559 results about "Cell nucleus" patented technology

The invention discloses a lung cancer gene mutation classification method based on frequency domain multi-scale fusion guidance, and relates to the technical field of medical image processing and gene detection. According to the MFHA mechanism provided by the invention, the pathological image is decoupled into low-frequency global and high-frequency detail sub-bands through wavelet transform, and extraction of key high-frequency features such as cell nucleus morphology and local texture is enhanced by combining multi-scale convolution and up-sampling guided by high-frequency information; the problems of insufficient feature detail mining and low feature fusion efficiency in a traditional pathological image analysis method are solved; key features are screened and focused through a channel, frequency domain-space feature deep fusion is realized through up-sampling, robust representation is constructed by combining space attention with cosine similarity and multi-dimensional statistical features, a frequency domain analysis-space focusing collaborative optimization mechanism is formed, information redundancy caused by simple feature splicing is avoided, and the robustness of the system is improved. And the classification stability of the model in a complex pathological scene is improved.

The invention belongs to the technical field of molecular biology, and particularly relates to a CeNF-YA3 gene, an expression vector and application of the CeNF-YA3 gene in vegetable fat regulation and control. The nucleotide sequence of the CeNF-YA3 gene is as shown in SEQ ID NO.1. After the CeNF-YA3 gene is over-expressed, the grease content of arabidopsis seeds and leaves can be remarkably increased; in addition, the invention also provides a series of vectors containing the CeNF-YA3 gene for subcellular localization, yeast hybridization and plant overexpression for the first time, and proves that the CeNF-YA3 protein has a transcriptional activation function, is localized in a cell nucleus and accords with the basic characteristics of transcription factors for the first time. Therefore, the technical scheme of the invention provides scientific guidance for the application of the CeNF-YA3 gene and the protein thereof.

The invention discloses application of an apple MdMYS1 gene in regulating and controlling the wax content of plant fruits and leaves, and belongs to the technical field of plant genetic engineering. According to the invention, the MdMYS1 gene with high expression quantity in apple varieties with more waxiness is separated, and the nucleotide sequence of the MdMYS1 gene is shown as SEQ ID NO. 1. Through subcellular localization, the transcription factor expressed by the MdMYS1 gene is found to be localized on a cell nucleus. Experimental results show that overexpression of the MdMYS1 gene can significantly increase the wax content of the apple fruits and leaves by promoting biosynthesis of wax, which indicates that the MdMYS1 gene plays a key role in regulation and control of the wax content of the apple fruits and leaves. The invention provides an efficient and rapid way for apple breeding, provides a gene material for improving apple quality, and has a wide application prospect in improving economic benefits and ecological benefits of apple planting.

The invention discloses a hepatocellular carcinoma postoperative early recurrence prediction method based on multi-modal fusion. The method comprises the following steps: firstly, integrating clinical data of a training set, a preoperative enhanced CT image and a postoperative full-view digital pathological image, and carrying out standardized correction; then, traditional image omics features and deep learning features are extracted from the CT image, cell nucleus morphological features and tumor microenvironment spatial configuration features are extracted from the pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy algorithm (mRMR) and LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal cross validation, foresight and external independent validation, risk layering is carried out based on the prediction probability, and individualized postoperative management is guided.

The invention belongs to the technical field of molecular biology, and particularly relates to a CeNF-YA1 gene, an expression vector and application of the CeNF-YA1 gene in vegetable fat regulation and control. The invention provides the CeNF-YA1 gene for the first time, the nucleotide sequence of the CeNF-YA1 gene is as shown in SEQ ID NO.1, the full length of a coding region of the CeNF-YA1 gene is 792 bp, a series of carriers containing the CeNF-YA1 gene, such as subcellular localization, yeast hybridization and plant overexpression, are constructed for the first time, and meanwhile, the CeNF-YA1 protein is proved to have a transcription activation function, is localized in a cell nucleus and conforms to the basic characteristics of transcription factors. It is found for the first time that overexpression of the CeNF-YA1 gene can significantly improve the grease content of arabidopsis seeds and leaves; therefore, the technical scheme provided by the invention can be used for improving the oil content of the plant seeds and the nutritional tissues, and shows the application prospect in improving the oil content of the seeds and the nutritional tissues.

The invention discloses a morphological feature-based cell segmentation and cell nucleus identification method for a turned unstained bone tissue pathological image. The method comprises the following steps of: 1, eliminating tool marks by adopting a tool mark elimination method combining local frequency domain analysis and directional suppression; 2, performing cell segmentation by using a K-means method, and performing morphological expansion and topological analysis on a segmented single cell image to identify a cell nucleus in the single cell image; step 3, calculating morphological characteristic indexes of each region; the method comprises the following steps: establishing a multi-dimensional Gaussian mixture model according to existing bone cell labeled sample information, performing outlier detection according to statistical data analysis, and removing results which do not conform to cell morphology; classifying different regions, and removing non-cell regions; by calculating morphological characteristic indexes of each region, different regions are distinguished according to the indexes, and cells are preliminarily screened. According to the method, high-precision cell segmentation and cell nucleus identification can be carried out on the cut undyed bone tissue pathological image.

The invention discloses a method for automatically constructing a pathological image data set and training cell nucleus detection and classification based on a space transcriptome technology, and belongs to the field of image processing and artificial intelligence auxiliary pathological diagnosis. According to the method, a spatial transcriptome public data set is obtained, and a data set containing image blocks, weak supervision / semi-supervision labels and cell nucleus boundary information is automatically constructed through preprocessing, deconvolution cell type annotation and cell nucleus instance segmentation, so that the dependence on manual annotation is reduced. Furthermore, a detection and classification model is designed, a multi-scale deformable attention encoder and a decoupled detection and classification decoder are adopted, a limited deformable cross attention mechanism is introduced into the classification decoder, KL divergence classification loss is combined, and instance-level cell nucleus categories are learned from region-level proportion labels. According to the method, end-to-end automation is realized, the cell nucleus detection and classification precision and efficiency are improved, and a high-quality pre-training model basis is provided for downstream pathological analysis.

The invention relates to the technical field of image analysis, in particular to a targeted drug curative effect prediction method based on image recognition, which comprises the following steps: acquiring tissue images and nuclear morphological parameters by a microscope, establishing a database in combination with transcripts, extracting an injury area, recognizing image features through a convolutional neural network, and constructing a prediction model; and inputting candidate drug molecular structures for molecular docking, calculating a repair progress by combining animal verification to establish a curative effect model, predicting drug scores and response time based on the curative effect model to generate a ranking list, screening high-score drug cells, verifying monitored survival, comparing, predicting and outputting a result. The method comprises the following steps: extracting a cell nucleus form, revealing a relation between damage and molecular abnormality in combination with a transcriptome, identifying a target spot corresponding to an abnormal mode and pathological change through deep learning, performing affinity prediction and animal verification on a drug structure, quantifying the repair progress by adopting image difference, and evaluating the curative effect with two dimensions of structure and function. And curative effect scores and response prediction are output to realize system sequencing, so that drug screening is more accurate and practical.

The invention relates to the field of anti-tumor drugs, and discloses an anti-diffuse large B-cell lymphoma pharmaceutical composition, a preparation method and application thereof, the composition comprises azacitidine and Sanilisoma, the composition can improve the accumulation amount of VDUP1 protein in a cell nucleus through the combination of the methylation effect of DNMT3b on VDUP1 protein and the inhibition of XPO1 activity by Sanilisoma, and the anti-diffuse large B-cell lymphoma pharmaceutical composition can be used for preparing anti-diffuse large B-cell lymphoma. The chemotherapy effect is remarkably improved, the sensitivity of the DLBCL to the Sanisole is remarkably improved, and particularly, the pharmaceutical composition has a remarkable curative effect on R / R DLBCL patients.

The invention provides a kiwi fruit transcription factor and application thereof in kiwi fruit canker, and belongs to the technical field of genetic engineering. The invention provides a transcription factor AcWRKY50, the nucleotide sequence of the transcription factor AcWRKY50 is shown as SEQ ID No.1, and the AcWRKY50 is positioned in a cell nucleus and has transcription activation capability. The AcWRKY50 disclosed by the invention can continuously respond to Psa infection, after Psa pathogenic bacteria infection, the expression of the AcWRKY50 gene is rapidly activated and continuously up-regulated, and after the AcWRKY50 gene is over-expressed, the resistance to Psa can be improved. Meanwhile, MeJA negatively regulates the resistance of the kiwi fruit to the Psa, and the kiwi fruit germplasm can be innovated based on the transcription factor disclosed by the invention.

The invention discloses a single cell activity evaluation method based on cell nucleus geometric parameters and a Bayesian framework, which comprises the following steps: establishing a HeLa cell activity gradient model through adriamycin induction, carrying out cell nucleus segmentation by adopting Cell pose 3.0 and extracting 26 geometric features, screening a high-confidence sample in combination with a Bayesian deep learning framework, and evaluating the activity of a single cell according to the high-confidence sample. And constructing a multi-modal feature fusion model to integrate geometric features and deep semantic features, and finally realizing unmarked and high-precision single cell activity evaluation. The method overcomes the limitation that a traditional technology depends on biochemical markers, has the advantages of being easy and convenient to operate, high in flux and high in interpretability, and provides an innovative technical means for tumor liquid biopsy and precise medical treatment.

A kit for detecting expression of AIRE and Treg in lymphatic tissue infected by EB virus comprises colloidal quantum well antibody conjugates, including a colloidal quantum well and protein AIRE and GITR conjugates and a colloidal quantum well and antibody CD25 and Foxp3 conjugates. The detection method comprises the following steps: (1) carrying out slicing treatment on a lymphatic tissue, and carrying out antigen repair by using an antigen repair liquid; (2) respectively adding colloidal quantum well proteins or antibodies coupled with different targets to dye the sections; (3) carrying out nucleus staining; and (4) collecting multi-channel fluorescence image information of the tissue slice, and judging expression levels and mutual relations of AIRE, Treg cells and dendritic cells in immune tolerance in combination with spatial positioning and intensity distribution of fluorescence signals of different channels. According to the invention, qualitative or quantitative multi-parameter and high-precision detection of the immune tolerance related cells in the lymph tissue is realized.

The present disclosure relates generally to methods of inhibiting a tripartite VAP-A, ORP3 and Rab7 (VOR) protein complex in multicellular organisms, to methods of identifying agents which inhibit such complex and to the medical use of those agents. Inhibition of the VOR complex causes interference with at least one mechanism of intercellular communication, wherein the intercellular communication is mediated by receptor-ligand interaction and / or EVs, and viral infection involving the transport of endocytosed biomaterials to the nucleus of recipient cells.

The invention relates to the technical field of biomedicine detection reagent manufacturing and reproductive medicine, in particular to an embryonic development potential sperm key protein kit and a use method thereof. The method is characterized in that an antibody combination capable of specifically recognizing a group of key proteins related to embryonic development potential in human sperms is prepared, and the antibodies, a fluorescence labeled secondary antibody, a cell nucleus dye, a concentrated washing liquid, a mounting medium and a pre-coated control porous glass slide are assembled into the special detection kit. The kit can be combined with a multi-mode imaging system (such as a fluorescence / confocal microscope and a common optical microscope) and image analysis software to realize the traditional analysis of sperm morphology and movement ability, and can perform synchronous and accurate qualitative and quantitative analysis with the expression level and subcellular localization of the key protein. The invention provides an integrated molecular detection solution, and provides a powerful tool for sperm quality screening and embryonic development potential prediction in an assisted reproduction technology.

The present invention relates to a method for diagnosing cancer and predicting cancer types using a methylated cell-free nucleic acid, and more particularly, to a method for diagnosing cancer and predicting cancer types using a method for extracting methylated nucleic acids from a biospecimen, generating vectorized data of nucleic acid fragments based on aligned reads by obtaining sequence information, and then inputting the data into a trained artificial intelligence model so as to analyze a calculated value. The method for diagnosing cancer and predicting cancer types using methylated cell-free nucleic acids according to the present invention is useful because it generates vectorized data and analyzes it using an AI algorithm, compared to methods that use a conventional step of determining the amount of chromosomes based on the read count or detection methods that use the concept of distance between aligned reads to utilize values related to reads as one by one structured values, so that a similar effect can be achieved even if the read coverage is low.

The present disclosure provides a method for obtaining a cancer risk prediction marker, a cancer risk assessment method, an electronic device, and a storage medium performed by a machine. The method for obtaining a cancer risk prediction marker includes: analyzing a plurality of peripheral blood circulating cell-free DNA (cfDNA) fragments of a biological sample of a subject to obtain sequence reads; obtaining data of a distribution of a nucleosome, and determining a predetermined interval upstream and downstream of a nucleosome center position as a nucleosome protection region; screening sequence reads of partial cfDNA fragments whose ends are located in the nucleosome protection region from the sequence reads of the plurality of cfDNA fragments; calculating a first fragmentation feature in the sequence reads of the plurality of cfDNA fragments, and calculating a second fragmentation feature in the sequence reads of the screened partial cfDNA fragments; and calculating one or more difference values of a change level of the second fragmentation feature relative to the first fragmentation feature as a cancer risk prediction marker.

The invention discloses a sugarcane ScPP2C49 gene and application thereof, and relates to the technical field of plant biology, the nucleotide sequence of the ScPP2C49 gene is as shown in SEQ ID No.1, and the amino acid sequence of protein coded by the ScPP2C49 gene is as shown in SEQ ID No.2. The gene is induced to express by drought and abscisic acid, and the encoded protein of the gene is positioned in a cell nucleus. Functional studies show that after overexpression of the ScPP2C49 gene in the plant, the drought resistance of the plant is negatively regulated through various mechanisms of inhibiting an abscisic acid signal channel, hindering stomatal closure under drought stress, weakening active oxygen scavenging ability, reducing photosynthetic efficiency, inhibiting root growth and the like. The invention also provides a recombinant overexpression vector containing the gene and a method for obtaining a transgenic plant with reduced drought resistance by using the vector through an agrobacterium-mediated method. According to the invention, a new gene resource is provided for deep analysis of a plant drought-resistant molecular mechanism, and an important target gene is provided for cultivation of high-stress-resistance crop varieties through a reverse genetics means (such as gene knockout).

Techniques for using combination stain types for machine learning models for digital pathology are described herein. In an example, a system accesses a first image of a sample comprising an immunohistochemistry (IHC) stain for a biomarker. The system accesses a second image of the sample comprising a hematoxylin and eosin (H&E) stain for nuclei. The system can segment tissue regions in the one or more first images and the second image, partitions the tissue regions in the one or more first images and the second image, and extracts features from the set of tiles using a feature extractor. The system can generate, by a machine-learning model, an output classification indicating a first phenotype based on the features extracted from the set of tiles. The machine-learning model can include one or more classifiers and an aggregation model that provides an aggregated output for the set of tiles.

The invention provides a cell classification system and application based on a cell microenvironment map attention network, and the method comprises the following steps: obtaining a mask of each cell nucleus in a TCT image and a centroid coordinate of each cell nucleus, and obtaining a morphological feature vector of each cell nucleus based on each cell nucleus mask, obtaining a depth visual feature vector of each single cell image based on the centroid coordinate of each cell nucleus; constructing an initial cell microenvironment structure chart and carrying out chart attention calculation on the initial cell microenvironment structure chart; and using a classifier to classify the image attention calculation result to obtain the regional cell category of the TCT image. According to the scheme, the cell microenvironment map is constructed based on each cell nucleus in the TCT image, and the node features in the cell microenvironment map are updated by using the map attention mechanism, so that the classification result of the cell nucleus is more accurate, and a key basis is provided for clinical diagnosis and treatment.

The invention discloses a rectal cancer postoperative recurrence risk prediction system and method based on multi-modal time sequence data, and relates to the technical field of medical artificial intelligence. The system comprises a data acquisition and preprocessing module, a feature extraction module and a multi-modal feature fusion and modeling module. The method comprises the following steps: constructing a cross-modal data set containing time sequence clinical data, a time sequence MR image and a biopsy digital pathological image; respectively extracting clinical features, radiomics and deep learning features of the MR image, and nucleus morphology and spatial distribution features of the pathological image; and fusing all the features by using a Transform network, and constructing a prediction model. According to the method, macroscopic images, micropathology and dynamic time sequence information are integrated, tumor heterogeneity is comprehensively quantified, the problem that prediction of a single-mode static model is not accurate is solved, the postoperative recurrence risk of the stage III rectal cancer patient can be evaluated more accurately, and clinical treatment decision making is assisted.

2-diarylmethyl-4-aminotetrahydropyran derivatives are disclosed. The compounds include the following genus:or a pharmaceutically acceptable salt thereof. The compounds activate cellular PP2A, suppress oncogenic kinase signaling and negatively regulate MYC and MYCN in cancer. The compounds also induce FOXO transcription factor translocation to the nucleus by modulating PP2A and, as a consequence, exhibit anti-proliferative effects. They are useful in the treatment of a variety of disorders, including as a monotherapy in cancer treatment, or used in combination with other drugs to restore sensitivity to chemotherapy where resistance has developed.

Systems and methods for multi-modal subcellular segmentation using photolysable biomarkers and / or transcriptomic readout density maps are disclosed. The systems and methods improve the accuracy of cell segmentation of the nucleus, cytoplasm, and cell membrane regions by using optical and bleach correction from a variety of photolysable morphological markers in combination with high quality 3D images acquired with high dynamic range scans and spatial transcriptomic readout density maps.

The invention provides a pathological image tissue classification method based on staining invariance and tissue morphological characteristics, which comprises the following steps of: 1, modeling an input pathological image based on a staining invariance module, reconstructing an absorption coefficient of the image to obtain staining invariance characteristics, and fusing the staining invariance characteristics into a backbone network; 2, carrying out channel separation on the image based on a tissue morphology module, respectively extracting morphological characteristics of cell nucleus and non-nuclear tissues, and fusing the morphological characteristics into each level of a backbone network layer by layer in a pixel-by-pixel addition mode; 3, constructing a comparison loss function guided by a prototype, representing each category as an updatable category prototype, dynamically updating the prototype by using a high-confidence sample, and optimizing the feature expression ability by maximizing the similarity between the sample and the similar prototype and minimizing the similarity between the sample and the heterogeneous prototype; and 4, performing end-to-end training on the model by combining the prototype comparison loss and the cross entropy loss, and outputting prediction results of each category, thereby realizing accurate tissue classification of the pathological image.

The invention discloses a medical record diagnosis auxiliary system for thyroid puncture pathology and clinical data association analysis, and relates to the technical field of medical diagnosis intelligent analysis. Comprising a regional risk sensing module, a self-adaptive partition compression module, a transmission priority scheduling module, a detail integrity verification module, a cross-domain feature fusion and detail enhancement module and a diagnosis result return and model optimization module, in the digital scanning process, a regional risk perception model based on a pathological diagnosis task is established, a high-sensitivity region is calibrated according to cell nucleus density distribution, cell nucleus morphological anomaly degree and chromatin particle change, and corresponding lossless fidelity demand data is generated. Pathology key detail fidelity transmission and multi-dimensional feature fusion are achieved under the condition that resources are limited, the early-stage tiny focus recognition rate is increased, misdiagnosis and missed diagnosis are reduced, and diagnosis accuracy and efficiency are continuously improved through closed-loop optimization.

The invention discloses application of fluorinated ionizable lipid as a gene drug delivery carrier, and belongs to the technical field of biological medicine. The gene drug delivery carrier provided by the invention is prepared from fluorinated ionizable lipids, cationic lipids, auxiliary lipids, structural lipids and PEG lipids, wherein the molar percentage of the fluorinated ionizable lipids in the gene drug delivery carrier is 1%-10%. According to the invention, the molar percentage of the fluorinated ionizable lipid in the total lipid is optimized to 1-10%, so that the cytotoxicity is obviously reduced, and more excellent nucleic acid transfection efficiency is realized in vivo and in vitro; various nucleic acid drugs including DNA, mRNA, siRNA and CRISPR-Cas9 ribonucleoprotein can be efficiently delivered, action targets of the nucleic acid drugs are expanded to cell nucleuses and cytoplasm, and the nucleic acid drugs are more accordant with the action mechanism of modern gene therapy and editing.

The invention discloses a pathological image virtual dyeing method and system based on a diffusion model, and belongs to the technical field of cross of pathological image processing, deep learning and computer vision. Through an end-to-end process of data preprocessing, model construction, model training and virtual dyeing reasoning, AF to Hamp is realized; performing non-paired virtual dyeing on the E; the method comprises the following steps: acquiring a non-paired AF pathological image and a Hamp through data preprocessing; e, acquiring a pathological image; generating a trunk through single-step diffusion, and constructing an STAIN model through a multi-modal semantic enhancement module, a local significance perception module and a composite loss function; the STAIN model is trained; and inputting a to-be-processed AF pathological image into the trained STAIN model for virtual dyeing reasoning. According to the method, the multi-step iteration bottleneck of a diffusion model is broken through, and single-step rapid reasoning is realized; cell nucleus and gland pathological structures are accurately reserved; it is guaranteed that the dyeing style conforms to Hamp; e, clinical standard; a lightweight adaptation strategy is adopted; background noise interference is suppressed; the method adapts to non-pairwise data scenes, and the practicability and generalization are improved.

The invention discloses a method and a device suitable for CUAMP. The invention discloses a method for extracting liriodendron tulipifera cell nucleuses by a Tag technology and application of the liriodendron tulipifera cell nucleuses. Hybridized liriodendron tulipifera calluses are used as materials, and protoplasts are released through enzymolysis solution vacuumizing and mild enzymolysis for 1.5 h; the method comprises the following steps: sequentially purifying by using a W5 solution and mannitol, detecting the activity by FDA, cracking by using an NE buffer solution, and washing by using a Wash buffer solution in two steps, thereby obtaining 2 * 10 < 6 >-3 * 10 < 6 > high-purity and high-integrity cell nucleuses. The obtained core is clean in background and complete in membrane structure, and can be directly used for CUTamp; tag is used for building a library, so that the magnetic bead capturing efficiency is improved by 46%, and protein-DNA interaction high-throughput sequencing under the condition of low sample size is realized. The method disclosed by the invention is simple and convenient to operate and good in repeatability, and provides key technical support for epigenetic research of rare tree species such as liriodendron tulipifera.

The present invention relates to specific natural or artificial RNA or DNA / RNA substrates for cleaving by a Cas nuclease. The invention furthermore relates to a complex comprising the specific artificial RNA or DNA / RNA substrate, at least one of a Cas nuclease enzyme and at least one preselected guide RNA binding to at least one target RNA. The present invention also relates to methods for cleaving the natural or artificial RNA or DNA / RNA substrate and methods for detecting at least one target RNA in a cell, tissue, cellular nucleus, and / or sample using the substrate or for eliminating a cell expressing the at least one target RNA.

The application discloses a photoacoustic spectrum signal detection system and a detection method. The system comprises a signal acquisition device and a detection signal processing terminal. The signal acquisition device comprises an ultraviolet pulse laser, a first plano-convex lens, a second plano-convex lens, a focusing objective lens, a refractive prism, a helium-neon laser, an electric displacement platform, a polarization modulation assembly, a light splitting assembly and a differential detector. The photoacoustic spectrum signal detection system uses the specific absorption of cell nuclei to ultraviolet light, obtains a differential detection signal through the differential detector and generates an analysis spectrum. The light sensing assembly collects a light sensing signal of the sample and generates a microscopic image. The detection system can realize photoacoustic spectrum analysis of a cell nucleus level micro-sized absorber, thereby obtaining a high-resolution analysis spectrum and a microscopic image reflecting the physical properties inside the cell nuclei. The label-free and non-staining photoacoustic spectrum signal detection does not need staining and can save detection time, and the accuracy of cell detection on biological samples is improved.

The application discloses a method, system, device and medium for recognizing point fluorescence signals in cell nuclei. The method comprises inputting a pre-processed tissue slice scanning image into a cell nucleus segmentation network model to obtain a binary segmentation mask image; determining a target cell nucleus mask based on the binary segmentation mask image; multiplying a point fluorescence signal channel with the target cell nucleus mask to obtain a multiplication feature result image; inputting the multiplication feature result image into a point fluorescence signal recognition model to obtain a signal point heat map and a preliminary segmentation mask; fusing the signal point heat map, the preliminary segmentation mask and the point fluorescence signal channel to obtain a fusion feature image; inputting the fusion feature image into a signal point instance segmentation model to obtain a plurality of single signal point instances; and determining a target ACD score of the tissue slice scanning image based on the plurality of single signal point instances. The application can improve the accuracy, stability and efficiency of recognizing point fluorescence signals in cell nuclei.