41 results about "Cell localization" patented technology

The invention discloses an improved convolution neural network model for leukocyte calculation, and relates to the field of artificial intelligence and medical image processing. Aiming at the problems of inaccurate feature extraction, strong background noise interference and insufficient multi-scale feature fusion in a traditional leukocyte calculation method, the model realizes fine processing and feature enhancement of a medical image through a multi-level modular design; the image preprocessing module is used for completing white blood cell positioning and image adaptive division and enhancement; multi-scale edge, texture and gray features are fused by means of an image feature extraction module, and a composite feature vector is constructed; through multi-scale convolution, a double-layer attention mechanism and convolution layer calculation of the improved convolution neural network model module, precise focusing of white blood cell contours and cell nucleus features, background noise suppression and calculation efficiency optimization are realized; and finally, a calculation result is output through a visual terminal, so that the accuracy and robustness of leukocyte counting are remarkably improved, and efficient technical support is provided for medical microscopic image analysis.

The invention discloses a handwritten table structure recognition and Excel cooperative processing method and system, relates to the technical field of computer vision, and comprises the steps of image preprocessing, table structure recognition, cell positioning, content recognition, Excel template analysis and text mapping. The system preprocesses the input image; respectively extracting horizontal and vertical lines based on morphological operation, and identifying a logic rank range of the merged cells; calculating a cell pixel bounding box through the cross points, and carrying out logic sorting and interference filtering; independently cutting each cell and calling a handwriting OCR (Optical Character Recognition) engine to extract text content; matching a preset Excel template according to the header or the code, and analyzing the coordinates of the combined and non-combined cells of the Excel template; and accurately mapping the identification content to the corresponding position of the Excel based on the row and column logic sequence numbers by taking the anchor point cell as a reference, and generating a spreadsheet. According to the method, the problems of inaccurate merging cell recognition, content dislocation and the like are effectively solved, and the electronization precision and efficiency of the handwritten form are remarkably improved.

The invention discloses a staining cell positioning method and system for pathological diagnosis, and relates to the technical field of staining cell positioning. The staining cell positioning method for pathological diagnosis comprises the following steps: acquiring an original pathological staining image based on an image acquisition device, and preprocessing the original pathological staining image to obtain a processed pathological staining image; feature processing is conducted on the processed pathological staining image, a staining area binary image and staining cell feature data of pixel points are obtained, and the staining cell feature data comprise shape features, texture features and color features; carrying out image segmentation on the dyed area binary image based on the dyed cell characteristic data of the pixel points to obtain a segmented pathological dyed image; and cell localization is carried out based on the segmented pathological staining image, so that the problem that the existing staining cell localization method is inconvenient to effectively separate cells by combining region growth and watershed algorithms is solved.

The invention relates to the fields of epigenetic regulation and tumor treatment, and aims to evaluate that the cell distribution of the mutant is consistent with that of H3 by constructing Bas-2B (H3K9R) and Bas-2B (H3K18R) stably transfected cell strains and analyzing the cell positioning condition of the H3K9R / H3K18R mutant through an immunofluorescence technique. According to the present invention, a Western blotting technology is adopted to detect the H3K9 / H3K18 lactic acid modification level of Bas-2B (H3K9R) and Bas-2B (H3K18R) cells compared to control cells so as to verify the influence of the H3K9R / H3K18R mutant on the H3K9 / H3K18 lactic acid modification; after Bas-2B (H3K9R), Bas-2B (H3K18R) cells and control cells of the Bas-2B (H3K9R) and the Bas-2B (H3K18R) cells are subjected to nickel compound exposure treatment, the influence of the H3K9R / H3K18R mutant on the nickel compound induced lung cancer is evaluated through a soft agar cloning formation experiment. An AGS cell line stably transfecting H2BK16R is constructed, and the cell localization condition of the H2BK16R mutant is analyzed through an immunofluorescence technology. According to the present invention, a Westernbloting technology is adopted to detect the lactic acid modification level of AGS (H2BK16R) cells so as to verify the influence of the H2BK16R mutant on the H2BK16 lactic acid modification; the influence of the H2BK16R on the malignant proliferation ability of the gastric cancer is researched through a soft agar clone formation experiment.

Methods of tagging cells with unique oligonucleotide “zipcode” constructs are provided. By these methods and associated compositions, cells in a multicellular structure such as a tissue section can be tagged with a construct, the unique composition of which is associated with the cells position in the multicellular structure. Subsequently, the multicellular structure can be dissociated into single cells and a single cell transcriptome analysis performed, as well as other types of single cell analyses. By preserving positional information in the analyzed single cells, biological processes within the tissue can be mapped. By these methods, the effects of the local environment surrounding a cell on its state and various functions can be elucidated, and intra-tissue processes can be mapped and observed. Likewise, coordinated actions by multiple cells within a tissue can be mapped and tracked over time.

High levels of nuclear NMT1 are associated with longer relapse free survival in ERα positive breast cancer patients. Both low levels of cytosolic and nuclear NMT1 correlated to very poor clinical outcomes. NMT2 also plays an important function in breast cancer signalling, regulated through phosphorylation. For example, NMT2 phosphorylation status is a key element in the progression of ER+ breast cancer cells. Specifically, nuclear localization of NMT2 is associated with poor outcomes in breast cancer patients.

The present application relates to the technical field of gastric cancer, and discloses a tumor cell quantification analysis method and system based on gastric cancer CLDN18, which comprises the following steps: presetting candidate points on a pathological section image; generating foreground and background auxiliary points for cell labeling points, and constructing a structured supervised target; extracting multi-scale semantic features through a pre-trained pathological large model, and generating an aggregated feature map through downsampling alignment and fusion operations; adaptively reconstructing candidate point features using implicit feature interpolation technology; predicting position offset and class confidence based on candidate point features, and determining cell position and class through threshold screening and offset optimization; optimizing the model in combination with an auxiliary point supervision mechanism, and outputting objective quantification results. The present application can significantly improve the positioning accuracy and classification consistency of CLDN18 positive tumor cells, and reduce the workload of doctors.

A method for detecting cell positioning anomalies is disclosed. Control plane signaling data packets are collected associated with multiple cells of a communications network. Distance and azimuth values for individual communication sessions are calculated for each cell. A machine learning model is executed using various communication parameters as input to generate a classification for each cell. A list identifying which cells are experiencing anomalies is generated.

The bioactive compounds of Ganoderma lucidum extract (GLE) responsible for anticancer activity were elucidated using NMR, X-ray crystallography and analogue derivatization, as well as anti-cancer activity studies. Structures of the seven most abundant GLE compounds are disclosed. Their selective efficacy against triple negative (TNBC) and inflammatory breast cancers (IBC) and other human cancer cell types (solid and blood malignancies) was shown, confirming potential their as anticancer agents.

The invention provides a single-cell space transcriptome cell localization and type deconvolution algorithm based on feature decoupling. A deep learning technology is utilized to carry out single-cell pseudo-space reconstruction and space transcriptome cell type deconvolution. The algorithm combines feature decoupling and cross-domain transfer learning methods, and specifically comprises encoder feature decoupling, feature transfer, single-cell pseudo-space prediction and space transcriptome cell type composition prediction. According to the method, cross-domain transfer learning is applied to simultaneous prediction of single-cell pseudo-space and space transcriptome cell type composition for the first time, and the model is constructed by using gene transcriptome data. In the embodiment, the Pearson's correlation coefficient between the predicted distance between the paired cells in the single cell space and the distance between the paired cells in the real space reaches 0.75, and the Pearson's correlation coefficient between the predicted space transcriptome cell type composition and the real cell type composition reaches 0.79; the method successfully realizes efficient prediction of single cell pseudo-space and space transcriptome cell type composition, and provides a new thought and method for the field of solving cell heterogeneity.

The invention provides application of MCT2 protein as a marker in preparation of products for ovarian cancer stem cell identification and treatment effect evaluation, and belongs to the technical field of biomedicine. According to the invention, primary ovarian cancer cells are obtained, an OCSC model is established by screening and culturing, an inhibitor with MCT2Ki specificity is adopted for intervention, an OCSC marker is identified by adopting flow cytometry and qRT-PCR, and the expression level and cell localization of MCT2 are detected by adopting a qRT-PCR method and an immunofluorescence staining method. A stem cell balling experiment and a matrigel migration chamber experiment are adopted to verify the influence of the MCT2 inhibitor on the dryness and invasiveness of the OCSC, and verify that the MCT2 is in a high expression state in the primary OCSC, and the dryness and invasiveness of ovarian cancer cells can be reduced by inhibiting the MCT2, so that the MCT2 can become a new target for treating high-grade ovarian cancer.

The present disclosure relates to computer-implement techniques for cell localization and classification. Particularly, aspects of the present disclosure are directed to accessing an image for a biological sample, where the image depicts cells comprising a staining pattern of a biomarker; inputting the image into a machine learning model; encoding, by the machine learning model, the image into a feature representation comprising extracted discriminative features; combining, by the machine learning model, feature and spatial information of the cells and the staining pattern of the biomarker through a sequence of up-convolutions and concatenations with the extracted discriminative features from the feature representation; and generating, by the machine learning model, two or more segmentation masks for the biomarker in the image based on the combined feature and spatial information of the cells and the staining pattern of the biomarker.

The invention relates to a cell counting method, and relates to the technical field of cell image analysis, a neural network model directly processes clear and discrete point coordinates from input to output, does not need complex composite loss, directly outputs instance-level coordinates, and basically completely eliminates background noise accumulation caused by density map integration. The invention further provides a query point generation mechanism for intelligently distributing calculation resources according to the actual distribution density of the cells, the counting speed is greatly increased while high precision is guaranteed, and the dilemma that a traditional method is redundant in calculation or unstable in matching is fundamentally solved. According to the method, each query point is creatively enabled to simultaneously query feature maps from different levels of the convolutional neural network. Therefore, the neural network model makes a final decision, the low-level detail features, the middle-level structural features and the high-level semantic features can be organically and adaptively fused, and the cell positioning precision and robustness are greatly improved.

The present invention provides a kind of rubber tree HbMAX2 gene, its nucleotide sequence is as shown in SEQ ID NO:1.The present invention clones and obtains rubber tree HbMAX2 gene from rubber tree for the first time, and this gene subcellular localization is in cell nucleus, and is expressed in all tissues of rubber tree, wherein the expression amount in flower and leaf is significantly higher than other tissues, and the expression amount in rubber tree high-yield germplasm is higher than low-yield germplasm, explanation HbMAX2 may be involved in the latex synthesis process of rubber tree, further study shows, this gene can improve the viability of yeast under oxidative stress, and illustrates the regulatory function of this gene on abiotic stress.The present invention provides new candidate gene for the research of improving aspects such as yeast and plant stress resistance, and is of great significance to in-depth study of rubber tree genetic improvement breeding.

A cell cultivation apparatus for cultivating microorganisms and growing cells at high density is provided. The apparatus includes a membrane comprising multiple surface features on a first side of the membrane for cell placement. The surface features comprising one or more compartments within which a cell can be located. The membrane includes a material that is at least partially permeable to gas. A second side of the membrane defines a gas region. The second side of the membrane is separated from the first side of the membrane by the membrane. The apparatus further includes a media region for receiving media. The compartments are configured to at least partially reduce media flow shear forces on one or more cells in the compartments. The surface features may be ridges, protrusions, fins, wells, and / or posts.

Methods of treating heart disease by modulating the cellular localization of the beta isoform calcium / calmodulin dependent protein kinase II delta (CaMKIIδ) via the disruption of post-transcriptional phosphorylation.

The present application relates to a single-cell protein subcellular localization model based on weakly supervised multi-instance learning, and relates to the technical field of biological information. The model comprises a cell feature extractor, an image branch, a cell branch and a class-aware adaptive pruning module. The cell feature extractor is used to encode single-cell images into single-cell feature vectors. An image-level classifier in the image branch is used to output an image-level protein subcellular localization prediction result according to the image-level feature representation. A cell-level classifier in the cell branch is used to output a single-cell-level protein subcellular localization prediction result according to the single-cell feature vector. The class-aware adaptive pruning module generates single-cell pseudo labels through several label sources. The model of the present application can solve the problems of cell label noise and class long-tail distribution under weak supervision, thereby realizing more accurate and stable single-cell localization prediction and providing a powerful tool for single-cell heterogeneity analysis.

A method and system for seeding cells on a well plate is disclosed. According to the present disclosure, a cell concentrating device is used that positions the cells at a particular location on the bottom floor of each well. The cell concentrating device improved sensitivity and uniformity when conducting analysis on the cells.

The invention relates to the technical field of biomedicine, in particular to a sepsis marker screening method based on a peripheral blood mononuclear cell gene co-expression network, and the method comprises the following steps: obtaining a sample; carrying out batch RNA sequencing, differential expression analysis, weighted gene co-expression network analysis, cross analysis and protein interaction network analysis on the sample, and screening hub genes; performing function enrichment analysis and immune cell infiltration analysis on the hub gene, and screening out a core gene; and carrying out expression verification and clinical correlation analysis on the hub gene. Starting from the overall perspective of a gene network, the screened Hub gene has higher biological significance and reliability, through cross screening of WGCNA and differential expression analysis, the range of candidate genes is greatly narrowed, the screening efficiency and accuracy are improved, bioinformatics analysis, scRNA-seq cell localization and protein level experimental verification are integrated, and the screening method has the advantages that the screening efficiency is greatly improved, and the screening cost is reduced. A complete evidence chain is formed, and the credibility of the marker is ensured.

The invention relates to a computer-implemented method for creating a searchable indexed data structure for biological sequence data. The invention further relates to searching in said data structure for any given biological sequence data or associated information. The invention relates to a computer-implemented method comprising: a. providing biological sequence input data, b. decomposing the biological input data into non-overlapping portions, c. indexing each portion of the biological input data with at least one associated information of said portion comprised within or provided with the biological input data, thereby creating a two-tiered index for each portion. The method may further comprise d. receiving at least one query from a user, wherein the query comprises biological sequence information, language-based prompts and / or queries regarding the metadata comprised within or provided with the biological input data. The method may further comprise e. providing at least one output comprising at least one biological sequence information, biological sequence identifier and / or metadata of one or more portions of the biological sequence input data corresponding to the query. The invention further relates to the use of the method of the invention for identifying tissue and / or cell localized expression of at least one nucleic acid, such as a gene, and / or protein of interest. The method further relates to a data structure and a computer-readable medium comprising a data structure, such as a database, constructed according to the invention.

The present application relates to the technical field of genetic engineering, and particularly relates to a MdHEMH2 gene for promoting apple anthocyanin synthesis and application thereof. The present application uses molecular biology means to screen a gene MdHEMH2 capable of regulating apple anthocyanin accumulation from apples. Through GUS reporter gene expression, biological information analysis, subcellular localization, transient and stable genetic expression and other tests, it is proved that the MdHEMH2 gene plays a positive regulation role in the accumulation of anthocyanin induced by ALA, and has the function of promoting the accumulation of apple anthocyanin. In addition, exogenous ALA can induce the expression of MdHEMH2 gene and participate in the accumulation of apple anthocyanin. The use of MdHEMH2 gene can improve fruit quality and provide a molecular basis for breeding new plant varieties.

To provide a screening method of polypeptides derived from glycosylated proteins and a polypeptide vaccine combination, having an effective and strong specific anti-tumor immune response effect.SOLUTION: The method comprises: a step 1 of screening N-mannan oligosaccharide-modified proteins and modification sites; a step 2 of identifying a sequence of the modified proteins, cellular localization, immunogenic regions, conservation of glycosylation modification sites, expression levels in tissue cells, site mutations, and effect thereof on protein stability; a step 3 of identifying conservation of residue expression of the modification sites; a step 4 of predicting antigen polypeptides having high HLA affinity and B cell linear epitopes from polypeptides containing the modification sites; and a step 5 of scanning the modification sites of the antigen polypeptides, simulating binding between HLA-A*24:02 molecules and the antigen polypeptides, and identifying parameters of binding stability.SELECTED DRAWING: None

A method of determining presence of biomarkers in a sample comprises receiving an image of the sample, the image comprising a plurality of cell-cross segments; dividing the image into a plurality of two-dimensional grid segments, each of the plurality of two-dimensional grid segments comprising a plurality of cell segments; determining, for each of the plurality of two-dimensional grid segments, a corresponding z-level of a cell contained within the segment; correcting a segment-quantified biomarker expression for each of the cells contained within each of the plurality of grid segments by regressing out z-level information; and returning a corrected expression approximating a complete biomarker expression for each of the cells contained within each of the plurality of grid segments as if a biomarker expression of an entirety of each of the cells contained within each of the plurality of the grid segments had been measured.

A cell retainer has a measuring surface that includes a work region in which a plurality of working electrodes are arranged, a reference electrode arranged outward of the work region, a plurality of first regions provided between the work region and the reference electrode, and a second region arranged between each two of the first regions, the two being adjacent to each other in a radial direction. A contact angle in the first region is greater than a contact angle in the work region and a contact angle in the second region. Thus, any of the first regions arranged outward of the work region or the second region serves to restrict the spread of a cell suspension. Moreover, the first regions arranged in multiple tiers enable selection of a drop region and a drop area from a plurality of drop regions and a plurality of drop areas. Accordingly, it is possible to adjust the thickness of a cell layer by selecting the drop area of the cell suspension.

The present invention relates to a microphysiological system sensor chip for real-time dynamic multi-parameter monitoring and its assembly method. The invention includes a cell positioning capture module, a multi-parameter cell sensing detection module, and a signal switching module. The cell positioning capture module includes multiple 3D cell or organoid capture and positioning microfluidic chips; the individual 3D cell or organoid capture and positioning microfluidic chips in the cell positioning capture module are combined and connected; the multi-parameter cell sensing detection module includes multiple sensors for real-time monitoring of 3D cells or organoids; the multi-parameter cell sensing detection module senses multiple parameters of multiple 3D cells or organoids in real time; and the signal switching module includes a switching circuit and an image acquisition area. The present invention performs high-throughput capture and positioning of different types of 3D cells or organoids based on the 3D cell or organoid capture and positioning microfluidic chip, constructs a microphysiological system composed of multiple 3D cells or organoids, and performs comprehensive evaluation of drugs.

The invention relates to the technical field of gastric cancer, and discloses a gastric cancer CLDN18-based tumor cell quantitative analysis method and system, and the method comprises the steps: presetting candidate points on a pathological section image; foreground and background auxiliary points are generated for the cell labeling points, and a structured supervision target is constructed; extracting multi-scale semantic features through a pre-trained large pathological model, and generating an aggregation feature map through down-sampling alignment and fusion operation; adopting an implicit feature interpolation technology to adaptively reconstruct candidate point features; based on candidate point features, predicting position offset and category confidence, and determining cell positions and categories through threshold screening and offset optimization; and combining with an auxiliary point supervision mechanism optimization model, and outputting an objective quantification result. According to the scheme, the positioning precision and classification consistency of the CLDN18 positive tumor cells can be remarkably improved, and the workload of doctors is reduced.

This invention discloses a deep learning-based method and related equipment for locating and counting cells in *Scenedesmus* (a type of algae). The method first acquires microscopic images of *Scenedesmus* populations and labels the cell locations to form a set of labeled cell points. Then, a *Scenedesmus* cell counting network is constructed, including a backbone network, density branches, and regression branches, to extract features from the images and model the spatial distribution of cells. Enhanced images are generated by preprocessing the microscopic images, and the network is trained using the labeled information as supervision. After model training, new microscopic images are input into the network to obtain initial cell location and counting results. Furthermore, by incorporating the biological prior constraint that the number of cells in a *Scenedesmus* population is even, the initial counting results are post-processed and corrected, finally outputting the cell location and counting results. This invention enables automatic location and accurate counting of cells in *Scenedesmus* populations, exhibiting high recognition accuracy and application value.

This invention belongs to the field of biomedical technology and discloses an in vitro evaluation method for GBM drug delivery systems based on tumor organoid chips. This method uses a PDMS tumor organoid chip as a carrier, co-culturing GBM cells and human umbilical vein endothelial cells to localize and culture GBM cells within the chip chamber. A biomimetic blood-brain barrier is constructed within the microchannels, and a perfusion system continuously maintains the cell growth microenvironment. After in vitro drug administration to the test drug delivery system, its anti-GBM effect is quantitatively detected from three dimensions: proliferation, invasion, and spheroidization. This invention can highly simulate the in vivo GBM tumor microenvironment and blood-brain barrier structure, achieving precise, real-time, and dynamic in vitro efficacy evaluation, while significantly reducing reliance on animal experiments, improving the reliability of results and experimental throughput. It provides an efficient and standardized platform for preclinical screening and mechanism of action research of anti-GBM drug delivery systems, and has significant scientific research value and clinical translational significance.

The application discloses a multi-omics integration method and system for identifying a key autocrine signal loop and core candidate genes in pancreatic cancer. The method comprises the following steps: acquiring and preprocessing batch transcriptome, proteome, single-cell RNA sequencing and spatial transcriptome data; identifying core candidate genes through multi-method cross screening and machine learning robustness sorting; locating the core genes to the target cell subpopulation by using single-cell data and analyzing the regulation mechanism thereof; identifying the autocrine signal loop participated by the core genes in the target subpopulation based on cell communication network analysis; and verifying the spatial co-localization and co-activity of the loop by using spatial transcriptome data. The application systematically connects the whole-chain analysis of molecular discovery, cell localization, function analysis and interaction verification, significantly improves the accuracy and reliability of the identification of key factors and autocrine loops, and provides new targets and complete evidence chain for pancreatic cancer targeted therapy.