8 results about "Microorganism classification" patented technology

The invention discloses a MALDI-TOF mass spectrum microbiological identification method based on the distance between spectrums, and the method comprises the following steps: 1, spectrum data preprocessing: carrying out denoising and baseline correction processing on original mass spectrum data, extracting and merging corrected data peaks, and carrying out normalization processing to form a data list; step 2, carrying out average reference spectrum construction on the data based on spectrum spacing dimension reduction, and collecting a plurality of representative spectrums for each microorganism; pairing the same spectrum by using a peak value matching algorithm; calculating the average position and intensity of each group of matching peaks to form an average reference spectrum; constructing an average reference spectrum, then carrying out inter-spectrum distance calculation, obtaining peaks corresponding to the sample spectrum and the average reference spectrum, then calculating an inter-peak distance, and generating a distance vector as a low-dimensional representation of the microorganism sample spectrum; and step 3, microorganism classification: performing binary classification and multi-classification on the microorganism sample to complete microorganism identification.

The present application relates to the technical field of health, environmental management and resource development, and discloses an alkaline-sulfur oxidizing bacteria and its application in water pollution treatment and bioleaching. The alkaline-sulfur oxidizing bacteria is named 381-2, which has been preserved in the China General Microbiological Culture Collection Center on December 23, 2024, and the preservation number is CGMCC No. 33017; the microbial classification name is Stutzerimonas sp. The present application isolates a new species of Stizolobium strain from a deep-sea hydrothermal area Stutzerimonas sp. . The strain has strong and rapid oxidation ability of thiosulfate ions, and also has the characteristics of producing alkali, so it can be applied to the fields of water pollution treatment and bioleaching.

The application provides a high-order interaction prediction method and device with mixed graph deep learning, which first constructs a drug molecule graph, a microorganism weighted graph, a disease weighted graph and a supergraph connecting the three based on multi-source heterogeneous data such as drug molecular structure, microorganism classification information and disease semantic network, forming a mixed graph structure. Subsequently, through a mixed graph deep learning module fusing a graph convolution network and a supergraph neural network, nonlinear structure features and high-order interaction features of each entity are extracted, and the adaptive fusion of the features is realized by using an attention mechanism. Then, the fused deep features are mapped to the prior expectation of the latent factor matrix in the Bayesian logic tensor decomposition model, a probabilistic graph model is constructed, and the joint adaptive inference of the model parameters, latent variables and deep learning mapping is carried out through a variational expectation maximization algorithm, so that the high-order correlation probability prediction of the whole tensor space is realized without negative sampling.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying the predicted type of one or more microorganisms. In one aspect, a system comprises a phase-contrast microscope and a microorganism classification system. The phase-contrast microscope is configured to generate a three-dimensional quantitative phase image of one or more microorganisms. The microorganism classification system is configured to process the three-dimensional quantitative phase image using a neural network to generate a neural network output characterizing the microorganisms, and thereafter identify the predicted type of the microorganisms using the neural network output.

The invention provides a high-order interactive prediction method and device with mixed graph deep learning, and the method comprises the steps: firstly constructing a drug molecule graph, a microorganism weighted graph, a disease weighted graph and a hypergraph connecting the three graphs based on multi-source heterogeneous data, such as a drug molecule structure, microorganism classification information and a disease semantic network, and forming a mixed graph structure; and then, through a mixed graph deep learning module fusing a graph convolutional network and a hypergraph neural network, nonlinear structure features and high-order interaction features of each entity are extracted, and adaptive fusion of the features is realized by using an attention mechanism. Next, the fused deep features are mapped into priori expectation of a potential factor matrix in a Bayesian logic tensor decomposition model, a probability graph model is constructed, and joint adaptive inference is performed on model parameters, latent variables and deep learning mapping through a variational expectation maximization algorithm, so that the probability graph model is obtained under the condition that negative sampling is not needed; and high-order association probability prediction of the full tensor space is realized.

This invention relates to the field of water treatment and discloses a strain of *Methylorubrum aminovorans* and its application in treating high-concentration formaldehyde wastewater. This *Methylorubrum aminovorans* strain is named 3-5-6, and its microbiological classification is *Methylorubrum aminovorans*. It was deposited at the China General Microbiological Culture Collection Center (CGMCC) on November 24, 2025, with accession number CGMCC No. 36747. This invention provides a formaldehyde-degrading bacterium that can tolerate high concentrations of formaldehyde and optimizes its degradation conditions to improve the treatment effect of high-concentration formaldehyde wastewater.

This invention discloses a strain of Bacillus thuringiensis and its application in the degradation of lignocellulose, belonging to the field of microbial technology. The microbial classification name is Bacillus thuringiensis, the strain name is LMU-81, and the accession number is CGMCC NO.36215. Strain LMU-81 was isolated from leaf litter soil and accumulated decaying wood in mangrove wetlands. It exhibits high lignocellulose enzyme activity, particularly lignin peroxidase (LiP), and can be used as a microbial inoculant in combination with other microorganisms to form a compound inoculant for the degradation of lignocellulose in sugarcane bagasse and other materials.

The application relates to the field of microbiological technology, and discloses a methylbacterium with high-efficiency zearalenone degradation and application thereof, the methylbacterium has been preserved in the China General Microbiological Culture Collection Center on January 2, 2024, the address is No. 1, Xiliujia, Beichen West Road, Chaoyang District, Beijing, the preservation number is CGMCC No. 29497, and the microbial classification name is methylbacterium. Methylobacterium extorquens The methylbacterium obtained by the application can efficiently degrade zearalenone.