12 results about "Sequence dependence" patented technology

The application discloses a modified crRNA, a light-controlled nucleic acid detection system, a kit and application, and belongs to the cross field of biotechnology, intelligent sensing and molecular diagnosis. In view of the technical defects of strong target sequence dependence and high ultraviolet irradiation requirement of the existing light-controlled CRISPR technology, the application innovatively introduces a photosensitive protection group 6-nitropiperidin oxymethyl (NPOM) at a specific key node of a stem loop skeleton of crRNA maintaining conformation. In the constant temperature amplification stage, preferred double-site cooperative modification can transiently inhibit RNP complex assembly to realize target non-interference enrichment; subsequently, only 10 mW / cm 2 of extremely low intensity ultraviolet light irradiation for 30 seconds can restore the crRNA conformation and activate the trans cleavage. The preferred technical scheme of the application can eliminate the target sequence limitation, realizes a detection limit of as low as 2 copies in a single reaction tube, and the result can be obtained within 15 minutes. The system is widely applicable to rapid diagnosis of infectious agents, high-specificity typing of single nucleotide polymorphism and portable intelligent molecular diagnosis terminal.

The invention discloses a novel protein structure prediction method based on AI, and particularly relates to the technical field of protein structure research, a protein amino acid sequence is converted into a multi-dimensional feature matrix, homologous fragment information is fused, and feature representation with physical and chemical properties and potential folding rules is obtained; extracting local residue relation and global sequence dependency information by combining graph convolution and a self-attention mechanism, constructing a multi-scale subsurface space, and realizing comprehensive description of a folding trend; candidate conformation generation and uncertainty indexes are introduced into the submerged space, and it is ensured that the prediction process has reliable quantization; ranking and screening the priorities of the candidate conformations in combination with an energy constraint function and structural similarity measurement, and outputting an optimized structure set with reasonable energy and coordinated trend; a prediction structure is generated through multi-modal information fusion, and credibility evaluation is performed, so that the problem that prediction accuracy and interpretability are difficult to consider at the same time in an existing method is effectively solved.

The invention discloses an in-vehicle cooperative distribution path solving method based on sequence dependence and adaptive weight updating. The method comprises the following steps: generating an initial solution of an in-vehicle cooperative distribution path by using a two-stage heuristic algorithm based on a time window and capacity limitation; introducing sequence dependency and defining a fixed-length damage-repair operator considering the sequence dependency; iterating the initial solution, and realizing solution improvement by selecting, applying and updating a damage-repair operator sequence; dynamically adjusting the operator selection probability according to the expression of the operator sequence in historical iteration by using a simulated annealing adaptive weight updating mechanism; and when a stopping condition is met, outputting the current optimal solution. According to the method, the high-quality distribution path can be efficiently generated by considering the correlation of the operator use sequence under the real multi-constraint conditions of operator dependency, multi-truck and multi-unmanned aerial vehicle cooperative distribution and the like, and scientific decision support is provided for scheduling and path planning of the high-quality distribution path.

The invention provides a method for evaluating the authenticity of an intelligent contract transaction sequence dependence vulnerability variation generation result. The method comprises the following steps of: firstly, finding a function containing transfer in a contract and a function which can indirectly influence the transfer amount or the transfer object of the transfer; the function where the TOD vulnerability is located is found by locally executing a transaction sequence containing the two functions. And secondly, setting a global variable as a lock, and fixing the execution sequence of tFun and cFun by using an assertion mechanism of the smart contract to achieve the effect of repairing the transaction sequence dependency vulnerability. Thirdly, the repaired vulnerabilities are handed over to a variation tool for variation, and a varied vulnerability data set is obtained; and finally, calculating a Jaccard similarity coefficient between the varied vulnerability and the vulnerability of the original contract, and obtaining the authenticity of the varied vulnerability of the variation tool.

The invention relates to a cough detection method and device, computer equipment, a storage medium and a program product, and can solve the problem of joint modeling of local features and long time sequence dependence. The method comprises the following steps: acquiring a to-be-detected audio signal; inputting the audio signal into a cough detection model; the cough detection model is used for acquiring continuous vibration feature information of the audio signal, acquiring local time-frequency feature information of the audio signal, fusing the continuous vibration feature information and the local time-frequency feature information to obtain fused feature information, and determining a cough event detection result of the audio signal according to the fused feature information; wherein the continuous vibration characteristic information reflects a long sequence dependency relationship, related to the cough event, of the audio signal; the local time-frequency feature information comprises short-time local features of the cough event contained in the audio signal and a time sequence dynamic mode reflecting the dynamic change of the short-time local features in the time dimension.

The invention relates to the technical field of artificial intelligence, provides a key frame extraction method and device based on few sample learning, equipment and a medium, is applied to financial and medical health care service scenes, can construct a loss function based on adversarial loss, perception loss and time sequence consistency loss, realizes collaborative optimization of feature generation, purification and selection, and improves the accuracy of feature extraction. The error accumulation problem of staged training is avoided; a BEGAN generator based on boundary balance generative adversarial network can supplement a large number of high-quality synthetic frames under the condition of few samples, and the problem of scarcity of training data is relieved; interframe time sequence dependence is captured through a space-time attention mechanism based on a time sequence perception discriminator, and the space-time consistency of features can be improved; and noise introduced by adversarial training can be eliminated based on the feature purification sub-module, and the purity of features is ensured, so that high-quality and accurate key frame extraction is realized under the condition of few samples.

The invention belongs to the field of biological medicine, and provides a method and a model for predicting the antibody-antigen binding possibility based on expansion convolution and a channel attention mechanism and application of the method and the model. According to the method, antibody-antigen binding possibility prediction is carried out based on expansion convolution and a channel attention mechanism, a deep learning model is constructed by collecting a training data set, expansion convolution is introduced in training to expand a receptive field, and feature expression ability is enhanced in combination with the channel attention mechanism, so that efficient prediction is realized. According to the method, the receptive field is effectively expanded through expansion convolution to capture long sequence dependence, key features are self-adaptively enhanced in combination with a channel attention mechanism, the prediction accuracy is remarkably improved under the condition of limited labeled data, and the method can be widely applied to the fields of antibody screening, drug design, immunotherapy and the like.

A distributed energy power generation power prediction method and system based on time-frequency characteristics, collect the historical power generation time series data of the distributed energy node to be predicted; input the historical power generation time series data into the encoder, the self-attention layer of the encoder extracts the sequence dependence feature, and the STFT layer of the encoder extracts the sequence time-frequency feature; then add the historical power generation time series data and the sequence dependence feature extracted by the self-attention layer of the encoder and the sequence time-frequency feature extracted by the STFT layer of the encoder, obtain the output encoding result of the encoder through the feedforward layer; finally, based on the output encoding result of the encoder, use the decoder to predict the power generation sequence of the distributed energy node. The application effectively captures the dependence relationship and time-frequency characteristics between long-term and large amount of distributed energy historical power generation data by using the self-attention mechanism and STFT, and introduces the sparse operation, effectively reduces the data operation amount, and improves the accuracy of sequence prediction.

The application discloses a social network link prediction method based on a space-time feature perception time sequence diagram network, which divides key features in a time sequence diagram into two categories of time sequence features and structure features, for the time sequence features, through adaptive fusion of a continuous time method and a discrete method, long sequence dependence and recent dependence are effectively captured and weighed, for the graph structure features, through co-occurrence neighbor coding, graph structure information is effectively explicitly coded, and a hash-based method is used to improve co-occurrence neighbor retrieval efficiency. Such a method not only exceeds previous methods in prediction accuracy by fully capturing time sequence features and structure features, for example, the average accuracy is improved by 14.18%, 15.89% and 32.48% respectively on USLegis, UNtrade and Unvote data sets compared with previous optimal methods, and better trade-off is achieved in inference efficiency. And it is simple to realize and easy to reproduce.

The invention discloses a social network link prediction method based on a spatial-temporal feature perception time sequence diagram network, and the method comprises the steps: dividing key features in a time sequence diagram into time sequence features and structural features, and carrying out the adaptive fusion of a continuous time method and an internal discrete method for the time sequence features, long-sequence dependence and recent dependence are effectively captured and balanced; for graph structure features, explicit coding is effectively carried out on graph structure information through co-occurrence neighbor coding, and meanwhile the co-occurrence neighbor retrieval efficiency is improved through a hash-based method. According to the method, the time sequence features and the structural features are fully captured, the prediction precision exceeds that of a preorder method, for example, the average precision of a USLegis data set, a UNtrade data set and a Unvote data set is improved by 14.18%, 15.89% and 32.48% compared with that of a preorder optimal method, and better balance is achieved in reasoning efficiency. The method is easy to implement and easy to reproduce.

The application discloses a kind of node zero trust trusted access methods and devices for computing power network, it is related to network security technical field.Based on the principle of zero trust, it is carried out in the registration phase and task execution phase of computing power node Trust evaluation.In the registration phase, the multi-source trust modeling mechanism of fusing identity attribute, capability observation and organization reputation is constructed, and the initial trust evaluation is realized by introducing Bayesian inference and graph regularization method;In the running phase, a multi-modal log anomaly detection method is designed, high-dimensional behavior modeling is carried out combined with semantic, time, parameter and quantity characteristics, and the abnormal identification ability is enhanced by using long sequence dependence;Further combined with time decay and user feedback mechanism, a dynamic trust adjustment strategy is proposed, to realize the continuous evolution of node trust and fine access control.The application realizes the trusted access of computing power node by scheduling system in the environment of computing power network, effectively improves the security and reliability of computing power network system.

The application belongs to the field of biological medicine, and provides a method and model for predicting antibody-antigen binding possibility based on dilated convolution and channel attention mechanism, and application thereof. The method is based on dilated convolution and channel attention mechanism for predicting antibody-antigen binding possibility, a deep learning model is constructed by collecting a training data set, dilated convolution is introduced in training to expand the receptive field, and the channel attention mechanism is combined to enhance the feature expression capability, so as to realize efficient prediction. The application effectively expands the receptive field by dilated convolution to capture long program sequence dependence, combines the channel attention mechanism to adaptively strengthen key features, significantly improves the prediction accuracy under the condition of limited labeled data, and can be widely applied in the fields of antibody screening, drug design and immunotherapy.