein of the micropterus salmoides rhabdovirus (MSRV) is directionally expressed through a genetic engineering technology, a large number of pathogenic viruses do not need to be cultured, the biological safety risk is greatly reduced, meanwhile, the defect that a prokaryotic expression system lacks protein post-translational modification is overcome, and the method is suitable for industrial production. The sf9 insect cell is used as an expression host, so that the recombinant G protein can be correctly folded to form a functional structure domain, and the immunogenicity is remarkably improved.

A real-time fluorescence RT-PCR detection method and a kit for a novel coronavirus 2019-nCoV. Specifically, the present invention relates to a kit and a method for detecting the nucleic acid of an E gene of the novel coronavirus 2019-nCoV. The kit and the method have extremely high sensitivity and specificity, and can significantly improve the accuracy of virus identification.

This invention discloses a primer design method and primer set. Through in-depth analysis of influenza surveillance network data nationwide, this application makes significant contributions to improving the accuracy and sensitivity of influenza virus identification. The primer revisions not only achieved significant results in experiments but also provided valuable insights for future influenza surveillance and research. A deeper understanding of the genetic characteristics and epidemiological patterns of influenza viruses is crucial for developing scientifically effective prevention and control strategies. In disease prevention and public health management, continuous improvement of surveillance methods and strategies will bring greater benefits to timely responses to infectious diseases such as influenza.

The invention discloses a Trojan virus detection method and device in the Internet field, and the method comprises the steps: configuring a Trojan virus environment, and collecting flow data in the Trojan virus environment; carrying out bidirectional flow division on the collected flow data according to a quintuple, and carrying out feature extraction to obtain flow feature data; obtaining training set data obtained by processing the traffic feature data, and training a machine learning model by using the training set data to obtain a Trojan virus recognition model; wherein the processing comprises the following steps of: performing normalization processing on the traffic characteristic data; and utilizing the Trojan virus identification model to identify the hiding mode and Trojan activity stage of the Trojan virus. According to the method, the flow of the Trojan horse is detected, the hiding mode of the Trojan horse is locked and the activity stage of the Trojan horse is identified under the condition of high hiding.

The invention provides an analysis method and system for virus metagenome sequencing data, and relates to the field of biological information.The method comprises the steps that quality filtering is conducted on original sequencing data, and filtered data is obtained; according to the filtered data, a sequence is assembled through a De Bruijn graph and an overlapping layout, and a virus metagenome sketch is constructed; comparing the virus metagenome sketch with a known virus database to identify known virus types; and identifying a new virus by combining the unknown sequence which is not compared with the virus sequence through clustering analysis so as to realize the sequence comparison of the virus metagene. By improving data quality and accuracy, enhancing virus identification and classification capability, improving variation detection and function annotation efficiency and optimizing gene prediction and community structure analysis, analysis of virus metagenome sequencing data is realized.

The application belongs to the technical field of biology. More particularly, it relates to a method for accurately identifying the genotype of African swine fever virus based on high-throughput sequencing. The application selects 29 SNP sites in the B646L (P72) gene region that are highly conserved and genotype-specific, calculates the proportion of specific sites by parallel alignment with double reference sequences and base depth quantification statistics, and realizes quantitative determination of the proportion of type I, type II and mixed infection of African swine fever virus. The application breaks through the limitation that the traditional method cannot determine the mixed infection of African swine fever virus, realizes the objective determination of mixed strains, and provides a reliable tool for virus identification under complex infection scenarios. The application is completed based on high-throughput sequencing data, the analysis process is standardized, the subjective error caused by manual judgment is reduced, the consistency and repeatability of the results are strong; at the same time, the output statistical table and the results are intuitive and traceable, and have good popularization and application value.

The application discloses a Teensy virus identification and protection method of a mobile hard disk, relates to the technical field of virus identification and protection, and breaks through the limitation of traditional single feature detection by combining hardware ripple characteristics with dynamic protocol response to effectively identify high-simulation attack equipment through composite equipment fingerprint construction. Meanwhile, a hidden Markov chain is used for real-time analysis of a protocol state transition probability model to accurately capture microsecond-level protocol switching abnormalities and solve the problem of missed detection caused by coarse time granularity of an existing scheme. In addition, a window context instruction association mechanism binds a system focus state to HID operation to block a hidden attack chain formed by a combination of legal instructions. A hierarchical fuse strategy integrates protocol endpoint control and physical layer isolation to realize attack blocking while ensuring the availability of storage functions, thereby avoiding the influence of normal use caused by full-port disabling in traditional schemes.

The application discloses an Internet Trojan virus detection method and device, the method comprises the following steps: configuring a Trojan virus environment, and collecting traffic data under the Trojan virus environment; dividing the collected traffic data according to five tuples in a bidirectional manner, and extracting features to obtain traffic feature data; obtaining training set data obtained by processing the traffic feature data, training a machine learning model by using the training set data, and obtaining a Trojan virus identification model; wherein the processing comprises: normalizing the traffic feature data; and identifying the hiding mode of the Trojan virus and the activity stage of the Trojan virus by using the Trojan virus identification model. The application realizes the detection of Trojan virus traffic, the locking of the hiding mode of the Trojan virus, and the identification of the activity stage of the Trojan virus under high concealment.

The invention discloses an attack virus detection method and apparatus, an electronic device and a medium, and belongs to the technical field of network security, for each node of a target cluster, collecting multi-dimensional behavior data of each container running on the node, and sliding a data feature type of target behavior data in a target window on the behavior data according to a preset step length, performing feature value conversion on the target behavior data, and combining the converted feature values to obtain a fusion feature vector; performing abnormal feature analysis on the fusion feature vector, and determining a first detection result of the target behavior data; if it is determined that the target behavior data is abnormal, feature reconstruction analysis is performed on the fusion feature vector, and a second detection result of the target behavior data is determined; determining a final result of the target behavior data based on the first detection result and the second detection result; and based on the final result of the target behavior data corresponding to each target window, determining whether the nodes in the target cluster have containers of attack viruses. And the attack virus identification precision is improved.