Abstract: To determine a peak signal corresponding to a particular part of a sample, a data signal for a cluster can be transformed into a set of localized functions. For example, the data signal can be transformed to obtain coefficients of a set of wavelets, which can span a variety of scales that define an exponential decay of the wavelet. The coefficients in a time region for which a peak signal is to be obtained can be replaced with coefficients that model the behavior of tails of other peaks. The tail model can be determined using nonlinear regression, which may need to only be performed once. An inverse transform can then provide a background signal that can be subtracted from the input data signal to provide the desired peak signal.

Abstract: Systems and methods are provided for analyzing data to determine properties of a PCR processor other process exhibiting amplification or growth. Data representing an amplification can be distinguished from data representing a jump or other error. A modified sigmoid function containing a drift term may be used in determining the properties. A multi-stage functional fit of the amplification data can provide increased accuracy and consistency of one or more of the properties. A baseline of the amplification data can be determined by analyzing an integrated area of a first derivative function of the data. A reference quantitation value can also be determined from locations of maxima of different derivative functions of the amplification data, e.g., a weighted average of the maxima locations for the second and third derivatives may be used.

Abstract: To determine a peak signal corresponding to a particular part of a sample, a data signal for a cluster can be transformed into a set of localized functions. For example, the data signal can be transformed to obtain coefficients of a set of wavelets, which can span a variety of scales that define an exponential decay of the wavelet. The coefficients in a time region for which a peak signal is to be obtained can be replaced with coefficients that model the behavior of tails of other peaks. The tail model can be determined using nonlinear regression, which may need to only be performed once. An inverse transform can then provide a background signal that can be subtracted from the input data signal to provide the desired peak signal.

Abstract: Systems and methods are provided for analyzing data to determine properties of a PCR processor other process exhibiting amplification or growth. Data representing an amplification can be distinguished from data representing a jump or other error. A modified sigmoid function containing a drift term may be used in determining the properties. A multi-stage functional fit of the amplification data can provide increased accuracy and consistency of one or more of the properties. A baseline of the amplification data can be determined by analyzing an integrated area of a first derivative function of the data. A reference quantitation value can also be determined from locations of maxima of different derivative functions of the amplification data, e.g., a weighted average of the maxima locations for the second and third derivatives may be used.

Abstract: Systems and methods are provided for analyzing data to determine properties of a PCR process or other process exhibiting amplification or growth. Data representing an amplification can be distinguished from data representing a jump or other error. A modified sigmoid function containing a drift term may be used in determining the properties. A multi-stage functional fit of the amplification data can provide increased accuracy and consistency of one or more of the properties. A baseline of the amplification data can be determined by analyzing an integrated area of a first derivative function of the data. A reference quantitation value can also be determined from locations of maxima of different derivative functions of the amplification data, e.g., a weighted average of the maxima locations for the second and third derivatives may be used.

Abstract: Systems and methods are provided for analyzing data to determine properties of a PCR process or other process exhibiting amplification or growth. Data representing an amplification can be distinguished from data representing a jump or other error. A modified sigmoid function containing a drift term may be used in determining the properties. A multi-stage functional fit of the amplification data can provide increased accuracy and consistency of one or more of the properties. A baseline of the amplification data can be determined by analyzing an integrated area of a first derivative function of the data. A reference quantitation value can also be determined from locations of maxima of different derivative functions of the amplification data, e.g., a weighted average of the maxima locations for the second and third derivatives may be used.

Abstract: Methods, apparatus, and systems are provided for processing a data set representing an amplification curve having a baseline portion and a growth portion. Peak objects are generated by taking a derivative of the data set. The first peak object having a value greater than a threshold is identified. An end of the baseline portion from a beginning of the first peak object is estimated.

Abstract: Systems and methods are provided for analyzing data to determine properties of a PCR process or other process exhibiting amplification or growth. Data representing an amplification can be distinguished from data representing a jump or other error. A modified sigmoid function containing a drift term may be used in determining the properties. A multi-stage functional fit of the amplification data can provide increased accuracy and consistency of one or more of the properties. A baseline of the amplification data can be determined by analyzing an integrated area of a first derivative function of the data. A reference quantitation value can also be determined from locations of maxima of different derivative functions of the amplification data, e.g., a weighted average of the maxima locations for the second and third derivatives may be used.

Abstract: Methods, apparatus, and systems are provided for processing a data set having noise to determine whether the data set exhibits statistically linear behavior. A true data signal is calculated based on local properties of the data, and an estimate of the noise in the data is calculated from the true data signal. A measure of the estimated noise is then compared to properties of a linear fit to the data set.

Abstract: Coupons which are displayed as a text message on a mobile telecommunication device and include a code uniquely identifying the coupon are provided for. Systems for generating coupons on demand are also provided for. The system comprises an agency computer having access to a coupon database. The coupon database comprises information on offers and a location value assigned to each offer. The agency computer is connectable to a mobile communications network or other network for transmitting electronic mail. It receives requests for coupons from consumers that are transmitted via the network. Preferably, the requests originate from cells phones or other mobile telecommunication devices and are transmitted to the agency computer via a mobile communications network. The request is a text message that includes information identifying the consumer's location.

Abstract: Methods, apparatus, and systems are provided for processing a data set representing an amplification curve having a baseline portion and a growth portion. Peak objects are generated by taking a derivative of the data set. The first peak object having a value greater than a threshold is identified. An end of the baseline portion from a beginning of the first peak object is estimated.

Abstract: Methods, apparatus, and systems are provided for processing a data set having noise to determine whether the data set exhibits statistically linear behavior. A true data signal is calculated based on local properties of the data, and an estimate of the noise in the data is calculated from the true data signal. A measure of the estimated noise is then compared to properties of a linear fit to the data set.