9results about How to "High scientific value" patented technology

The invention discloses a confocal laser microscope detection method for micro-plastic surface biofilm components and biomass, and belongs to the field of micro-plastic pollution monitoring. The method comprises the following steps: carrying out stratified sampling and polymer component identification on micro-plastic in an environmental water body; the method comprises the following steps: marking algae, blue-green algae, bacteria and extracellular polymeric substances in a biological membrane through specific fluorescent staining, carrying out high-resolution three-dimensional imaging by utilizing a confocal laser microscope, and realizing batch quantitative analysis of each component of the biological membrane by adopting ImageJ software. The technical problems that an existing detection method is insufficient in parameter dimension, evaluation result deviation is caused only based on partial component quantification, and the influence of the overall structure of the biological membrane on the micro-plastic migration behavior cannot be truly reflected are solved. According to the method, the biomass and spatial distribution of each component of the biological membrane can be comprehensively and accurately quantified, the accuracy of analyzing the vertical migration mechanism of the micro-plastic is improved, and multi-dimensional data support is provided for ecological risk assessment.

The application discloses a method for constructing a lung cancer xenograft tumor model, which comprises the following steps: S1, constructing an asthma model by using a sensitizing agent causing asthma on a rodent; S2, establishing the lung cancer xenograft tumor model by transplanting lung cancer cells from the rodent into the asthma model. The lung cancer xenograft tumor model constructed by the application can simulate the findings of clinical research in basic research, and improve the scientific value and application value of the lung cancer xenograft tumor model.

The invention belongs to the technical field of medical treatment, and discloses a BMSC (bone mesenchymal stem cell) specific HIF-2alpha gene knockout mouse model based on a Cre-LxoP + technology and a construction method of the BMSC specific HIF-2alpha gene knockout mouse model. The method comprises the following steps: carrying out an in-vivo experiment on a mouse (Hif-2 [alpha] fl / fl mouse), and using a wild type (WT) same nest mouse (Hif-2 [alpha] fl / fl) with matched age and gender as a contrast; hif-2 [alpha] fl / fl and Prx1-Cre are selected from the group consisting of Prx1-Cre Hif-2 alpha fl / fl mice are subjected to ovariectomy, dexamethasone (DEX) treatment or semi-lethal radiation, and the mice with the same age (6-8 weeks), gender and genotype are randomly grouped for subsequent experiments (researchers are not covered); the method comprises the following steps: preparing Prx1-Cre; no gender-related difference has been observed in Hif-2 [alpha] fl / fl mice. According to the invention, a mouse with BMSC-specific HIF-2alpha knockout is generated and is used for evaluating the physiological function of HIF-2alpha in an early osteoblast lineage.

The invention provides a method for inverting a gravitational field and an internal structure of an asteroid based on machine learning, which is characterized in that when a planet deep neural gravitational field network model based on machine learning is trained, the internal density distribution of the asteroid is speculated according to external observation information, and the process is also called gravity inversion. According to the invention, gravity inversion is carried out by using the planetary deep neural gravity field model based on deep learning, and prior density constraints in various forms can be carried out through a loss function mode, so that the search space of gravity inversion optimization is reduced, and the stability and accuracy of gravity inversion are improved. The planetary deep neural gravity field model can integrate multi-source gravity observation data processing, and can simultaneously encode a gravity field model, an internal structure model and a three-dimensional shape model of an asteroid. According to the method, the scientific value and practical significance of planet exploration are improved, and beneficial theoretical support and practical guidance are provided for deep space exploration.

The invention discloses a test method for testing the choked flow characteristic of a pile foundation open type breakwater, and relates to the technical field of breakwater choked flow characteristic tests, and the method comprises the following steps: S1, determining the size of a test water tank and a model scale; s2, configuring a test water tank, and arranging a pile foundation open type breakwater model in the test water tank; s3, determining upstream and downstream water flow stable areas, and respectively selecting a test section in the upstream and downstream water flow stable areas; s4, carrying out a characteristic test experiment, and after the flow field is stable, obtaining the water depth and the average flow velocity of the upstream test section and the downstream test section; and S5, based on the energy equation and the momentum equation, calculating head loss and water flow resistance of a hydrodynamic field caused by the pile foundation open type breakwater model. According to the method, the structural complexity of the pile foundation open type breakwater is fully considered, and the choked flow characteristic test can be rapidly and accurately carried out.

The application provides a land use and cover grid data downscaling method and system based on AlphaEarth Foundations and machine learning in future years, which identifies coarse scale pixels in which land classes change in future years by comparing coarse scale data in future years and a base year; maps the coarse scale pixels to fine scale data in the base year to determine a target area; counts the number of fine scale pixels of each land class in the target area to obtain the net increase of fine scale pixels of each land class in future years; in the target area, generates a conversion probability corresponding to each land class for each fine scale pixel by using a land class conversion probability prediction model; constructs an optimization model with the sum of the conversion probabilities corresponding to the land classes to which all fine scale pixels in the target area are assigned as an objective function, and solves the optimization model to obtain an optimal land class assignment result of the fine scale pixels in the target area; and fuses the optimal land class assignment result with the fine scale data in the base year to generate fine scale land use and cover grid data in future years after downscaling.

This invention relates to the field of medical and health information processing technology, specifically disclosing an early risk stratification and warning system and method for diabetic nephropathy. The system includes a data fusion preprocessing module, a multi-scale causal network construction module, a dynamic risk trajectory calculation module, and a stratified warning decision-making module, connected sequentially. By fusing multimodal data to construct an interpretable causal network and utilizing a network-constrained prediction model, it achieves dynamic trajectory prediction of future renal function indicators and comprehensive risk indices. Based on the prediction results, it triggers different levels of risk warnings and outputs decision support information, thereby achieving early, dynamic, and causally interpretable risk stratification and warning.

This application relates to the field of underwater robot navigation and environmental perception technology, and discloses a positioning method and system for underwater environments. The positioning method includes: acquiring flow velocity profile data, temperature data, salinity data, and depth data at sampling points; obtaining normalized flow field feature vectors and physical feature vectors corresponding to each sampling point; calculating the cosine similarity of the normalized flow field feature vectors and the physical feature similarity of the normalized three-dimensional physical feature vectors; weightedly fusing the cosine similarity and physical feature similarity to obtain a fusion similarity; combining the fusion similarity with preset constraint rules to obtain position revisit constraint factors; adding the position revisit constraint factors to the factor graph to construct a complete factor graph model; and using the iSAM2 algorithm to solve the factor graph to obtain the globally optimized position sequence of the underwater vehicle. This method can correct cumulative drift errors and effectively ensure the reliability of the AUV's trajectory in visually and acoustically limited scenarios.

The application discloses a method for constructing a lung cancer xenograft tumor model, which comprises the following steps: S1, constructing an asthma model by using a sensitizing agent causing asthma on a rodent; S2, establishing the lung cancer xenograft tumor model by transplanting lung cancer cells from the rodent into the asthma model. The lung cancer xenograft tumor model constructed by the application can simulate the findings of clinical research in basic research, and improve the scientific value and application value of the lung cancer xenograft tumor model.