Patent classifications
C12Q1/06
METHODS AND COMPOSITIONS FOR PRESERVING BACTERIA
The disclosure provides methods and compositions for the preservation of bacteria.
Biomarker for mental disease
A marker for determining a mental disease is provided. The marker can be used in an objective diagnosis of such a mental disease. The marker contains one or more enterobacteria of Bifidobacterium, Lactobacillus, Lactobacillus brevis, Lactobacillus reuteri subgroup, Lactobacillus sakei subgroup, Atopobium cluster, Bacteroides fragilis group, Enterococcus, Clostridium coccoides group, Clostridium leptum subgroup, Staphylococcus, Clostridium perfringens, and Enterobacteriaceae.
Biomarker for mental disease
A marker for determining a mental disease is provided. The marker can be used in an objective diagnosis of such a mental disease. The marker contains one or more enterobacteria of Bifidobacterium, Lactobacillus, Lactobacillus brevis, Lactobacillus reuteri subgroup, Lactobacillus sakei subgroup, Atopobium cluster, Bacteroides fragilis group, Enterococcus, Clostridium coccoides group, Clostridium leptum subgroup, Staphylococcus, Clostridium perfringens, and Enterobacteriaceae.
Microbiological testing device, method for provision and use of such a device
A microbiological testing device for testing a liquid to be analysed that is liable to contain at least one microorganism, includes a closed inner space, a microbiological filtration member and an inlet port. The device has a nutritive layer in contact with the filtration member, and in that, in a configuration for providing the device an open/close member of the inlet port is in a closed state; the absolute gas pressure inside the closed inner space is strictly less than the standard atmospheric pressure, such that the device is able to create suction through the inlet port during a first opening of the open/close member.
Microbiological testing device, method for provision and use of such a device
A microbiological testing device for testing a liquid to be analysed that is liable to contain at least one microorganism, includes a closed inner space, a microbiological filtration member and an inlet port. The device has a nutritive layer in contact with the filtration member, and in that, in a configuration for providing the device an open/close member of the inlet port is in a closed state; the absolute gas pressure inside the closed inner space is strictly less than the standard atmospheric pressure, such that the device is able to create suction through the inlet port during a first opening of the open/close member.
Computer-readable storage medium storing control program, control method, and control device
A non-transitory computer-readable storage medium storing a control program for causing a computer to execute for acquiring, from a sensor, multiple monitored values in multistep processes including a process related to fermentation of microbes; setting probability distributions for multiple specific parameters related to unmonitored data and included in multiple parameters included in a nonlinear mathematical model related to the fermentation of the microbes corresponding to the multistep processes; generating monitoring predicted values at next monitoring time of the mathematical model based on the multiple monitored values and the probability distributions; using a distribution of the monitoring predicted values and values monitored at the next monitoring time to update the multiple parameters; and controlling the mathematical model so that errors of the multiple specific parameters generated using the mathematical model including the updated multiple parameters are reduced.
Computer-readable storage medium storing control program, control method, and control device
A non-transitory computer-readable storage medium storing a control program for causing a computer to execute for acquiring, from a sensor, multiple monitored values in multistep processes including a process related to fermentation of microbes; setting probability distributions for multiple specific parameters related to unmonitored data and included in multiple parameters included in a nonlinear mathematical model related to the fermentation of the microbes corresponding to the multistep processes; generating monitoring predicted values at next monitoring time of the mathematical model based on the multiple monitored values and the probability distributions; using a distribution of the monitoring predicted values and values monitored at the next monitoring time to update the multiple parameters; and controlling the mathematical model so that errors of the multiple specific parameters generated using the mathematical model including the updated multiple parameters are reduced.
ANALYSIS OF MICROBIOME FOR DIAGNOSIS AND TREATING OF URINARY STONE DISEASE
A method of determining the risk that a subject will develop urinary stone disease (USD) or hyperoxaluria is described. The method includes conducting a differential abundance analysis of the bacteria present in a stool and/or urine sample obtained from the subject, determining the ratio of bacteria associated with health to bacteria associated with USD or hyperoxaluria present in the subject’s stool and/or urine sample, and assigning a level of risk for developing USD or hyperoxaluria based on the ratio.
ANALYSIS OF MICROBIOME FOR DIAGNOSIS AND TREATING OF URINARY STONE DISEASE
A method of determining the risk that a subject will develop urinary stone disease (USD) or hyperoxaluria is described. The method includes conducting a differential abundance analysis of the bacteria present in a stool and/or urine sample obtained from the subject, determining the ratio of bacteria associated with health to bacteria associated with USD or hyperoxaluria present in the subject’s stool and/or urine sample, and assigning a level of risk for developing USD or hyperoxaluria based on the ratio.
Method for analysis of yeast
A method for analysis of yeast includes: receiving a microscopic image of yeast by a cloud server (2901), the microscopic image including a scaling pattern for determining a magnification; determining the magnification by the cloud server based on the scaling pattern (2902); and analyzing, by the cloud server, the microscopic image based on the magnification to obtain an analysis result (2903).