Abstract: A compound useful as an active ingredient of a pharmaceutical composition for treatment of cancer related to activation of immune cells or cancer resistant to anti-PD-1 antibody/anti-PD-L1 antibody therapy is provided. The present inventors have conducted studies on a compound useful as an active ingredient of a pharmaceutical composition for treatment of cancer related to activation of immune cells or cancer resistant to anti-PD-1 antibody/anti-PD-L1 antibody therapy, and confirmed that a heteroaryl carboxamide compound has DGK ? (DGKzeta) inhibitory effect, leading to completion of the present invention. The heteroaryl carboxamide compound of the present invention has DGK ? inhibitory effect, and can be used as a therapeutic agent for treatment of cancer related to activation of immune cells or cancer resistant to anti-PD-1 antibody/anti-PD-L1 antibody therapy.

Abstract: A number analyzing method, a number analyzing device, and a storage medium for number analysis are disclosed, which enable, with high accuracy, analysis of the number or number distribution of particulate or molecular analytes according to the kinds of the analytes. A computer control program is executed on the basis of a data group of particle-passage detection signals which are detected by a nanopore device in accordance with passage of subject particles through a through-hole. Also, a particle type distribution estimating program is executed, to estimate probability density on the basis of a data group based on feature values indicating feature of the waveforms of pulse signals which correspond to the passage of particles and which are obtained as the particle-passage detection signals. Thus, the number of particles can be derived for each particle type.

Abstract: A number analyzing method, a number analyzing device, and a storage medium for number analysis are disclosed, which enable, with high accuracy, analysis of the number or number distribution of particulate or molecular analytes according to the kinds of the analytes. A computer control program is executed on the basis of a data group of particle-passage detection signals which are detected by a nanopore device in accordance with passage of subject particles through a through-hole. Also, a particle type distribution estimating program is executed, to estimate probability density on the basis of a data group based on feature values indicating feature of the waveforms of pulse signals which correspond to the passage of particles and which are obtained as the particle-passage detection signals. Thus, the number of particles can be derived for each particle type.

Abstract: A number analyzing method, a number analyzing device, and a storage medium for number analysis are disclosed, which enable, with high accuracy, analysis of the number or number distribution of particulate or molecular analytes according to the kinds of the analytes. A computer control program is executed on the basis of a data group of particle-passage detection signals which are detected by a nanopore device in accordance with passage of subject particles through a through-hole. Also, a particle type distribution estimating program is executed, to estimate probability density on the basis of a data group based on feature values indicating feature of the waveforms of pulse signals which correspond to the passage of particles and which are obtained as the particle-passage detection signals. Thus, the number of particles can be derived for each particle type.

Abstract: Provided is a novel analysis method which, when a chemical sensor is used to perform a measurement, makes it possible to identify a sample without controlling or monitoring a change in the time the sample is introduced. According to the present invention, a sample can be identified without knowing a change in the time the sample is introduced, by using a chemical sensor having a plurality of channels each having different characteristics to perform a measurement, and performing an analysis on the basis of responses obtained from each channel.

Abstract: Provided is a novel analysis method that enables identification of a sample even when any sample is introduced during measurement carried out by using a chemical sensor. An input in which the amount of an unknown sample changes over time is provided to the chemical sensor, a response which is from the chemical sensor and which changes over time is measured, a sensor function (transmission function) of the chemical sensor with respect to the unknown sample is calculated on the basis of the input and the response, and the unknown sample is identified on the basis of the sensor function of the chemical sensor with respect to the unknown sample.

Abstract: It is an object to improve detection accuracy of an object as compared with prior arts. A flow passage (10) provided in a detection device (10) includes a substrate (1) and a covering member (2) provided at a position corresponding to the substrate (1). A covering member opening (HL2) of the covering member (2) is provided such that a substrate opening (HL1) of the substrate (1) is not covered with the covering member (2). The covering member (2) is arranged onto the substrate (1) such that a substrate capacitance and a covering member capacitance are connected in series. The covering member capacitance is lower than the substrate capacitance.

Abstract: A search device includes: a relationship calculation means for calculating a relationship by using a plurality of sets, each set including a parameter value and a numerical value indicating a degree of occurrence of a target event in a case of the parameter value, the relationship being a relationship between the parameter value and the numerical value; a frequency calculation means for calculating, for a plurality of bins obtained by dividing up a range that can be taken by the numerical value, a frequency at which the numerical value is included in a bin; a number setting means for performing, for at least some bins of the plurality of bins, a setting relating to a number of the parameter value such that the lower the frequency of the bin, the greater a number of the parameter value serving as calculation subjects of the numerical value, by using the frequency; and a parameter setting means for setting parameter values by the set number.

Abstract: Target state inference unit infers a target state of a system and a partial target state thereof between a first state of the system and the target state thereof based on the first state, inference knowledge, and quantitative knowledge, the system being configured to be operated based on a manipulation procedure. Manipulation sequence inference unit infers a manipulation for a transition to the partial target state based on a manipulation derivation rule. Learning setting generation unit generates a learning setting for the inferred manipulation based on a learning setting derivation rule. A learning agent creates information about detailed manipulations in the manipulation based on the learning setting for the manipulation.

Abstract: An analysis device applies, for each of a plurality of candidates set according to a update target parameter value, the update target parameter value and the candidate to a machine learning result to acquire information, the information indicating a degree of difference of an evaluation target value in a case of the candidate with respect to an evaluation target value in a case of the update target parameter value; calculates, for each candidate, an evaluation target value in a case of the candidate, based on the degree of difference of the evaluation target values and the evaluation target value in the case of the update target parameter value; and compares the evaluation target values in a case of each of the plurality of candidates and selects a candidate from the plurality of candidates based on a result of the comparison.

Abstract: An analysis device applies, for each of a plurality of candidates for an updated parameter value set according to an update target parameter value, the update target parameter value and the candidate to a plurality of machine learning results to acquire, for each machine learning result, information indicating a degree of difference of an evaluation target value in a case of the candidate with respect to an evaluation target value in a case of the update target parameter value; calculate, for each candidate and for each machine learning result, an evaluation target value in the case of the candidate based on the degree of difference and the evaluation target value in the case of the update target parameter value; and calculate a selection index value for each candidate using a variation in the evaluation target values for each machine learning result, compare the selection index value of each candidate.

Abstract: It is an object to improve detection accuracy of an object as compared with prior arts. A flow passage (10) provided in a detection device (10) includes a substrate (1) and a covering member (2) provided at a position corresponding to the substrate (1). A covering member opening (HL2) of the covering member (2) is provided such that a substrate opening (HL1) of the substrate (1) is not covered with the covering member (2). The covering member (2) is arranged onto the substrate (1) such that a substrate capacitance and a covering member capacitance are connected in series. The covering member capacitance is lower than the substrate capacitance.

Abstract: A PU classification device includes a classifier that performs maximum likelihood classification of an instance to be classified as a positive instance or a negative instance based on a magnitude relationship between a first probability that the instance is sampled from a population distribution for learning as the positive instance and a second probability that the instance is sampled from the population distribution for learning, when the instance to be classified is given, and a processor that learns the classifier by estimating a distribution function of the first probability from a set of positive instances sampled from the population distribution for learning and by estimating a distribution function of the second probability from a set of instances that are sampled from the population distribution for learning and are unknown whether they are positive or negative, wherein an instance to be classified is classified as the positive instance or the negative instance by using the classifier learned by the

Abstract: The present invention provides an identification method by which appropriately identifies nonconforming data from a measurement data set, for example, contributes to improve the reliability of measurement results by the advanced sensing device, a classification analysis method which can perform the classification analysis with high accuracy for the measurement data, an identification device, a classification analysis device, a storage medium for identification and a storage medium for classification analysis. A feature value is obtained in advance which indicates the feature of waveform of pulse signal, and the feature value obtained is set as the learning data for machine learning.

Abstract: The present invention provides a classification analysis method, a classification analysis device, and a storage medium for classification analysis, which enable, with high accuracy, the classification analysis of particulate or molecular analytes. As a means for solving the problem, a data group of particle-passage detection signals is based which are detected by a nanopore device 8 in accordance with passage of subject particles through a through-hole 12. A feature value is obtained in advance which indicates the feature of the waveform of the pulse signals corresponding to the passage of the predetermined analyte and the feature value obtained in advance is set as the learning data for the machine learning. The feature value obtained from the pulse signals of said analyzed data is set as a variable and the classification analysis on the predetermined analytes in the analyzed data can be performed by executing a classification analysis program due to the machine learning.

Abstract: Provided is a novel analysis method which, when a chemical sensor is used to perform a measurement, makes it possible to identify a sample without controlling or monitoring a change in the time the sample is introduced. According to the present invention, a sample can be identified without knowing a change in the time the sample is introduced, by using a chemical sensor having a plurality of channels each having different characteristics to perform a measurement, and performing an analysis on the basis of responses obtained from each channel.

Abstract: Provided is a novel analysis method that enables identification of a sample even when any sample is introduced during measurement carried out by using a chemical sensor. An input in which the amount of an unknown sample changes over time is provided to the chemical sensor, a response which is from the chemical sensor and which changes over time is measured, a sensor function (transmission function) of the chemical sensor with respect to the unknown sample is calculated on the basis of the input and the response, and the unknown sample is identified on the basis of the sensor function of the chemical sensor with respect to the unknown sample.

Abstract: The present invention provides a number analyzing method, a number analyzing device, and a storage medium for number analysis, which enable, with high accuracy, analysis of the number or number distribution of particulate or molecular analytes according to the kinds of the analytes. A computer control program is executed on the basis of a data group of particle-passage detection signals which are detected by a nanopore device (8) in accordance with passage of subject particles through a through-hole (12). Also, a particle type distribution estimating program, which is a number deriving means, is executed, to estimate probability density on the basis of a data group based on feature values indicating feature of the waveforms of pulse signals which correspond to the passage of particles and which are obtained as the particle-passage detection signals. Thus, the number of particles can be derived for each particle type.

Abstract: A frequent changing pattern extraction device which extracts a frequent changing pattern from an ever-changing network structure includes: a conversion unit which converts each of a plurality of graph sequences into an operator sequence by expressing changes, from a first graph included in the graph sequence to a second graph which is temporally adjacent to the first graph, using operators indicating operations necessary to transform the first graph into the second graph, each graph sequence including a plurality of graphs that show temporal changes in the graphs and each of the graphs including a vertex corresponding to a data piece and an edge corresponding to a link between data pieces; and an extraction unit which extracts an operator subsequence that appears at least a predetermined number of times in the plurality of operator sequences corresponding to the plurality of graph sequences, based on the anti-monotonicity used in the Apriori algorithm.

Abstract: A frequent changing pattern extraction device (100) which extracts a frequent changing pattern from an ever-changing network structure includes: a conversion unit (12) which converts each of a plurality of graph sequences into an operator sequence by expressing changes, from a first graph included in the graph sequence to a second graph which is temporally adjacent to the first graph, using operators indicating operations necessary to transform the first graph into the second graph, each graph sequence including a plurality of graphs that show temporal changes in the graphs and each of the graphs including a vertex corresponding to a data piece and an edge corresponding to a link between data pieces; and an extraction unit (18) which extracts an operator subsequence that appears at least a predetermined number of times in the plurality of operator sequences corresponding to the plurality of graph sequences, based on the anti-monotonicity used in the Apriori algorithm.