Abstract: Disclosed are methods and apparatus for diagnosis, detection of host response, monitoring, treatment or management of sepsis, infection-negative systemic inflammatory response syndrome (SIRS) and post-surgical inflammation in mammals. More particularly, the present invention discloses marker genes and their splice variant transcripts as well as their expression products, which are useful for distinguishing between sepsis and infection-negative SIRS, including post-surgical inflammation, and to the use of these markers in grading, monitoring, treatment and management of these conditions.

Abstract: A computer-implemented method of analysis of melt curve data comprising the steps of: (a) parameter model fitting the melt curve data; (b) performing a principal component analysis of the melt curve data; and (c) utilizing the principal components for clustering the melt curve data into groups. Such a method allows an efficient analysis of variations in melt curve shape and position and allows to statistically quantify these variations for both supervised and unsupervised data sets. Current melt analysis methods neither allow for statistical measures of each unknown nor do they allow for the determination of unsupervised data sets (i.e., unknown number of groups present). Particularly, the method according to the invention can be advantageous for identifying a specific sequence of dsDNA in a sample after performing a polymerase chain reaction (PCR).

Abstract: A computer-implemented method of analysis of melt curve data comprising the steps of: (a) parameter model fitting the melt curve data; (b) performing a principal component analysis of the melt curve data; and (c) utilizing the principal components for clustering the melt curve data into groups. Such a method allows an efficient analysis of variations in melt curve shape and position and allows to statistically quantify these variations for both supervised and unsupervised data sets. Current melt analysis methods neither allow for statistical measures of each unknown nor do they allow for the determination of unsupervised data sets (i.e., unknown number of groups present). Particularly, the method according to the invention can be advantageous for identifying a specific sequence of dsDNA in a sample after performing a polymerase chain reaction (PCR).

Abstract: A method of selecting one or more variables for use with a statistical model, the method comprising the steps of: creating a plurality of unique subsets of variables of multivariate data; determining the performance of a discriminant rule when used with each of the subsets, the discriminant rule being based on multivariate normal class densities each having substantially diagonal covariance matrices; and selecting the one or more variables from at least one of the subsets that result in a desired performance of the discriminant rule.