Battelle Memorial Institute

Lars J Kangas

Richard Dale Smith

Paul Edwin Keller

Gordon A Anderson

James R Skorpik

Bary Wallace Wilson

Frank L Greitzer

Thomas W Edgar

Thomas E Carroll

Thomas A Ferryman

Satish Chikkagoudar

Daniel M Johnson

Kenneth J Auberry

Konstantinos Petritis

Kiri J Oler

Kristine M Terrones

Melody A Maynard

Kristine M Arthur

Ronald A Pawlowski

A network analysis tool receives network flow information and uses deep learning&#x2014;machine learning that models high-level abstractions in the network flow information&#x2014;to identify dependencies between network assets. Based on the identified dependencies, the network analysis tool can discover functional relationships between network assets. For example, a network analysis tool receives network flow information, identifies dependencies between multiple network assets based on evaluation of the network flow information, and outputs results of the identification of the dependencies. When evaluating the network flow information, the network analysis tool can pre-process the network flow information to produce input vectors, use deep learning to extract patterns in the input vectors, and then determine dependencies based on the extracted patterns. The network analysis tool can repeat this process so as to update an assessment of the dependencies between network assets on a near real-time basis.

Extracting dependencies between network assets using deep learning

A method of predicting whether a peptide present in a biological sample will be detected by analysis with a mass spectrometer. The method uses at least one mass spectrometer to perform repeated analysis of a sample containing peptides from proteins with known amino acids. The method then generates a data set of peptides identified as contained within the sample by the repeated analysis. The method then calculates the probability that a specific peptide in the data set was detected in the repeated analysis. The method then creates a plurality of vectors, where each vector has a plurality of dimensions, and each dimension represents a property of one or more of the amino acids present in each peptide and adjacent peptides in the data set. Using these vectors, the method then generates an algorithm from the plurality of vectors and the calculated probabilities that specific peptides in the data set were detected in the repeated analysis. The algorithm is thus capable of calculating the probability that a hypothetical peptide represented as a vector will be detected by a mass spectrometry based proteomic platform, given that the peptide is present in a sample introduced into a mass spectrometer.

Method for predicting peptide detection in mass spectrometry

A method and computer-based apparatus for monitoring the degradation of, predicting the remaining service life of, and/or planning maintenance for, an operating system are disclosed. Diagnostic information on degradation of the operating system is obtained through measurement of one or more performance characteristics by one or more sensors onboard and/or proximate the operating system. Though not required, it is preferred that the sensor data are validated to improve the accuracy and reliability of the service life predictions. The condition or degree of degradation of the operating system is presented to a user by way of one or more calculated, numeric degradation figures of merit that are trended against one or more independent variables using one or more mathematical techniques. Furthermore, more than one trendline and uncertainty interval may be generated for a given degradation figure of merit/independent variable data set. The trendline(s) and uncertainty interval(s) are subsequently compared to one or more degradation figure of merit thresholds to predict the remaining service life of the operating system. The present invention enables multiple mathematical approaches in determining which trendline(s) to use to provide the best estimate of the remaining service life.

Method and apparatus to predict the remaining service life of an operating system

A method for predicting the elution time of a peptide in chromatographic and electrophoretic separations by first providing a data set of known elution times of known peptides, then creating a plurality of vectors, each vector having a plurality of dimensions, and each dimension representing the elution time of amino acids present in each of these known peptides from the data set. The elution time of any protein is then be predicted by first creating a vector by assigning dimensional values for the elution time of amino acids of at least one hypothetical peptide and then calculating a predicted elution time for the vector by performing a multivariate regression of the dimensional values of the hypothetical peptide using the dimensional values of the known peptides. Preferably, the multivariate regression is accomplished by the use of an artificial neural network and the elution times are first normalized using a transfer function.

Method for enhanced accuracy in predicting peptides using liquid separations or chromatography

The present invention is a method and apparatus for collecting EEG data, reducing the EEG data into coefficients, and correlating those coefficients with a depth of unconsciousness or anesthetic depth, and which obtains a bounded first derivative of anesthetic depth to indicate trends. The present invention provides a developed artificial neural network based method capable of continuously analyzing EEG data to discriminate between awake and anesthetized states in an individual and continuously monitoring anesthetic depth trends in real-time. The present invention enables an anesthesiologist to respond immediately to changes in anesthetic depth of the patient during surgery and to administer the correct amount of anesthetic.

Neurometric assessment of intraoperative anesthetic

The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

Artificial neural network cardiopulmonary modeling and diagnosis

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West Richland, WA, United States of America

Lars J Kangas