7964 results about "Gene" patented technology

The present disclosure provides methods and compositions for detecting and spatially locating analyte interactions and gene expression in a biological sample. For example, provided herein are methods of determining a location of at least one analyte in a biological sample using analyte-binding moieties, proximity ligation, and an array including capture probes.

The invention relates to the field of medical informatization. The invention discloses a thyroid cancer electronic medical record system based on multi-modal data fusion. The thyroid cancer electronic medical record system comprises a multi-modal data acquisition module which acquires patient texts, ultrasonic images, genes, biochemical indexes and clinical data and performs standardized calibration to generate standard data; the multi-modal feature extraction module extracts semantic, structure, mutation, change and fluctuation features of each standard data through multiple technologies; the single-mode prediction model construction module constructs single-mode prediction models of texts, images and the like based on the features and outputs results; and the multi-modal fusion prediction module fuses the single-modal model based on the deep learning framework to output a multi-modal fusion prediction result. According to the invention, multi-modal data are integrated, and the accuracy and comprehensiveness of thyroid cancer diagnosis are improved. The system ensures consistency through standardized data processing, and assists doctors to accurately judge pathological types, recommend therapeutic schedules and evaluate prognosis by means of a multi-modal feature extraction and fusion mechanism.

The invention relates to the technical field of data processing, and discloses a heterogeneous computing acceleration method and system based on a deep learning framework network. The method comprises the following steps: acquiring performance parameters of heterogeneous computing equipment to obtain an equipment characteristic data set; receiving a calculation task and analyzing the calculation task into an operation sequence; the operation sequence is coded into a gene sequence, task decomposition is optimized through a genetic recombination algorithm, and a subtask set marked with acceleration characteristics is obtained; performing matching analysis on the sub-task set and the equipment characteristic data set to obtain a task allocation scheme; deploying the subtasks to corresponding equipment according to the allocation scheme to obtain a distributed execution framework; and monitoring the running state of the framework in real time, and dynamically adjusting resource allocation to obtain a calculation result of speed-up ratio improvement. According to the method, a self-adaptive task decomposition and resource allocation and dynamic load balancing mechanism can be realized, and the execution efficiency and the resource utilization rate of the deep learning task are improved.

The invention discloses a benign and malignant nodule grading evaluation system based on large model fusion ultrasonic imaging and thyroid gene markers, which can organically fuse non-invasive examination and serological detection, can simulate and diagnose multi-grade risk probability information provided by a gold standard, realizes similar risk grading estimation in a non-invasive mode, and has a wide application prospect. The thyroid nodule risk assessment method can provide visual explanation conforming to clinical logic based on comprehensive information of iconography and molecular biology, can significantly improve the accuracy of thyroid nodule risk assessment, can also effectively improve clinical decision-making efficiency and patient credibility, and has important clinical application prospects. The system comprises a data acquisition module, an ultrasonic image feature extraction module, a gene marker feature extraction module, a multi-modal fusion and hierarchical reasoning module and a generation module.

The invention relates to the technical field of seed processing analysis, in particular to a big data-based rape seed quality evaluation model, which is characterized in that a data pool is constructed by converging multi-source heterogeneous rape data, and high-quality input is provided for subsequent cross-domain feature association; a cross-domain feature interaction graph is constructed, a graph neural network is used for learning, cross-domain feature interaction is visually presented, and a gene-environment interaction relationship is analyzed; through a four-branch multi-head and cross-modal attention analysis atlas, focusing key features, fusing optimization information and generating a high-quality feature vector, high-order feature nonlinear fusion is realized, and the characterization capability of the model on multi-source heterogeneous data is remarkably improved; an evaluation model is constructed through decoding vectors, and high-yield variety data is extracted, so that efficient screening of rape varieties is facilitated; by deploying the generative adversarial network and transferring the learning adaptive model, the evaluation capability is quickly expanded to a new variety, and breeding intelligence is promoted.

The invention provides a scientific and technological achievement intelligent matching and transaction recommendation method and system based on a knowledge graph, and relates to the technical field of science and technology, and the method comprises the steps: extracting a technical gene sequence based on a scientific and technological achievement data set, generating a technical state vector, constructing a technical association intensity matrix and a technical iteration path graph, and calculating a technical potential energy distribution field; and forming an initial knowledge graph. And monitoring the fluctuation of the knowledge graph state feature set through the intelligent sensor, and dynamically optimizing the knowledge graph according to the fluctuation condition. Converting technical requirements of a user into a query state, performing state similarity calculation with the knowledge graph, identifying an optimal technical unit combination, predicting a state evolution trajectory, evaluating a regional adaptation degree, and generating a technical implementation scheme. By dynamically optimizing the knowledge graph and accurately matching the user demands, the efficiency and accuracy of scientific and technological achievement matching and transaction recommendation can be effectively improved, and scientific and technological achievement conversion is promoted.

Provided is a compound useful as an active ingredient in a pharmaceutical composition for treating cancer.　The present inventors have examined a compound useful as an active ingredient in a pharmaceutical composition for cancer treatment, and have completed the present invention by finding that: a heterocyclic compound represented by formula (I) and formula (I-I) has excellent an action of inducing degradation of a mutant KRAS protein and a wild-type KRAS protein, and in particular, an action of inducing degradation of a KRAS protein having a G12C mutation, G12D mutation, G12V mutation, or G13D mutation; and said heterocyclic compound has mutant KRAS inhibition and the inhibitory activity of KRAS amplified by a wild-type KRAS gene, and in particular, inhibitory activity of KRAS having a G12C mutation, G12D mutation, G12V mutation, or G13D mutation. The heterocyclic compound according to the present invention or a salt thereof can be used as a therapeutic agent for cancer.

The invention relates to a data processing method and system for multi-source complex biological information data. According to the method, expression profile data, gene variation data and clinical survival data are collected, and unified standardization processing is carried out on the collected data. Feature alignment is performed on different source data based on sample identifiers, a joint feature expression matrix is constructed, and a context dependency relationship across data types is maintained. On the basis, multi-stage feature screening is carried out through Lasso regression and information gain evaluation in sequence, and an optimal feature subset used for modeling is obtained. And further training a risk scoring model by adopting a Cox proportional risk regression method, and calculating a risk scoring value of the sample by utilizing the constructed continuous scoring function. And finally, dividing the score value into a plurality of risk levels, and generating a survival curve of each level in combination with a Kaplan-Meier estimation method so as to verify the risk layering effect and prediction significance of the model. According to the method, the accuracy of biological information modeling can be improved, and the method has good universality and practical value.

The invention discloses an intelligent monitoring management system and method for a wire harness production line, and relates to the technical field of wire harness manufacturing, and the method comprises the steps: extracting data of crimping force and mold temperature from historical wire harness production data, coding the data into a real number coding gene chain, generating an initial gene pool in combination with material attributes, and constructing a digital twin model; the method comprises the steps of simulating stress field distribution and a heat conduction effect in a wire harness crimping process through finite element simulation, obtaining a twinning predicted value, pre-verifying process parameters in an initial gene bank based on a digital twinning model, screening process parameter combinations with deformation errors smaller than an error threshold value, and issuing the process parameter combinations to a crimping machine through a production line control center. According to the method, data of real-time wire harness deformation quantity is obtained, the data of the real-time wire harness deformation quantity is compared with a twinborn predicted value frame by frame, a deformation error rate is generated, and an abnormal traceability report is generated based on the deformation error rate, pre-verification is performed by constructing an initial gene pool and combining a digital twinborn model, so that optimization of process parameters is more accurate and effective.

The invention discloses a multi-task lung cancer brain metastasis lifetime prediction method based on multi-modal data fusion, and relates to the field of medical image processing, and the method comprises the steps: obtaining non-small cell lung cancer CT image data, gene data and clinical data; performing focus segmentation on the non-small cell lung cancer CT image data; feature extraction is carried out based on the focus segmentation result, and radiomics features and depth image features are obtained; performing feature extraction on the gene data to obtain gene features; missing value deletion and numeralization preprocessing are carried out on the clinical data to obtain clinical features; local cross-modal attention fusion is carried out on the radiomics features and the depth image features; performing global cross-modal attention fusion on the preliminary mixed image features, the gene features and the clinical features to obtain original mixed features; and performing conditional guidance diffusion based on the original mixed features to obtain a lung cancer brain metastasis classification judgment result and a lifetime prediction risk. According to the invention, the lifetime prediction accuracy and stability can be improved.

Provided herein are devices, methods, and systems for in situ gene sequencing of a target nucleic acid in a cell in an intact tissue. Methods of screening a candidate agent to determine whether the candidate agent modulates gene expression of a nucleic acid in a cell in an intact tissue are also provided herein.

Provided herein are methods for analyzing a biological sample that include contacting the biological sample with a non-conductive substrate; and contacting a matrix to a surface of the biological sample. Further methods optionally include performing mass spectrometry analysis; determining presence of a first analyte in the biological sample; and / or analyzing a second analyte on the surface of the biological sample to determine presence of the first and the second analyte in the biological sample.

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 belongs to the technical field of biochemical engineering, and provides a method for increasing L-carnosine yield of escherichia coli by increasing PRPP supply, and the yield and yield of L-carnosine are remarkably increased by up-regulating expression of a prs gene, increasing PRPP content in cells and promoting synthesis of L-histidine. Experiments show that the yield of the L-carnosine in different recombinant strains is increased by 55.56%-140%, and L-histidine and beta-alanine do not need to be added in the production process.

The invention discloses a transcriptomics spatial domain identification method, and belongs to the technical field of transcriptomics. The objective of the invention is to solve the problems of low data noise reduction precision and poor recognition effect of an existing spatial transcriptional spatial domain recognition method. The method comprises the following steps: firstly, obtaining an undirected neighborhood graph according to a gene expression matrix, obtaining embedded representation of the gene expression matrix by utilizing an encoder, obtaining a corresponding reconstruction matrix by utilizing a decoder, and further determining reconstruction loss; meanwhile, a ZINB model is used for fitting a reconstruction matrix, and a ZINB loss function is obtained; then, an augmented graph is constructed based on the undirected neighborhood graph, respective embedded matrixes are obtained through an encoder, the comparison loss of the undirected neighborhood graph and the comparison loss of the augmented graph are obtained through a comparison representation learning mechanism, and then the neighbor comparison loss is obtained; total target loss is obtained based on all losses, a joint optimization strategy is adopted for training, and after training of the whole model is completed, dimensionality reduction and spatial domain recognition are carried out on a generated reconstruction matrix.

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

The invention relates to a recombinant A type humanized XVII type collagen as well as construction and application thereof, the protein is the humanized XVII type collagen which is composed of an intracellular region, a transmembrane region and an extracellular region, and the amino acid sequence is as shown in SEQ No. 1. The hair follicle repairing agent has the advantages of hair follicle repairing, hair regeneration, good biological activities of cell proliferation, cell adhesion and the like, and can be widely applied to the fields of medicines, medical instruments, biological materials, tissue engineering, cosmetics and the like. The gene sequence of the recombinant A type XVII collagen is 100% identical to the corresponding part of the gene sequence of the recombinant A type XVII collagen, so that the recombinant A type XVII collagen has good hydrophilicity and high activity, and has application potential in the fields of biomedical materials, beauty cosmetics, skin care products and the like.

The invention discloses a radish bolting and flowering gene RsSUF4 regulated by m6A demethylase and application of the radish bolting and flowering gene RsSUF4, relates to the technical field of bioengineering, and is technically characterized in that the gene is RsSUF4. The functions of the demethylase RsALKBH10B and the flowering inhibition gene RsSUF4 in the flowering process are analyzed in a targeted manner, and a specific association mechanism between m6A modification and radish flowering regulation and control is defined.

The invention discloses a spatial domain identification method based on a multi-view weighted fusion GCN network, and the method comprises the steps: taking the gene expression and spatial position of spatial transcriptome data as input, and combining the spatial information based on different similarity measurement modeling with the gene expression after modeling to construct a multi-view weighted fusion graph convolutional network model; and reconstructing a gene expression matrix by using a decoder, capturing global information of ST data, calculating spatial regularization constraint loss by using similarity information and spatial neighbor information, and completing identification of a spatial transcriptome spatial domain. According to the method provided by the scheme, the model can extract the spatial information from the whole situation and the local situation, so that the spatial information is more fully and comprehensively mined, and accurate spatial domain identification is realized.

The invention belongs to the technical field of gene engineering, and particularly relates to application of a ZmDIR11 gene in regulating and controlling salt stress resistance of corn. After salt stress treatment, it is found that the ZmDIR11 gene mutant corn plant growth vigor and material accumulation are significantly lower than those of a wild type B73 material, an oxidative metabolism regulation path, proline accumulation, chlorophyll synthesis, lignan accumulation and a Na < + > / K < + > transport pathway are inhibited, and related metabolic indexes are significantly lower than those of the wild type B73 material. It is shown that the ZmDIR11 gene positively regulates and controls the salt-resistant metabolic pathway of the corn in the corn, and the salt tolerance of the corn is reduced after the ZmDIR11 gene is mutated. The research provides a new gene resource for corn salt-tolerant breeding by utilizing gene engineering.

Platforms, systems, and methods for genetic generalization in synthetic biology development. According to one aspect, there is provided a method for predicting performance associated with genetic edits, the method comprising: receiving, by a platform, information about a strain of a microorganism, wherein the information about the strain comprises information describing a plurality of genetic edits to a base strain of the microorganism; generating, by the platform, a set of genetic embeddings based on the information about the strain, wherein the generating comprises processing the information about the strain using one or more embedding models, wherein each of the one or more embedding models: receives the information about the strain of the microorganism as input; and applies computational transformations to the input using a corresponding embedding model to generate a multi-dimensional vector representation for each of the plurality of genetic edits.

The invention relates to the field of white carbon black modifier production, and particularly discloses an activity monitoring and early warning method of a white carbon black modifier based on Internet of Things, which comprises the following steps: acquiring reaction temperature, catalyst dosage and equipment operation parameters in real time through an Internet of Things sensor, and constructing a parameter and efficiency mapping table to form a multi-dimensional process database; encoding the temperature, the stirring rate and the raw material ratio into chromosome genes by utilizing a genetic algorithm, and constructing a multi-target model by taking the bifunctional grafting rate as an optimization target and combining the equipment load rate and the energy consumption constraint; a high-adaptability parameter set is generated by adopting iterative optimization, and a three-level response mechanism of local fine tuning, population resetting and historical optimal parameter loading is realized in combination with a hierarchical early warning system. According to the method, the problems that the synthesis conditions of the bifunctional silane coupling agent cannot be accurately controlled and the continuous production data response is delayed in the traditional process are solved, and the activity dynamic optimization and green and efficient production of the white carbon black modifier are realized.

The invention discloses a method for improving beef quality, an action mechanism and an experimental method, in a cattle body, vitamin A can activate the expression of EBF2 through an active metabolite RA of the vitamin A, and the EBF2 can inhibit the transcription process of CYP26B1 in a targeted manner to maintain the activity of a retinol signal channel, so that PPAR gamma and downstream lipid metabolism related genes thereof are activated, and the activity of the retinol signal channel is improved. Fatty acid transport and lipid accumulation in fat cells in the cattle muscle are promoted, fat deposition in the cattle muscle is promoted, and the beef quality is improved. By constructing the molecular network for regulating and controlling the formation of the fat in the cattle muscle, a complex regulation and control mechanism for controlling the fat deposition in the cattle muscle can be understood more deeply, a method for improving the beef quality based on regulating and controlling the vitamin A mediated key factor EBF2 is provided, and a new way is provided for improving the beef quality in a targeted manner.

The invention discloses a group of gene markers for predicting or evaluating neoadjuvant chemotherapy response of breast cancer and application of the gene markers. The gene markers comprise CCNA2, CCND1, CD52, ESR1 and FAM234B. The system for predicting or evaluating the neoadjuvant chemotherapy reaction of the breast cancer is constructed on the basis of the gene marker, and can accurately predict or evaluate whether the breast cancer patient has the risk that complete remission cannot be achieved after neoadjuvant chemotherapy, and discriminate whether the breast cancer patient can benefit from the neoadjuvant chemotherapy reaction; therefore, whether the breast cancer patient can be subjected to neoadjuvant chemotherapy or not is accurately predicted, medical intervention is performed in time, and the method has important clinical reference value.

The invention belongs to the field of gene engineering, and particularly relates to application of an XC3225 gene in increasing the yield of xanthomonas campestris pv. Campestris xanthan gum. The invention finds that the XC3225 gene has a negative regulation effect on the xanthan gum yield, and the xanthan gum yield is remarkably improved by inhibiting or knocking out the XC3225 gene in the genome of xanthomonas campestris pv. Campestris, so that a new gene resource is provided for xanthan gum preparation.

The invention discloses a cherax quadricarinatus oocyte targeted penetrating peptide VgSP and a living body delivery method thereof, and belongs to the technical field of biology. The invention provides the cherax quadricarinatus oocyte targeted penetrating peptide VgSP on one hand, and provides application of the cherax quadricarinatus oocyte targeted penetrating peptide VgSP on the other hand. The invention provides a polypeptide VgSP capable of being recognized by a cherax quadricarinatus oocyte surface receptor and mediated into an oocyte, and an injection and detection method of the polypeptide in sub-adult and adult female cherax quadricarinatus. The breakthrough lays a technical foundation for establishing a gene editing element delivery method with oocyte targeting, and is expected to be applied to the field of cherax quadricarinatus gene editing.

The present invention relates to compounds of formula (I) and salts, stereoisomers, tautomers or N-oxides thereof that are useful as modulators of STING (Stimulator of Interferon Genes). The present invention further relates to the compounds of formula (I) for use as a medicament and to a pharmaceutical composition comprising said compounds.

The invention discloses a multi-omics causal structure relation learning method based on comparative learning, which comprises the following steps: firstly, respectively constructing corresponding encoders for preprocessed gene mutation and gene expression data, and respectively carrying out feature extraction on two kinds of omics data; then, constructing a projection head with shared parameters to realize cross-modal feature alignment; then, using the aligned features as nodes, and constructing causal graph data through a learnable causal graph structure; constructing a graph neural network to learn causal graph representation, and constructing a contrast loss function; and finally, a model prediction result is obtained through a multi-layer perceptron, a survival prediction loss function is constructed, and a total loss function is obtained for multi-omics causal structure model training. Based on gene mutation and gene expression data, a cross-omics causal structure relationship is constructed and learned through comparative learning, more accurate prognosis prediction is provided for diseases such as acute myelogenous leukemia and the like, and potential biomarkers and key regulatory factors are helped to be found.

The invention discloses a method for efficiently screening saline-alkaline tolerant rice. According to the method, controllable indoor saline-alkali environment simulation conditions are constructed, various salts are mixed in proportion to prepare solutions with different salinity gradients, the pH value is adjusted, and temperature, humidity and illumination parameters are accurately controlled; the method comprises the following steps: disinfecting rice seeds, carrying out a germination test, transplanting rice seedlings, and regularly measuring growth indexes; in the treatment process, physiological indexes such as malondialdehyde content, antioxidant enzyme activity, proline content and the like and the expression level of salt and alkali tolerance related genes are detected, key indexes are screened by applying statistics and multivariate analysis methods, and an evaluation system is established for comprehensive score screening. The method can quickly perform saline-alkali stress treatment on a large number of rice seeds or seedlings, is not limited by natural environment, shortens the screening period, can accurately simulate different saline-alkali soil environments, scientifically evaluates the saline-alkali tolerance of rice from multiple dimensions, and provides an efficient and reliable technical means for breeding saline-alkali tolerant rice varieties.

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.