415 results about "Cell cluster" patented technology

The invention provides an analysis method of multiple immunotissue fluorescent staining images and related equipment, and is applied to the technical field of data processing. The method comprises the steps of obtaining original biological sample data; slicing the original biological sample data to generate a plurality of pieces of initial biological slice information; processing the initial biological section information to generate a fluorescent staining section image; processing the fluorescent staining slice image to generate an aligned fluorescent staining slice image; performing fusion processing on the aligned fluorescent staining slice images to generate a target fluorescent staining image; processing the target fluorescent dyeing image to generate initial region information; the initial area information is processed, images of a plurality of different fluorescence channels are generated, and the images of the plurality of different fluorescence channels are single fluorescence channel images; and processing the images of the plurality of different fluorescence channels to generate cell clustering information.

The embodiment of the invention discloses a single cell transcriptome sequencing data annotation method. The method comprises the following steps: acquiring single cell transcriptome sequencing data associated with a target disease, and clustering a plurality of cells corresponding to the single cell transcriptome sequencing data to obtain a plurality of cell clusters; aiming at each marker gene corresponding to the target disease, according to single cell transcriptome sequencing data, obtaining the cell cluster gene expression quantity of the marker gene in each cell cluster; for each cell cluster and each cell type corresponding to the target disease, accumulating the cell cluster gene expression quantity of each marker gene corresponding to the cell type in all the marker genes in the cell cluster to obtain the total gene expression quantity; and aiming at each cell cluster, according to the total gene expression quantity corresponding to the cell cluster under each cell type, annotating a target cell type for the cell cluster from all the cell types. According to the technical scheme provided by the embodiment of the invention, the accuracy of cell type annotation can be ensured.

Facilitating energy aware multi-cell admission control in advanced communication networks is provided herein. A method includes, based on notification of a denial of an admission request by a first cell, facilitating a transmission of the admission request to a second cell and at least a third cell. The second cell and the third cell are neighbor cells of the first cell. The admission request can be received from a user equipment. The method also includes, based on receipt of an acceptance of the admission request from the second cell and the third cell, and based on a determination that a first utility of the second cell is higher than a second utility of at least the third cell, selecting the second cell as an admission cell for the user equipment. Further, the method includes facilitating admission of the user equipment to a cell cluster via the second cell.

The invention discloses a spatial transcriptomics cell clustering method based on multi-scale contrast learning. The method comprises the following steps: S1, carrying out data preprocessing; s2, performing graph construction by using the processed data; s3, performing data enhancement; s4, extracting cell gene expression information by using GCN; s5, enriching node information by using a ContraNorm layer; and S6, further learning information by using multi-scale image comparison learning, and performing biological analysis by using final information obtained by learning. According to the method, computer-aided cell type analysis is utilized, a large amount of high-quality data is not needed, only gene expression data and cell space position information data are utilized, which cells belong to the same category can be predicted, and more effective information is provided to help researchers to identify the onset of cancer and the development of diseases.

The invention relates to the technical field of single cell data processing, in particular to a database visualization method based on single cells. The method comprises the following steps: acquiring a single cell data set; standardizing the single cell data set to obtain standardized single cell data; performing high-dimensional feature extraction on the standardized single-cell data to obtain single-cell high-dimensional feature data; and performing nonlinear dimension reduction processing on the single-cell high-dimensional feature data to obtain single-cell dimension reduction data. Performing data clustering on the single cell dimension reduction data to obtain single cell clustering data; performing gene differential expression extraction on the single cell clustering data to obtain differential expression gene data; multi-modal data fusion analysis is carried out on single cell data through a data processing technology, a feature mapping technology and a visualization technology; and dynamic weight division is carried out on the single cell database, so that the effect of visualization of the single cell database is enhanced.

The invention relates to a preservation method and application of panax notoginseng germplasm resources, and relates to the technical field of panax notoginseng germplasm preservation. The preservation method comprises the following steps: (1) suspending pseudo-ginseng embryogenic calluses and a WPM culture medium, and screening and collecting to obtain pseudo-ginseng embryogenic cell clusters with the size of 0.45-1.5 mm; (2) transferring the panax notoginseng embryogenic cell cluster into a cryopreservation tube, and adding a loading solution for loading treatment to obtain a loaded embryogenic cell cluster; (3) carrying out vitrification treatment on the loaded panax notoginseng embryogenic cell cluster and a vitrification solution in a cryopreservation tube to obtain a vitrified panax notoginseng embryogenic cell cluster; (4) placing the cryopreservation tube filled with the vitrified pseudo-ginseng embryogenic cell cluster in liquid nitrogen for preservation; and after preservation, carrying out recovery culture on the pseudo-ginseng embryogenic cell cluster. According to the method, the survival rate reaches 90% after preservation, pseudo-ginseng plants can be rapidly differentiated, the differentiated pseudo-ginseng plants survive after being transplanted under natural conditions, the transplanting survival rate reaches 90%, and the pseudo-ginseng plants grow well in the later period.

The invention provides a cell specific transcription factor regulatory network analysis method and a visualization platform, and relates to the technical field of bioinformatics, the method comprises the following steps: constructing a gene regulatory network through a GRNBoost2-cisTarget-AUCell-Cell GRN workflow based on a transcription factor in combination with a motif database; screening a direct regulation relationship in combination with the database, and calculating an activity score of a regulator in each cell; based on activity scores and cell type annotation results, grouping the single cell data by using a unified manifold approximation and projection (UMAP) dimensionality reduction method and a Leiden clustering algorithm, and displaying the following results through an interactive visualization tool: a cell clustering UMAP graph, performing color marking according to cell types; a UMAP graph and a heat map of transcription factor regulator activity; according to the visual map of the gene regulation and control network, transcription factors and target genes are distinguished through node shapes, and regulation and control relations are marked through line weights and colors. According to the invention, an accurate regulation and control network can be provided.

The invention discloses an image contour recognition method and device for a cell cluster, and relates to the technical field of biological image processing, and the method comprises the steps: converting an input cell cluster image into a binary image; carrying out expansion operation and distance transformation on the image after morphological operation, and carrying out subtraction operation on the two obtained images to obtain a third image containing an unknown region; detecting connected regions in the accurate foreground region, and marking each independent connected region, background region and unknown region to obtain a marked image; performing region segmentation on the original image to obtain segmented marked regions, generating a corresponding binary mask image, and extracting the contour of each marked region by using the binary mask image; identifying and filtering the contour of each marked area to obtain an effective contour; the size of each effective profile is converted into an equivalent sphere diameter. According to the invention, accurate segmentation of adjacent or adhesion areas in the image can be efficiently realized, and accurate counting and size measurement of cell clusters can be obtained.

The invention provides a beam weight adjustment method, electronic equipment and a storage medium, and relates to the technical field of communication. The method comprises: obtaining any cell cluster in a preset area and at least one target working parameter corresponding to the cell cluster, the target working parameter being a working parameter corresponding to an uplink signal; according to at least one target working parameter corresponding to the cell cluster, adjusting the beam weight of each communication cell in the cell cluster to obtain a target beam weight of each communication cell under the condition that the target working parameter is optimal; and updating the real-time beam weight of each communication cell according to the target beam weight of each communication cell. Therefore, the real-time beam weight of each communication cell can meet the requirement of the communication link quality, the terminal can reside in the communication cell with the optimal communication quality of the uplink, and the communication efficiency is improved.

The invention discloses an animal single cell data cell type annotation method, which is characterized by comprising the following steps: collecting Bulk RNA-seq data of purified cell types in various tissues and organs of a specific animal, and integrating the Bulk RNA-seq data into a reference data set after preprocessing; the method comprises the following steps: acquiring single-cell RNA-seq original data, screening the single-cell RNA-seq original data to obtain high-quality cells, screening high-variation genes from the high-quality cells, processing the high-variation genes, extracting principal components, performing dimensionality reduction on the principal components, and performing cell clustering based on a dimensionality reduction result to obtain cell clusters; and calculating expression similarity between the cell clusters and the reference data set based on the high-variation genes, determining initial cell types of the cell clusters according to a similarity result, and carrying out iterative tuning on the initial cell types with similar scores to obtain a final cell type annotation result. The method provides efficient and accurate technical support for animal single cell research.

The disclosure relates to a computer-implemented method for configuring at least one operation parameter in a network cell (140) served by a network node (110) of a communication network (100), the method comprising: (S210) obtaining at least one cell feature for each one of a plurality of network cells (140), the cell feature representing a cell state of respective network cell (140), (S220) clustering a number of network cells (140) into a number of cell clusters (160), wherein the clustering of network cells (140) is performed based on the obtained at least one cell feature of respective network cell (140), (S230) determining one network cell (140) of respective cell cluster (160) to be a representor cell (170), and for at least one cell cluster (160): (S240) determining at least one action (a) to be performed for configuring at least one operation parameter for a number of network cells (140) of respective cell cluster (160).

Disclosed is a computer-implemented method for real-time classification of cells in a body tissue that includes capturing one or more images of a cytology slide sample of the body tissue, pre-processing the captured one or more images, detecting and extracting a plurality of regions of interest in the pre-processed images using a cell detection process, wherein each region of interest includes one or more cells or cell clusters of interest, segmenting each individual cell or cluster in the regions of interest and extracting pixel content of each segmented cell or cluster, extracting semantic features from each segmented cell image using an embedding extraction model, classifying each individual cell or cell cluster based on corresponding extracted sematic features, and predicting a class of the cytology slide sample, based on one or more positions and relationships among one or more classified cells, using a slide level decision model.

The invention discloses a human-derived iPSC (induced pluripotent stem cell)-induced liver organ and a preparation method thereof. The method comprises the following steps: digesting human induced pluripotent stem cells obtained by culture into single cells; single cells are prepared into cell clusters through self-balling, or cell matrigel microspheres are prepared by using a T-type microfluidic technology and then are distributed one by one, so that one cell cluster is formed in each pore of the low-adsorption 96-pore plate; carrying out step-by-step induction culture on the obtained cell cluster, and culturing in a culture medium I for 5 hours; replacing with a culture medium II and culturing for 2 days; a culture medium III is used for culturing for 2 days; replacing with a culture medium IV and culturing for 5 days; replacing with a culture medium V, and culturing for 6 days; and replacing with a culture medium VI, and culturing for 10 days to obtain the mature human liver organ. According to the invention, liver organs with human normal liver cell characteristics and protein expression can be formed within 25 days, and the method can realize homogenization and high efficiency of iPSC induction.

The invention relates to a multi-model fusion and knowledge base enhancement-based cell clustering prediction system and method, and the system comprises the following modules: a comprehensive knowledge base construction module, a knowledge base enhancement module, a knowledge base enhancement module, a knowledge base enhancement module, a knowledge base enhancement module, a knowledge base enhancement module and a knowledge base enhancement module. Constructing a species-tissue-cell group three-level knowledge sub-library; when cell dimension reduction clustering analysis is carried out, firstly, an existing knowledge database is used, key identification genes and modes are transmitted into a large model, and scoring is carried out through the large model; secondly, performing significance scoring on each cell cluster based on a specific algorithm, and performing accuracy evaluation and optimization on a grouping result by applying a grouping accuracy evaluation algorithm; in addition, a large amount of data is sorted in combination with a large model RAG and a large model fine tuning technology, and the large model is adjusted in combination with an artificial correction prediction result, so that the prediction accuracy is improved; and the calculation resources and the calculation efficiency of the model are reduced through a large model rectification technology.

The invention provides a cell type annotation method and device based on plant single cell transcriptome data and a readable storage medium. Annotation is achieved through multi-level data integration, wherein preliminary annotation is expressed based on cell type marker genes or homologous genes; calculating expression correlation auxiliary annotations of the to-be-analyzed data and the known transcriptome data set; performing function enrichment on the cell cluster differential genes to deduce cell types; carrying out quasi-timing analysis on the heterogeneous cell clusters and annotating subgroups; and finally integrating and generating a comprehensive annotation. The method solves the problem that the prior art depends on artificial experience and is insufficient in basic data set, and is suitable for mode and non-mode plants.

The invention discloses a biconvex crystalline lens organ relying on stiffness-controllable hydrogel external directional induction as well as a construction method and application thereof. A crystalline lens organ model provided by the invention is jointly realized by virtue of stiffness-controllable hydrogel and a composite growth factor; and the pluripotent stem cells are rapidly and directionally induced into crystalline lens organs with high quality in vitro. According to the technical scheme, the method comprises the following steps: (1) directionally inducing and differentiating the pluripotent stem cells into primary ectoderm cell clusters; (2) preparing hydrogel with specific rigidity and carrying out surface cell adhesion treatment; (3) planting the primary ectoderm cells on the cleaned polyacrylamide hydrogel and inducing the primary ectoderm cells into a neural ectoderm cell cluster; and (4) inducing the neural ectoderm cells to directionally differentiate into mature crystalline lens organs. The three-dimensional lens organ-like model obtained by the method has a sphere-like biconvex morphology with a top surface and a bottom surface, is transparent and has structures such as capsule membranes, lens epithelial cells and lens fibrocytes, and the bottom surface is also wrapped by a complete lens capsule membrane. Compared with the prior art, the method realizes the formation of a complete capsule film on the bottom surface and a sphere-like form. The obtained crystalline lens organ can be used for research on crystalline lens embryonic development, research on cataract pathogenesis and research, development and screening of cataract drugs.

The invention discloses an auxiliary diagnosis system for renal transplantation rejection and typing thereof based on a peripheral blood immune cell map. Comprising a peripheral blood single cell acquisition unit, a mass spectrum flow cytometry unit, an original data preprocessing unit, a data conversion and normalization processing unit, a peripheral blood immune cell grouping acquisition unit, a peripheral blood immune cell map construction unit, a feature vector acquisition unit, a rejection prediction unit and a rejection typing prediction unit. According to the invention, a kidney transplantation two-stage diagnosis architecture is created for the first time; rejection detection and typing identification are realized through classification of an independently trained random forest binary classifier, and different TCMR / ABMR immune mechanisms are accurately matched; the traditional biopsy limitation is broken through, an auxiliary diagnosis scheme with high precision, low damage, moderate cost and typing identification capability is provided for the kidney transplantation rejection, and the method has important significance on timely discovery of the rejection of a kidney transplantation patient and improvement of prognosis.

The present disclosure relates to automated systems and methods adapted to quickly and accurately train a neural network to detect and / or classify cells and / or nuclei. The present disclosure also relates to automated systems and methods for using a trained cell detection and classification engine, such as one including a neural network, to classify cells within an unlabeled image.

The invention discloses a single cell clustering statistics significance test method and system based on manifold learning, and the method comprises the following steps: obtaining original single cell data, carrying out the standardization processing of the original single cell data, and removing the deviation and noise in the data; performing dimension reduction on the standardized data by using a manifold learning method; dividing the dimension reduction data based on a pre-calculated clustering label, and performing hierarchical clustering on the divided data to obtain a hierarchical tree; hypothesis testing is carried out step by step from the bottom to the top of the hierarchical tree by adopting a Monte Carlo simulation method, and whether the clusters are combined or not is determined according to a hypothesis testing result; and finally, taking the combined hierarchical tree as a single cell clustering correction result. According to the method, the reliability and the accuracy of the clustering statistical test can be improved, and unnecessary group splitting and misjudgment can be avoided.

The invention discloses a single cell RNA sequencing data clustering method based on graph transformer, and relates to the technical field of deep learning and biological information, and the method comprises the following steps: firstly, constructing a cell specific signal network according to scRNA-seq data; then training the same graph neural network by using the cell specific signal network to learn the specific graph representation of the cells, thereby obtaining a node embedding matrix of the cell specific signal network; and finally, performing cell clustering based on graph representation according to the node embedding matrix. According to the single-cell RNA sequencing data clustering method based on the graph transformer, a specific network of each cell is constructed, and then GNN is used for learning specific graph embedding of each cell, so that heterogeneity between the cells is revealed, and the application range of a model is expanded; the novel cell clustering method not only improves the accuracy and robustness of clustering, but also provides abundant information for subsequent biological analysis and research.

The utility model provides a micro-fluidic chip for unmarked or fluorescently marked single cell sorting and a single cell sorting system, which do not adopt means for damaging a sorting target, such as high voltage, strong electric field, high-voltage sheath fluid and the like, and can be used for sorting the unmarked or fluorescently marked single cells on the premise of keeping activity. For unlabeled or labeled (such as fluorescence labeled) cells, bacteria, proteins, viruses, genes (fragments or whole segments), cell clusters and other microorganisms, or liquid drops, liquid segments, bubbles or blocky solids containing the biological particles, high-efficiency, accurate and reliable single-individual detection, control and sorting (especially including but not limited to single-cell sorting) are carried out.

The invention relates to the technical field of preparation of organoids, and discloses a method for regulating and controlling human induced pluripotent stem cells to differentiate into kidney organoids, which comprises the following steps: resuspending a cell cluster formed by human induced pluripotent stem cells by using a basic differentiation culture solution containing a GSK-3beta inhibitor, inoculating the cell cluster into an ultralow adsorption cell culture plate, and starting to culture, marking the time as the 0th hour of differentiation; and after culturing for 70-76 hours, replacing the basic differentiation culture solution containing the GSK-3beta inhibitor with a basic differentiation culture solution containing a Knockout TM serum substitute (KSR), and continuing to carry out differentiation culture. By adopting the method disclosed by the invention, the human induced pluripotent stem cells can be efficiently, quickly, simply, conveniently and stably subjected to directional induced differentiation into kidney organs containing podocytes, renal tubules and other structures, and the time for constructing a kidney in-vitro model can be shortened, so that the research on kidney diseases, kidney development and the like can be greatly assisted; and a powerful platform is provided for kidney toxicity assessment.

The invention discloses a target micro-area laser cutting and collecting device and method for tissue slices on a blade, the target micro-area laser cutting and collecting device for the tissue slices on the blade comprises a carrier table, the blade, a laser cutting system, a control module and a collector, the laser cutting system comprises a laser, a beam expander, a galvanometer and an objective lens, and the scanning angle theta of the galvanometer is adjusted, and the scanned laser is focused in combination with the objective lens, so that the target micro-area on the tissue slice is cut and bounced up. According to the invention, laser microdissection and mechanical slicing are combined, and non-contact collection of a specific micro-area of a tissue slice placed on an inclined blade is realized under the action of laser edge ejection combined with dielectrophoretic force, so that the integration of the form of a sample in the whole acquisition process is ensured, and three-dimensional position information of a target cell or a cell population is accurately reserved; the cutting efficiency is improved, non-contact cutting can be achieved, and samples are prevented from being polluted.

The invention discloses a breast cancer metastasis state analysis system based on image recognition. The system comprises an image collection processing module, a feature fusion improvement module, a cell cluster space-time modeling module, a prognosis mapping module and a metastasis state analysis module. The invention belongs to the technical field of breast cancer metastasis state analysis, a tumor cell evolution process is modeled through multi-source pathological image preprocessing, adaptive feature fusion and a space-time diagram convolutional network, treatment response is predicted in combination with multi-omics data, and finally the breast cancer metastasis state is comprehensively judged. According to the system, the accuracy of focus feature extraction, the dynamic modeling capability and the comprehensiveness of prognosis prediction are improved, and efficient and intelligent technical support is provided for precise medical treatment.

The utility model provides an energy storage system. The energy storage system comprises a box body; the energy storage device is arranged in the box body, the energy storage device comprises N battery clusters arranged in the first direction, each battery cluster comprises M battery packs connected with one another in the second direction perpendicular to the first direction, N is larger than or equal to 4 and smaller than or equal to 6, and M is larger than or equal to 5 and smaller than or equal to 8. The number of parts in the energy storage system can be reduced, the space utilization rate of the box body is improved, the manufacturing time is effectively reduced, and the system manufacturing cost is reduced.

The invention provides a control method and device, electronic equipment and a storage medium, and the method comprises the steps: issuing AC active power to a corresponding bidirectional energy storage converter so as to adjust the current distributed by each bidirectional energy storage converter, and controlling each DC power converter based on a current distribution target ratio, therefore, the current distributed to each battery cluster is adjusted. According to the invention, by controlling the given AC active power issued to the bidirectional energy storage converter, the current of each DC bus is controlled, each cell stack is fully charged or fully discharged at the same time, on the other hand, the current proportion of the cell clusters in the same stack is distributed, the system difference is reduced, and the system reliability is improved. And the consistency, the service life, the working efficiency and the charge-discharge depth of the whole battery energy storage system are greatly improved.

The invention discloses a lightweight optimal clustering selection method for scRNA-seq data. The lightweight optimal clustering selection method comprises the following steps: 1, acquiring and preprocessing an scRNA-seq data matrix; 2, performing layer-by-layer dimensionality reduction on data by using a multi-layer perceptron encoder to obtain a low-dimensional feature matrix; and 3, constructing an undirected cell map, and enhancing and removing cell map noise through an NE network. And 4, reconstructing features by using graph convolution, inputting the low-dimensional feature matrix and the cell graph into a simplified graph convolution module, and updating the reconstructed feature matrix round by round. And 5, carrying out cell clustering on the characteristic matrix after each round of updating by using self-optimization clustering to obtain cell representation. And 6, judging and evaluating the clustering performance of the current round, storing an optimal clustering result, and carrying out feature reconstruction and self-optimization clustering of the next round. And 7, outputting and analyzing final optimal feature information. According to the method, scRNA-seq data can be well processed, low-dimensional embedded features can be extracted, tag prediction is more accurate, the requirement for hardware resources is low, and an efficient method is provided for analyzing biological characteristics of single cell data.

A method for designing a beam hopping pattern of a low-orbit satellite belongs to the field of low-orbit satellite communication, and comprises the following steps: dynamically clustering ground cells by using a hybrid clustering cell clustering algorithm according to the service requirements of the cells in a beam hopping scene, and enabling the cells in the same cluster to approach as much as possible on the basis of load balancing; dividing user satisfaction indexes into three levels to guide three dynamic operations; selecting an optimal individual to generate a progeny by using the overall user satisfaction of all cells in the whole beam hopping period; performing individual gene crossover by using the community user satisfaction of each cluster; performing individual gene variation by using the user satisfaction of a single cell; and fine tuning of beam power is carried out when each beam of the beam hopping pattern is served in the same time slot. According to the method, load balancing and geographic position compactness are comprehensively considered, the same-frequency interference is reduced, the resource utilization efficiency is improved, the calculation overhead is reduced, and the overall calculation efficiency is improved.

The present disclosure relates to a system and method for neighbour management in a network The method comprises: receiving, at a centralized self-organizing network server [300], a neighbour cell addition request for registration of a network node to a cell cluster in the network; determining, a geographical location of the network node; evaluating a distance between the geographical location and the cell cluster to be below a distance threshold; determining a count of a plurality of network nodes included within the cell cluster to be below a count threshold; and registering the network node to the cell cluster; receiving a neighbour cell deletion request for removal of the network node; determining the node parameters to be present in a Neighbour Relation Table (NRT) based on a validation procedure; and removing the network node from the cell cluster based on the determination of the plurality of node parameters.

Methods, systems, and computer program products are provided for monitoring a cell culture, utilizing a predictive model to improve cell culture processes, measuring cell dynamics of a cell culture, managing cell clusters in a cell culture system, and training a machine learning model for managing cell clusters in a cell culture system. In various embodiments, a cell culture may be maintained in a cell culture chamber. The cell culture may comprise one or more cell colonies. One or more properties of the cell culture may be measured. One or more perturbations may be applied to all or a portion of the cell culture. The one or more properties of the cell culture may be measured after applying the one or more perturbations. The one or more properties may be compared before and after the one or more perturbations. The cell culture may be managed based on the comparison.