10263 results about "Gene" patented technology

The invention relates to a power transmission line forest fire risk assessment early warning system and method, and belongs to the technical field of power transmission line risk assessment. The system comprises an environment DNA power transmission line forest fire risk assessment and early warning system, a quantum sensing power transmission line fire remote monitoring system, a power transmission line forest fire early warning emergency training system, a digital twin power transmission line fire risk dynamic management system, and a multi-node power transmission line fire early warning system of an edge AI and a low orbit satellite. The system comprises a thermal imaging and deep learning integrated power transmission line fire risk distribution prediction system, an AI-driven power load dynamic adjustment and fire prevention system, an intelligent fire-fighting equipment linked power transmission line fire spreading path prediction system and a distributed quantum computing power transmission line fire emergency simulation and optimization response system. According to the method, the inflammable vegetation density and biocenosis change can be dynamically analyzed through a plant and microorganism gene sequencing 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 belongs to the technical field of medicinal chemistry, and particularly relates to a hydroxyl-substituted dihydroisoquinolinone compound. The compound has a good KRAS G12V mutant gene inhibition effect, and can be used for treating tumor diseases related to KRAS G12V gene mutation.

The present disclosure, in some aspects, provides one or more disease state-associated genes for repeat expansion diseases (e.g., a repeat expansion disease associated with spliceopathy, such as DM1). In some embodiments, each of the disease-state associated gene described herein is a disease-state associated gene for a repeat expansion disease (e.g., DM1). Methods of measuring the splicing activity of disease state- associated genes, e.g., for determining disease state and monitoring patient as well as treatment efficacy are also provided.

The present disclosure relates to the field of biological information, and in particular to a gene regulatory network inference method based on spatiotemporal transcriptomic data, and the use thereof. In the present disclosure, on the basis of a graph neural network, a graph variational auto-encoder is used, an adjacency matrix A is used as node connection information, cells and genes are used as node feature information, and position information of the cells is fully used, so as to reconstruct a feature matrix of nodes by means of three hidden layers, thereby performing more accurate cell clustering and dimensionality reduction analysis while inferring a gene regulatory network.

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 relates to a method and a system for constructing a dynamic gene regulation network, computer equipment, a computer readable storage medium, a computer program product and a computer program. The method comprises the following steps: constructing a plurality of single cell quasi-time sequence differentiation trajectories based on single cell transcriptome data of a plurality of single cells; determining a dynamic change gene in a target quasi-timing sequence differentiation trajectory based on the plurality of single cell quasi-timing sequence differentiation trajectories; constructing an initial static gene interaction network based on the dynamic change gene; and performing inspection based on the initial static gene interaction network to obtain the dynamic gene regulation network. According to the method, key gene regulation mechanisms corresponding to different differentiation stages of the cells can be rapidly determined, and the real dynamic change condition of the gene regulation relationship in the biological process is reflected.

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.

The invention relates to the technical field of genomics, in particular to a gene detection data cleaning method and system based on artificial intelligence. The method comprises the following steps: acquiring gene detection data, and performing preliminary filtering on the gene detection data based on removal of low-quality reads to obtain preliminary filtering data; performing linker sequence identification and removal on the preliminarily filtered data, and performing sequencing error correction to generate gene sequence data; performing GC content analysis and repetitive sequence identification on the gene sequence data to obtain sequence feature data; performing dynamic quality threshold adjustment according to the sequence feature data, and performing secondary filtering on the gene sequence data to generate high-quality sequence data; and performing sequence pattern recognition on the high-quality sequence data to obtain sequence pattern feature data. According to the method, deep analysis is carried out by utilizing sequence characteristics such as GC content and nucleotide distribution, and the recognition capability on sequence complexity and variation modes is improved.

The invention belongs to the technical field of optimized coding genes, and discloses a recombinant human I-type collagen as well as a preparation method and application thereof, and the amino acid sequence of the recombinant human I-type collagen is shown as SEQ ID No.1. The recombinant I-type collagen provided by the invention is relatively high in expression quantity, has relatively good cell migration promoting, cell proliferation promoting and cell adhesion promoting properties, is high in safety, and can be widely applied to the fields of medicines, medical instruments, biological materials, tissue engineering, cosmetics and the like.

The invention discloses an intestinal flora and obesity personalized diagnosis method based on a graph attention network, and relates to the technical field of obesity diagnosis, and the method comprises the following steps: collecting gene data, building a graph structure-based model for the data to predict obesity risk and provide personalized health suggestions, each piece of microbiome data is expressed as a weighted undirected graph, a microbiome graph is constructed, the association strength between microbiome groups is calculated, and the microbiome data is expressed as a graph structure. According to the method, the graph structure model considering the relevance between the microbiome groups is constructed, the self-adaptive graph convolutional network and the heterogeneous graph attention network are utilized, multi-modal information interaction between the microbiome and clinical data is deeply mined, the processing capacity of high-dimensional and complex microbiome data is improved, and the multi-modal information interaction between the microbiome and clinical data is improved. The accurate prediction of obesity risks and the provision of personalized health suggestions are realized, the prediction accuracy and generalization ability of the model are significantly improved, and a more effective personalized obesity diagnosis effect is achieved.

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 relates to the technical field of biological information, in particular to a method and system for online integration of multi-source single cell data. The method comprises the following steps: acquiring multi-source single cell transcriptome data; performing data quality evaluation based on gene expression quantity, mitochondrial gene proportion and cell data depth on the single cell transcriptome data to obtain initial quality control data; setting standardized filtering parameters according to the initial quality control data, and performing quality control parameter filtering on the single cell transcriptome data to obtain filtered cell data; performing gene expression characteristic analysis on the filtered cell data to generate high-variation gene expression characteristic data; and performing gene expression spatio-temporal distribution statistics according to the high-variation gene expression feature data to generate gene expression statistical feature data. According to the method, through refined quality control and batch effect correction, data from different sources can be seamlessly integrated on the premise of ensuring data quality, so that noise caused by different batches and technical platforms is eliminated.

The invention discloses a soil quality improvement and microbiological analysis integrated system, and relates to the technical field of soil improvement and microbiological analysis, the system comprises a microbial agent adding module, an organic acid adjusting module, a soil improvement effect monitoring module, a data analysis and processing module and a control and feedback module. According to the invention, high integration of soil improvement and microbiological analysis is realized, the efficiency and effect of soil improvement are improved through real-time monitoring and accurate regulation and control, and the microbial community is comprehensively and dynamically analyzed, so that deep understanding of a microbial action mechanism in the soil improvement process is facilitated, and the soil improvement efficiency and effect are improved. By monitoring succession of microbial community structures, expression changes of functional genes and diversity of carbon source utilization functions, the effects of microorganisms on straw degradation, soil structure improvement and the like are accurately grasped, problems are found in time through an early warning function, improvement measures are adjusted in time, and the stability and sustainability of a soil ecosystem can be effectively maintained.

The invention discloses acid-resistant pichia kudriavzevii and an application of the acid-resistant pichia kudriavzevii. The acid-resistant pichia kudriavzevii is Pichia kudriavzevii S9 and is preserved in the Guangdong Microbial Culture Collection Center on September 20, 2024, and the preservation number of the acid-resistant pichia kudriavzevii S9 is GDMCC.No: 65148. The acid-resistant pichia kudriavzevii S9 has the advantages that the acid-resistant pichia kudriavzevii S9 is used for According to the invention, a low-pH-resistant pichia kudriavzevii strain is screened from surface microorganisms of grapes, and the pichia kudriavzevii strain can tolerate an environment with pH of 1.8 and ferment organic acid under the condition of not adding a neutralizing agent; besides, genetic modification is carried out based on pichia kudriavzevii, including knocking out succinic acid degradation related genes, knocking out ethanol and glycerol production pathways and enhancing succinic acid transporter expression, so that engineering bacteria for high-yield succinic acid are obtained, and the fermentation yield of succinic acid reaches 31.74 g / L under the condition that a neutralizer is not added.

The invention provides a method for predicting the relative abundance of antibiotic resistance genes in surface water and application. The method comprises the following steps: acquiring ARGs relative abundance, water quality physicochemical indexes and genus level bacteria relative abundance of a water sample to be detected, and judging potential bacterial hosts of ARGs by Spearman correlation; the method comprises the following steps: taking water quality physicochemical indexes and relative abundance of potential bacterial hosts as characteristic variables, and respectively taking relative abundance of multidrug ARGs and total ARGs as target variables; adopting automatic machine learning to screen an optimal algorithm; determining an optimal feature set through feature importance ranking and model comparison; and establishing an optimal prediction model to predict the relative abundance of the multi-drug ARGs and the total ARGs of the water sample to be detected. According to the method, the water environment ARGs are predicted based on environmental factors and bacterial indexes, the model accuracy is high, and main driving factors and potential bacterial hosts influencing propagation and proliferation of the ARGs can be identified through interpretable analysis between features and target variables.

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.

This disclosure features chemical entities (e.g., a compound or a pharmaceutically acceptable salt, and / or hydrate, and / or cocrystal, and / or drug combination of the compound) that inhibit (e.g., antagonize) Stimulator of Interferon Genes (STING). Said chemical entities are useful, e.g., for treating a condition, disease or disorder in which increased (e.g., excessive) STING activation (e.g., STING signaling) contributes to the pathology and / or symptoms and / or progression of the condition, disease or disorder (e.g., cancer) in a subject (e.g., a human). This disclosure also features compositions containing the same as well as methods of using and making the same.

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 an engineering bacterium of high-yield ornamental blue as well as a construction method and application of the engineering bacterium, and belongs to the technical field of natural dye synthesis by a biological method. The engineering bacterium capable of highly producing the ornamental blue, disclosed by the invention, expresses icd, bpsA, glnAY405F and gd hA; acnR, yggB, glsK, aceA and ldh are knocked out from the engineering bacteria. According to the application, yggB and aceA genes are knocked out, site-directed mutagenesis (Y405F) is carried out on a glnA gene, a glsK gene is replaced by a glnAY405F gene, an acnR gene is replaced by an icd gene, an ldh gene is replaced by a gdhA gene, and a bpsA gene is integrated, so that the formation of by-products such as lactate and succinic acid in a blue-viewing synthesis process can be blocked; the flow direction of a passage from citric acid to isocitric acid to alpha-ketoglutaric acid to glutamic acid to glutamine is improved, so that the concentration of glutamine in cells is improved, and the yield of the ornamental blue is improved.