366 results about "Cell based" patented technology

The embodiment of the invention provides an annotation model training method, a cell type annotation method and related equipment, and belongs to the technical field of biological information analysis. The method comprises the following steps: inputting acquired spatial transcriptome data and single cell sequencing data into an initial annotation model; determining reference annotation information corresponding to each target single cell in each spatial site of the sample tissue data according to the single cell sequencing data by taking the spatial transcriptome data as a constraint condition; determining prediction annotation information corresponding to each target single cell based on the reference annotation information and the spatial transcriptome data; performing joint annotation processing on each target single cell according to the reference annotation information and the predicted annotation information to obtain a sample annotation result corresponding to the sample tissue data; and adjusting model parameters of the initial annotation model according to a sample annotation result to obtain a trained annotation model. The cell type annotation accuracy of the biological sample tissue can be improved.

The invention relates to the technical field of bioinformatics, in particular to a method for identifying an elncRNAs regulation target gene in a single cell based on single cell ATAC-seq. According to the method provided by the invention, a regulatory network of an elncRNAs-target gene on a single cell whole genome is identified by utilizing chromatin co-accessibility based on scATAC-seq data. Meanwhile, the method is applied to immune cell differentiation, and the elncRNAs-target gene regulation dynamic state of immune cell differentiation is accurately recognized. In addition, the biological functions of the elncRNAs in different cell types are further analyzed. The method provided by the invention can help related researchers in the field of biomedicine to more elaborately and accurately predict the regulation effect and function of the elncRNAs under the single cell resolution.

The invention is suitable for the technical field of cell analysis, and particularly relates to a cell mechanical phenotype analysis method and device based on deep learning, and the method comprises the following steps: obtaining a time sequence image, mechanical response data and culture environment parameters of a target cell; based on the mechanical response data and the culture environment parameters, performing region segmentation on the time sequence image, determining a mechanical sensitive region on the surface of the target cell and local deformation features of a subcellular structure, and realizing refined and targeted feature recognition; generating a mechanical phenotype descriptor of the target cell according to the mechanical sensitive area, the local deformation characteristics and the culture environment parameters to realize multi-modal data fusion; a mechanical phenotype analysis model obtained by training a sample mechanical phenotype descriptor of a sample cell in advance is used for processing the mechanical phenotype descriptor to obtain a heterogeneity prediction result of a target cell, the heterogeneity of the mechanical characteristics of the cell in a complex culture environment is automatically identified, and the dynamic nature and generalization ability of heterogeneity classification are remarkable.

Aspects of the disclosed technology include computer-implemented systems and methods for integrating machine-learned generative models with code editing tools. A code editor is configured to execute computer-executable code within code cells of a code editor interface including a first interface portion and a second interface portion. The interface portion is configured to receive user input for defining and editing a set of code cells within the first interface portion. Each code cell of the set of code cells is independently executable by the code editor application. The second interface portion is configured to receive user input for defining and submitting user queries to a machine-learned generative model. The code editor is configured to modify at least one code cell of the set of cells based at least in part on an output of the machine-learned generative model in response to a user query.

The invention relates to the technical field of computational mechanics and geological disaster simulation, and discloses a full-process numerical simulation method suitable for simulating continuous medium damage to discontinuous medium movement. The invention provides a novel chain-type disaster simulation method for solving the problems that calculation efficiency and large deformation precision are difficult to consider at the same time and the evolution process of a slope from a continuum to a fragmented body cannot be dynamically reflected in the prior art. The core of the method is that a dynamic criterion is set based on a unit real-time damage variable, a failed finite element (FEM) unit is adaptively converted into smoothed particle hydrodynamics (SPH) particles, and the physical state of the particles is accurately mapped; and a virtual particle coupling algorithm is adopted to process a two-domain interface, so that bidirectional transmission of mechanical parameters is realized. The method is executed circularly, the whole process of'continuous deformation-fragmentation flow 'of the side slope is simulated dynamically, high-precision integrated simulation of a'seismic source-propagation-response-movement' disaster chain in the same frame is achieved, and an efficient tool is provided for disaster risk assessment and prevention and control.

The invention discloses a measurement processing method, a measurement configuration method, devices, equipment and a readable storage medium. The method comprises the following steps: a terminal obtains a first predicted value of a beam in a first beam set of a first cell based on an AI model; the terminal generates a first predicted measurement value of the first cell according to the first predicted value; wherein the input of the AI model comprises a first actual measurement value of a beam in a second beam set of the first cell.

According to implementations of the present disclosure, there is provided a solution for auto-formatting of a data table. A computer-implemented method comprises obtaining values of at least one attribute for a plurality of cells in a data table, the values of the at least one attribute indicating at least one of a semantic meaning of data filled in the cells or a structure of the data table, the cells being arranged in rows and columns in the data table; extracting a feature representation of the values of the at least one attribute In using a first learning network; and determining respective formats for the cells based on the feature representation using at least the first learning network. In this way, it is possible to implement automatic mapping from attribute settings of cells to cell formats by using a learning network, thereby achieving auto-formatting of the data table.

The invention discloses a method for detecting a diallele editing cell based on a CRISPR / Cas12a technology. The invention provides crRNA, and the nucleotide sequence of a binding target of the crRNA is SEQ ID NO.6. The crRNA is obtained by in-vitro transcription of a transcription template; the transcription template is a product obtained by annealing a single-stranded DNA (Deoxyribose Nucleic Acid) molecule as shown in SEQ ID NO.10 and a single-stranded DNA molecule as shown in SEQ ID NO.13. The invention provides crRNA of the CD71 gene, a CRISPR / Cas12a system is constructed by using the crRNA, CRISPR / Cas12a detection is performed, screening of CRISPR / Cas9 induced CD71 gene diallele editing cells is realized, and the method is simple, convenient, quick and convenient for economically and effectively screening a large number of mutant cells.

The invention provides a method and a device for constructing a three-dimensional digital geological model. The construction method comprises the following steps: constructing a geologic model area range and discretizing the geologic model area range into n unit lattices; mapping the drilling data to n unit lattices to construct an initial geological model; constructing a neighborhood system of each unit lattice, and setting spatial constraints in seven directions; based on the label value of each unit lattice in the initial geologic model, obtaining a prediction result comprising M sampling label values of each unknown unit lattice according to a label probability prediction formula and an updating iteration formula of a granularity coefficient; based on the prediction result, obtaining the marginal probability of each unknown unit lattice and the prediction label value of each unknown unit lattice; and updating the initial geologic model based on the predicted label value of each unknown unit lattice, and constructing a three-dimensional digital geologic model. The three-dimensional digital geologic model constructed by adopting the construction method is accurate in simulation result and can represent the uncertainty of the stratum at the same time.

The invention discloses an aluminum electrolysis cell internal magnetic field prediction method based on a physical information network (PINN), and the method comprises the following steps: inputting related data of a to-be-predicted magnetic field into a trained PINN model, and outputting prediction data; the specific content of constructing the trained PINN model comprises the following steps: S1, constructing a physical model of an internal magnetic field of a target aluminum electrolysis cell; s2, a PINN model is constructed, a loss function is constructed according to the physical model of the internal magnetic field of the target aluminum electrolysis cell, physical loss in the back propagation process is calculated through the loss function through iterative training and back propagation, if a preset condition is met, training is completed, and the PINN model used for predicting the internal magnetic field of the target is obtained; and S3, verification of the PINN model is completed, and a trained PINN model is obtained.

The invention relates to the technical field of medical information systems, in particular to a blood smear abnormal cell intelligent recognition system and method based on deep learning. The multi-stage convolutional neural network module is in data connection with the image acquisition module, and the abnormal cell bank module is in data connection with the multi-stage convolutional neural network module; the knowledge graph module is in data connection with the abnormal cell bank module, and the blood index calculation module is in data connection with the multistage convolutional neural network module and the abnormal cell bank module and is used for calculating various blood indexes; generating a graphical display result of the blood indexes; the result output module is in data connection with the knowledge graph module and the blood index calculation module; according to the blood disease diagnosis method and system, the whole process from image acquisition to diagnosis report generation can be automatically completed, detailed analysis results and diagnosis suggestions can be provided for doctors, and the efficiency and accuracy of blood disease diagnosis are greatly improved.

The invention discloses an scRNA-seq missing data prediction method based on depth map contrast learning. The scRNA-seq missing data prediction method comprises the following steps: 1, acquiring an scRNA-seq data matrix; 2, the scRNA-seq matrix is preprocessed; and 3, constructing a cell map by using the preprocessed scRNA-seq data. And 4, inputting the cell map data into a contrast learning module, and learning and obtaining the embedded representation of the cells. And 5, determining a neighbor cell of each cell based on the cell embedding representation, calculating a related weight of the neighbor cell and the neighbor cell through a least square method, and finally filling a missing value of each cell. And 6, evaluating the accuracy of the predicted value through downstream analysis. According to the method, the missing value can be well recovered, the clustering of the cell subpopulation is promoted, and the deduction of the cell development trajectory is facilitated.

The invention discloses a space-time fusion knowledge tracking method based on large model annotation enhancement, and the method comprises the steps: firstly collecting and sorting historical interaction data of students in an intelligent teaching system, and determining questions, knowledge points and a corresponding relation thereof; and then various relationships among knowledge points are labeled through a large model, an initial graph is constructed, units and relationships among the units are labeled, then a labeling result is detected and optimized, and errors are corrected so as to improve the labeling quality. Then, knowledge point embedding is processed through a time sequence learning module based on an attention mechanism, question embedding is obtained in combination with difficulty, and a time sequence embedding code is obtained; and feature extraction, integration and comparative learning are carried out through the fusion learning module. And finally, node embedding is dynamically updated based on the unit relation graph, various information is fused, and a loss function is predicted and calculated by using a neural network in combination with a forgetting factor. According to the method, the accuracy and interpretability of knowledge tracking are improved, and personalized teaching is assisted.

The invention discloses a section output method, device and equipment of a three-dimensional model and a storage medium, and relates to the field of computer graphics, the section output method of the three-dimensional model comprises the steps that the three-dimensional model based on unit expression is acquired, and units are formed by connecting point data in the three-dimensional model; determining a binary space tree corresponding to the three-dimensional model based on a set maximum level, wherein each space node of the binary space tree comprises at least one unit; calculating a bounding box of each space node of the binary space tree, and screening out section units in the units according to positive and negative states of the bounding boxes and sections; and performing section calculation based on the section unit, and outputting a section model corresponding to the three-dimensional model. According to the method and the device, the technical effects of realizing section efficient performance calculation in a large model data set, eliminating a traversal calculation process of unnecessary units and greatly reducing calculation overhead can be realized.

The invention discloses a method for evaluating the activity of a single CTC cell based on a deep learning method, and the method is characterized in that the method comprises the steps: 1) carrying out the preprocessing of the initial image data of CTC cells in different states, obtaining the feature image of the single CTC cell, constructing a data set corresponding to a state classification label, randomly dividing the data set of each state into a training set, a verification set and a test set; (2) respectively inputting the training set and the verification set into an OffficientNet-B0 model to carry out vitality interval judgment training so as to obtain an optimal model; 3) inputting the training set and the verification set into a YOLOv5s model to carry out single CTC cell positioning and CTC cell activity value prediction training to obtain an optimal model; and (4) respectively inputting the image data of the cells to be detected into the optimal OffficientNet-B0 model and the optimal YOLOv5s model, and calculating to obtain the activity value of the single CTC cell, and the method has the advantages of high efficiency, rapidness and accuracy.

The invention belongs to the technical field of single cell imaging and analysis, and provides an unmarked single cell two-dimensional light scattering imaging modal amplification method and system. The method comprises the following steps: acquiring a two-dimensional light scattering image of an unmarked single cell, and preprocessing the two-dimensional light scattering image; according to the proposed fusion condition adversarial loss LcGAN, L1 distance loss LL1 and a new imaging modal amplification model of perception loss # imgabs0 # based on a VGG network, generating a corresponding single-cell bright field image and a fluorescence image by using the processed light scattering image, and realizing amplification of a single-cell multi-modal image; multi-modal fusion and classification identification are carried out on the single cell two-dimensional light scattering image, the corresponding bright field image and the fluorescence image, so that the cancer single cell analysis accuracy is improved; the method can generate the corresponding single-cell bright field image and fluorescence image according to the unmarked single-cell two-dimensional light scattering image, is especially suitable for diagnosis and classification of cancer cells, and facilitates promotion of biomedical research and clinical application.

The invention relates to the technical field of energy storage, in particular to a mobile energy storage system cluster energy balance management method and system. According to the method, multi-mode monitoring data are collected in real time, comprehensive sensing of cluster dynamic changes, topological connection relations and unit states is achieved, the problem of unit mutation sensing lag is solved, a topological connection matrix and a topological disturbance field are obtained based on unit mutation data and topological structure data, the influence of unit mutation on topology is quantified, and the overall performance of unit mutation sensing is improved. Then, a cluster balance feature set is obtained in combination with unit state data, a dynamic adaptive topological structure is constructed, the dynamic adaptive topological structure is adjusted in real time along with unit state changes, then energy balance regulation and control parameters are generated accordingly, and the problem that regulation and control parameters are disjointed with the topological balance state is solved; and then a regulation and control expected index obtained through the energy balance regulation and control parameters is compared with a target balance index based on analogue simulation, and a regulation and control strategy immobilization problem is solved through a closed-loop adjustment mechanism, so that cluster energy balance stability is ensured.

A computer-implemented method receives a first table and a second table into a table evaluation system. Each of a plurality of cells of the first table and the second table are converted into a two-dimensional point with a cell weight related to the size of the cell. An edit distance matrix is computed between the cells of the first table and the cells of the second table, where the edit distance matrix being obtained is based on contents of cells. An optimal transport distance matrix is calculated between the cells of the first table and the cells of the second table by using the two-dimensional point and the cell weight for each of the plurality of cells. An output is generated that indicates a correspondence between cells in the first table and cells in the second table by using the edit distance matrix and the optimal transport distance matrix.

The invention discloses a method for designing a bionic bone scaffold based on TPMS unit and lattice unit multi-plane fusion, and belongs to the technical field of bionic bone scaffolds. The porosity is adjusted by adjusting the wall thickness, so that an S and SC homogeneous unit porous structure with the target porosity is formed, and then a Schwarz unit cell and a simple cubic unit cell are subjected to plane smooth transition fusion in a fusion region or fusion function tan (x / w) mode; and respectively importing the established fusion model into mechanical simulation software to carry out quasi-static compression finite element simulation so as to obtain the elastic modulus and the yield strength which accord with the thighbone. According to the design, different types of TPMS units and traditional lattice units are subjected to heterogeneous unit fusion, the fused novel porous structure model has different function grading expressions, and a new thought is provided for design of the porous structure as a bionic bone scaffold.

The invention provides a medical cell detection method. The medical cell detection method comprises the following steps: S1, collecting a cell image for preprocessing; s2, carrying out structured processing and coding processing on the multi-modal data; s3, performing feature extraction on the cell image and the multi-modal data; s4, fusing the cell image and the multi-modal data; s5, inputting the fused feature vector into a segmentation head of the FastSAM; s6, generating a segmentation mask of the image; s7, judging whether the cells are abnormal or not; s8, re-sampling the abnormal cells for cell culture; s9, collecting a cell image in the cell culture equipment in real time, and preprocessing; s10, inputting the cell image into a FastSAM model, and analyzing various indexes of the cell based on the generated segmentation mask; according to the method, cell images, cases, clinical examination results and other multi-modal data are combined, the characteristics of the cells are known more comprehensively, and the detection accuracy is improved.

The invention discloses a super capacitor monomer health state estimation method based on charging and discharging data, which belongs to the technical field of measurement, and comprises the following steps: carrying out de-noising processing on circulating charging and discharging data of a tested super capacitor monomer, calculating instantaneous frequency and instantaneous energy of the charging and discharging data after de-noising; energy feature distribution of time domain and frequency domain decoupling of the de-noised charging and discharging data is obtained, an energy feature distribution decomposition result is synchronously input into the health state estimation model based on the convolutional neural network, and the real-time capacity and the maximum capacity of the measured super capacitor monomer are fed back to the health state estimation model. And obtaining a predicted value of the capacity of the tested super capacitor monomer after training, and estimating the health degree of the super capacitor monomer. According to the method, collaborative analysis is carried out by fusing the time-domain dynamic characteristics and the frequency-domain steady-state characteristics of the charging and discharging data of the supercapacitor monomer, the limitation of traditional single-dimensional data representation can be broken through, and the detection sensitivity of the degradation phenomenon of the lithium ion supercapacitor is improved.

The invention discloses a cell interaction identification method based on cell space transcriptome data, which comprises the following steps: constructing cell pairs according to cell space proximity, and calculating an interaction signal matrix and space information of each pair of cells by combining a ligand-receptor database; then generating cell pair embedding representation by using a graph neural network, calculating the similarity between cell pairs, and calculating the similarity between cells based on a gene expression matrix; updating the similarity matrix through iteration until convergence to obtain a final similarity matrix between cells and between cell pairs; and clustering the similarity matrix, and constructing a cell type and cell pair type model, thereby realizing accurate cell interaction identification. According to the method, space and expression information can be fully utilized, inter-cell heterogeneity and context specificity interaction can be captured, the analysis precision and biological authenticity of a cell communication network are improved, and an effective tool is provided for research on tissue development, disease mechanisms, immune response and the like.

The invention discloses a sweet potato plot area division and background removal method based on unmanned aerial vehicle multispectral remote sensing. According to the method, the random forest model is adopted for training, so that remarkable advantages are brought. Firstly, the comprehensiveness of data preparation and the accuracy of feature selection jointly improve the prediction accuracy of the model. By integrating multiple characteristics such as multispectral remote sensing data, vegetation indexes and topographic parameters, the model can comprehensively consider multiple factors influencing the growth of sweet potatoes, so that the results of cell division and background removal are predicted more accurately. The feature importance evaluation further screens out the feature which has the largest influence on the model, interference caused by irrelevant factors is reduced, the model is more focused on key information, and the prediction accuracy and reliability are improved. An integrated learning mechanism of multiple decision trees is combined with randomly selected features and samples, so that an overfitting problem is effectively avoided, and stable performance of the model on unknown data is ensured.

The invention discloses a multi-agent 5G base station intelligent energy-saving method and device based on large and small model fusion, and relates to the field of wireless communication network energy conservation, and the method can automatically recognize a network deployment scene and configure an initial energy-saving strategy corresponding to the scene through the technology of fusion of a large language data model and a generative AI model. And screening out an energy-saving cell based on the initial energy-saving strategy corresponding to each cell or region, obtaining an energy-saving strategy and an energy-saving interaction instruction corresponding to the energy-saving cell, and controlling network equipment corresponding to the energy-saving cell to enter an energy-saving state. And further iteratively optimizing a corresponding energy-saving strategy on line for the cell entering the energy-saving state according to the energy-saving effect and the change area of the load performance index, and searching a balance point between the energy-saving threshold parameter and the network performance, thereby realizing maximum energy saving.

The invention relates to the technical field of biology, provides a cell characterization model pre-training method, a cell downstream task processing method, equipment, a readable storage medium and a program product, and can improve the cell characterization accuracy. The cell characterization model pre-training method comprises the following steps: determining a plurality of feature genes of a cell sample set, and obtaining first feature gene sequences corresponding to a plurality of sample cells in the cell sample set; matching according to the plurality of characteristic genes to obtain a plurality of gene pathways for realizing different biological functions; determining cell coding information corresponding to each sample cell by a to-be-trained cell characterization model according to each first feature gene sequence and each gene pathway, and predicting a second feature gene sequence corresponding to each sample cell according to the cell coding information; and adjusting the cell characterization model according to the difference between the second characteristic gene sequence corresponding to the sample cell and the first characteristic gene sequence until a training ending condition is met, thereby obtaining a pre-trained cell characterization model.

The present disclosure provides methods and systems for performing multiplex image analysis of cells. A method may comprise: (a) obtaining an image of a plurality of cells; (b) obtaining a plurality of trained machine learning models, each configured to analyze image data of cells to classify or score the cells based on a different pre-defined criterion among a plurality of pre-defined criteria; (c) for each of the plurality of trained machine learning models, processing the image of the plurality of cells to classify or score the plurality of cells based on a different pre-defined criterion; (d) identifying a region of interest in the image of the plurality of cells based at least in part on the classifying or said scoring; and (e) generating, based at least in part on the region of interest, a visualization of said plurality of cells that distinguishes each of the different pre-defined criteria.

Disclosed is a single cell analysis device for analyzing a tumor using artificial intelligence, the device including, a memory, and a processor, and the processor is configured to acquire single cell data on a tumor of a particular patient through the communication part; pre-process the single cell data on the tumor through a data pre-processing module; classify a type of the single cell based on the data pre-processed by a cell type defining module; predict a phenotype derivable from RNA expression of the tumor of the particular patient through a single-cell state predicting module materialize heterogeneity of the tumor of the particular patient through the tumor analyzing module; and report and provide an analysis result of the tumor of the particular patient through an analysis result generating module.

The invention relates to the technical field of medical information processing, in particular to a cervical cancer cell early screening and intelligent evaluation system based on multi-modal data, which comprises a data acquisition module, a form detection module and a screening early warning module, the data acquisition module is used for acquiring multi-modal data of a current detection object in a plurality of inspection processes; the morphology detection module is used for calculating a cellular morphology score of a current detection object in any target inspection process; the screening early warning module is used for determining similar detection objects of the current detection object, and correcting differences between the current detection object and the similar detection objects in the aspects of cellular morphology scores and lesion detection indexes according to the approximation degree between different types of lesion detection indexes of the current detection object; and obtaining the screening early warning coefficient of the current detection object by using the relative change condition of the correction result. According to the invention, the early screening and evaluation effects on cervical cancer cells are improved.

The invention belongs to the technical field of crossing of rock soil, geological engineering and computer modeling, and discloses a three-dimensional fractured rock anisotropic fragmentation and expansion structure generation method and system, and the method comprises the following steps: generating tightly embedded initial blocks in an expansion domain according to the number of control blocks and the initial equivalent size of the blocks; on the basis of the node information of the initial block, anisotropic fragmentation and expansion mapping geometric transformation is carried out on the cell elements, and node coordinate information after fragmentation and expansion and form control is carried out is obtained; calculating the volume ratio of blocks in the target area to obtain a current volume fraction; extracting a target domain in the extended domain, performing constraint smoothing, abandoning an external block, and retaining node coordinate information in the target domain; and exporting the reserved node coordinate information, the current volume fraction and the geometric structure to obtain the fragmentation and expansion structure.