292 results about "Cell cluster" patented technology

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 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 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.

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 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.

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.

The invention belongs to the technical field of medical image analysis, and particularly relates to a cerebrospinal fluid cell intelligent analysis and diagnosis system and method based on deep learning. The method comprises the following steps: cutting an original image through a multi-scale segmentation model, and recording and storing position information of each cell image; segmenting all single cell and cell cluster images by a multi-scale segmentation model; training a cell classification model for endowing image category attributes to the single cells and the cell clusters; counting classification results output by the cell classification model to generate quantitative indexes; and generating a category visualization image and a diagnosis report through the positioning information of the single cell and cell cluster images. According to the method, a digital image processing technology and a deep learning algorithm are utilized, a traditional method is combined with an intelligent algorithm, effective image information is fully utilized, a detection result is visualized, checking by a doctor is facilitated, and finally the functions of automatic positioning, accurate classification and quantitative index of single cells and cell clusters in a cerebrospinal fluid sample are achieved.

The invention belongs to the technical field of cytobiology and regenerative medicine, and particularly relates to a porcine-derived corneal limbus epithelial stem cell culture method and application, during culture, pretreatment is firstly carried out, then a culture dish is coated, cells are cultured and passaged after centrifugation, dryness identification is carried out through cell immunofluorescence color, and the porcine-derived corneal limbus epithelial stem cells are obtained. The stem cell engineering vector can be applied to damage repair of corneal diseases including corneal limbal stem cell deficiency, during repair, the stem cell engineering vector is compounded with a biological material to form the stem cell engineering vector, the stem cell engineering vector is implanted into a damaged part, the separation and culture method is simple in step, the cell obtaining amount is large, and the stem cell engineering vector is suitable for being separated from eyeballs of a large animal-pig; the corneal limbus epithelial stem cells cultured in vitro can form cell clusters called stem cell islands, the obtained corneal limbus epithelial stem cells have phenotypes of BMI1 + and delta Np63 alpha +, and CK14 + and ABCG < 2 + >, and a new way can be provided for cell transplantation based on experimental research and clinical research.

The present invention discloses a method for establishing and applying a caudal fin-induced pluripotent stem cell line of Erythroculter ilishaeformis. By the tissue adherence method, a cell line with high proliferation performance and stemness is isolated from the caudal fin of Erythroculter ilishaeformis. In the early stage, the cells of this cell line are significantly fibroblast-like. Later, the cells become smaller but still maintain a spindle shape, without obvious contact inhibition phenomenon, and can spontaneously form cell clusters similar to induced pluripotent stem cells. Alkaline phosphatase staining is positive and it expresses stemness marker genes such as Oct4, Sox2, Klf4, Nanog, and c-Myc. The present invention uses a single cAMP agonist small molecule compound to reverse the phenomenon of weakened stemness after continuous subculture of this cell line, enabling it to maintain the characteristics of high proliferation performance and high stemness, which is suitable for future research on the induction and differentiation of fish stem cells and provides more choices for the research and application of fish stem cells.

The invention discloses an access detection method, device, equipment and system, a storage medium and a program product. The energy storage equipment comprises a positive electrode busbar, a negative electrode busbar and N battery clusters, voltages of the first battery cluster to the Nth battery cluster are sequentially reduced, and the method comprises the following steps: controlling the first battery cluster to be accessed into the positive electrode busbar and the negative electrode busbar, and performing access detection on the first battery cluster based on a detection voltage when the first battery cluster is accessed, the detection voltage is determined based on the voltage difference between the positive electrode busbar and the negative electrode busbar, and the access detection result comprises whether the positive electrode and the negative electrode of the battery cluster are reversely connected or not; under the condition that the positive electrode and the negative electrode of the first battery cluster are not reversely connected, the negative electrodes of the second battery cluster to the Nth battery cluster are controlled to be sequentially connected to the positive electrode busbar or the negative electrode busbar, and access detection is conducted on all the battery clusters based on the detection voltage when all the battery clusters in the second battery cluster to the Nth battery cluster are connected, and obtaining an access detection result of each battery cluster.

The invention relates to a cell network optimization method and device, equipment, a storage medium and a product, and relates to the technical field of communication. The method comprises the following steps: acquiring a performance index matrix of a target cell cluster; the target energy-saving strategy is simulated and executed based on the performance index matrix of the target cell cluster to obtain a simulation execution result, the target energy-saving strategy is an energy-saving strategy in the strategy iterative optimization process, and the energy-saving strategy comprises at least one of the following: changing the activation state of each cell and changing the activation state of each cell; adjusting a target parameter, used for adjusting the coverage range of the cell, of the cell under the condition that the user perception condition is met; evaluating the simulation execution result based on the comprehensive optimization target to obtain an evaluation result; the comprehensive optimization target is constructed based on a user perception index and an energy-saving index; if the evaluation result reaches an optimization completion condition, executing a target energy-saving strategy; through the method, the perception experience of the user is ensured while the energy is saved, and the energy conservation of the base station and the effective optimization of the cell load are realized.

The invention belongs to the technical field of biological extraction, and discloses a separation method for improving the yield of human lymphocytes. The method comprises the following steps: collecting blood cells, diluting the blood cells, obtaining lymphocytes and recovering the lymphocytes. The method is characterized in that a blood cell diluent is prepared and comprises human serum albumin, EDTA-2Na (Ethylene Diamine Tetraacetic Acid-2Na) and a calcium-magnesium ion-free PBS (Phosphate Buffer Solution). The method is applied to lymphocyte separation, the osmotic pressure of cells in the separation process can be improved, the normal form of the cells can be maintained, the viscosity between the cells can be reduced, cell adhesion can be prevented, meanwhile, the part which is adhered to form a membrane layer or a cell cluster is separated twice, the lymphocyte separation yield is remarkably increased, and the method is simple to operate and easy to implement. The realization is easy. The method is especially suitable for blood samples of people with immune system diseases, blood samples and blood viscous samples of old and sick people, non-fresh blood samples and the like.

A disaggregation system is disclosed. The system includes a reservoir containing a solution including multiple cell clusters. The system further includes a cluster processing system. The processing system includes at least one separation device having multiple separation elements spaced apart from each other, each separation element including an edge portion configured to apply a separation force to cell clusters that contact the edge portion. The processing system further includes one or more channels in fluid communication with the reservoir, the one or more channels configured to receive the solution from the reservoir and direct the solution to pass through a corresponding separation device, causing at least one cell cluster to contact the edge portion of one or more of the separation elements, causing the at least one cell cluster to disaggregate into at least two separate cell clusters.

Some embodiments provide a microfluidic cartridge for automatically hydrodynamically loading objects (e.g., cell clusters) into traps in parallel trapping channels with one object per trap, methods of making such cartridges and methods of use of such cartridges.