Abstract: Disclosed herein are nucleotide sequences encoding an insecticidal protein exhibiting Lepidopteran inhibitory activity, as well as novel insecticidal proteins referred to herein as a BCW 001, BCW 002, BCW 003, and BCW toxic protein-containing chimeras and BCW toxin insecticide, transgenic plants expressing the chimeras or the insecticide, and methods for detecting the presence of the nucleotide sequences or the insecticide in a biological sample.

Abstract: Disclosed herein are nucleotide sequences encoding an insecticidal protein exhibiting Lepidopteran inhibitory activity, as well as novel insecticidal proteins referred to herein as a BCW 001, BCW 002, BCW 003, and BCW toxic protein-containing chimeras and BCW toxin insecticide, transgenic plants expressing the chimeras or the insecticide, and methods for detecting the presence of the nucleotide sequences or the insecticide in a biological sample.

Abstract: A computer system selects features of a dataset for predictive modeling. A first set of features that are relevant to outcome are selected from a dataset comprising a plurality of cases and controls. A subset of cases and controls having similar values for the first set of features is identified. The subset is analyzed to select a set of additional features relevant to outcome. A first and second predictive model are evaluated to determine that the second predictive model more accurately predicts outcome, wherein the first predictive model is based on the first set of features and the second predictive model is based on the first set of features and the additional features. The second predictive model is utilized to predict outcomes. Embodiments of the present invention further include a method and program product for selecting features of a dataset for predictive modeling in substantially the same manner described above.

Abstract: Disclosed herein are nucleotide sequences encoding an insecticidal protein exhibiting Lepidopteran inhibitory activity, as well as novel insecticidal proteins referred to herein as a BCW 001, BCW 002, BCW 003, and BCW toxic protein-containing chimeras and BCW toxin insecticide, transgenic plants expressing the chimeras or the insecticide, and methods for detecting the presence of the nucleotide sequences or the insecticide in a biological sample.

Abstract: Aspects of the invention include includes identifying a respective estimated clinical risk score for each of a first group of patients and a second group of patients. An alternative probability estimate is generated using a same set of inputs used to determine each respective estimated clinical risk score. An unreliability of a patient's clinical risk score is determined based at least in part on a feature of the patient and on a difference between the alternative probability estimate and the determined respective estimated clinical risk score.

Abstract: A computer system trains a predictive model. A plurality of subsets of features are selected from a dataset comprising a plurality of cases and controls and a plurality of features. Cases and controls are matched to select a plurality of case-control subsets for each subset of features, each case-control subset having similar values for the corresponding subset of features. For each case-control subset, a statistical significance of each feature of the plurality of features absent from the subset of features used to match the case-control subset is identified. A final subset of features is selected based on satisfying a statistical significance of each feature for the plurality of case-control subsets. A predictive model is trained using the final subset of features. Embodiments of the present invention further include a method and program product for training a predictive model in substantially the same manner described above.

Abstract: A computer system selects features of a dataset for predictive modeling. A first set of features that are relevant to outcome are selected from a dataset comprising a plurality of cases and controls. A subset of cases and controls having similar values for the first set of features is identified. The subset is analyzed to select a set of additional features relevant to outcome. A first and second predictive model are evaluated to determine that the second predictive model more accurately predicts outcome, wherein the first predictive model is based on the first set of features and the second predictive model is based on the first set of features and the additional features. The second predictive model is utilized to predict outcomes. Embodiments of the present invention further include a method and program product for selecting features of a dataset for predictive modeling in substantially the same manner described above.

Abstract: A computer system trains a predictive model. A plurality of subsets of features are selected from a dataset comprising a plurality of cases and controls and a plurality of features. Cases and controls are matched to select a plurality of case-control subsets for each subset of features, each case-control subset having similar values for the corresponding subset of features. For each case-control subset, a statistical significance of each feature of the plurality of features absent from the subset of features used to match the case-control subset is identified. A final subset of features is selected based on satisfying a statistical significance of each feature for the plurality of case-control subsets. A predictive model is trained using the final subset of features. Embodiments of the present invention further include a method and program product for training a predictive model in substantially the same manner described above.

Abstract: Aspects of the invention include includes identifying a respective estimated clinical risk score for each of a first group of patients and a second group of patients. An alternative probability estimate is generated using a same set of inputs used to determine each respective estimated clinical risk score. An unreliability of a patient's clinical risk score is determined based at least in part on a feature of the patient and on a difference between the alternative probability estimate and the determined respective estimated clinical risk score.

Abstract: Disclosed herein are nucleotide sequences encoding an insecticidal protein exhibiting Lepidopteran inhibitory activity, as well as novel insecticidal proteins referred to herein as a BCW 001, BCW 002, BCW 003, and BCW toxic protein-containing chimeras and BCW toxin insecticide, transgenic plants expressing the chimeras or the insecticide, and methods for detecting the presence of the nucleotide sequences or the insecticide in a biological sample.

Abstract: Disclosed herein are nucleotide sequences encoding an insecticidal protein exhibiting Lepidopteran inhibitory activity, as well as novel insecticidal proteins referred to herein as a BCW 001, BCW 002, BCW 003, and BCW toxic protein-containing chimeras and BCW toxin insecticide, transgenic plants expressing the chimeras or the insecticide, and methods for detecting the presence of the nucleotide sequences or the insecticide in a biological sample.

Abstract: Disclosed herein are nucleotide sequences encoding an insecticidal protein exhibiting Lepidopteran inhibitory activity, as well as novel insecticidal proteins referred to herein as a BCW 001, BCW 002, BCW 003, and BCW toxic protein-containing chimeras and BCW toxin insecticide, transgenic plants expressing the chimeras or the insecticide, and methods for detecting the presence of the nucleotide sequences or the insecticide in a biological sample.

Abstract: A method for the machine generation of a model for predicting patient outcome following the occurrence of an event. In one embodiment the method includes the steps of obtaining a physiological signal of interest, the physiological signal having a characteristic; obtaining a time series of a signal characteristic; dividing the time series into a plurality of window segments; converting the time series from time-space to beat-space; computing the power in various frequency bands of each window segment; computing the 90th percentile of the spectral energies across all window segments for each frequency band; and inputting the data into a machine learning program to generate a weighted risk vector.

Abstract: A method for the machine generation of a model for predicting patient outcome following the occurrence of an event. In one embodiment the method includes the steps of obtaining a physiological signal of interest, the physiological signal having a characteristic; obtaining a time series of a signal characteristic; dividing the time series into a plurality of window segments; converting the time series from time-space to beat-space; computing the power in various frequency bands of each window segment; computing the 90th percentile of the spectral energies across all window segments for each frequency band; and inputting the data into a machine learning program to generate a weighted risk vector.

Abstract: A method and apparatus for predicting patient outcome from a physiological segmentable signal of a patient. In one embodiment, the method comprises the steps of obtaining the physiological segmentable signal of the patient; segmenting the physiological segmentable signal into a plurality of separate segmentable components; calculating a time series of the morphological distance between adjacent separate segmentable components of the plurality of separate segmentable components; and predicting patient outcome in response to the time series of the morphological distance.

Abstract: A method and apparatus for predicting patient outcome from a physiological segmentable signal of a patient. In one embodiment, the method comprises the steps of obtaining the physiological segmentable signal of the patient; segmenting the physiological segmentable signal into a plurality of separate segmentable components; calculating a time series of the morphological distance between adjacent separate segmentable components of the plurality of separate segmentable components; and predicting patient outcome in response to the time series of the morphological distance.

Abstract: A method and apparatus for predicting patient outcome from a physiological segmentable signal of a patient. In one embodiment, the method comprises the steps of obtaining the physiological segmentable signal of the patient; segmenting the physiological segmentable signal into a plurality of separate segmentable components; calculating a time series of the morphological distance between adjacent separate segmentable components of the plurality of separate segmentable components; and predicting patient outcome in response to the time series of the morphological distance.

Abstract: The application relates a methodology and apparatus for identifying predictive patterns for acute clinical events in the absence of prior knowledge. Principles of conservation are used to identify activity that consistently precedes an outcome in patients, and describe a two-stage process that allows us to more efficiently search for such patterns in large datasets. This is achieved by first transforming continuous physiological signals from multiple patients into symbolic sequences, and by then searching for patterns in these reduced representations that are strongly associated with an outcome.

Abstract: The invention relates to collagen peptides, as well as related methods. The invention also relates to methods and products for modulating cytokine production and/or inflammation.

Abstract: The invention relates in part to methods for partitioning a plurality of patients into risk profile groups comprising the steps of: recording a physiological signal from each patient of a plurality of patients; segmenting the physiological signal into a plurality of components for each patient of a plurality of patients; grouping the components into a plurality of information classes for each patient of a plurality of patients; assigning a representation to each information class for each patient of a plurality of patients; and grouping the patients in response to the representations of their respective information classes.