Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model.

The methods described herein enable the evaluation of compounds on subjects to assess their therapeutic efficacy or toxic effects. The target of analysis is the underlying biochemical process or processes (i.e., metabolic process) thought to be involved in disease pathogenesis. Molecular flux rates within the one or more biochemical processes serve as biomarkers and are quantitated and compared with the molecular flux rates (i.e., biomarker) from control subjects (i.e., subjects not exposed to the compounds). Any change in the biomarker in the subject relative to the biomarker in the control subject provides the necessary information to evaluate therapeutic efficacy of an administered drug or a toxic effect and to develop the compound further if desired.

Aspects relate to calculating energy expenditure values from an apparatus configured to be worn on an appendage of a user. Steps counts may be quantified, such as by detecting arm swings peaks and bounce peaks in motion data. A search range of acceleration frequencies related to an expected activity may be established. Frequencies of acceleration data within a search range may be analyzed to identify one or more peaks, such as a bounce peak and an arm swing peak. Novel systems and methods may determine whether to utilize the arm swing data, bounce data, and/or other data or portions of data to quantify steps. The number of peaks (and types of peaks) may be used to choose a step frequency and step magnitude. At least a portion of the motion data may be classified into an activity category based upon the quantification of steps.

A system is disclosed in which a plurality of in-vitro diagnostic (IVD) devices each include a network communication device for connecting to a publicly accessible data network. For example, IVD devices are provided with a cellular modem for connecting to a public cellular network. These IVD devices connect to the data network upon completion of a diagnostic test, and upload results of the test, as well as other appropriate data, to a remote device which is also on the network. The IVD devices also download appropriate data from remote network elements. The remote network element may be a network element such as a Hospital Information System (HIS) or Laboratory Information System (LIS) database. Alternatively, the remote device may be a remote server or another IVD device. This connectivity enables the system to accumulate diagnostic test data, and to analyze, report, and/or update the IVD devices based on the accumulated data.

In general, one aspect of the subject matter described in this specification is embodied in operations of processing sequence data by selecting a distribution key according to a type of one or more tasks to be performed on the data. The key is one or more data fields of a sequence data file, e.g., a sequence alignment/map (SAM) format or binary sequence alignment/map (BAM) format file, or derived from one or more data fields of a sequence data file. The sequence data is then distributed to multiple nodes of a parallel processing relational database system. The system performs the tasks of processing the sequence data by executing database queries. The system executes the database queries on multiple nodes in parallel. The system can use query optimization functions built into the database to expedite performance of each task.

Disclosed is a computerized method and system for identifying a medicinal substance from a plurality of different machine-readable codes that are each compliant with a different coding standard. A code reader reads a machine-readable code and transmits a signal indicative of the machine-readable code in response. A recognition identifies the coding standard with which the machine-readable code complies. Based on the identification by the recognition unit, computer-executable instructions specific to decoding information according the identified standard are selected and executed to decode the information encoded pursuant to the identified coding standard.

Processes, as well as associated systems and computer program (software) products, are disclosed for the biological conversion of CO into desired end products such as ethanol. The control methodologies used for these processes can advantageously result in a reduced time required for a batch operation or other initial operating period, prior to achieving a continuous operation, which may be demarcated either by the addition of fresh culture medium at a defined flow rate or by another process initiation target. The control methodologies may alternatively, or in combination, improve a process performance parameter, such as productivity of the desired end product or bacterial growth rate, during this batch operation or other initial operating period.

Provided herein is a method for distinguishing an aberrant methylation level for DNA from a first cell type, including steps of (a) providing a test data set that includes (i) methylation states for a plurality of sites from test genomic DNA from at least one test organism, and (ii) coverage at each of the sites for detection of the methylation states; (b) providing methylation states for the plurality of sites in reference genomic DNA from one or more reference individual organisms, (c) determining, for each of the sites, the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each site; and (d) weighting the normalized methylation difference for each site by the coverage at each of the sites, thereby determining an aggregate coverage-weighted normalized methylation difference score. Also provided herein are sensitive methods for using genomic DNA methylation levels to distinguish cancer cells from normal cells and to classify different cancer types according to their tissues of origin.

Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model.

The present disclosure, among other things, methods and systems for digital quantification of single molecule analytes.