Abstract: Sensor systems can be used to measure an analyte concentration. Sensor systems can include a base having a distal side configured to face towards a person's skin. An adhesive can couple the base to the skin. A transcutaneous analyte measurement sensor can be coupled to the base and can be located at least partially in the host. A transmitter can be coupled to the base and can transmit analyte measurement data to a remote device.

Abstract: Detecting analytes using proximity-induced tagmentation, strand invasion, restriction, or ligation is provided herein. In some examples, detecting an analyte includes coupling a donor recognition probe to a first portion of the analyte. The donor recognition probe includes a first recognition element specific to the first portion of the analyte, a first oligonucleotide corresponding to the first portion, and a transposase coupled to the first recognition element and the first oligonucleotide. An acceptor recognition probe is coupled to a second portion of the analyte. The acceptor recognition probe includes a second recognition element specific to the second portion of the analyte and a second oligonucleotide coupled to the second recognition element and corresponding to the second portion. The transposase is used to generate a reporter polynucleotide including the first and second oligonucleotides. The analyte is detected based on the reporter including comprising the first and second oligonucleotides.

Abstract: Systems, methods, and apparatus are described herein for training machine learning models to predict probe intensity values using sample-specific image data and/or applying the predicted probe intensity values. As described herein, sample-specific image may include a signal associated with a sample for a process probe in a microarray relating to a single individual. The machine learning model may be trained, using the sample-specific image data, to predict a probe intensity value. The probe intensity value may be a raw probe intensity value or a normalized probe intensity value. After being trained, the machine learning model may receive as input a probe sequence or probe features. The machine learning model may be used to predict a total probe intensity value based on the probe sequence or the one or more probe features.