Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Provided herein are processes for preparing an oligomer (e.g., a morpholino oligomer). The synthetic processes described herein may be advantageous to scaling up oligomersynthesis while maintaining overall yield and purity of a synthesized oligomer.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Provided herein are processes for preparing an oligomer (e.g., a morpholino oligomer). The synthetic processes described herein may be advantageous to scaling up oligomer synthesis while maintaining overall yield and purity of a synthesized oligomer.

Abstract: A method of making a gaseous fluid data sensor assembly for acquiring data regarding the ambient environment adjacent a surface of an airframe with adjacent air speeds below 40 knots (or another aerodynamic structure with low speed gaseous fluid flow adjacent thereto) having a flexible substrate adhesively conforming to the airframe surface, a conformable cover layer and a relatively thin air data sensor for sensing air pressure between the substrate and the cover layer. The method includes forming a flexible printed circuit on a polymeric film, attaching thin air data sensor to the printed circuit and attaching a flexible substrate to form a conformal air data sensor. The method may also include attaching a data acquisition circuit to the printed circuit and may still further include providing an optical interconnection between the air data sensor and the data acquisition circuit.

Abstract: A method of making a gaseous fluid data sensor assembly for acquiring data regarding the ambient environment adjacent a surface of an airframe with adjacent air speeds below 40 knots (or another aerodynamic structure with low speed gaseous fluid flow adjacent thereto) having a flexible substrate adhesively conforming to the airframe surface, a conformable cover layer and a relatively thin air data sensor for sensing air pressure between the substrate and the cover layer. The method includes forming a flexible printed circuit on a polymeric film, attaching thin air data sensor to the printed circuit and attaching a flexible substrate to form a conformal air data sensor. The method may also include attaching a data acquisition circuit to the printed circuit and may still further include providing an optical interconnection between the air data sensor and the data acquisition circuit.

Abstract: A gaseous fluid data sensor assembly for acquiring data regarding the ambient environment adjacent a surface of an airframe with adjacent air speeds below 40 knots (or another aerodynamic structure with low speed gaseous fluid flow adjacent thereto) having a flexible substrate adhesively conforming to the airframe surface, a conformable cover layer and a relatively thin air data sensor for sensing air pressure between the substrate and the cover layer. The assembly also includes a fiber optic communication link, a battery, a data acquisition subsystem, and a flexible printed circuit, all between the substrate and the cover layer. The cover layer is formed of a polymer film.

Abstract: A gaseous fluid data sensor assembly for acquiring data regarding the ambient environment adjacent a surface of an airframe with adjacent air speeds below 40 knots (or another aerodynamic structure with low speed gaseous fluid flow adjacent thereto) having a flexible substrate adhesively conforming to the airframe surface, a conformable cover layer and a relatively thin air data sensor for sensing air pressure between the substrate and the cover layer. The assembly also includes a fiber optic communication link, a battery, a data acquisition subsystem, and a flexible printed circuit, all between the substrate and the cover layer. The cover layer is formed of a polymer film.