50 results about "Sequence design" patented technology

The invention belongs to the technical field of essential gene prediction, and particularly relates to an essential gene prediction method based on DNA large model and time-frequency domain deep learning fusion, and the method comprises the steps: taking a domain DNA large model as a core representation layer, and obtaining special gene representation through cross-species corpus pre-training and task fine tuning; a T-Block and F-Block dual-channel time-frequency fusion structure is adopted, and the local dependence and long-range regulation relation of a gene sequence is synchronously captured by expanding DFT (Discrete Fourier Transform), complex value attention and iDFT (Initial Discrete Fourier Transform) conversion; designing an efficient modeling reasoning scheme of sliding window slices and gene-level aggregation aiming at an ultra-long sequence; in combination with class imbalance and a noise robust training strategy, cross-cell line / cross-platform transferable threshold output is realized through temperature scaling calibration, an uncertainty quantization and structured interface is matched, and drug target screening and experimental design decision are supported. The system supports the realization of multiple programming languages, and can complete low-delay end-to-end reasoning in a conventional hardware environment.

Disclosed are an auxiliary pilot adaptive optimization method, apparatus and device, and a medium, which relate to the technical field of wireless communications, and solve the problems of static configuration, resource waste and single function of the existing auxiliary pilot. An auxiliary pilot frequency sequence is designed into a synchronous prefix and an estimation core section, and the synchronous prefix adopts a pseudo-random sequence with high autocorrelation and is used for timing synchronization (positioning through a sequence autocorrelation peak value) and frequency synchronization (calculating frequency offset through a phase difference) in a demodulation process; the estimation core section adopts an orthogonal sequence with a low peak-to-average ratio and is used for accurate channel estimation; the double-segment sequence is connected through a preset code pattern, and it is ensured that a demodulation end can perform synchronous analysis. By sensing the channel state in real time, parameters of the auxiliary pilot frequency are dynamically adjusted, and a synchronization function is fused, so that the demodulation performance and the resource utilization rate are improved.

The invention discloses an autoclave temperature offline simulation and online prediction method based on Hybrid KAN feature fusion. The autoclave temperature offline simulation and online prediction method comprises the steps that historical temperature time sequence data and static process parameter data of different batches are collected; performing feature screening and preprocessing, and constructing a time sequence data input sequence and a static process parameter input sequence; designing a Hybrid KAN neural network model, taking the time sequence data input sequence and the static process parameter input sequence as input, and outputting a predicted value of the temperature of the autoclave at the next moment through feature extraction and fusion; taking minimization of a loss function as a target, training the model, and obtaining an optimal model by using a structured pruning strategy; and deploying an optimal model, and realizing off-line simulation and on-line prediction of the temperature of the autoclave through rolling time window iteration multi-step prediction. The method is high in accuracy, wide in universality, small in model parameter scale, high in reasoning efficiency and suitable for autoclave temperature prediction in complex scenes.

The invention discloses a diffusion model reasoning acceleration method based on optimal time step sequence search and knowledge distillation, which constructs a unified search space covering a time step sequence and corresponding model architecture configuration on the premise of not performing overall fine adjustment on a pre-trained diffusion model, and searches an optimal time step sequence. And designing a knowledge distillation training method based on the optimal diffusion time step sequence obtained by searching, constructing a main distillation loss function in a discrete time step subspace limited by the optimal diffusion time step sequence, and introducing adjacent time step consistency loss to relieve discrete errors introduced by a non-uniform time step span. In the reasoning stage, a teacher model is frozen, a distilled student model is combined with an efficient sampler ACDMS based on UniPC and fused with flow matching dynamic correction, finite step sampling is carried out on an optimal time step sequence, and reasoning acceleration under generation quality maintenance is achieved.

The invention discloses a time series prediction method based on a multi-modal enhanced large language model, which comprises the following steps: acquiring historical time series data, constructing a standardized input matrix and setting core task parameters; dividing data blocks through a sliding window mechanism, and converting the data blocks into uniform dimension features through linear embedding; a semantic prototype is generated based on a large language model vocabulary, and time sequence features and semantic features are fused through multi-head cross-attention; alternately splicing the data blocks and the corresponding semantic information, and constructing a self-multi-modal input sequence; designing a three-level structured prompt including context, task target and modal guidance, and fusing the three-level structured prompt with a multi-modal sequence; and training the lightweight model by adopting a frozen training strategy, and outputting a prediction result of a specified time step in the future. According to the method, through single-source data enhancement and prompt guidance, the time sequence reasoning capability of a small-parameter large language model is activated, high-precision prediction is guaranteed, efficient deployment is achieved, and the method adapts to long-term and short-term prediction tasks in the fields of electric power, traffic, meteorology and the like.

The application discloses an mRNA vaccine sequence design system and method based on a large language model intelligent agent, and the mRNA vaccine sequence design system based on the large language model intelligent agent comprises a user interaction and task analysis module, a task planning and intelligent agent scheduling module, an RNA design skill library and skill description module, a multi-target evaluation and closed-loop optimization module and a data and model management module. The application integrates a large language model and various mRNA sequence analysis and optimization tools, automatically completes mRNA vaccine sequence generation, evaluation and optimization for a target antigen in a computer environment, is suitable for candidate sequence design and screening of a preventive or therapeutic mRNA vaccine, improves design efficiency, and reduces the degree of dependence on manual experience and manual script arrangement.

This invention relates to an RNA sequence design method based on isovariant geometry and graph neural networks, comprising: first, RNA three-dimensional structure data and corresponding sequences; then, after dataset encapsulation and feature extraction, vector embeddings that can be processed by graph neural networks are generated; next, the data is fed into the Equiformer+GVP model for modeling, and the model outputs the probability distribution of four bases at each nucleotide position; finally, the bases are selected according to the probability distribution to form the predicted RNA sequence. Among these methods, E(3) isovariant constraints are introduced to achieve geometric consistency expression, and multi-scale geometric feature encoding is combined to enhance the model's understanding of local and global structures, significantly improving the model's physical rationality, prediction accuracy and universality; and the three-dimensional geometric skeleton of the RNA molecule is used as the only input, eliminating the dependence on secondary structures or external contact diagrams, and learning and generation are performed directly at the three-dimensional spatial coordinate level, thereby achieving significant improvements in physical consistency, geometric completeness and generalization ability.

Various schemes are described for long training field (LTF) sequence design for distributed tone resource units (DRUs) for wider bandwidth in wireless communications. A device (e.g., a working station (STA)) generates a DRU LTF sequence of a DRU using a predefined DRU LTF base sequence. The device transmits the DRU LTF sequence of the DRU in wireless communications of a bandwidth of 160 MHz or more.

The invention discloses a communication system of an improved parallel cascade LDPC code structure based on a single encoder, and belongs to the technical field of aviation platform laser communication. Aiming at the bottlenecks that the traditional PCGC depends on double encoders, the hardware overhead is large, the decoding delay is high and the complementarity requirement on component codes is harsh, the system adopts a single encoder architecture design: firstly, a basic verification sequence is generated through a single LDPC encoder, and differential verification constraints are constructed through interleaving and repeating operations; meanwhile, a zero syndrome and non-zero syndrome dual-check sequence design is introduced, so that the minimum distance of a code word is increased; a decoding end fuses a normalized minimum sum algorithm and a hierarchical message updating and dynamic early stop mechanism, combines channel sensing soft information construction and a self-adaptive interleaving and redundancy allocation strategy, and adapts to composite channel characteristics of an aviation platform.

The application provides a method and system for generating and predicting the function of mRNA untranslated region sequence based on coding sequence. The method comprises: constructing a pre-training data set and a downstream task data set; using the pre-training data set to perform autoregressive training on the constructed conditional generation model to obtain a UTR sequence generation model; using the downstream task data set to fine-tune the UTR sequence generation model to obtain a downstream task function prediction model; generating candidate UTR sequences based on the UTR sequence generation model, and evaluating the generation ability of the UTR sequence generation model in the non-coding region sequence generation task; and predicting the function attribute of the UTR sequence based on the downstream task function prediction model, and evaluating the prediction ability of the downstream task function prediction model in multiple UTR related downstream tasks. The application considers the synergistic relationship between UTR and CDS in the UTR sequence generation process, and improves the efficiency and rationality of UTR sequence design.

The invention discloses an mRNA vaccine sequence design system and method based on large language model intelligent proxy. The mRNA vaccine sequence design system based on the large language model intelligent agent comprises a user interaction and task analysis module, a task planning and intelligent agent scheduling module, an RNA design skill library and skill description module, a multi-target evaluation and closed-loop optimization module and a data and model management module. According to the method, the large language model and various mRNA sequence analysis and optimization tools are integrated, mRNA vaccine sequence generation, evaluation and optimization aiming at the target antigen are automatically completed in a computer environment, the method is suitable for candidate sequence design and screening of prophylactic or therapeutic mRNA vaccines, the design efficiency is improved, and the design cost is reduced. And the degree of relying on artificial experience and manual script arrangement is reduced.

Various schemes pertaining to four times (4×) long-training field (LTF) sequence design for wide bandwidths in wireless communications are described. A processor of an apparatus generates an LTF of a physical-layer protocol data unit (PPDU) with a 78.125 kHz subcarrier spacing by using a predefined LTF base sequence. The apparatus then performs a wireless communication in a 240 MHz, 480 MHz or 640 MHz bandwidth with the PPDU.

The invention discloses a quantitative prediction method of anti-candida peptide activity and a sequence design method of high-activity anti-candida peptide, and relates to the technical field of bioinformatics and artificial intelligence crossing, the quantitative prediction method comprises the following steps: inputting an antibacterial peptide sequence of which the activity is to be predicted into a pre-trained activity quantitative prediction model to obtain an anti-candida MIC (Minimum Inhibitory Concentration) value; carrying out fusion processing on anti-candida multisource heterogeneous information in the antibacterial peptide sequence by utilizing a feature preprocessing module to obtain a preprocessed antibacterial peptide sequence; the core reasoning module comprises a random forest sub-model and a CatBoost sub-model, a first MIC predicted value is output through the random forest sub-model, and a second MIC predicted value is generated through the CatBoost sub-model; and converting the second MIC predicted value into an anti-candida MIC value in the antibacterial peptide by using a result conversion module. According to the method, the activity characteristics of the antibacterial peptide can be automatically learned from a large amount of sequence data, the activity of the antibacterial peptide is predicted, and the prediction accuracy and reliability are improved.

The embodiment of the invention relates to a communication method, equipment, a device and a medium. In accordance with an example embodiment of the present disclosure, a first device determines a first frequency from a first set of frequencies and a second frequency from a second set of frequencies. The first device also generates a complex exponential sequence based on the first frequency and the second frequency. The complex exponential sequence includes a first complex exponential subsequence associated with a first frequency and a second complex exponential subsequence associated with a second frequency, the first frequency being different from the second frequency. The first device also sends the complex exponential sequence to the second device in response to the first device being in an active state. In this way, more accurate, rapid and extensible user activity detection can be constructed through the leader sequence design disclosed by the invention.

The invention discloses an RNA sequence design method and device based on reinforcement learning and a submerged space diffusion model, equipment and a medium, and relates to the technical field of artificial intelligence. A mixed attention mechanism in the submerged space diffusion model is used for processing an initial RNA sequence and an RNA three-level structure; performing rotation position coding, activation function optimization and mask processing on the processed data to obtain a first RNA sequence; carrying out reward value calculation on the second RNA sequence by utilizing a reinforcement learning optimization engine and based on a preset reward mechanism to obtain each reward value, carrying out fusion and structural analysis on each reward value to obtain a target reward value, and determining a third RNA sequence corresponding to the target reward value from the second RNA sequence; and carrying out multi-objective optimization on the third RNA sequence by utilizing a Pareto frontier method to construct a target RNA, so that the model training time and resource consumption are reduced, and the RNA sequence design based on reinforcement learning and the submerged space diffusion model is realized.

This disclosure provides methods, components, devices and systems for tone mapping, enhanced long range (ELR)-mark sequence designs, and signal field designs for ELR transmissions. Some aspects more specifically relate to ELR-signal (ELR-SIG) field designs within a physical layer (PHY) protocol data unit (PPDU) associated with an ELR format to facilitate a parsing of ELR-data that follows an ELR-SIG field. Some further aspects more specifically relate to mechanisms according to which devices may support a set of ELR sequences associated with relatively low peak-to-average-power ratios (PAPRs) and usable to convey information that a receiving device may use to determine whether to perform an early drop of a received ELR PPDU. In some examples, various devices may support a set of ELR sequences with each ELR sequence of the set indicative of a respective basic service set (BSS) information value.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a signal that identifies a set of sequences for low-power (LP) signal transmissions from respective network entities in a set of network entities, each sequence defining a unique sequence of samples for the respective unique network entities when modulating the LP signal transmissions. The UE may receive the LP signal transmissions from the set of network entities in accordance with the set of sequences, a first set of samples of a first sequence from a serving network entity in the set of network entities comprises a different set of samples relative to a second set of samples of a second sequence from a non-serving network entity in the set of network entities. The UE may perform an operation with the serving network entity in accordance with the first set of samples of the first sequence.

This invention discloses a meme island optimization method and related apparatus for complementary sequence sets, belonging to the field of complementary sequence design. The method includes: constructing multiple island subpopulations that evolve in parallel; initializing a global archive for recording the history of search space region visits; performing population evolution in parallel on multiple islands; evaluating each candidate complementary sequence set according to a fitness function that incorporates region guidance; exchanging elite individuals among multiple islands when a preset migration condition is met; and outputting the optimal complementary sequence set found among all islands when an iteration condition is satisfied. By introducing region guidance based on the global archive into the fitness function, the search is actively guided to unexplored regions, systematically avoiding the search process from stagnating in the same basin, avoiding getting trapped in local optima, significantly improving global exploration capability, and reducing the risk of premature convergence.

The invention discloses a deep learning-based de novo enzyme design and catalytic activity prediction system. The system comprises a sequence generation module, a three-dimensional structure construction module, a molecular feature coding module, a catalytic activity prediction module, an iterative optimization module, a data enhancement module, a structural stability correction module and an experimental verification feedback module. The method comprises the following steps: a sequence generation module outputs an amino acid sequence; a construction module generates three-dimensional coordinates; a molecular feature coding module tensions coding structure information; a catalytic activity prediction module calculates an activity score; and the experimental verification feedback module forms closed-loop updating. The method has the beneficial effects that the de novo enzyme sequence design is accelerated, the prediction accuracy of the catalytic activity is improved, the structural stability is ensured, and the high efficiency and controllability of enzyme design are realized.

The invention discloses a time-frequency synchronization method and system, electronic equipment and a storage medium, and relates to the technical field of communication. The method comprises the following steps: acquiring a received signal, and performing iterative equivalent matched filtering on the received signal based on symbol delay to obtain a burst signal preliminary positioning result; and generating a local training symbol based on a preset frequency domain spread spectrum sequence, and performing parallel matched filtering by using the local training symbol and the initial positioning result of the burst signal to obtain a joint detection result of frequency offset estimation and time domain synchronization. Through innovative iteration equivalent matching detection, frequency domain sequence design and parallel matching processing, the carrier synchronization performance of the OFDM satellite communication system is greatly improved, synchronization is faster, frequency offset estimation is more accurate, coverage frequency offset is larger, weak signal adaptability is higher, meanwhile, implementation complexity and resource overhead are reduced, and the method is suitable for large-scale popularization and application. And the method has obvious engineering application value.

1.The name of the design product: electronic device with enzyme calculation platform graphical user interface. 2.The use of the design product: an electronic device. 3.The design points of the design product: the interface content of the graphical user interface. 4.The picture or photo that best shows the design points: interface change state diagram 3. 5.No design points, omit rear view; no design points, omit left view; no design points, omit right view; no design points, omit top view; no design points, omit bottom view. 6.The use of the graphical user interface: for enzyme-related parameter calculation, mutant sequence design, molecular structure visualization, enzyme reaction simulation, enzyme agent intelligent auxiliary design and result analysis, to help users efficiently complete the calculation task of enzyme design and optimization. 7.Other circumstances that need to be explained: the front view is the initial interface, in the front view, slide down to enter interface change state diagram 1, input calculation requirement information and reference text in the text box of interface change state diagram 1, click the submit button (the button with a round shape and an upward arrow inside) to enter interface change state diagram 2, or select the “go to” button corresponding to the item below the “recommended for you” column in the front view or interface change state diagram 1 to enter interface change state diagram 2, after the result output shown in interface change state diagram 2 is completed, click the “think again” button to output the result again, after the result output shown in interface change state diagram 2 is completed, click the “start calculation” button to enter interface change state diagram 3, after the result output shown in interface change state diagram 3 is completed, click the “next stage” button to enter interface change state diagram 4, after the result output shown in interface change state diagram 4 is completed, click the “next stage” button to enter interface change state diagram 5, after the result output shown in interface change state diagram 5 is completed, click the “next stage” button to enter interface change state diagram 6.

The invention provides a protein sequence design method based on a three-position structure, relates to the technical field of artificial intelligence, is a two-stage protein map neural network model based on a state space model, and aims to fully utilize context information of a long sequence to generate all residues at one time, so that the design efficiency is improved. And the design effect is improved while the model reasoning speed is ensured. In the first stage, comprehensive physical information is adopted to construct node features of the protein map, and the sequence design effect is remarkably improved; meanwhile, in the second stage, a method of combining a selection state space model and a graph neural network is used for updating node features of protein graph data, global information is processed more effectively, context features are extracted, adjacent residue information is fully learned in the continuous iteration process, the global information is considered at the same time, and the method is more efficient. The efficiency and effect of protein sequence generation are both considered.

The invention discloses a single-input single-output channel data analysis and model construction method oriented to an urban environment. In order to improve the reliability and efficiency of a communication system, the method comprises the following steps of: constructing a test platform, designing a detection sequence and constructing a statistical model to realize a complete theoretical closed loop from channel feature extraction to modeling. In a data processing and feature extraction stage, a set of efficient signal processing flow is provided, and high-precision time synchronization is realized through cross-correlation calculation of sliding correlation. According to the modeling method, a sine wave superposition algorithm based on LP-Norm is adopted for a Gaussian channel, and fitting of Doppler power spectrum density is achieved by optimizing Doppler coefficients and harmonic parameters. For non-Gaussian characteristics, a sphere invariant process theory is introduced, channel fading is modeled into a product form of a Gaussian process and a heavy-tailed random variable, the combined influence of multipath and shadow effect in an urban environment is captured, and the two fading components are decoupled through Mellin transform. The effectiveness of the method is verified through simulation.

The invention provides a protein sequence design method and system based on deep learning and 3D features. The method comprises the following steps: acquiring 3D structure data of a target protein; extracting 3D space structure features based on a physical and chemical environment coding framework; obtaining pseudo multi-sequence comparison information of the target protein, and extracting multi-sequence context features from the pseudo multi-sequence comparison information; constructing a memory tuning network with a bidirectional gating mechanism, and performing dynamic weight fusion on the 3D spatial structure features and the multi-sequence context features to generate a fused residue state; performing multiple rounds of iterative refining on the residue state by using a memory tuning network; and determining a final amino acid category of the residue position according to the amino acid category probability distribution corresponding to the maximum prediction confidence obtained by each residue position in all iteration rounds, and obtaining a final protein design sequence of the target protein. According to the method, the sequence design accuracy in the protein reverse folding task can be improved.

This application discloses a primer design method, system, and computer storage medium based on nucleic acid consistency. The method includes: obtaining a target nucleic acid sequence; classifying the target nucleic acid sequence to obtain multiple taxonomic units; using at least one target nucleic acid sequence from each taxonomic unit as a representative sequence; designing candidate primers for the representative sequence; sequentially selecting at most one pair of candidate primers from each taxonomic unit to establish a candidate primer pool; and excluding candidate primers in the candidate primer pool that exhibit non-specific amplification to obtain the final primer pool. This application is simple to operate and can handle large nucleic acid libraries, making it particularly suitable for universal primer design for pathogen libraries. Compared to similar software, the method in this application is more systematic and comprehensive, resulting in a primer pool with high coverage and low host contamination rate.

The application belongs to the field of composite material structure design, and particularly relates to a composite material layer sequence design method, which is characterized by the following: based on given layer proportion and total number, under the constraints of engineering rules such as symmetry, balance, outer ±45°, and continuous layer not more than 4 layers, the number of layers in 0°, ±45° and 90° directions is calculated first; then, a concept of predefined minimum layer unit group is introduced; then, according to the number of layers, the number of different unit groups is determined; then, the unit groups are arranged in a specific order, and pure 0° unit groups are inserted therebetween to generate an initial layer sequence; finally, a database of all typical layer schemes meeting the conditions is automatically output according to the layer rules. The application realizes the automation and systematization of layer design, can efficiently generate multiple schemes meeting the engineering constraints, and is particularly suitable for composite material structures with a large number of layers or requiring variable-thickness continuity design.

The application discloses a primer design method, system and computer storage medium based on minimum degeneracy, the method comprising: obtaining nucleotide sequences; dividing the nucleotide sequences into multiple classification units; selecting a representative sequence from each classification unit; designing a corresponding primer pair set for the representative sequence based on the base frequency in the representative sequence; determining the coverage of the primer pair set relative to all nucleotide sequences, and selecting multiple primer pairs in each primer pair set as a candidate primer pair set according to the coverage of each primer pair set; and selecting multiple candidate primers in the candidate primer pair set to establish a primer pool. The application considers the mismatch between primer pairs and the coverage in the primer design process, thereby allowing the primer pairs to have extremely high coverage and lower degeneracy.

The invention provides an automatic operation time sequence design method for a mercury ion microwave frequency standard, which is automatically operated by software according to a time sequence, and comprises the following steps: when parameters of microwaves, crystal oscillators and mercury lamps are read back to be consistent with set values and the mercury lamps are lightened on time, starting microwave frequency sweeping; after frequency sweeping is finished, it is judged that the scanning range and the semaphore are abnormal, and fluorescence signal data fitting is carried out; fitting to obtain center frequency and fluorescence spectrum line width data, and setting the center frequency and the line width if no abnormity exists; if the read-back data is not abnormal, crystal oscillator pressure sweeping is carried out; after voltage sweeping is finished, it is judged that the scanning range and the semaphore are not abnormal, and crystal oscillator signal data fitting is carried out; fitting to obtain a center voltage value, and setting the center voltage value for the crystal oscillator if no abnormity exists; and if the read-back data is not abnormal, starting to lock the whole machine. Real-time monitoring is carried out in the locking process, it is ensured that the system is locked under the condition that the important peripheral working condition is normal, and it is ensured that data is true and credible.

The application discloses a command sequence anomaly detection method and system based on semantic representation and context perception, the method comprises the following steps: constructing a multi-layer command filtering and cleaning mechanism, adopting a three-level processing flow of session reconstruction, command deduplication and noise filtering to convert original commands into standardized token sequences; designing a semantic representation learning module, training an encoder-decoder network through a mask language model to learn deep semantic representation of commands; constructing a context perception modeling module, capturing context dependency of command sequences based on a state space model and a sliding window mechanism; implementing a two-stage anomaly detection strategy; the system comprises a command preprocessing module, a context semantic modeling module and an anomaly detection module. The application realizes fine detection from the session level to the command level, effectively controls the computing overhead while ensuring the detection accuracy, greatly reduces the model construction and maintenance cost, and improves the practicability and deployability of the system.