1321 results about "Cell nucleus" 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.

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

The invention provides a Chinese rose heat shock protein family gene RcHSP70-8, the nucleotide sequence of the Chinese rose heat shock protein family gene RcHSP70-8 is shown as SEQ ID No. 1, and the amino acid sequence of the Chinese rose heat shock protein family gene RcHSP70-8 is shown as SEQ ID No. 2. The invention discloses an application of the Chinese rose heat shock protein family gene RcHSP70-8 in delaying flowering of plants, and the RcHSP70-8 protein is positioned in a cell nucleus and can delay flowering through overexpression in arabidopsis thaliana. The invention is helpful for enriching the research on the flowering regulation and control mechanism of the Chinese rose, and has important significance for genetic engineering breeding.

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 relates to the technical field of data processing, and discloses a cell nucleus segmentation model construction method, a cell nucleus segmentation method and a construction device.The construction method comprises the steps that a cell image data set is acquired; a cell nucleus segmentation network structure is constructed, the cell nucleus segmentation network structure comprises a patch embedding layer, an encoder module, a tokenized KAN network module, a decoder module and a projection layer which are connected in sequence, and the encoder module and the decoder module are further connected through a frequency domain learnable module; wherein the frequency domain learnable module is used for transmitting low-level features in the encoder module to the decoder module; the tokenized KAN network module is used for deep feature extraction; and training the cell nucleus segmentation network based on the cell image data set to obtain a cell nucleus segmentation model. The cell nucleus segmentation model provided by the invention pays more attention to the cell nucleus boundary effect, and the cell nucleus segmentation precision is greatly improved.

The invention relates to the technical field of biomedical engineering and digital image processing, in particular to a digital image enhancement method based on cytopathology. The method comprises the following steps: eliminating noise and artifacts caused by non-uniform dyeing, section folding and dust factors; the problem of dyeing difference caused by different scanning devices is solved; key areas including cell nucleuses and cell membranes are highlighted, so that the visual effect of a low-contrast image is improved; segmenting an area, including cell nucleuses and cytoplasm, of the cells, and extracting morphological characteristics; the detail resolution of a low-resolution image, especially the definition of a small cell structure, is improved; the brightness component Y is corrected to compensate for the difference of different dyeing methods and dyeing qualities in brightness response, then the chromatic value is adjusted according to the color correction factor to make the colors of the images more consistent, and finally, the images which are acquired by different dyeing methods and dyeing qualities and subjected to brightness and chroma correction are fused to make the color of the images more consistent. And a final corrected image is obtained.

The invention discloses a pathological image cell nucleus classification method based on a graph neural network. The method comprises the following steps: firstly, acquiring a pathological tissue image containing a cell nucleus and an instance segmentation result of the pathological tissue image; then, constructing a shape-guided three-branch fusion network; inputting the pathological tissue image containing the cell nucleus into the depth feature branch to extract global features to obtain a pixel-level feature map and obtain the depth feature of each cell nucleus; extracting form, color and texture features of a cell nucleus from an instance segmentation result by using a manual feature branch to obtain a manual feature, and raising dimensionality to obtain a manual feature vector; shape feature branches are adopted to extract shape features based on contour points from the instance segmentation result; finally, sending to a feature gating fusion mechanism for fusion to obtain a fusion vector; and inputting the fusion vector into a classification output module for classification, and outputting a classification result. According to the method, the shape information of the cell nucleuses is fully utilized, so that the network has better distinguishing capability on the cell nucleuses with complex forms, and the classification performance is improved.

The invention discloses a corn NAC transcription factor and application thereof in enhancing disease resistance. The corn NAC transcription factor can endow transgenic rice with wide resistance and does not affect the rice yield, and the amino acid sequence of the corn NAC transcription factor is SEQ ID No.2. The sequence of the coding gene of the gene is SEQ ID No.1. Research shows that the corn NAC transcription factor is located in a cell nucleus, the resistance of rice to rice blast caused by magnaporthe oryzae and rice bacterial leaf blight caused by Xanthomonas oryzae can be improved by overexpression of the corn NAC transcription factor in rice, meanwhile, the agronomic traits of the rice are evaluated, it is found that the gene does not affect the ear length and the grain length of the rice, and the corn NAC transcription factor can be used for improving the rice resistance to rice blast caused by magnaporthe oryzae and the rice bacterial leaf blight caused by Xanthomonas oryzae. Thousand seed weight and single plant yield.

The invention belongs to the technical field of kidney histopathology and immunological evaluation, and particularly relates to a kidney pathology detection kit, a detection method and application, the detection kit comprises 37 rare metal element labeled monoclonal antibodies combined with antigens of in-situ tissue cells, and the detection method comprises the following steps: (1) collecting a sample to be detected, preparing a pre-dyed paraffin section; (2) mixing 37 monoclonal antibodies labeled by rare metal elements, and carrying out antibody staining on various antigen targets on the surfaces of cells in the in-situ tissue; (3) carrying out nucleus DNA staining on the stained tissue sample; and (4) detecting the stained tissue sample through an imaging mass spectrum flow system, and collecting original information data through laser spot firing. The method is an analysis method for fine grouping identification and relative quantification of structural cells, immune cells and pathological injury cells in the clinical nephropathy sample in-situ tissue. And auxiliary diagnosis information is provided for diagnosis, grading, treatment and prognosis detection of various clinical nephropathy.

The invention relates to an application of a regulator in improving the maturation quality of frozen pig oocytes, the regulator is N-(2-(2-(4-chlorphenyl) thiazole-4-yl) ethyl) butyryl, and the regulator improves the maturation quality of the frozen pig oocytes by improving the mitochondrial function of the frozen pig oocytes after in-vitro maturation. The invention provides a new application of N-(2-(2-(4-chlorphenyl) thiazole-4-yl) ethyl) butyramide, namely the N-(2-(2-(4-chlorphenyl) thiazole-4-yl) ethyl) butyramide is used for developing a novel oocyte vitrification freezing solution and an in-vitro maturation culture solution, and the obtained high-quality oocytes can be beneficial to embryo biotechnology researches such as in-vitro fertilization and somatic cell nuclear transplantation.

The invention discloses a weak supervision cell nucleus segmentation method based on wavelet differential convolution and region expansion, and relates to the technical field of image processing, and the method comprises the following steps: obtaining an image sample containing a cell nucleus, and marking the position of the cell nucleus through central point annotation; a wavelet differential convolution module is designed, multi-scale features are extracted through discrete wavelet transform, and cell nucleus boundary and detail information are enhanced in combination with the differential convolution module; a region expansion module is constructed, pseudo labels are generated based on point annotation iteration, a complete cell nucleus region is expanded step by step, and the problems of noise and nucleus missing detection are reduced; building a segmentation network, adding a wavelet difference convolution module to extract detail features, and optimizing network performance by using a pseudo tag as a weak supervision signal; carrying out segmentation prediction, and outputting the accurate position and shape of the cell nucleus; therefore, high-precision segmentation is realized under a small amount of annotation information, the annotation dependence is reduced, and particularly, the effects of reducing adjacent cell nucleus boundary adhesion and small cell nucleus leak detection are remarkable.

Some aspects of this disclosure provide strategies, systems, reagents, methods, and kits that are useful for the targeted editing of nucleic acids, including editing a single site within the genome of a cell or subject, e.g., within the human genome. The disclosure provides fusion proteins of nucleic acid programmable DNA binding proteins (napDNAbp), e.g., Cas9 or variants thereof, and nucleic acid editing proteins such as cytidine deaminase domains (e.g., novel cytidine deaminases generated by ancestral sequence reconstruction), and adenosine deaminases that deaminate adenine in DNA. Aspects of the disclosure relate to fusion proteins (e.g., base editors) that have improved expression and / or localize efficiently to the nucleus. In some embodiments, base editors are codon optimized for expression in mammalian cells. In some embodiments, base editors include multiple nuclear localization sequences (e.g., bipartite NLSs), e.g., at least two NLSs. In some embodiments, methods for targeted nucleic acid editing are provided.

The invention relates to a cervical TCT image cell segmentation method based on improved U-Net, and belongs to the field of image processing technology and medical technology. The problems that complex morphological characteristics of cells are not fully considered and fine cell segmentation is insufficient in cervical cancer intelligent screening research based on cervical TCT images are solved. Comprising the following steps: preprocessing a cervical cell TCT image; constructing a segmentation network based on the EGE-UNet; evaluating the cell image segmentation model based on qualitative and quantitative evaluation; and obtaining a cervical cell segmentation result based on the evaluated model. According to the method, high-quality cervical TCT cell image segmentation and fine segmentation of cervical cell nucleuses and cytoplasm can be realized by using the EGE-UNet model, and related research of subsequent cervical cell intelligent diagnosis is facilitated; based on an accurate cell segmentation result, the method can assist in extracting various cell morphological characteristics, has clinical guiding significance, and provides more interpretable quantitative data for subsequent cervical cell research.

The invention discloses a specific primer for distinguishing rice and barnyard grass and application of the specific primer, and belongs to the technical field of biological species identification. The sequences of the specific primers are as shown in SEQ ID NO. 1 and SEQ ID NO. 2. The specific primer is designed according to the nuclear gene ITS sequence of the rice, the specific primer can accurately identify the rice through PCR, the identification method is stable, the detection time is short, the specificity is high, the detection limit is low, the identification method is not affected by material characters, rapid and accurate identification of the rice and barnyard grass can be achieved, and the identification efficiency is high. The problem that a method for effectively and rapidly distinguishing rice and barnyard grass does not exist at present is solved.

Disclosed are compositions comprising a nucleic acid molecule. The nucleic acid molecule may encode an exonic sequence or portion thereof of a target ribonucleic acid (RNA) sequence. The nucleic acid molecule may further encode one or more stabilization domains. The one or more stabilization domains may be configured to reduce a cellular nuclease activity compared to a nucleic acid molecule that does not comprise the one or more stabilization domains.

The invention relates to the technical field of image processing, and discloses a gynecological cell morphology intelligent identification method and system based on machine vision, and the method comprises the steps: collecting an original image of gynecological cells, carrying out the color normalization processing of the collected original image, carrying out the denoising processing through employing a wavelet threshold value, and carrying out the image enhancement through employing adaptive histogram equalization; according to the method, color consistency errors of different batches of dyed images are reduced, noise introduced in the image acquisition process is eliminated, cell structure details are reserved, and the accuracy of diagnosis is improved. And then image enhancement is carried out, the contrast ratio of cell nucleuses and cytoplasm is improved, so that the characteristics in the cells are more obvious, segmentation can be more accurately completed during subsequent image segmentation, the lesion level of the gynecological cells can be accurately identified, and the accuracy of an identification result is ensured.

The invention provides a method and system for interpreting expression of ER and PR in breast cancer by using artificial intelligence, and relates to the technical field of artificial intelligence, the method comprises the following steps: collecting labeled breast cancer tissue pathological section images, and performing data enhancement to obtain a sample image set; performing chromatic aberration balance on the image data in the sample image set to obtain a preprocessed data set; training a preset initial segmentation network by using the preprocessed data set; inputting the breast cancer tissue pathological section image into the trained cell nucleus segmentation model to obtain a cell nucleus region image; performing morphological processing on the cell nucleus region image to obtain a processed target region; and extracting color features and morphological features in the target area, and inputting the color features and the morphological features into the classification model to obtain an interpretation result. According to the method, cell nucleus segmentation and feature extraction are carried out through the deep learning model, subjectivity and errors of manual interpretation are reduced, and the interpretation accuracy 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 belongs to the field of digital pathological image processing, and particularly relates to an intelligent auxiliary diagnosis system and method based on cell contours. The intelligent auxiliary diagnosis method based on the cell contour comprises the following steps: S10, acquiring a stained pathological tissue image, separating an HE staining channel in the pathological tissue image through a color deconvolution algorithm, and identifying the position of a cell nucleus in a marked image according to a separation result as an initial position; calculating the staining intensity of each cell nucleus according to the separation result, constructing a contour characteristic spectrum based on the staining intensity of the cell nucleus, and extracting tissue microenvironment indication information in the pathological tissue image; s20, defining a dyeing concentration peak region as a first core region, and defining a cell nucleus edge diffusion region as a second peripheral region; and combining the geometrical morphology characteristics of the cell nucleus with the gradient distribution of the dyeing intensity. According to the scheme, the cells in the pathological tissue image can be accurately segmented and classified, a quantitative diagnosis basis can be provided, and the working pressure of doctors is relieved.

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 multimodal-based cell experiment image processing method and system and a storage medium, and the method comprises the steps: obtaining a single-channel gray image or a multi-channel fluorescence image containing cytoplasm and cell nucleus characteristics, carrying out the Gaussian filtering of a cytoplasm channel, and carrying out the median filtering of a cell nucleus channel; performing threshold segmentation on the preprocessed cytoplasm channel image by using a maximum between-class variance method to generate an initial cytoplasm mask; performing adaptive threshold segmentation on the preprocessed cell nucleus channel image to generate an initial cell nucleus mask; performing logic OR operation on the initial cytoplasm mask and the initial cell nucleus mask to generate an initial cell region; and finally, calculating each morphological parameter of each cell region and removing the cell regions of which the morphological parameters exceed a preset range to generate a segmentation result of the complete cell region, the morphological parameters and spatial distribution information. The problems that in the prior art, cytoplasm and cell nucleus noise processing segmentation precision is low, overlapped nucleus regions are difficult to separate, and morphological parameter analysis reliability is poor are solved.

The invention discloses a stem cell fusion degree detection method and system based on artificial intelligence, and a storage medium. The method comprises the following steps: carrying out image preprocessing on a cell microscope image; automatically calculating an optimal threshold value by using an image threshold value segmentation algorithm to obtain a cytoplasm mask; median filtering is carried out on the original image to reduce noise, then an adaptive threshold segmentation method is adopted, a local threshold is calculated according to local area gray level distribution, and a cell nucleus binary image is generated; performing connected region marking on the cell nucleus binary image, calculating the area attribute of each region, and performing filtering according to a cell nucleus removal ratio parameter to obtain a cell nucleus mask; performing logic OR operation on the cytoplasm mask and the cell nucleus mask to obtain a complete cell segmentation result; and calculating the fusion degree of the stem cells based on the cell segmentation result. Therefore, the problems of accuracy and consistency of judging the fusion degree of the stem cells by observing microscope images with human eyes in the prior art are solved, and accurate detection of the fusion degree of the stem cells is realized.

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 discloses a multiple myeloma bone marrow image recognition system based on computer vision, relates to the technical field of cell recognition, solves the problem that traditional manual microscopic examination is large in limitation, and accurately recognizes burr change segments by conducting angle analysis on the edge contour of a feature area, so that the accuracy of recognition is improved. According to the comprehensive proportion of the burr change section on the overall edge contour, reliably judging whether the to-be-detected cell is a tumor cell or not, and calibrating and displaying the tumor cell in a gray image; according to the mode, the obvious difference of the tumor cells and normal cells in cell nucleus morphology is effectively utilized, rapid and accurate identification of the tumor cells is achieved, a visual and clear result is provided for clinical diagnosis, doctors are assisted to accurately judge multiple myeloma in time, the diagnosis efficiency and the treatment effect are improved, mean value processing and radial ratio analysis are conducted on contour area gray values, and the diagnosis accuracy is improved. The contour region associated with the cell nucleus can be accurately locked from the to-be-detected cell and is calibrated as the feature region, and a key cell nucleus feature basis is provided for tumor cell recognition.

The invention discloses an NF (nuclear factor)-kappa B inhibitor. The NF-kappa B inhibitor comprises N-trans-p-coumaroyl tyramine; the invention further discloses a preparation method of the NF-kappa B inhibitor. The invention also discloses an application of the NF-kappa B inhibitor in medicines for preventing or treating diseases related to body inflammation. According to the application disclosed by the invention, the influence of the N-trans-p-coumaroyl tyramine on a TLR4 / MyD88 / NF-kappa B inflammation signal channel is detected through an immunofluorescence technology, and a result shows that the N-trans-p-coumaroyl tyramine can be used for reducing the expression of TLR4 and MyD88 proteins and inhibiting the transfer of NF-kappa B proteins to cell nucleuses, so that the N-trans-p-coumaroyl tyramine can be used as an NF-kappa B inhibitor and can be used for preparing an anti-inflammatory drug. The composition is used for preventing or treating body inflammatory response.

The invention provides a signal point identification method and a related product. According to a specific embodiment of the method, instance segmentation is performed on a cell nucleus image based on a cell instance segmentation model, and a cell segmentation result of each cell in the cell nucleus image is obtained; based on a fusion feature extraction model, carrying out image feature extraction on the fluorescence signal point image of each channel to obtain a fusion feature map; then, based on the cell segmentation result of each cell, extracting a cell region fusion feature map of each cell from the fusion feature map; then, based on a cell region mask feature extraction model, obtaining a masked cell region fusion feature map of each cell; and finally, based on a signal point identification model, performing signal point identification on the masked cell region fusion feature map of each cell to obtain a signal point identification result of the corresponding cell in each channel. Therefore, end-to-end cell segmentation and signal point identification can be realized, cell segmentation and signal point identification can be realized based on the cell nucleus image and the multi-channel fluorescence signal point image, cell segmentation and signal point identification can be completed at the same time with fewer model parameters, the calculation amount is saved, and the algorithm processing time is accelerated.

A nanostructured low-immunogenic biological artificial blood vessel, a preparation method therefor and a use thereof. The preparation method comprises: decellularizing a pre-treated animal blood vessel to obtain a decellularized blood vessel; treating the decellularized blood vessel in an enzyme solution to obtain an enzyme-treated blood vessel, wherein the enzyme solution comprises nuclease and / or a biological enzyme; and using a cross-linking agent to cross-link the enzyme-treated blood vessel to obtain a nanostructured low-immunogenic biological artificial blood vessel, which is used as a blood vessel transplantation material. The artificial blood vessel overcomes the defects of decreased mechanical properties in decellularized biological tissues, in-vivo calcification after long-term use, and the presence of immunogenicity, antigenic components such as vascular wall cells and cell nuclei are fully removed, and original collagen and other extracellular matrix structural proteins in the tissue are retained to a greater extent, avoiding the in-vivo calcification of the blood vessel upon long-term implantation, enhancing the durability of use of the blood vessel; and the method has a short preparation cycle, low cost and high long-term patency rate.

This provides a system and method for analyzing and diagnosing conditions, such as cancer, based upon stained tissue sample slides prepared by users from patient tissue taken, for example, in a biopsy procedure. A convolutional neural network (CNN) is generated at training time. Slide images are acquired / scanned, and individual cell nuclei from the images are annotated, using IHC. H&E images are acquired / scanned prior to a washout intermediate step from the same slides / tissue samples. IHC is performed on the same tissue layer, which was scanned and used to retrospectively annotate the H&E WSI. IHC is used to annotate a ground truth mask for machine learning. The resultant process achieves a high degree of spatial resolution in detecting individual IHC positive nuclei, yielding process that is almost entirely automated, employing materials that are commonly available in clinical practice. In runtime, users access the CNN to perform analysis on patient slides.

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 relates to the technical field of image analysis, in particular to a data visualization pathological diagram analysis system which comprises a pathological image noise reduction and enhancement module, a lesion area judgment module, a local texture and global structure fusion module, a self-adaptive multi-scale segmentation module and an error correction and optimization module. According to the method, by analyzing color channels, cell structure edge features and background noise distribution in the pathological image, accurately screening noise and optimizing image filtering, the image quality is effectively enhanced, the noise is reduced, the recognition accuracy of a lesion area is improved, and the recognition accuracy of the lesion area is improved in combination with cell nucleus gradient information and tissue edge distribution. The accuracy is further improved by using gray statistics and cell density, region division is weighted and optimized through the contrast and entropy of lesion tissues, the segmentation accuracy is ensured, the image segmentation scale is accurately adjusted in combination with the cell density and color gradient information, errors are reduced, accurate segmentation of lesion regions is ensured, and the accuracy of image segmentation is improved. And finally, the precision and reliability of overall image analysis are improved.