27 results about "Microorganism classification" patented technology

The invention relates to the field of microorganisms, and discloses application of lactococcus lactis in a product for improving sleep. The lactococcus lactis is named as WHH2078, is separated from yoghurt butter collected from a country in Sankarst city in Tibet Autonomous Region in China, is preserved in China General Microbiological Culture Collection Center on February 25, 2021, has a preservation number of CGMCCNO.21831, and is classified and named as Lactococcus lactis. The Lactococcus lactis is named as Lactococcus lactis, and the Lactococcus lactis is named as Lactococcus lactis. The lactococcus lactis has the effects of shortening the sleep latency, prolonging the total sleep time, improving 5-hydroxytryptamine and gamma-aminobutyric acid of the brain, serum and colon, lowering the corticosterone level, regulating back the diversity of intestinal microorganisms alpha, regulating up the abundance of intestinal acidobacilli and lowering the abundance of actinomycetes, desulfurization bacilli and verruca microflora. The sleep disorder can be effectively improved.

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 discloses a stress-resistant heterotrophic nitrification-aerobic denitrification nitrogen removal bacterium and application thereof. The pseudomonas is named as H1, the microorganism classification name of the pseudomonas is Pseudomonas sihuiensis, the preservation number of the pseudomonas is CGMCC (China General Microbiological Culture Collection Center) NO: 33481, the preservation date of the pseudomonas is January 20, 2025, and the preservation place of the pseudomonas is China General Microbiological Culture Collection Center. The pseudomonas H1 disclosed by the invention can still realize efficient denitrification in a heterotrophic nitrification-aerobic denitrification way in a high-salt and high-nitrogen water body environment containing toxic pollutants with benzene ring functional groups, so that the pseudomonas H1 has great application potential in biological treatment of nitrogen-containing wastewater.

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.

Disclosed herein are methods for determining oral health of an animal by compositional analysis of oral microbiome. The abundance or relative abundance of one or more classified population of microorganisms is determined using qPCR and compared to modeling data produced by a canine having a healthy or periodontal disease state.

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 invention relates to the technical field of microbial denitrification, and discloses paracoccus denitrificans and application thereof. The paracoccus denitrificans is named as 392A9 and is preserved in the China General Microbiological Culture Collection Center on October 20, 2023, the preservation number of the paracoccus denitrificans is CGMCC No.28691, and the classification of the microorganisms of the paracoccus denitrificans is named as paracoccus denitrificans. The paracoccus denitrificans 392A9 provided by the invention can survive and realize efficient denitrification under aerobic and anaerobic conditions and higher nitrate concentration, and can adapt to a wider pH (Potential of Hydrogen) range, so that the paracoccus denitrificans 392A9 has a better application prospect in denitrification treatment of nitrate wastewater.

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.

The present invention discloses an Aspergillus aculeatus and its application in the production of tannase and gallic acid, belonging to the field of microorganisms and tannin degradation technology. The microorganism is classified and named Aspergillus aculeatus WYT2101, which has been deposited in the Guangdong Provincial Microbial Culture Collection Center with a deposit number of GDMCC No. 62691, a deposit date of August 10, 2022, and a deposit address of 5th Floor, Building 59, Institute of Microbiology, Guangdong Academy of Sciences. The Aspergillus aculeatus can use its tannase gene to prepare tannase with high enzyme activity (the tannase activity of the fermentation supernatant can reach 44.45U / mL) and good thermal stability; it can also use pomegranate peel to efficiently produce tannase (2.14U / mL) and gallic acid (the output reaches 1.7mg / mL), and has the potential to be applied to industrial mass production of tannase and gallic acid.

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.

The invention relates to the technical field of microorganisms, and discloses methylobacterium torquens capable of efficiently degrading zearalenone and application of the methylobacterium torquens, the methylobacterium torquens is preserved in China General Microbiological Culture Collection Center on January 2, 2024, the preservation address is Institute of Microbiology, Chinese Academy of Sciences, No. 3, Beichen West Road, Chaoyang District, Beijing, and the preservation number is CGMCC NO.9687. The preservation number of the bacillus subtilis is CGMCC (China General Microbiological Culture Collection Center) No.29497, and the bacillus subtilis is classified and named as Methylobacterium extorquens by microorganisms. The methylobacterium torquens obtained by the invention can be used for efficiently degrading zearalenone.

The invention relates to an eight-one agricultural paenibacillus and an application of the eight-one agricultural paenibacillus in production of xylanase. The strain is paenibacillus sp. (Paenibacillus sp.) QZ-Y1, and is preserved in the China Center for Type Culture Collection (CCTCC), the preservation number is CCTCC NO: M 2025559, and the preservation date is March 21, 2025. Through microbial classification and identification, the strain is confirmed to be a new species of paenibacillus. According to the present invention, the Eight-one paenibacillus agricultural strain QZ-Y1 can produce the xylanase by using the corncob fermentation, has advantages of simple operation, low cost, high xylanase enzyme activity and the like, and is expected to provide the simple and economical fermentation process for the industrialization of the production of the xylanase by using the microbial fermentation method.

The present invention relates to a Brucella WYQ-2, a bacterial agent and a use thereof. The Brucella WYQ-2 is classified as Brucella ( Brucella sp. ), deposited in China Center for Type Culture Collection, with a deposit number of CCTCC No: M 20242175, and a deposit date of October 14, 2024; the thalli of Brucella WYQ‑2 are small dots, white and opaque, full in shape, and without flagella; the bacterial lawn grows along the streak. Brucella WYQ‑2 and the bacterial agent of the present invention, under the condition of having an electron donor, especially with reduced iron powder as an electron donor, can break through the chemical barrier of extremely high chemical energy required for the conversion of carbon dioxide into other high value-added products, and realize effective fixation of carbon dioxide. In addition, reduced iron powder can be used as a trace element to regulate the secretion of protein during the growth of microorganisms in the fixation reaction.

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 present invention belongs to the technical field of probiotics, and particularly relates to a Pediococcus acidilactici with acid resistance, bile salt resistance and antibacterial activity and its application. The Pediococcus acidilactici TR19 of the present invention has a preservation number of CGMCC NO. 31330, and the microbial taxonomic name is Pediococcus acidilactici Pediococcus acidilactici , this strain has good acid resistance and bile salt resistance characteristics, and at the same time has strong antibacterial effects on Escherichia coli, Bacillus cereus, Staphylococcus aureus and Salmonella typhimurium. In addition, this strain has good degradation ability for both nitrite and cholesterol, and has strong applicability.

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.

The invention relates to the technical field of microorganisms, and discloses an aerobic denitrifying bacterium and application thereof. The aerobic denitrifying bacterium is named as 392B6, is preserved in the China General Microbiological Culture Collection Center (CGMCC) on October 20, 2023, has the preservation number of CGMCC No.28692, and is named as Stutzerimonas nitritiolerans in a classification manner, and the aerobic denitrifying bacterium is named as Stutzerimonas nitritiolerans. The aerobic denitrifying bacterium disclosed by the invention can tolerate relatively high nitrate concentration, realizes efficient removal of nitrate under an aerobic condition, can adapt to a relatively wide pH range, and has relatively high alkali resistance.

The invention relates to the technical field of microorganisms, and discloses a temperature-resistant Jutanberg's heldnella yeast and an application thereof, and the Jutanberg's heldnella yeast is Jutanberg's heldnella yeast 413, and the temperature-resistant Jutanberg's heldnella yeast is Jutanberg's heldnella yeast 413. The invention relates to a Jerbudzea jadinii strain which is characterized in that the Jerbudzea jadinii strain 413 is preserved in Guangdong Province Microbial Culture Collection Center on November 11, 2024, the preservation address is the 5th floor of the building 59, No. 100 Courtyard, Xianlie Middle Road, Guangzhou City, the preservation number is GDMCC NO: 65456, and the microorganism classification is named as the Jerbudzea jadinii 413. According to the present invention, the lindnella gedinieri has good temperature resistance, and can be fermented at the fermentation temperature of 28-38 DEG C so as to obtain the protein feed with the high protein content;

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.

The present invention discloses a digital image target detection method and system based on medical microbiological inspection, which relates to the technical field of digital image processing. It includes taking pictures of microbial samples to obtain multi-spectral images of different wavelengths, taking pictures of the same microbial sample by adjusting the incident light angle to obtain polarization images; denoising and removing artifacts from the obtained multi-spectral images and polarization images and adjusting the image contrast; using principal component analysis (PCA) to fuse the processed multi-spectral images and polarization images to obtain a multi-dimensional data set; using an image processing algorithm to extract the type characteristics of different microorganisms from the multi-dimensional data set; using a deep learning algorithm to construct a microbial classification model, inputting the type characteristics into the microbial classification model to distinguish different microbial species and output the microbial species; comparing the output microbial species with known microbial samples to verify the accuracy, and adjusting the parameters of the microbial classification model according to the verification results.