284 results about "Mutation detection" patented technology

The invention discloses an intelligent substation communication link fault self-healing regulation and control method and system, and relates to the technical field of fault self-healing regulation and control, and the method comprises the following steps: constructing a link disturbance intensity sequence reflecting link quality fluctuation; performing trend fitting and mutation identification on the link disturbance intensity sequence based on a sliding window mechanism, and generating a link state transaction index; when the link state transaction index exceeds a first threshold value, identifying an affected key easy-to-disturb service set according to the service path topological data of the abnormal link; constructing a context-aware path migration cost model based on the key easy-to-disturb service set, and generating an optimal switching path sequence; and implementing hierarchical link regulation and control according to the path switching optimal sequence. According to the method, trend fitting and mutation detection are carried out by adopting an overlapped sliding window mechanism and a mutation scoring function, so that intermittent or hidden link degradation abnormity which is difficult to capture can be effectively identified, and a link state transaction index has higher judgment precision.

The invention provides an SNP (Single Nucleotide Polymorphism) site of an ABO blood group variant, and an A subtype variant blood group identification kit and method, and relates to the technical field of biology. The SNP sites of the ABO blood group variant comprise the 155th c.155Ggt from an initiation codon of an ABO gene coding region, and the 155th c.155Ggt from the initiation codon of the ABO gene coding region; mutation of T. A substance for detecting the SNP site of the ABO blood group variant can be applied to preparation of an A-subtype variant blood group identification method and product so as to be effectively applied to rapid detection of patients and blood donors, the A-subtype variant blood group of an individual can be defined, and possible blood transfusion reaction can be prevented in time.

The invention provides an SNP (Single Nucleotide Polymorphism) site of an ABO blood group variant, and a B subtype variant blood group identification kit and method, and relates to the technical field of biology. The SNP (Single Nucleotide Polymorphism) sites of the ABO blood group variant comprise the 842th c.842Cgt from an initiation codon of an ABO * B.01 allele coding region of an ABO gene; mutation of T. A substance for detecting the SNP site of the ABO blood group variant can be applied to preparation of a B subtype variant blood group identification method and product, so that the substance can be effectively applied to rapid detection of patients and blood donors, the B subtype variant blood group of an individual can be defined, and potential possible blood transfusion reaction can be prevented in time.

The invention relates to the field of medical image analysis, in particular to a lung adenocarcinoma EGFR gene mutation detection system and method based on PET / CT deep learning, and the system comprises a data collection module which is used for obtaining PET / CT image data and clinical information of a lung adenocarcinoma patient; the image preprocessing module is connected with the data acquisition module and is used for carrying out standardization processing and ROI extraction on the PET / CT image data; the feature extraction module is connected with the image preprocessing module and used for extracting depth features, metabolic parameter features and CT sign features from the PET / CT image data; the multi-modal data fusion module is connected with the feature extraction module and used for fusing the extracted multi-modal features; and the prediction model module is connected with the multi-modal data fusion module and is used for predicting the lung adenocarcinoma EGFR gene mutation state and prognosis based on the fusion features, and the prediction accuracy is improved through multi-modal data fusion and deep learning technologies.

The invention relates to the technical field of power system monitoring, in particular to a bus duct temperature and humidity abnormity monitoring method, which comprises the following steps of S1, periodically acquiring temperature and humidity data through a temperature and humidity sensor, and performing data preprocessing to obtain processed data; s2, mutation detection and trend analysis are carried out according to the processed data, and mutation data and trend data are obtained respectively; s3, according to the abrupt change data and the trend data, a weighting strategy is adjusted in a self-adaptive mode; when the method is used, the credibility of data can be improved, the noise and errors of the sensor can be effectively reduced, the adaptability of the system can be improved, the data subjected to denoising and abnormal value elimination can be used as high-quality input of mutation detection, trend analysis and time sequence prediction, the accuracy of the whole temperature and humidity abnormity monitoring is improved, and the reliability of the system is improved. And short-time window detection and long-time window detection are integrated, so that the accuracy of anomaly monitoring is improved, false alarm caused by purely based on mutation detection is avoided, and the response speed of the method is improved.

The invention relates to the technical field of debugging systems, in particular to an adaptive debugging system and method for touch screen FW parameters, and the system comprises a signal acquisition module, a trend triggering module, a temperature compensation calculation module, a window generation module and a parameter output module. According to the invention, the edge of the driving voltage is carefully collected, the sliding difference algorithm is introduced to filter out high-frequency noise, meanwhile, real-time sampling of environment temperature and baseline capacitance adjustment are combined, the influence of external variables on signals can be dynamically corrected, and accurate judgment of capacitance fluctuation is improved by utilizing periodic delay deviation and regression trend analysis. By means of main frequency feature extraction and zero crossing point sudden change detection, threshold setting can be refined, self-adaptive window expansion can be achieved, touch signal response is kept sensitive and consistent all the time, adjustment and optimization can still be automatically completed even under the condition that the temperature changes frequently or signal interference is complex, parameter drift and debugging failure are effectively prevented, and user experience is improved. And the parameter automatic matching efficiency and the finished product consistency in batch production are improved.

The invention relates to the technical field of plant phenotype data processing, in particular to a plant phenotype automatic acquisition and feature processing method oriented to a multi-element environment. According to the method, the plant multi-modal phenotypic data are synchronously collected through the Internet of Things nodes, the coupling strength is calculated in combination with the environmental factors, the phenotypic parameters are divided into an environment sensitive type and a steady type, and the data classification accuracy is improved. A growth trend field is constructed by means of phenological phase decoupling analysis and geographic space superposition, abnormal segments are dynamically eliminated by combining physiological synergy degree and a mutation detection algorithm, and trend analysis stability is enhanced. A phenotype-environment response map is generated through standardized processing and an environment equivalent value model, a key environment sensitive window is recognized, a regional and staged cross-environment regulation scheme is output, intelligent and accurate management of the crop growth environment is achieved, and the method is suitable for agricultural yield increase and breeding optimization.

A method of biological sample analysis includes subjecting a biological sample comprising nucleic acid to an amplification reaction conducted in the presence of a detector probe and a calibrator probe, wherein the detector probe is configured to specifically interact with a first region of a wild type target nucleic acid sequence and emit a first emission signal in response to the interaction, and wherein the calibrator probe is configured to specifically interact with a second region of the wild type target nucleic acid sequence and emit a second emission signal in response to the interaction, the first region and the second region differing from each other. The method further includes calculating a ratio of the change in the first emission signal to the change of the second emission signal at a point in time of the amplification reaction.

The invention provides a cattle behavior recognition method based on a three-axis attitude angle, and belongs to the technical field of intelligent animal husbandry management. According to the method, three-axis attitude angle signals such as the yaw angle (Yaw), the pitch angle (Pitch) and the roll angle (Roll) of the head of a cattle are collected in real time through a gyroscope sensor arranged in wearable equipment such as a cattle ear tag. The method does not depend on images, accelerometers, microphones or other modalities, only based on attitude angle signals, multiple algorithms such as a time sequence sliding window, frequency domain analysis, weighted scoring, sudden change detection and state transition modeling are utilized, and multiple typical behaviors such as ingestion, rumination, water drinking, bed lying, limping, licking, fright jumping and low activity are recognized. The method is suitable for multiple cattle breeds such as dairy cattle, beef cattle and buffalo, has the advantages of low power consumption, lightweight calculation, low deployment cost, accurate identification and the like, and is convenient for realizing behavior monitoring and health management of large-scale cattle individuals. The algorithm path can be flexibly selected, but a core input signal is only limited to attitude angle data, so that the technical boundary of the patent protection of the method is formed.

The invention discloses a student mental health prediction method based on artificial intelligence, and particularly relates to the technical field of mental health prediction. A time sequence sample fusing multi-source behavior data is constructed, a deep learning model of an abrupt change detection mechanism is combined, the ability of recognizing short-term psychological abnormal changes of students is effectively improved, psychological risk signals are dynamically output when abrupt change occurs, and an individual intervention decision module is called to generate intervention suggestions, so that the accuracy of the short-term psychological abnormal changes is improved. Closed-loop management from risk identification to intelligent intervention is realized, the early warning capability and intervention timeliness of the system for sudden psychological crisis are remarkably enhanced, and the system has relatively high practical value and popularization prospect.

The invention belongs to the technical field of power system operation control, and relates to a real-time estimation method and system for comprehensive inertia containing a network construction VSG, a storage medium and electronic equipment, and the method comprises the following steps: S1, constructing a system model; s2, inertia constant state conversion is carried out; s3, mutation detection and correction are carried out; aiming at the problem of real-time evaluation when inertia sudden change occurs in a power system, on the basis of an improved unscented Kalman filtering algorithm, conversion from parameter estimation to real-time state estimation and verification and correction of inertia sudden change conditions are realized, the real-time performance and accuracy of an inertia evaluation result are ensured, and the real-time performance of the power system is improved. And the evaluation method of inertia real-time estimation is enriched.

The invention discloses an underground pipe network water quality sudden change detection system and method. Comprising a multi-parameter distributed sensing network module, a data transmission module, a data processing and analyzing module, a space-time traceability positioning module and a user interaction and alarm module, wherein the multi-parameter distributed sensing network module comprises various types of water quality sensors and hydraulic sensors; the data acquisition module is used for acquiring water quality parameter data and hydraulic parameter data of each node in real time; the invention relates to the technical field of water quality monitoring. According to the underground pipe network water quality abrupt change detection system and method, multi-dimensional data are collected in real time through a distributed anti-interference sensing network, and an intelligent closed loop from data collection to abrupt change recognition is constructed in combination with'mixed transmission architecture + improved Bayesian network fusion analysis + LSTM-AE deep learning detection '; a complex sudden change mode of multi-parameter cooperative change can be rapidly identified, and risk expansion caused by monitoring lag or shallow layer analysis is avoided.

The present disclosure relates to somatic mutation determinations. An example method includes performing whole genome sequencing using a high-throughput sequencing system on nucleic acids from a sample obtained from a subject to generate raw data, transmitting the raw data from the high-throughput sequencing system to a computing system, processing the raw data by the computing system to generate candidate variants and a set of feature values for each candidate variant of the candidate variants, processing the feature values by the computing system using a trained machine learning model to identify somatic mutations from the candidate variants, transmitting results based on the somatic mutations from the computing system to an end device, and displaying a report on the end device based on the results.

The invention discloses a psychological state dynamic evaluation and early warning system based on multi-modal behavior data, which belongs to the field of medical care informatics and comprises a multi-modal behavior data hierarchical coding module, a time sequence causal atlas construction and reasoning module, a double-stage self-adaptive early warning decision module and a context awareness intervention strategy generation module. A cross-modal association mode is extracted through a double-layer coding mechanism, a time sequence graph containing a causal relationship is constructed, causal reasoning is performed, a double-stage mechanism of short-term mutation detection and long-term trend prediction is adopted to generate graded early warning, and an optimal intervention strategy is selected based on a deep Q network according to a user situation. According to the method, the accuracy, timeliness and intervention effectiveness of psychological health assessment are improved, and dynamic monitoring and early warning of the psychological state are realized.

A molecular marker related to drought resistance of tomatoes and a use of the molecular marker, relating to the technical field of molecular breeding. The nucleotide sequence of the molecular marker is shown as SEQ ID NO. 4, and T / G mutation is present at position 22 bp of the sequence shown in SEQ ID NO. 4. A PARMS primer group for detecting the molecular marker comprises two allele specific primers and one marker site specific primer, the drought resistance of tomatoes can be accurately identified by using the PARMS primer group, and the drought resistance of materials can be determined at the time of young tomato seedling, so that the seed production cost is reduced, and the breeding efficiency is improved. Additionally, the transformation process of new tomatoes or other fruiting vegetables can also be accelerated.

The invention relates to the technical field of pathogenic bacterium detection, in particular to a method and a kit for detecting helicobacter pylori gene mutation in excrement. According to the technical scheme, the kit for detecting helicobacter pylori gene mutation in excrement is developed by combining multiple advanced technical means such as ARMS-PCR design, blocking primers, locked nucleic acid modification (peptide nucleic acid modification), MGB probes, nested PCR design and degenerate base design. The kit can detect nine gene mutation sites related to the drug resistance of helicobacter pylori at one time, and has a wide coverage range. The kit in the scheme can be used for detecting the drug resistance of quinolone antibiotics and / or clarithromycin antibiotics of helicobacter pylori, and the technical problem that a high-specificity and high-sensitivity excrement helicobacter pylori gene mutation detection method is lacked in the prior art is solved. The method is simple and convenient to operate and can be completed within 1-2 hours, the detection cost is reduced, and the method has ideal application and popularization prospects.

The invention belongs to the technical field of visual identification, and particularly discloses a thick plate saddle-shaped weld groove feature extraction and size measurement method and system. Comprising the steps that based on multi-scale geometric feature mutation detection, a current frame weld groove three-dimensional point cloud is segmented into a branch pipe face, a groove side wall, a backing weld surface and a main pipe face, and four area three-dimensional point clouds are obtained; according to the three-dimensional point cloud of each area, determining an intersection point of a branch pipe surface and a groove side wall, an intersection point of the groove side wall and a backing welding surface, an intersection point of the backing welding surface and a main pipe surface, an intersection point of the branch pipe surface and the main pipe surface, and a virtual intersection point of the groove side wall and an extension line of the main pipe surface as weld groove feature points; and constructing vectors between the feature points, and calculating the geometric dimension of the weld groove through vector operation. The method is based on multi-scale geometric feature mutation detection and adaptive point cloud segmentation, a fitting curve slope threshold value does not need to be preset, and the method can be adaptive to different included angles between the branch pipes and the main pipe in the pipe tubular joint.

The invention relates to the field of intelligent medical treatment, in particular to a prediction method for predicting MET 14 jump mutation. Comprising the following steps: S1, acquiring MET gene data of a patient, including a chromosome number, a physical coordinate, a reference genome basic group and a mutant basic group; s2, calculating the basic group change number of the reference genome basic groups and the mutation basic groups to obtain the basic group change number of the reference genome and the change number of the mutation basic groups; s3, the chromosome number, the physical coordinates, the reference genome base change number and the mutation base change number are input into a prediction model, and a prediction result whether MET 14 jump mutation exists or not is obtained. The method can be used for detecting classical shearing and hidden shearing of MET 14 jump mutation, the mutation detection rate is increased, and the method has good clinical value.

The invention relates to the technical field of bioinformatics, and discloses a method, a system, equipment and a medium for searching for base mutation aiming at third-generation full-length transcript sequencing data, the base mutation is accurately positioned to a specific transcript by directly processing the third-generation full-length transcript sequencing data, and the method and the system for searching for the base mutation aiming at the third-generation full-length transcript sequencing data are provided. The co-occurrence relation of a plurality of mutations on the same transcript is accurately analyzed, and the defects of calculation redundancy, function misjudgment and the like caused by the fact that a mutation transcript source cannot be determined due to fragmentation splicing and the co-occurrence is simulated by depending on permutation and combination in a second-generation short-read-long sequencing technology are effectively overcome; the recall rate of transcripts which are not mapped due to a high-variation region is improved through mapping correction assisted by structural variation, and false positive is effectively inhibited through a multi-dimensional filtering condition, so that the sensitivity and reliability of mutation detection are remarkably improved; particularly, mutation events with function remodeling due to reading frame change can be accurately recognized in scenes such as neoantigen prediction where mutation function consequences need to be accurately evaluated, and the method has important value in application in the fields of precision medical treatment and the like.

The invention discloses a current abrupt change identification method based on comparative learning. The method comprises the following steps: S1, collecting three-phase current time sequence data and generating a sample; s2, performing normalization and noise suppression on the sample to obtain a standardized time sequence vector; s3, constructing positive and negative sample pairs based on the current change trend for comparative learning; s4, inputting the sample pair into the multiple perceptual hash network to generate a fusion vector; s5, through channel difference calculation and residual fusion, extracting mutation features; s6, introducing a time position coding gating mechanism, and dynamically adjusting the channel weight; s7, optimizing a model structure and parameters by adopting an improved black hole optimization algorithm; and S8, performing mutation detection based on similarity calculation and outputting a time position. According to the invention, high precision and high robustness of current abrupt change detection are realized, the power system monitoring efficiency is improved, and the method is suitable for intelligent fault diagnosis in a complex power environment.

The invention provides a typhoon path prediction method and system fusing physical constraint and path mutation recognition. The method comprises the steps that a multi-dimensional input feature tensor is obtained through a feature tensor generation module; performing time modeling through a multi-scale modeling module to obtain a deep feature tensor, and performing significance guidance through a significance guidance module to obtain a prediction path sequence; the bending event identification module identifies a bending angle to obtain a plurality of path abnormal scores, the adversarial disturbance analysis module introduces a plurality of preset disturbances to determine the path confidence, and the path prediction output module outputs a path prediction result. According to the technical scheme of the embodiment of the invention, refined modeling can be carried out on the typhoon path, the PINNs physical constraint module is utilized to ensure that the prediction result meets the physical conservation principle, the path mutation detection capability is enhanced through bending event recognition, uncertainty evaluation is realized through disturbance analysis, and the accuracy of the typhoon path detection is improved. And a typhoon path prediction result which is more accurate and credible and has physical consistency is output.

The invention relates to the technical field of biological medicine, in particular to an RNF / CLR / Cas / crRNA compound as well as a preparation method and application thereof. The RNF / CLR / Cas / crRNA compound disclosed by the invention is composed of an RNA nanoflower (RNF), a circular fluorescent reporter molecule (CLR) and a Cas / crRNA compound, and the circular fluorescent reporter molecule and the Cas / crRNA compound are combined on the RNA nanoflower through base complementary pairing; the Cas / crRNA compound is composed of a Cas protein and crRNA, and the crRNA is used for specifically recognizing the crRNA of a nucleic acid molecule. The RNF / CLR / Cas / crRNA compound can be used for nucleic acid molecule specificity detection and nucleic acid molecule base mutation detection, can also be used for fluorescence in situ hybridization tissue imaging, and has a good application prospect.

The invention belongs to the technical field of gene detection, and particularly relates to application of a CFTR gene mutation combination, an amplification reagent and a product in preparation of a CF risk assessment product. The CFTR gene mutation combination consists of an intron region mutation combination and an exon region mutation combination, based on the CFTR gene mutation combination, the invention further develops an amplification reagent and a CFTR gene mutation detection product, the amplification reagent and the CFTR gene mutation detection product comprise a primer combination for multiplex PCR amplification of the CFTR gene mutation combination, and the primer combination has the characteristics of high accuracy, high specificity and high sensitivity, can accurately detect related gene mutation, and provides a reliable basis for risk assessment and diagnosis of CF. When the amplification reagent and the CFTR gene mutation detection product are combined with sweat chlorine detection for use, the diagnosis rate of CF can be remarkably improved, and the amplification reagent has important application value in clinical diagnosis of CF and is expected to provide more powerful support for early diagnosis and treatment of CF patients.

The invention belongs to the technical field of genes, and particularly relates to an amplification primer group of a renal cyst disease gene PKD1, a mutation detection method and application, the amplification primer group comprises four pairs of primers for specifically amplifying first to 34 exons of the PKD1 gene; the mutation detection method comprises the following steps: acquiring genome DNA (Deoxyribose Nucleic Acid); carrying out ultra-long fragment PCR amplification on the genome DNA through the primers, carrying out electrophoresis detection, and purifying a target fragment; mixing the purified amplification product with genome DNA in proportion, constructing a sequencing library, and performing high-throughput sequencing through target area capture; and analyzing sequencing data, and screening pathogenic PKD1 gene mutation. Compared with the prior art, the PKD1 ultra-long fragment PCR amplification primer and the renal cyst disease gene PKD1 mutation detection method have the advantages that true and false genes of PKD1 can be distinguished, the diagnosis accuracy is greatly improved, the operation process is simplified, the timeliness is improved, and the detection flux is improved.

The invention discloses a multiple qPCR (quantitative polymerase chain reaction) primer probe group for drug-resistant gene mutation detection of eleusine indica and application of the multiple qPCR primer probe group. The multiple qPCR primer probe group comprises ALS-F, ALS-R, ALS-P, GS1-1-GF2, GS1-1-GB2, GS1-1-R, GS1-1-P, EPSP-F, EPSP-R, EPSP-P2, 102I-T, 102-B, 106-P and 106-B. The multiple qPCR primer probe group is used for detecting drug-resistant gene mutation of eleusine indica. The invention also discloses a kit comprising the primer probe group and application of the primer probe group and the kit in detection of drug-resistant gene mutation of eleusine indica. The method has the advantages of high throughput, simplicity and convenience in operation, visual result, more time saving and the like.

The invention discloses a complex stratum dual-mode shield construction rock ballast particle size distribution evaluation method and system, and the method comprises the steps: obtaining a rock ballast image, carrying out the preprocessing and denoising, and obtaining a first image; utilizing a deep learning algorithm to identify the edge of the rock ballast, and separating overlapped rock ballast to obtain a second image; analyzing the shape of the rock ballast through morphological parameters, and performing three-dimensional reconstruction correction on the non-spherical particles to obtain a third image; performing data fusion on the third image in combination with laser particle size analysis data to obtain initial particle size distribution; aiming at a fine particle adhesion phenomenon, correcting particle size distribution by adopting a detection algorithm; dynamically updating particle size distribution according to the change of a crushing mechanism during shield mode switching; non-linear mutation in the complex stratum is analyzed through a mutation detection algorithm, and data smoothing is carried out; and finally, analyzing the error by using an error accumulation model, and if the error exceeds a threshold value, reprocessing until the error meets the requirement. According to the method, the precision and reliability of rock ballast particle size distribution evaluation are remarkably improved.

The invention discloses a variation detection method based on an end-to-end assembled genome, and relates to a variation detection method. The objective of the invention is to solve the problems of incomplete detection, inaccurate detection, incapability of identifying complex variation types and low genotyping accuracy existing in a current method for detecting structural variation based on splicing. The variation detection method based on the end-to-end assembly genome comprises the following steps: step 1, processing a variation signal to obtain a processed variation signal; 2, performing mixed sorting on the processed variation signals obtained in the step 1, performing spatial distribution clustering on the sorted variation signals, judging whether the variation signals subjected to spatial distribution clustering are potential complex variation or not, if yes, judging the type of the complex variation, outputting the complex variation, obtaining complex variation signals, and executing the step 3; if not, indicating that the signal is a simple variation signal, and executing the step 3; and step 3, carrying out genetic typing on the variation signal obtained in the step 2. The method is applied to the field of variation detection.

The invention relates to a pig abnormal behavior recognition method based on a three-axis attitude angle, and belongs to the technical field of intelligent breeding and animal behavior monitoring. The method comprises the following steps: acquiring three-axis attitude angle data of a pig, wherein the three-axis attitude angle data comprises a yaw angle, a pitch angle and a roll angle; performing time sequence processing, frequency domain analysis, statistical feature extraction or state sequence modeling on the attitude angle data; and constructing a behavior recognition model, and judging whether an abnormal behavior exists or not. The behavior recognition model may include weighted scoring functions, spectral energy analysis, Z-score mutation detection, or behavior chain modeling based on state transition diagrams. According to the method, multi-dimensional fusion of identification logic is realized, the method is suitable for monitoring various abnormal behaviors of pigs such as oestrus, parturient, fright, rolling and manic-depressive pigs, has the characteristics of low power consumption, simple equipment, extensible algorithm and the like, and is suitable for being deployed in a wearable intelligent terminal of the pigs.

The invention provides a time sequence data mutation detection method and system based on multi-stage dynamic optimization and a storage medium, and belongs to the field of radiotherapy equipment state monitoring and quality control. The invention provides the equipment state change point detection method which does not need penalty parameters and is based on'multi-stage dynamic optimization '. According to the method, on the basis of Dynp and RBF models in a ruptures tool library, the accuracy and stability of change point detection are effectively improved through candidate change point supplementation, local refinement, redundant change point deletion and other stage processing. The method can be used for detecting time sequence data mutation related to quality control in the radiotherapy equipment, and the stability and safety of the radiotherapy equipment in the using process are guaranteed.

The invention relates to a tandem repeat related gene mutation detection system and a non-disease diagnosis purpose method. The system comprises an acquisition module, a Y-shaped pre-library preparation module, a targeted enrichment module, a single molecule real-time sequencing library construction module and a single molecule real-time sequencing analysis module, wherein target genes comprise ATXN1, ATXN3, ZFHX3, CACNA1A, ATXN7, ATXN8OS, ATXN10, PPP2R2B, TBP, FGF14, BEAN1-TK2, NOP56, DAB1, THAP11, FXN, GLS, RFC1, LRP12, GIPC1, RILPL1, VWA1, AR and DMPK; detection is carried out through three-generation sequencing, mutation setting is accurate, and the problem that an existing detection scheme is not practical is solved.