Abstract: A defined peak region residing between about 3.2 and 3.4 ppm of a proton NMR spectrum of an in vitro biosample is electronically evaluated to determine a level of trimethylamine-N-oxide (“TMAO”). The biosample may be any suitable biosamples including human serum with a normal biologic range of between about 1-50 ?M or urine with a normal biologic range of between about 0-1000 ?M.

Abstract: Systems and methods for a framework for generating predictive models for media planning. In some aspects, control circuitry receives, from a database, reference data associated with a program. The control circuitry receives a future date for insertion of an advertisement during transmission of the program. The control circuitry determines a prediction period between a current date and the future date. The control circuitry determines whether the prediction period exceeds a threshold period. If the prediction period does not exceed the threshold period, the control circuitry selects a first type for a predictive model. If the prediction period exceeds the threshold period, the control circuitry selects a second type for the predictive model. The control circuitry trains the predictive model according to the selected type and based on the reference data. The control circuitry predicts, based on the predictive model, an average audience for insertion of the advertisement.

Abstract: Systems and methods for a framework for generating predictive models for media planning. In some aspects, control circuitry receives, from a database, reference data associated with a program. The control circuitry receives a future date for insertion of an advertisement during transmission of the program. The control circuitry determines a prediction period between a current date and the future date. The control circuitry determines whether the prediction period exceeds a threshold period. If the prediction period does not exceed the threshold period, the control circuitry selects a first type for a predictive model. If the prediction period exceeds the threshold period, the control circuitry selects a second type for the predictive model. The control circuitry trains the predictive model according to the selected type and based on the reference data. The control circuitry predicts, based on the predictive model, an average audience for insertion of the advertisement.

Abstract: Methods and systems for performing non-linear optimization of reach are described herein. The methods and systems may be used to compute the reach associated with different advertisement campaigns involving different combinations of spots. The method includes computing probabilities that a user will access a certain spot. The method includes retrieving a weight associated with the targeted user demographic. The method includes computing a first reach value using a first non-linear function, and a second reach value using a second non-linear function. The method includes comparing the two reach values to each other and to a predetermined reach threshold. The higher reach value larger than the predetermined reach threshold is selected and information associated with the selected reach value is provided to the user.

Abstract: Methods and systems for performing non-linear optimization of reach are described herein. The methods and systems may be used to compute the reach associated with different advertisement campaigns involving different combinations of spots. The method includes computing probabilities that a user will access a certain spot. The method includes retrieving a weight associated with the targeted user demographic. The method includes computing a first reach value using a first non-linear function, and a second reach value using a second non-linear function. The method includes comparing the two reach values to each other and to a predetermined reach threshold. The higher reach value larger than the predetermined reach threshold is selected and information associated with the selected reach value is provided to the user.

Abstract: Methods and systems are presented for creating and retrieving a matrix wherein a first dimension represents viewers, a second dimension represents media assets, and each data point represents a probability that a viewer accessed a media asset. The methods further include reducing the number of rows and columns of the matrix based on a desired size of the matrix and calculating a reach value for a media asset based on a the data points in the modified matrix.