272 results about "Cell based" patented technology

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 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 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 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 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.

A monitoring device of biological cells including: a fluidic channel in which a fluid including biological cells is made to flow, including a first and second glass walls placed in parallel and a reflective surface adjoined to the second wall; and a piezoelectric transducer, the piezoelectric transducer emitting acoustic waves creating a force acting on the biological cells, making them flow substantially along a single motion plane. The external interface between the first wall and the exterior of the fluidic channel and the reflective surface are configured to function as a Fabry-Perot interferometer, such that the motion planes sensibly perpendicular to the optical axis of the Fabry-Perot interferometer. The finesse of the interferometer is lower than 10. The monitoring device further includes a detecting unit detecting a fringe pattern stemming from the Fabry-Perot interferometer and an analysing device, analysing the fringe pattern and configured to output data on physical properties of the cells based on the pattern.

Content relevance based table query answering is described. In one or more examples, a query and a table are received. The table includes a plurality of cells. A plurality of scores for calculated that correspond to the plurality of cells based on the query. One or more machine-learning models are then leveraged to generate a search result from the query, table, and scores, which is presented in a user interface for display.

Provided herein are compositions and methods for identifying the cell type origin of cells based on RNA sequencing (RNA-seq) data.The method provided herein may include: receiving a plurality of sequencing read counts; providing a set of detected cell types and a related gene set G for each cell type, such that each gene set G comprises a plurality of genes g; scoring the sequencing read counts to generate an assignment score; and assigning each cell among the plurality of cells to the cell type with the highest assignment score, thereby identifying the cell type origin of each cell.This method can be implemented, for example, using a computer system.

The application discloses a kind of artificial cells for single-cell mass spectrometry and preparation method thereof, wherein artificial cell includes internal water phase, intermediate oil phase and external water phase;Internal water phase includes polyethylene glycol and polyvinyl alcohol aqueous solution, intermediate oil phase includes L-alpha-phosphatidylcholine chloroform and hexane mixture;External water phase includes PVA and F-68 aqueous solution.Compared with natural cell sample, the novel artificial single cell based on microfluidic self-assembly developed in the application has better uniformity, stability and controllability, effectively avoids significant measurement difference between single-cell individual samples, and effectively solves the problem of difficult stable preservation of biological samples.The proposed artificial cell preparation method will largely solve the problem of lack of standard reference material in the field of single-cell mass spectrometry, making the results of single-cell mass spectrometry method more accurate, reliable and mutually recognized.

The invention discloses a multi-immunofluorescence staining cell target detection method and device based on environmental information fusion. The method comprises the following steps: 1) constructing an image detection framework combined with environmental information fusion; 2) detecting network anchor point adjustment and positioning; 3) accurately positioning a feature map and generating cells in a classified manner; and 4) performing feature accurate positioning target detection based on environmental information guidance. According to the method, target category image target detection based on environment information conversion is realized through a feature vector accurate positioning form based on environment information of a DAPI image.

The invention relates to the technical field of biological tissue cell detection, in particular to an in-situ biological tissue cell free radical detection device and method based on EPR. The technical problems that in the prior art, a fluorescent probe is prone to being oxidized, the detection accuracy is affected, a traditional EPR method needs to freeze or fix a sample, a cell sample is prone to being inactivated, and free radicals in the sample are inactivated or attenuated are solved. According to the technical scheme, the in-situ biological tissue cell free radical detection device based on the EPR comprises a culture cabin, a central controller, a culture dish, a cleaning fluid storage rack, a control assembly, a detection assembly and a temperature control assembly; through the design of the movable sterile liquid suction head and the liquid conveying channel, automatic sampling and conveying are achieved, the problem that free radicals are inactivated or attenuated in traditional EPR detection is solved, the authenticity and reliability of a detection result are ensured, and real-time, dynamic and in-situ detection of biological free radicals is achieved through automatic operation of a central control system.

A method for gene editing in cells based on the CRISPR-Cas system is provided, wherein the Cas enzyme is a Cas9 enzyme with an enzyme activity of 0-1 nmol. A method for constructing universal T cells is also provided, along with the prepared T cells and their applications, wherein the TCR and MHC genes of the T cells are edited using gene editing technology. A gRNA construct is also provided.

The present invention provides a prospecting prediction method and device based on fuzzy feature analysis of geoscience big data. The method includes: establishing a geoscience big data spatial database of a target prospecting area; dividing the target prospecting area to generate multiple grid cells; selecting multiple model cells from the multiple grid cells and selecting initial prediction variables; determining a fuzzy membership function for any initial prediction variable in each model cell; obtaining the fuzzy membership of the initial prediction variable of the model cell based on the fuzzy membership function; calculating a fuzzy matching coefficient between any initial prediction variable and any other initial prediction variable based on the fuzzy membership of the initial prediction variable of the model cell; determining a weight coefficient for each initial prediction variable based on the fuzzy matching coefficient; selecting a target prediction variable based on the weight coefficients of all initial prediction variables; and calculating the metallogenic favorability of the target prospecting area based on the fuzzy membership function and the weight coefficient of the target prediction variable. The present invention can improve prediction accuracy.