The present invention is related to novel methods for categorizing and treating a population of subjects that are at risk for increased pulmonary exacerbation and disease progression.

The present invention relates to methods for the treatment or prevention of fatigue associated with disordered sleep, for example, in multiple sclerosis, fibromyalgia, Fabry's disease, Parkinson's disease, or traumatic brain injury, using cyclobenzaprine. The present invention further relates to a biomarker for the therapeutic effects of a cyclobenzaprine treatment.

The present invention relates to methods for the treatment or prevention of fatigue associated with disordered sleep, for example, in multiple sclerosis, fibromyalgia, Fabry's disease, Parkinson's disease, or traumatic brain injury, using cyclobenzaprine. The present invention further relates to a biomarker for the therapeutic effects of a cyclobenzaprine treatment.

A device for compressing subject data. the device comprises a communication link, the communication link capable of receiving a set of subject data; a compression module, the compression module configured to apply a compression algorithm to the set of subject data, the compression algorithm compressing the set of subject data using a reference string of subject data; and a transmission module, the transmission module configured to transmit the compressed subject data. The device further comprising an encryption module for encrypting the subject data.

A device for compressing subject data. the device comprises a communication link, the communication link capable of receiving a set of subject data; a compression module, the compression module configured to apply a compression algorithm to the set of subject data, the compression algorithm compressing the set of subject data using a reference string of subject data; and a transmission module, the transmission module configured to transmit the compressed subject data. The device further comprising an encryption module for encrypting the subject data.

Examples of methods and apparatus are described that permit an analyte concentration to be estimated from a measurement in the presence of compounds that interfere with the measurement. In one example, the method can reduce the error in the estimation of analyte concentration in the presence of interferents. The method can include the use of one or more calibration set to determine analyte concentration. From a sample measurement, each calibration set can be tested to determine if it is eligible to estimate the analyte concentration in the sample. An estimate of analyte concentration can then be produced, based at least in part on the eligible calibration sets and on the sample measurement. In some implementations, if no calibration sets are eligible, an action is taken such as not outputting an estimate, displaying an alarm or alert, or providing a notification.

Examples of methods and apparatus are described that permit an analyte concentration to be estimated from a measurement in the presence of compounds that interfere with the measurement. In one example, the method can reduce the error in the estimation of analyte concentration in the presence of interferents. The method can include the use of one or more calibration set to determine analyte concentration. From a sample measurement, each calibration set can be tested to determine if it is eligible to estimate the analyte concentration in the sample. An estimate of analyte concentration can then be produced, based at least in part on the eligible calibration sets and on the sample measurement. In some implementations, if no calibration sets are eligible, an action is taken such as not outputting an estimate, displaying an alarm or alert, or providing a notification.

A method for building and using soil models that determine soil properties from soil spectrum data is provided. In an embodiment, building soil model may be accomplished using soil spectrum data received via hyperspectral sensors from a land unit. A processor updates the soil spectrum data by removing interference signals from the soil spectrum data. Multiple ground sampling locations within the land unit are then determined based on the updated soil spectrum data. Soil property data are obtained from ground sampling at the ground sampling locations. Soil models that correlate the updated soil spectrum data with the soil property data are created based on the updated soil spectrum data and the soil property data. The soil models are sent to a storage for future use.

A method for calibrating forecasts involving temperature, precipitation, and other weather related variables is provided. In an embodiment historical ensemble-based forecasts and historical observations are received by an agricultural intelligence computing system. Historical differences are determined between the forecasts and the observations corresponding to the forecasts and stored in the volatile memory of the agricultural intelligence computing system. The agricultural intelligence computing system receives current ensemble-based forecasts and a request for improved forecasts. The agricultural intelligence computing system retrieves the historical differences and uses a combination of the historical differences and the current ensemble-based forecasts to create probability distributions for the weather for each lead day. The agricultural intelligence computing system then samples from the probability distributions to create improved ensemble-based forecasts at the requested location.

A method for determining national crop yields during the growing season is provided. In an embodiment, a server computer system receives agricultural data records for a particular year that represent covariate data values related to plants at a specific geo-location at a specific time. The system aggregates the records to create geo-specific time series for a geo-location over a specified time. The system creates aggregated time series from a subset of the geo-specific time series. The system selects a representative feature from the aggregated time series and creates a covariate matrix for each specific geographic area in computer memory. The system determines a specific state crop yield for a specific year using linear regression to calculate the specific state crop yield from the covariate matrix. The system determines a national crop yield for the specific year using the sum of the state crop yields for the specific year nationally adjusted.