Abstract: A modular gas delivery system for delivering gas to a plurality of non-human animals simultaneously is provided, the system including: an upstream module including a gas inlet; a downstream module including a gas outlet; and an intermediate module assembly including a first intermediate module including a first gas delivery outlet, and a second intermediate module including a second gas delivery outlet, in which the intermediate module assembly is engageable with and disengageable from the upstream module and the downstream module, in which the first intermediate module is engageable with and disengageable from the upstream module, in which the second intermediate module is engageable with and disengageable from the upstream module, in which the upstream module, the downstream module, and the intermediate module assembly are configured to be assembled into a first system configuration and a second system configuration, and in which the second system configuration is a parallel system configuration.

Abstract: Disclosed herein are methods and systems for implementing a multi-model computer architecture for entity identification. A method includes receiving data regarding a plurality of entities. The method includes generating a plurality of entity profiles for the plurality entities and a network graph data structure (e.g., a node graph) comprising edges between nodes for the plurality of entity profiles. The method includes executing a model using identifiers of the plurality of entity profiles, an event topic, and the edges between the nodes as input to generate one or more composite scores for the plurality of entity profiles. The method includes selecting one or more entities for the event based on the generated one or more composite scores. The method includes generating a record comprising associations between identifications of the entities and the event.

Abstract: Disclosed herein are methods and systems for implementing a machine learning architecture for detecting early adopters. A method includes receiving clinical data; training, using a supervised or unsupervised learning technique, a machine learning model (e.g., a neural network, a support vector machine, or a random forest) to generate a score identifying a likelihood to prescribe a medical product; generating, by the machine learning model, a score indicating a likelihood of a first medical personnel to prescribe a type of medical product within a defined time period; and causing a display at a client device based on the score.