Abstract: An assay device includes a colorimetric testing assembly including a detection area, a fluid inlet, and a microfluidic network including a first path extending to the detection area and a second path extending to the detection area. When a fluid (e.g., a buffer fluid or a combined buffer and sample solution) is provided to the fluid inlet, a first portion of the fluid rehydrates a first dried reagent (e.g., a dried enzyme label) disposed along the first path to produce a first rehydrated reagent and a second portion of the fluid rehydrates a second dried reagent (e.g., a dried substrate) to produce a second rehydrated reagent. The first rehydrated reagent and the second rehydrated reagent are then sequentially delivered to the detection area by capillary-driven flow to perform the assay.

Abstract: A treatment instrument can include a fluid platform comprising a chamber and an access port to provide fluid communication between a treatment region of the tooth and the chamber, the chamber having a central axis. The treatment instrument can include a first fluid supply port disposed to direct a fluid stream of a first material into the chamber along a stream axis non-parallel to the central axis. The treatment instrument can include a second supply port distal the first fluid supply port, the second supply port disposed to direct a second material to be entrained with the fluid stream into the chamber.

Abstract: A method of creating a decision engine including a Bayesian network. The method includes retrieving data from a client database and forming a focus database; applying a set of initial rules to the focus database to form at least two nodes; applying a first learning process to determine a set of arcs to be applied between the at least two nodes; applying a second learning process to determine a set of states to be applied within each node; applying a third learning process to determine a set of probabilities applicable to the states learned in the second learning process; and applying a fourth learning process to update a structure of the at least two nodes, the set of arcs, the set of states within each node, and the set of probabilities for the states.

Abstract: A method of creating a decision engine including a Bayesian network. The method includes retrieving data from a client database and forming a focus database; applying a set of initial rules to the focus database to form at least two nodes; applying a first learning process to determine a set of arcs to be applied between the at least two nodes; applying a second learning process to determine a set of states to be applied within each node; applying a third learning process to determine a set of probabilities applicable to the states learned in the second learning process; and applying a fourth learning process to update a structure of the at least two nodes, the set of arcs, the set of states within each node, and the set of probabilities for the states.

Abstract: A method of creating a decision engine including a Bayesian network. The method includes retrieving data from a client database and forming a focus database; applying a set of initial rules to the focus database to form at least two nodes; applying a first learning process to determine a set of arcs to be applied between the at least two nodes; applying a second learning process to determine a set of states to be applied within each node; applying a third learning process to determine a set of probabilities applicable to the states learned in the second learning process; and applying a fourth learning process to update a structure of the at least two nodes, the set of arcs, the set of states within each node, and the set of probabilities for the states.