Abstract: A physiological monitoring system may process a physiological signal such a photoplethysmograph signal from a subject. The system may determine physiological information, such as a physiological rate, from the physiological signal. The system may use search techniques and qualification techniques to determine one or more initialization parameters. The initialization parameters may be used to calculate and qualify a physiological rate. The system may use signal conditioning to reduce noise in the physiological signal and to improve the determination of physiological information. The system may use qualification techniques to confirm determined physiological parameters. The system may also use autocorrelation techniques, cross-correlation techniques, fast start techniques, and/or reference waveforms when processing the physiological signal.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may receive a calculated value indicative of a physiological rate. The system may generate value pairs from a first collection of values of the physiological signal and another collection of corresponding value of the physiological signal spaced from the first collection based on the calculated value. The system may determine a best fit linear relationship based on the value pairs and determine at least one statistical metric based on the linear relationship and the value pairs. The system may qualify or disqualify the calculated value based on the at least one statistical metric.

Abstract: The present invention relates to a method for improving protein yield. The method comprises modifying the value of a set of relevant protein features to fall within an optimal range or to become more close to an optimal value for one or more protein features in the eukaryotic host.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may generate a difference signal based on the physiological signal and sort the difference signal to generate a sorted difference signal. The system may identify a midpoint of a first segment of the difference signal and a midpoint of a second segment of the difference signal. The first segment may correspond to positive values of the difference signal and the second segment may correspond to negative values of the difference signal. The system may determine an algorithm setting based on the first midpoint and the second midpoint. The algorithm setting may, for example, affect the amount of filtering applied to the physiological signal.

Abstract: A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may generate a difference signal based on the physiological signal. The system may determine positive areas associated with positive regions of the difference signal and negative areas associated with negative regions of the difference signal. The system may determine area ratios based on adjacent positive and negative regions of the difference signal. The system may determine an algorithm setting based on the area ratios. The algorithm setting may, for example, affect the amount of filtering applied to the physiological signal.

Abstract: The invention is directed to methods for determining clonotypes and clonotype profiles in assays for analyzing immune repertoires by high throughput nucleic acid sequencing of somatically recombined immune molecules. In one aspect, the invention comprises generating a clonotype profile from an individual by generating sequence reads from a sample of recombined immune molecules; forming from the sequence reads a sequence tree representing candidate clonotypes each having a frequency; coalescing with a highest frequency candidate clonotype any lesser frequency candidate clonotypes whenever such lesser frequency is below a predetermined value and whenever a sequence difference therebetween is below a predetermined value to form a clonotype. After such coalescence, the candidate clonotypes is removed from the sequence tree and the process is repeated.

Abstract: The compositions and methods provided herein allow for identification of causative genetic biomarkers for a disease condition or drug response.

Abstract: A method, computer product, and computer system of minimizing surprisal data comprising: at a source, reading and identifying characteristics of a genetic sequence of an organism; receiving an input of rank of at least two identified characteristics of the genetic sequence of the organism; generating a hierarchy of ranked, identified characteristics based on the rank of the at least two identified characteristics of the genetic sequence of the organism; comparing the hierarchy of ranked, identified characteristics to a repository of reference genomes; and if at least one reference genome from the repository matches the hierarchy of ranked, identified characteristics, comparing nucleotides of the genetic sequence of the organism to nucleotides from the at least one matched reference genome, to obtain differences and create surprisal data.

Abstract: A system and method for converting static/still medical images of a particular patient into dynamic and interactive images interacting with medical tools including medical devices by coupling a model of tissue dynamics and tool characteristics to the patient specific imagery for simulating a medical procedure in an accurate and dynamic manner.

Abstract: The present invention relates to methods of optimization of a protein coding sequences for expression in a given host cell. The methods apply genetic algorithms to optimise single codon fitness and/or codon pair fitness sequences coding for a predetermined amino acid sequence. In the algorithm generation of new sequence variants and subsequent selection of fitter variants is reiterated until the variant coding sequences reach a minimum value for single codon fitness and/or codon pair fitness. The invention also relates to a computer comprising a processor and memory, the processor being arranged to read from and write into the memory, the memory comprising data and instructions arranged to provide the processor with the capacity to perform the genetic algorithms for optimization of single codon fitness and/or codon pair fitness.

Abstract: A method, computer product and computer system of transmitting a compressed genome of an organism: a computer at a source reading an uncompressed sequence and a reference genome from a repository; the computer comparing nucleotides of the genetic sequence of the organism to nucleotides from a reference genome, to find differences where nucleotides of the genetic sequence of the organism which are different from the nucleotides of the reference genome; the computer using the differences to create surprisal data, the surprisal data comprising a starting location of the differences within the reference genome, and the nucleotides from the genetic sequence of the organism which are different from the nucleotides of the reference genome; and the computer transmitting, to a destination, a compressed genome comprising: surprisal data and an indication of the reference genome, discarding sequences of nucleotides that are the same in the sequence of the organism and reference genome.

Abstract: The determination of the nucleotide sequence of HTLV-III DNA; identification, isolation and expression of HTLV-III sequences which encode immunoreactive polypeptides by recombinant DNA methods and production of viral RNA are disclosed. Such polypeptides can be employed in immunoassays to detect HTLV-III.

Abstract: A robust classification method for cancer detection from mass spectrometry data includes inputting the mass spectrometry data, preprocessing the spectrometry data, conducting robust feature selection, generating predictions for the test data sets using multiple data classifiers, the multiple data classifiers including artificial neural networks, support vector machines, weighted voting on data patterns, classification and regression trees, k-nearest neighbor classification, and logistic regression, and constructing and validating a meta-classifier by combining individual predictions of the multiple data classifiers to generate a robust prediction of a phenotype. The test data sets are used exclusively for validation of the meta-classifier.

Abstract: Methods and systems for evaluating biological dataset profiles relating to toxic agents including candidate pharmaceuticals, environmental agents, biowarfare and chemical warfare agents are provided, where datasets comprising information for multiple cellular parameters are compared and identified, and used in the evaluation of candidate agents.

Abstract: A method provides an index that is indicative of the state of a subject, as to a biological condition, based on a sample from the subject. An embodiment of this method includes: deriving from the sample a profile data set, the profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables evaluation of the biological condition; and in deriving the profile data set, achieving such measure for each constituent under measurement conditions that are substantially repeatable; and applying values from the profile data set to an index function that provides a mapping from an instance of a profile data set into a single-valued measure of biological condition, so as to produce an index pertinent to the biological condition of the subject.

Abstract: A method for prognosing a rheumatoid arthritis phenotype using the outcomes of selected single nucleotide polymorphisms (SNPs) and clinical variables. A method for genotyping multiple rheumatoid arthritis associated genetic variations comprising use of a DNA microrarray. A microarray for use in the described methods.

Abstract: A kit and test panel utilizing a multivariate diagnostic method based on optimizing diagnostic likelihood ratios through the effective use of multiple diagnostic tests is disclosed. The Neyman-Pearson Lemma provides a mathematical basis to produce optimal diagnostic results.

Abstract: The present invention relates to genetic analysis and evaluation utilizing copy-number variants or polymorphisms. The methods utilize array comparative genomic hybridization and PCR assays to identify the significance of copy number variations in a subject or subject group.

Abstract: The present invention relates to a scoring system for the prediction of cancer recurrence by selecting genes and/or proteins whose expression patterns associated with recurrence of cancer, and generating formulae with the selected genes and/or proteins for the prediction of cancer recurrence. The present invention relates to a kit for determining the likelihood of recurrence of cancer, comprising DNA chip, oligonucleotide chip, protein chip, peptides, antibodies, probes and primers that are necessary for effecting DNA microarrays, oligonucleotide microarrays, protein arrays, northern blotting, in situ hybridization, RNase protection assays, western blotting, ELISA assays, reverse transcription polymerase-chain reaction to examine the expression of at least 2, 4, 6, 10, 12 or more genes and/or proteins, that are indicative of cancer recurrence.

Abstract: The attached disclosure provides a systems approach based on mathematical modelling of the kinetics of essential biochemical pathways involved in organ homeostasis. When this in silico model is coupled with in-vitro and/or in-vivo measurements to quantify drug-induced perturbations, a powerful platform that allows accurate and mechanistic-level prediction of drug-induced organ injury can be generated. The method described in this disclosure demonstrates that several physiological situations can also be accurately modelled in addition to the effect of perturbations induced by drugs. It can also be used along with high-throughput “omics” data to generate testable hypotheses leading to informed decision-making in drug development.