Abstract: Provided herein are systems and methods for detecting predefined patterns in streaming data being transmitted in a distributed computing system that contains a plurality of computing devices wherein the plurality of computing devices are collectively configured to execute a distributed software program. In one or more examples, a detection engine can be implemented on a computing device and can be configured to receive streaming data that is being transmitted between computing devices of the system. The detection engine can be further configured to analyze the streaming data to determine if the data matches one or more patterns that is specified by a watch point which can be converted into a happened before language expression. In one or more examples, the streaming data can be converted into an intermediate log file that is compared against patterns expressed by a regular expression and if a match is found, an alert can be generated.

Abstract: Predefined patterns are detected in streaming data being transmitted in a distributed computing system that contains a plurality of computing devices wherein the plurality of computing devices are collectively configured to execute a distributed software program. In one or more examples, a detection engine can be implemented on a computing device and can be configured to receive streaming data that is being transmitted between computing devices of the system. The detection engine can be further configured to analyze the streaming data to determine if the data matches one or more patterns that is specified by a watch point which can be converted into a happened before language expression. In one or more examples, the streaming data can be converted into an intermediate log file that is compared against patterns expressed by a regular expression and if a match is found, an alert can be generated.

Abstract: Provided are methods for identifying pancreatic cancer subtypes in a subject and treating the same. In some embodiments, the method comprise obtaining gene expression levels for each of the following genes in the biological sample: GPR87, KRT6A, BCAR3, PTGES, 1TGA3, C16orf74, S100A2, KRT5, REG4, ANXA10, GATA6, CLDN18, LGALS4, DDC, SLC40A1, CLRN3; performing pair-wise comparisons of gene expression levels for combinations of these genes, and calculating a Raw Score for the biological sample, wherein the Raw Score is indicative of the pancreatic cancer subtype in the subject. Also provided are methods for identifying differential treatment strategies for subjects diagnosed with PDAC, methods for treating PDAC patients based on the subtype of PD AC the patients have; and methods for classifying subjects diagnosed with PDAC as having a basal-like subtype or a classical subtype of PDAC.

Abstract: Methods for generating a prognostic and/or subtype signature for a subject with pancreatic ductal adenocarcinoma (PDAC) are provided. In some embodiments, the methods include determining expression levels for one or more genes listed in Tables 2-5, 9, 10, or 11, and/or the DE-S and/or DE-T subset of genes in PDAC cells obtained from the subject, wherein the determining provides a prognostic and/or subtype signature for the subject. Also provided are methods for classifying a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC) as having an activated stroma subtype or a normal stroma subtype of PDAC and/or a basal subtype or a classical subtype of PDAC; and methods for identifying a differential treatment strategy for a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC).

Abstract: Provided herein are systems and methods for detecting predefined patterns in streaming data being transmitted in a distributed computing system that contains a plurality of computing devices wherein the plurality of computing devices are collectively configured to execute a distributed software program. In one or more examples, a detection engine can be implemented on a computing device and can be configured to receive streaming data that is being transmitted between computing devices of the system. The detection engine can be further configured to analyze the streaming data to determine if the data matches one or more patterns that is specified by a watch point which can be converted into a happened before language expression. In one or more examples, the streaming data can be converted into an intermediate log file that is compared against patterns expressed by a regular expression and if a match is found, an alert can be generated.

Abstract: Methods for generating a prognostic and/or subtype signature for a subject with pancreatic ductal adenocarcinoma (PDAC) are provided. In some embodiments, the methods include determining expression levels for one or more genes listed in Tables 2-5, 9, 10, or 11, and/or the DE-S and/or DE-T subset of genes in PDAC cells obtained from the subject, wherein the determining provides a prognostic and/or subtype signature for the subject. Also provided are methods for classifying a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC) as having an activated stroma subtype or a normal stroma subtype of PDAC and/or a basal subtype or a classical subtype of PDAC; and methods for identifying a differential treatment strategy for a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC).

Abstract: The present invention relates to an aerated frozen confection with an overrun of between 120% and 160%, which includes acacia gum as all or part of a natural emulsifier. A process for the preparation of this frozen confection is also disclosed.

Abstract: Methods for generating a prognostic and/or subtype signature for a subject with pancreatic ductal adenocarcinoma (PDAC) are provided. In some embodiments, the methods include determining expression levels for one or more genes listed in Tables 2-5, 9, 10, or 11, and/or the DE-S and/or DE-T subset of genes in PDAC cells obtained from the subject, wherein the determining provides a prognostic and/or subtype signature for the subject. Also provided are methods for classifying a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC) as having an activated stroma subtype or a normal stroma subtype of PDAC and/or a basal subtype or a classical subtype of PDAC; and methods for identifying a differential treatment strategy for a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC).

Abstract: A device worn by a human performing a method using accelerometer data to classify human activities is disclosed. The method uses memory and computation efficiently. A stream of samples is divided into a sequence of sampling periods; each sample has an acceleration value for each axis (e.g., a 3-D accelerometer). Standard deviation of the values for each axis during each sampling period are calculated. A running sum of each axis' values can be maintained sample-by-sample. Each value is sorted into a bin of a histogram, by quantifying a deviance from a respective mean in standard deviations. A standard deviation produced from samples of a previous period can be used. The histogram is compared with histograms associated with particular activities and a classification output can be produced. Classification outputs from multiple sampling periods can be used for voting. A threshold amount of activity can be required to begin activity classification.

Abstract: The present invention relates to an aerated frozen confection with an overrun of between 120% and 160%, which includes acacia gum as all or part of a natural emulsifier. A process for the preparation of this frozen confection is also disclosed.

Abstract: A device worn by a human performing a method using accelerometer data to classify human activities is disclosed. The method uses memory and computation efficiently. A stream of samples is divided into a sequence of sampling periods; each sample has an acceleration value for each axis (e.g., a 3-D accelerometer). Standard deviation of the values for each axis during each sampling period are calculated. A running sum of each axis' values can be maintained sample-by-sample. Each value is sorted into a bin of a histogram, by quantifying a deviance from a respective mean in standard deviations. A standard deviation produced from samples of a previous period can be used. The histogram is compared with histograms associated with particular activities and a classification output can be produced. Classification outputs from multiple sampling periods can be used for voting. A threshold amount of activity can be required to begin activity classification.

Abstract: A cheese sauce comprising a water-in-oil emulsion containing, based on the weight of the sauce, from 1 to 25% by weight of cheese and from 3 to 10% by weight of a homogenized modified starch or a mixture of homogenized modified and unmodified starch containing up to 40% by weight of unmodified starch based on the weight of the mixture of the starches.