15 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 invention relates to the field of microorganisms and water treatment, and discloses pseudomonas capable of degrading fluoroquinolone antibiotics, a complex microbial inoculant and application of the pseudomonas and the complex microbial inoculant. The pseudomonas is named as 11-10, the preservation number is CGMCC No.33680, the microorganism classification is named as pseudomonas sp., the preservation unit is China General Microbiological Culture Collection Center, the preservation date is February 28, 2025, and the preservation number is CGMCC No.33680, the microorganism classification is named as Pseudomonas sp., the preservation unit is China General Microbiological Culture Collection Center, and the preservation date is February 28, 2025. Firstly, the new pseudomonas strain obtained through screening has high tolerance to the fluoroquinolone antibiotics and can efficiently degrade the fluoroquinolone antibiotics; and secondly, in the composite microbial agent disclosed by the invention, the new pseudomonas species, the bacillus tequilensis and the paenibacillus deserticola can play a relatively good synergistic effect, so that the endurance capacity and the degradation capacity on the fluoroquinolone antibiotic wastewater can be further improved.

The invention relates to the technical field of sanitation, environmental governance and resource development, and discloses an alkali-producing sulfur oxidizing bacterium and application thereof in water pollution governance and bioleaching. The alkali-producing sulfur-oxidizing bacterium is named as 381-2, is preserved in China General Microbiological Culture Collection Center (CGMCC) on December 23, 2024, and has a preservation number of CGMCC No.33017; the microbial classification is named as Stutzerimonas sp., and the microbial classification is named as Stutzerimonas sp. According to the invention, a new species strain Stutzerimonas sp. Of Stutzerimonas sp. Is obtained by separation from a deep sea hydrothermal zone. The bacterial strain has relatively strong capability of rapidly oxidizing thiosulfate ions and also has alkali production characteristic, so that the bacterial strain 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 relates to the field of biological fermentation, and discloses red monascus for highly producing monacolin K and monascus pigment and application of the red monascus. The red monascus purpureus is named as SHM1108, the microorganism classification name of the red monascus purpureus is Monascus purpureus, the preservation number of the red monascus purpureus is CGMCC (China General Microbiological Culture Collection Center) NO.41565, the preservation date of the red monascus purpureus is September 24, 2024, and the preservation place of the red monascus purpureus is China General Microbiological Culture Collection Center. The red monascus not only can produce ring-opening monacolin K and monascus pigment at the same time, but also does not produce citrinin, and the monascus obtained through fermentation can be applied to the fields of food, health food, medicine and the like.

An embodiment of the present invention provides a technology relating to an apparatus capable of accurately performing real-time analysis of types of microorganisms in bioaerosols in the air, and the like. The apparatus for classifying and measuring airborne microorganisms, according to an embodiment of the present invention, comprises: a measurement unit including a microfluidic chip having microchannels formed therein and an optical sensor configured to measure light generated when microorganisms passing through the microchannels react with a luminescent agent; a supply operation unit configured to supply, to the microfluidic chip, a feed solution containing microorganisms, a lysis buffer which lyses microbial cells to extract adenosine triphosphate (ATP), and a luminescent agent; and an analysis control unit configured to receive signals from the optical sensor of the measurement unit and classify the types of microorganisms.

The invention discloses a dairy product microorganism content real-time monitoring method, system and equipment based on an impedance method, and relates to the technical field of microorganism monitoring. The method is realized by virtue of a specific biological sensitive membrane modified micro-fluidic chip sensor, a broadband impedance analysis module and an embedded machine learning analysis unit, wherein an automatic sampler extracts a dairy product sample and injects the dairy product sample into the sensor, and target microorganisms are captured and enriched through specific bacteriophages or aptamers on the surface of an electrode; after injecting a low background culture medium and controlling the temperature, periodically scanning in a preset frequency range by a broadband impedance analysis module to obtain complex impedance spectrum data; the embedded unit analyzes data through a pre-trained lightweight model, and outputs a microorganism classification result and content prediction; according to a preset threshold value, whether standard exceeding early warning is generated or not is judged, after early warning, a chip is automatically cleaned, equipment is calibrated, a system integrates a corresponding module to execute the method, rapid, accurate and real-time monitoring of dairy product microorganisms is achieved, and the in-situ early warning requirement of a production line is met.

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.

A method for classifying a microorganism, including (S1) obtaining a first mass spectrum resulting from mass spectrometry of a first microorganism belonging to the order Enterobacterales that has been cultured under conditions for producing an acid shock protein, classification of the first microorganism at and below one of the family, genus, and species levels being unknown; (S2) obtaining a first m / z corresponding to the acid shock protein from the first mass spectrum; and (S3) performing classification at or below one of the unknown levels of the first microorganism by analyzing the first m / z.

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.

The present invention relates to a method of predicting the responsiveness to Immune Checkpoint Inhibitor (ICI)-based treatments in cancer subjects at baseline based on the relative abundances of at least 2 of 8 microbial taxa in a stool sample from the patient. The method produces a binary classification of patients as responders and non-responders to ICI-based treatments. More particularly, the stratification method relates to stratifying hepatocellular carcinoma patients into 2 classes. The invention also relates to ICI-based treatments that can be administered to responders. While the identified biomarkers partially overlap the previously reported ones, the novelty of this invention is based on a) a combination of mixed-level taxonomy (combining genus- and family-level taxonomic information) and b) development of an original machine learning pipeline to leverage this type of input. Also provided is a method of normalisation and look-up table for carrying out the invention on arbitrary datasets, shown to exactly reproduce results from a deep learning model. As a result, while the markers are partially known, their context of use such as combination with markers of different nature and in-house machine learning architecture resulted in the predictive performance that was never seen before for this task and data type.