Abstract: The present disclosure generally relates to compositions, methods, sorters, systems, devices and uses for screening for bioactive substances in emulsion droplets. In some embodiments, the compositions are a continuous phase formulation for stable emulsions, In some embodiments, the methods are for preparing a monodisperse polyethylene glycol acrylamide (PEGA) co-polymer resin, or for preparing a core-shell bead. In some embodiments, the systems and devices include a sorter, comprising an inlet channel, first and second outlet channels meeting the inlet channel at a junction, and first and second electrodes proximate respective first and second sides of the junction sorter.

Abstract: A sensor assembly includes a multilayer circuit, a first magnetic field sensor, and a second magnetic field sensor. The multilayer circuit extends between a proximal end and a distal end along a longitudinal axis. The multilayer circuit includes a plurality of electrical pads positioned at the proximal end. The first magnetic field sensor is coupled to the multilayer circuit and has a primary sensing direction aligned with the longitudinal axis. The second magnetic field sensor is coupled to the multilayer circuit and oriented with respect to the first magnetic field sensor such that the second magnetic field sensor has a primary sensing direction aligned with an axis orthogonal to the longitudinal axis.

Abstract: The present disclosure is directed to a frame assembly. The frame assembly includes, for example, a bottom support structure, a handle housing coupled to the bottom support structure, a holder coupled to the handle housing, and a diagonal support structure diagonally positioned and coupled to the handle housing via the holder. The handle housing, the holder, and the diagonal support structure together form an integrated structure.

Abstract: The invention provides systems and method for generating device-specific artificial neural network (ANN) models for distribution across user devices. Sample datasets are collected from devices in a particular environment or use case and include predictions by device-specific ANN models executing the user devices. The received datasets are used with existing datasets and stored ANN models to generate updated device-specific ANN models from each of the stored instances of the device ANN models based on the training data.

Abstract: The invention provides systems and method for generating device-specific artificial neural network (ANN) models for distribution across user devices. Sample datasets are collected from devices in a particular environment or use case and include predictions by device-specific ANN models executing the user devices. The received datasets are used with existing datasets and stored ANN models to generate updated device-specific ANN models from each of the stored instances of the device ANN models based on the training data.

Abstract: The invention provides systems and method for generating device-specific artificial neural network (ANN) models for distribution across user devices. Sample datasets are collected from devices in a particular environment or use case and include predictions by device-specific ANN models executing the user devices. The received datasets are used with existing datasets and stored ANN models to generate updated device-specific ANN models from each of the stored instances of the device ANN models based on the training data.

Abstract: The invention provides systems and method for generating device-specific artificial neural network (ANN) models for distribution across user devices. Sample datasets are collected from devices in a particular environment or use case and include predictions by device-specific ANN models executing the user devices. The received datasets are used with existing datasets and stored ANN models to generate updated device-specific ANN models from each of the stored instances of the device ANN models based on the training data.

Abstract: The invention provides systems and method for generating device-specific artificial neural network (ANN) models for distribution across user devices. Sample datasets are collected from devices in a particular environment or use case and include predictions by device-specific ANN models executing the user devices. The received datasets are used with existing datasets and stored ANN models to generate updated device-specific ANN models from each of the stored instances of the device ANN models based on the training data.

Abstract: An apparatus for coring out or dissecting an anal sphincter and mobilizing a rectum of an animal includes a probe attached to a first side of a support member and at least one knife blade attached to the support member a fixed distance from the probe. The distance from the surface of the probe to the knife blade is greater than a thickness of a rectal wall.