1842results about "Systems biology" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

A federated distributed computational system enables secure, multi-institutional biological data analysis and genomic medicine through interconnected, decentralized nodes in a federated distributed graph architecture. A federation manager coordinates computational resource allocation, control and data flows, establishes privacy and security boundaries, implements multi-scale spatiotemporal analysis and simulation modeling, models cross-species or intrapopulation elements, and maintains cross-institutional knowledge relationships. Each node includes a local processing unit for biological data analysis, including multiomics and gene editing, privacy-preserving protocols for secure multi-party computation, a hierarchical knowledge graph for managing multi-domain biological relationships across spatial and temporal scales, and encrypted network connections. The system implements cross-species genetic analysis via phylogenetic integration, environmental response modeling through spatiotemporal tracking, and multi-scale tensor-based data integration with adaptive dimensionality control. This architecture enables research institutions to collaborate on complex biological analyses and genomic medicine applications while maintaining strict data privacy and security controls.

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

The invention relates to the technical field of breeding planning, in particular to an intelligent breeding planning and decision-making method and system based on a large model. The method comprises the following steps: acquiring a multi-source breeding data set; constructing a structured breeding knowledge graph based on the multi-source breeding data set; performing breeding data association on the structured breeding knowledge graph according to a preset large model to generate a special breeding basic model; obtaining a breeding instruction input by a user; performing user semantic recognition on a breeding instruction input by a user to generate breeding semantic recognition data; inputting the breeding semantic recognition data into a breeding special basic model for breeding intention analysis, and generating user breeding intention data; and determining data information needing to be called based on the breeding intention data of the user, analyzing and screening to generate germplasm resource screening data and a breeding plan / breeding decision scheme. According to the method, the intelligence and operability of breeding planning are improved through integration of multi-source data, intelligent semantic recognition, combined genetic analysis and executable evaluation.

The invention discloses a microbial fermentation strain breeding control system based on a neural network model, and relates to the technical field of digital fermentation, and the system comprises a thallus data acquisition module, a biological feature recognition module, a prediction classification module, a breeding decision module, an optimization training module, an early warning monitoring module and a sorting regulation module. The bacterial data acquisition module preprocesses bacterial data; the biological feature recognition module constructs a bacterial feature library; the prediction classification module predicts the adaptability of bacterial strains and classifies the bacterial strains; the selection decision module selects the bacterial strains and generates culture parameters; the sorting regulation and control module generates a three-dimensional scatter diagram and a hereditary character report. According to the method, the precision and efficiency of breeding of the microbial fermentation strain are remarkably improved, intelligence and automation of the breeding process are achieved, the labor cost is reduced, the stability and controllability of the fermentation process are enhanced, and powerful technical support is provided for innovative development of the microbial fermentation industry.

The invention relates to the technical field of data processing. The fermentation process intelligent regulation and control method and system based on multi-modal perception are provided, and the method comprises the following steps: carrying out image feature extraction processing on microorganism image data to generate a morphological feature vector, and carrying out metabolic feature dimension reduction processing on metabonomics data to generate a metabolic feature matrix; performing time sequence alignment processing to generate a fusion feature matrix, and performing abnormal marking processing on the metabonomics data to generate abnormal marking data; carrying out correlation intensity calculation processing on the morphological change of the microorganisms and the concentration fluctuation of the metabolites to generate a dynamic correlation intensity curve; constructing a cross-dimensional anomaly recognition model and a multi-modal collaborative prediction model, and generating a regulation and control parameter suggested value; the parameters of the multi-modal collaborative prediction model are updated through a feedback learning mechanism, the feature fusion weight of the fusion feature matrix is optimized, the accuracy of anomaly detection and regulation decision is improved, and the risk of stability fluctuation in the fermentation process is reduced.

The invention relates to the technical field of crop saline-alkaline tolerance analysis, in particular to a rice saline-alkaline tolerance prediction method and system based on big data analysis, and the method comprises the following steps: collecting soil environment data, rice physiological phenotype data and historical stress response data, and constructing a multi-dimensional data cube by using a space-time alignment engine; quantization feature analysis is adopted to carry out quantum probability modeling on Na < + > / K < + > ion transmembrane transport and PSII photosynthetic exciton transfer features, and a quantum probability cloud chart of rice physiological response is generated. Based on a geodesic line domain adaptation model of geodesic line mapping, a soil conductivity thermodynamic diagram and a quantum probability cloud chart are fused, a rice saline-alkaline tolerance adaptability prediction model is established, finally, a three-dimensional saline-alkaline tolerance performance prediction map is output, and the adaptability of rice varieties in specific saline-alkaline land is visually displayed. According to the method, the regional generalization ability of saline-alkali tolerant varieties is effectively improved, the limitation of a traditional statistical model is broken through, and a scientific basis is provided for saline-alkali tolerant rice breeding and cultivation and saline-alkali soil agricultural optimization management.

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 discloses a colorectal cancer drug relocation method based on multi-omics integration. The system comprises a multi-omics data acquisition and preprocessing module, a tumor microenvironment analysis module, a specific disease network construction module, a multi-dimensional drug relocation module and a result evaluation module. And the tumor microenvironment analysis module comprises cell heterogeneity identification, cell map construction, cell annotation and tumor cell subset annotation. The specific disease network construction module comprises tumor feature expression program extraction, expression program screening, meta-program construction, clinical related meta-program recognition and specific disease protein interaction network construction. And the multi-dimensional drug relocation module comprises a module for identifying diseases by using a random walk algorithm, carrying out drug screening based on disturbance data, carrying out drug screening based on network proximity and carrying out comprehensive drug relocation. From the perspective of single cell data, element programs related to colorectal cancer survival are excavated, corresponding modules are designed, and the efficiency and precision of colorectal cancer targeted drug screening are improved.

The invention discloses a global function domain-introduced Cas protein classification method and system. The method comprises the following steps of: reading a Cas protein sequence file, preprocessing the Cas protein sequence file and constructing positive and negative sample pairs; a LoRA dynamic rank adjustment mechanism based on Cas protein sequence global functional domain priori is introduced on a pre-trained protein language model, the trained protein language model is used for Cas protein sequence classification, functional domain coverage frequency of each position in a Cas protein sequence is used for quantizing functional importance of each position to generate a global hotspot functional domain vector, and the global hotspot functional domain vector is used for classifying the global hotspot functional domain. And dynamically guiding different LoRA layer rank parameters in the LoRA model, and adjusting weight parameters of the protein language model. According to the technical scheme, on the basis of a prior LoRA dynamic rank adjustment mechanism of a global functional domain of a Cas protein sequence and through a layered dynamic strategy, rank distribution is highly consistent with functional domain evolution conservative property and structural characteristics, and the model is endowed with higher biological interpretability.

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

A federated distributed computational system enables secure drug discovery and resistance tracking through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates molecular dynamics simulations with machine learning models for drug discovery analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for molecular dynamics simulation and resistance pattern detection. Through a distributed graph architecture, the system enables real-world clinical data integration, resistance evolution tracking, and multi-scale tensor-based analysis with adaptive dimensionality control. The system implements real-time drug response prediction through multi-modal data analysis, enabling pharmaceutical companies and research institutions to collaborate on complex drug discovery projects while maintaining strict data privacy controls.

The invention relates to the technical field of artificial intelligence, and discloses a method and a system for dynamically predicting the risk of harm of feed mycotoxin to livestock and poultry. The method comprises the following steps: constructing a metabolic digital twin model based on a multi-organ coupling dynamics mechanism, connecting a gastrointestinal tract, a liver and a kidney through blood flow parameters to form a closed loop topology network, establishing a toxin migration rate equation, and verifying bioavailability; collecting feed toxin concentration and livestock and poultry physiological data in real time; after distributing the initial toxin load, iteratively generating target organ dynamic concentration distribution by adopting a Runge-Kutta method; an organ exposure index is calculated based on an organ specific hazard threshold, key organs are positioned, and low / high risk levels and toxin accumulation paths are output. The method solves the problems that a traditional static model is low in efficiency and poor in organ difference adaptability when being used for dynamically predicting the risk of harm to livestock and poultry caused by feed mycotoxin.

The invention provides an IBS micro-ecological transplantation intelligent prediction method and system based on multi-omics driving, and relates to the technical field of biomedicine. The method comprises the following steps: establishing a multi-omics data fusion subsystem to collect metagenome, metabolome, host genome and clinical phenotype group data of a target patient; inputting the data into a flora-metabolite combined network analysis model to construct an interaction network and extracting features; generating an incidence matrix based on the features and the host genome data and calculating indexes; generating indexes through a dynamic response algorithm in combination with the clinical phenotypic data and the indexes; and outputting a curative effect prediction result by using a transfer learning framework combined with modeling. The system comprises a data acquisition module, a network analysis module, a correlation calculation module, a dynamic response module and a joint modeling module. According to the method, multiple omics data are integrated, the flora and host relation is accurately mined, intelligent prediction of the micro-ecological transplantation curative effect is achieved, powerful support is provided for IBS personalized treatment, and meanwhile data processing and safety guarantee measures are taken.

A method for calculating carbon storage in a mixed forest ecosystem is provided. The method includes: acquiring basic geographic data, meteorological data, eco-physiological parameter, thinning management history data, and validation data; proposing an improved biome-biogeochemical cycles (Biome-BGC) model suitable for simulating carbon storage of a mixed forest ecosystem under management by improving a phenology module, adding a thinning operation management module, and optimizing the eco-physiological parameter, based on an existing Biome-BGC model; simulating, by taking a pine-oak mixed forest as a research object, the carbon storage based on the improved Biome-BGC model; validating the improved model; analyzing sensitivity of the eco-physiological parameter by an extended Fourier amplitude sensitivity test (EFAST) method; and selecting a highly sensitive parameter, and analyzing an effect of the highly sensitive parameter on the carbon storage by a path analysis method. The improved model exhibits good performance in calculating the carbon storage of the mixed forest.

The invention discloses a biological in-vitro sample aging quality control detection method, and relates to the technical field of biological samples, and the method specifically comprises the following steps: screening metabolic markers based on biological physiological characteristics, and constructing an in-vitro quality attenuation model; collecting and preprocessing sample data; detecting the concentration of the metabolic marker through detection equipment; inputting marker concentration data into the model for dynamic comparison; and outputting aging classification, and guiding experiment scheduling operation. According to the method, effective use time efficiency is adopted for rapid judgment, the problem that experimental data is influenced by metabolic degradation of a sample after in-vitro is solved, time efficiency grading can be completed in a short time through multi-marker dynamic comparison and a machine learning model, the method is remarkably superior to a traditional method, and by combining a micro-fluidic detection device and an LSTM model, the detection accuracy is greatly improved. According to the method, the samples can be scheduled to proper experiment operation according to the effective time limit of the samples, waste of the samples is avoided, and after failure samples are detected, a user can be reminded to collect the samples again, so that the experiment process is prevented from being delayed.

The invention relates to the technical field of organic fertilizer production, in particular to a bio-organic fertilizer production process data system and an extraction method. The method comprises the following steps: acquiring a raw material information set and a process parameter set; calculating a reference flora rate based on microbial activity characteristics of the raw material information set; marking a time sequence timestamp for the raw material information set and the process parameter set, and constructing a fermentation behavior track; analyzing the microbial activity characteristics of the fermentation behavior track, and calculating the flora reaction speed; therefore, by constructing a multi-source heterogeneous data-driven fermentation behavior track and quality mapping system, the problems of data isolation, state recognition lag and process deviation and incapability of accurate positioning in traditional bio-organic fertilizer production are solved, and the intelligent diagnosis capability and quality controllability of the production process are improved.

A disease marker structure evolution characteristic change point determination method belongs to the field of disease markers, and comprises the steps of obtaining and preprocessing time sequence structure characteristic data of a disease marker, constructing a characteristic transformation image and calculating characteristic intensity distribution, establishing a structure characteristic fitting model and calculating a time evolution coefficient and an intensity evolution coefficient, determining a structural feature evolution trajectory and calculating a correlation index; establishing a structural feature piecewise function and identifying a feature mutation point; calculating a structural feature contribution value and generating a feature evolution matrix; calculating an evolution stability index and determining a structural feature change point; hierarchical clustering is carried out, main structural feature change points and secondary structural feature change points are determined, a feature weight distribution diagram is constructed, a change point time sequence table is generated, a time sequence corresponding relation is established, and a structural feature change point determination result is output; and fine analysis and accurate description of structural evolution characteristics of complex disease markers are realized.

The invention discloses a method for evaluating enhancement and release seedling resources based on environmental DNA polymerization analysis. The method comprises the following steps: carrying out gridding partition on a target water area, collecting a water sample through a designed sampling scheme, and carrying out DNA extraction and high-throughput sequencing to obtain species sequence information of each sampling point. Sequencing data is subjected to species identification by using a bioinformatics method, released species are identified, a spatial abundance model is established, and a preliminary distribution map is generated. And establishing a DNA degradation kinetic model in combination with water area environmental parameters, and carrying out reverse correction on abundance distribution. And through a resource inversion model coupled with hydrodynamics, analyzing biomass distribution characteristics and migration laws of the release group, and obtaining a resource evaluation result. And finally, a species environment preference model is constructed based on migration path analysis, an optimal release area is matched in a target water area, a scientific scheme including release point locations, opportunities and quantity is generated, and a whole-process technical support and a decision basis are provided for enhancement and release.

The invention discloses an intelligent marinating regulation and control method for leisure old marinated claw snacks based on multi-source sensing, which comprises the following steps: acquiring environmental parameters and material parameters in a marinating process through a multi-source sensor, and analyzing texture change of old marinated claws by utilizing a collagen triple-helix structure denaturation kinetic model; a dynamic time warping smell recognition algorithm is adopted to recognize a smell change stage, a color change rule is analyzed through a Maillard reaction kinetics coupling model, multiple parameters are fused to establish an incidence matrix, and the heating power of marinating equipment, the marinating liquid circulation rate and the ventilation quantity are regulated and controlled according to the matrix. According to the method, multi-parameter collaborative monitoring and deep analysis are realized, the regulation and control accuracy and the automation level are improved, the defects that a traditional method is insufficient in key component change analysis and poor in smell recognition and multi-parameter fusion collaboration are overcome, the product quality stability is guaranteed, and the standardized production requirement is met.

The invention belongs to the technical field of microbial fermentation analysis, and discloses a microbial fermentation analysis method and system based on a digital twin technology. Comprising the following steps: acquiring and processing key parameters in a microbial fermentation process; constructing a cell metabolism response index by using the key parameters; analyzing the cell metabolism response index, judging whether the microbial fermentation process is abnormal or not, setting an alarm mechanism, and automatically triggering when the microbial fermentation process is abnormal; a microbial fermentation simulation model based on the digital twinborn technology is combined with the cell metabolism response index, and technological parameters of the microbial fermentation process are optimized; according to the method, comprehensive monitoring, anomaly detection and intelligent optimization of the microbial fermentation process are achieved, the stability and efficiency of the fermentation process are improved, the decision support capacity is enhanced, and technical support is provided for intelligent transformation of the microbial fermentation industry.

The invention discloses a high-quality fruit wine microbial community dynamic optimization method based on multi-source data fusion. The method comprises the following steps: S1, constructing a fruit wine microbial parameter set; s2, preprocessing the collected fruit wine microorganism parameter set to form a preprocessed multi-source data set; s3, constructing a microbial community dynamic evolution model in the fruit wine fermentation process based on the preprocessed multi-source data set; s4, obtaining an optimal combination of the microbial communities; s5, according to the optimal combination of the microbial communities, combining a fruit wine microbial parameter set collected in real time, and dynamically adjusting the composition structure of the microbial communities in the fruit wine fermentation process by adopting an intelligent control system; s6, after the fruit wine fermentation process is finished, based on the collected preprocessed multi-source data set and the quality detection data of the fermented finished fruit wine, self-adaptive adjustment is conducted on the microbial community dynamic optimization method, and a continuously-improved microbial community regulation and control strategy is formed. According to the method, the time for the flora to tend to be stable is shortened, and the abundance of the target strain is improved.

The invention provides a drug screening and curative effect evaluation method and system based on a glioma organ-like model, and relates to the technical field of biological material analys.The method includes the steps that a three-dimensional fluorescence image of the glioma organ-like model is collected, signal intensity analysis and compensation processing are carried out, a growth feature mapping model is constructed, and the three-dimensional fluorescence image of the glioma organ-like model is obtained; and phenotypic characteristics for representing the glioma organoid heterogeneity are obtained. Inputting the phenotypic characteristics into a drug response evaluation network, and calculating a drug sensitivity score; the drug molecular feature library and the drug collaborative analysis model are combined, the synergistic interaction index of different drug combinations is calculated, the optimal drug combination is screened, the drug administration dosage and the drug administration time sequence are determined in combination with phenotypic features, and finally an individualized treatment scheme is generated. According to the invention, the curative effect of the drug on the glioma organs can be effectively evaluated, and the optimal drug combination and administration scheme can be screened out, so that the accuracy and effectiveness of glioma treatment are improved.

The invention discloses a water microorganism risk analysis method based on reinforcement learning. The method comprises the following steps: S1, generating a standardized observation data set; s2, constructing a dynamic water body map based on the standardized observation data set; s3, executing dynamic graph representation learning for the dynamic water body graph structure, and generating a space-time risk heat matrix on the basis of the node vector representation matrix; s4, constructing prior distribution of a Bayesian reward function, initializing the reward function and establishing a reward function parameter set; s5, performing incremental learning on the award function parameter set by adopting a maximum posterior reverse reinforcement learning model, and outputting a first propagation potential field; s6, obtaining an optimal diffusion path sequence; and S7, performing path reconstruction error analysis on the optimal diffusion path sequence and the standardized observation data set to obtain a dynamic feedback index. According to the method, pollution diffusion response delay caused by emergencies in actual measurement is greatly shortened, and the emergency modeling capability of the system in an emergent pollution scene is remarkably improved.

The invention discloses a craft IPA beer flora optimization method based on an improved bat algorithm. The method comprises the following steps: S1, constructing an initial state space of yeast flora proportion dynamic optimization; s2, standardized real-time process data are formed; s3, fusing the standardized real-time process data with the initial state space to obtain an updated comprehensive state vector, and dividing the whole fermentation process into a sugar collection stage, a main fermentation stage and an after-ripening stage according to a time sequence; s4, obtaining a staged entropy weight vector; s5, forming a multi-objective optimization problem; s6, obtaining an optimal flora proportion parameter set and an optimal fermentation regulation parameter set; and S7, transmitting the optimal flora proportion parameter set and the optimal fermentation regulation and control parameter set to a microfluidic bacterium throwing device, a material supplementing control unit and a frequency conversion temperature control unit to realize the optimization of the crafted IPA beer flora. According to the method, an optimization path can be effectively guided to be close to a flavor synergistic expression area, and the spatial consistency of yeast metabolism and aroma generation is greatly improved.

The invention relates to the technical field of talent training, and discloses an artificial intelligence-based large-health customized talent training system, which comprises a multi-modal data acquisition module, which is used for acquiring user biological index data, behavior log data, psychological assessment data and industry demand data; and the data processing and knowledge fusion module is used for performing time sequence alignment and missing value filling on the acquired data, and constructing a skill association knowledge graph based on industry demand data. According to the invention, through the personalized ability modeling and reinforcement learning recommendation module, the personalized training path of each user is dynamically generated, it is ensured that each user can obtain training matched with health requirements, ability levels and industry requirements, and compared with a universal training scheme in the prior art, the training efficiency is greatly improved. An accurate and customized training scheme can be provided according to real-time biological indexes, psychological evaluation and behavior data of the user, and the training effect is remarkably improved.

To provide a pharmaceutical composition for treating a cancer patient having a tumor having a total passenger gene mutation amount larger than the background mutation amount of the tumor.SOLUTION: A pharmaceutical composition for treating a subject having a tumor with a total passenger gene mutation load that is greater than the background mutation load of the tumor, wherein the background mutation load has been determined based on randomly selected genes of the tumor, comprising antibodies that bind to PD1 as an active ingredient. Antibodies that bind PD1 comprise a heavy chain variable region (HCVR) comprising the amino acid sequence of SEQ ID NO: 21 and / or comprise a light chain variable region (LCVR) comprising the amino acid sequence of SEQ ID NO: 22.SELECTED DRAWING: Figure 1

The invention discloses a farmland environment intelligent monitoring system and method based on multi-modal sensor fusion, and relates to the technical field of farmland environment monitoring, and the method comprises the steps: obtaining first integrated data in a farmland, and carrying out the preprocessing of the first integrated data; constructing a sound wave attenuation model based on the preprocessed first comprehensive data, and inverting the air humidity and the vegetation density through the sound wave attenuation model; conflict detection and resolution processing are carried out based on an inversion result; and constructing a three-dimensional microclimate field based on a conflict resolution result, and carrying out verification and iterative optimization by fusing a crop physiological model. According to the method, farmland space-time heterogeneity is adapted by means of a dynamic space-time weight coefficient, so that a three-dimensional microclimate field covering horizontal and vertical dimensions is constructed, and a'monitoring-verification-optimization 'closed loop is formed through crop physiological model association and iterative optimization; the problems of limitation of a single sensor, low inversion precision, poor data continuity, extensive monitoring and lack of decision association in the prior art are effectively broken through.