A sample analysis system is provided with: a reference substance database including measurement results and component classification information of reference substances obtained by each analysis device on information of each reference substance; a reference substance designation unit; a measurement result collation unit to obtain the commonality of the components, the difference between the physical quantities of the respective components, and the degree of coincidence of the measurement results for each analysis device for the designated reference substance; an integration coincidence degree calculation unit to obtain an integration degree of coincidence; and a judgment unit to judge whether or not the difference between the contents of contained components is within an allowable range and classify the corresponding component based on the component classification information.

A sample analysis system is provided with: a reference substance database including measurement results and component classification information of reference substances obtained by each analysis device on information of each reference substance; a reference substance designation unit; a measurement result collation unit to obtain the commonality of the components, the difference between the physical quantities of the respective components, and the degree of coincidence of the measurement results for each analysis device for the designated reference substance; an integration coincidence degree calculation unit to obtain an integration degree of coincidence; and a judgment unit to judge whether or not the difference between the contents of contained components is within an allowable range and classify the corresponding component based on the component classification information.

A computer implemented method for training a neural network to capture a structural feature specific to a set of chemical compounds is disclosed. In the method, the computer system reads an expression describing a structure of the chemical compound for each chemical compound in the set and enumerates one or more combinations of a position and a type of a structural element appearing in the expression for each chemical compound in the set. The computer system also generates training data based on the one or more enumerated combinations for each chemical compound in the set. The training data includes one or more values with a length, each of which indicates whether or not a corresponding type of the structural element appears at a corresponding position for each combination. Furthermore, the computer system trains the neural network based on the training data for the set of the chemical compounds.

A computer implemented method for training a neural network to capture a structural feature specific to a set of chemical compounds is disclosed. In the method, the computer system reads an expression describing a structure of the chemical compound for each chemical compound in the set and enumerates one or more combinations of a position and a type of a structural element appearing in the expression for each chemical compound in the set. The computer system also generates training data based on the one or more enumerated combinations for each chemical compound in the set. The training data includes one or more values with a length, each of which indicates whether or not a corresponding type of the structural element appears at a corresponding position for each combination. Furthermore, the computer system trains the neural network based on the training data for the set of the chemical compounds.

A program for operating a cell culture support apparatus causes a computer to function as a first acquisition unit, a second acquisition unit, a first derivation unit, and an output control unit. The first acquisition unit acquires a learned model, derived by performing machine learning on the basis of a set of time-series data for learning indicating a time transition of an amount of each of plural types of components constituting a medium used for cell culture and good/bad data indicating good or bad of a result of the cell culture in correspondence with the time-series data for learning, indicating a guideline of the amount. The second acquisition unit acquires time-series data for analysis indicating the time transition of the amount. The first derivation unit derives quantitative guideline information of the amount for obtaining a good result in the cell culture, with respect to at least one of the plural types of components, from the learned model acquired in the first acquisition unit and input data of at least a part of the time-series data for analysis acquired in the second acquisition unit. The output control unit performs a control for outputting the guideline information.

A method is provided for evaluating a set of measurement data from an oral glucose tolerance test. The method may include calculating a similarity measure that quantifies the similarity between a time profile of the series of measured data of the glucose concentration and a corresponding glucose reference profile. The method may include calculating a further similarity measure that quantifies the similarity between the profile of the series of measured values of the further analyte concentration and the corresponding analyte sample profile, wherein the data set is represented by a point in a vector space that comprises coordinate axes that are formed by the similarity measures, whereby the coordinates of said point contain the calculated values of the similarity measures. The method also may include evaluating the position of the point with respect to reference points, which each represent a defined state of health, in order to calculate a parameter that specifies the state of the glucose metabolism of the patient.

A method is provided for evaluating a set of measurement data from an oral glucose tolerance test. The method may include calculating a similarity measure that quantifies the similarity between a time profile of the series of measured data of the glucose concentration and a corresponding glucose reference profile. The method may include calculating a further similarity measure that quantifies the similarity between the profile of the series of measured values of the further analyte concentration and the corresponding analyte sample profile, wherein the data set is represented by a point in a vector space that comprises coordinate axes that are formed by the similarity measures, whereby the coordinates of said point contain the calculated values of the similarity measures. The method also may include evaluating the position of the point with respect to reference points, which each represent a defined state of health, in order to calculate a parameter that specifies the state of the glucose metabolism of the patient.

This disclosure provides for a highly-efficient and scalable compression tool that compresses quality scores, preferably by capitalizing on sequence redundancy. In one embodiment, compression is achieved by smoothing a large fraction of quality score values based on k-mer neighborhood of their corresponding positions in read sequences. The approach exploits the intuition that any divergent base in a k-mer likely corresponds to either a single-nucleotide polymorphism (SNP) or sequencing error; thus, a preferred approach is to only preserve quality scores for probable variant locations and compress quality scores of concordant bases, preferably by resetting them to a default value. By viewing individual read datasets through the lens of k-mer frequencies in a corpus of reads, the approach herein ensures that compression lossiness does not affect accuracy in a deleterious way.

This disclosure provides for a highly-efficient and scalable compression tool that compresses quality scores, preferably by capitalizing on sequence redundancy. In one embodiment, compression is achieved by smoothing a large fraction of quality score values based on k-mer neighborhood of their corresponding positions in read sequences. The approach exploits the intuition that any divergent base in a k-mer likely corresponds to either a single-nucleotide polymorphism (SNP) or sequencing error; thus, a preferred approach is to only preserve quality scores for probable variant locations and compress quality scores of concordant bases, preferably by resetting them to a default value. By viewing individual read datasets through the lens of k-mer frequencies in a corpus of reads, the approach herein ensures that compression lossiness does not affect accuracy in a deleterious way.

Methods for analyzing a fluid sample can include providing a sensor comprising a non-conductive housing and having a first face and an electrode array mounted in the first face. The method can include disposing the first face of the housing into a fluid sample to be analyzed, selecting a mode of operation, and initiating sensor operation. Modes of operation can include electrochemical operation and conductivity analysis, and can be selected via positioning a switch. The method can include receiving information from the sensor regarding at least one parameter of the fluid. Such parameters can include a concentration of a target constituent in the fluid sample, combined concentrations of different species within the fluid sample, and/or information indicative of the conductivity of the fluid sample.