903 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 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 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 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.

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 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 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.

The invention discloses a Light GBM breast cancer prediction method and system based on WOA-SHAP feature selection, and the method comprises the steps: extracting cell nucleus features in a digital image through a data set preprocessing module in the system, and carrying out the data preprocessing of a feature value; the data set division module divides the feature data set into a training set and a test set; the data set equalization module carries out equalization processing on the feature data set; the feature selection module performs feature selection on features by fusing SHAP values based on a whale optimization algorithm; the LightGBM model training module is used for training a LightGBM model on the basis of the feature subsets after feature selection; the LightGBM model optimization module optimizes hyper-parameters of the model based on a particle swarm optimization algorithm; the model evaluation module performs effect evaluation on the trained model; and the prediction module performs prediction based on the trained LightGBM model. According to the method, the most influential feature can be effectively identified, and the detection accuracy is improved.

The invention relates to the technical field of medical examination, in particular to a method for calculating the definition of a leukocyte microscopic image based on deep learning, which comprises the following steps: preparing and expanding a data set of the microscopic leukocyte image, outputting a clear image and a blurred image, and enabling each image to have a corresponding definition evaluation index; performing multi-feature fusion definition evaluation on the microscopic leukocyte image based on comprehensive evaluation calculation of a Sobel operator, a Laplacian operator and information entropy; accessing multi-feature fusion definition evaluation into a deep learning model, and building a definition evaluation model; the definition evaluation model is trained, and in the training process, parameters of the definition evaluation model are adjusted by optimizing a loss function; the trained definition evaluation model is deployed in an automatic microscope system, and the problems that definition evaluation is carried out on the whole view, the definition evaluation algorithm is limited, and cell nucleus and cytoplasm characteristics are not distinguished in the prior art are solved.

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.

According to the endometrial cancer cell detection method and system provided by the embodiment of the invention, firstly, an image block is paired with a pathological report text, and unified semantic features are obtained by utilizing multi-positive sample comparison learning; performing semantic entropy-driven dynamic shielding-random shuffling self-supervision training on the visual neural network; secondly, after cell nucleuses are positioned, a weighted cell graph is constructed with cells as nodes and proximity relations as edges, topological features are obtained, and then the topological features, visual features and semantic features are subjected to cross-scale attention and gating fusion to generate multi-modal features; a detection head jointly outputs a category, a frame and a mask, and comprehensive cross-modal consistency loss is subjected to supervision or weak supervision training; and a'cancer-normal 'preference pair is automatically generated by further utilizing high and low probability regions output by the initial model, a reward model is trained, and a PPO strategy is adopted for iterative fine tuning, so that cell-level cancer focus detection can be accurately and robustly completed without a large number of pixel-level labels, and the pathological screening efficiency and accuracy are remarkably improved.

The invention discloses a senescent cell recognition system and method based on mitochondrial morphology. The system is a classification network, and the input data of the system is a distribution image of mitochondria in subcellular level cells, the distribution image of mitochondria in cells, the shape and / or number of mitochondria distributed at different positions in a three-dimensional structure for displaying cells, and preferably the shape and number of mitochondria distributed at different positions in the three-dimensional structure for displaying cells. According to the senescence cell recognition system based on the mitochondrial morphology, the senescence cells jointly show mitochondrial morphological changes including mitochondrial distribution, mitochondrial shapes and mitochondrial number changes, the senescence cell recognition method is easy to conduct through computer vision, good robustness is achieved, and the senescence cell recognition system based on the mitochondrial morphology is suitable for being applied to senescence cell recognition. Compared with other senescence cell identification methods based on cell nucleus morphological characteristics, the senescence cell identification method based on the general characteristics of the senescence cells has good universality for different cell types.

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.

Disclosed are a Solanum tuberosum drought tolerance-related transcription factor gene StPIF4 and the use thereof. A CDS sequence of StPIF4 has a length of 1554 bp, has a nucleotide sequence shown as SEQ ID No. 1, and encodes 517 amino acids, the amino acid sequence being shown as SEQ ID No. 2. The gene is located in a cell nucleus. The gene reduces the water loss of Solanum tuberosum seedlings under the drought stress by adjusting the opening degree of stomas, thus enhancing the drought tolerance of Solanum tuberosum. The present invention provides a new genetic material and a theoretical basis for analyzing the drought-tolerant molecular mechanism and drought-tolerant breeding improvement of Solanum tuberosum.

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

The 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.