5results about How to "Good correlation" patented technology

The invention discloses a biomarker for diagnosing Alzheimer's disease as well as a screening method and application thereof, relates to the technical field of biomarkers for Alzheimer's disease, and aims to solve the technical problems that the Alzheimer's disease lacks dynamic pathological monitoring indexes and stereoisomeric modification is difficult to systematically detect. According to the invention, the stereoisomeric modification level of peptide fragments KLDLSNVQSK and AKTDHGAEIVYK is used as the core biomarker for the first time, the modification level is remarkably related to the Braak staging of AD, and a detection method and a combined diagnosis model containing the marker are provided. A novel molecular target with high correlation and dynamic monitoring potential is provided for AD, the model diagnosis precision is remarkably improved, and a brand new solution is provided for disease mechanism research and drug development.

The application relates to the technical field of nanobiosensors, and discloses a bimodal nanoprobe for uric acid detection and a preparation method and application thereof. The bimodal nanoprobe is a fluorescence-photothermal bimodal nanoprobe, and is a core-shell structure nanoparticle, which comprises, from the inner layer to the outer layer, a) a fluorescent inner core composed of carbon dots and a silica layer coated outside the carbon dots; b) a manganese dioxide layer coated outside the fluorescent inner core; and c) a molecularly imprinted polymer layer coated outside the manganese dioxide layer. The bimodal nanoprobe in the application integrates a fluorescence signal source (carbon dots), a signal modulation and photothermal unit and a specific recognition layer (a uric acid molecularly imprinted polymer) through ingenious structure design, realizes bimodal sensing of "specific recognition -> MnO2 etching -> fluorescence recovery / photothermal weakening" on UA, and effectively solves the problems of poor selectivity, complicated operation and high cost of traditional methods.

This invention relates to a method, apparatus, equipment, and medium for predicting deformation during the assembly of diesel engine connecting rods, belonging to the field of mechanical assembly technology. The method acquires multi-dimensional assembly data during the assembly process of marine diesel engine connecting rods. Based on assembly process knowledge, the multi-dimensional assembly data is weighted to obtain a weighted input vector set. A pre-trained Bayesian network model is used to infer the deformation prediction probability distribution from the weighted input vector set. Statistical analysis is performed on the deformation prediction probability distribution to obtain deformation trend characteristics and distribution dispersion characteristics. Key deformation risk factors are identified based on these characteristics. A diesel engine connecting rod assembly deformation prediction report is generated based on the deformation prediction probability distribution, deformation trend characteristics, distribution dispersion characteristics, and key deformation risk factors. This invention improves the assembly quality and product reliability of marine diesel engine connecting rods.

This invention discloses a mobile phenotypic information acquisition platform and method based on the entire plant growth cycle, belonging to the field of intelligent plant phenotypic detection technology. It includes acquiring image sequences and depth point cloud data of multiple key growth stages of a target plant, and constructing a multi-dimensional phenotypic feature tensor representing the joint information of the plant's three-dimensional morphological structure and color texture through spatiotemporal registration and fusion. The tensor is input into a learnable phenotypic parsing network, and a dynamic phenotypic evolution map is generated by iteratively enhancing the plant organ feature response and suppressing the background feature response. Key phenotypic trait parameter sequences from budding to maturity are extracted, and the growth trend degree and developmental stability scores of each sequence are calculated, sorted, and integrated to form a hierarchical full-cycle phenotypic atlas. This method can achieve multi-dimensional phenotypic information fusion representation, weaken background interference, accurately depict the dynamic evolution process of phenotypic changes, and clearly present the correlation between phenotypic states at each growth stage.