Abstract: A server is programmed to predict high burden behavioral health insurance plan participants. The server receives health data from several data sources. The data is associated with the insurance plan participants. The server develops data variables and risk metrics relating to behavioral health of the participants. Sample data is collected from the health data. The sample data includes a first sample data set including a plurality of first values relating to the plurality of data variables, and a second sample data set including a plurality of second values relating to the plurality of risk metrics. The server analyzes the first and second sample data sets to determine one or more correlations between the first values and the second values. The server configures a software model to implement the correlations to predict which of the participants are at risk of becoming burden behavioral health insurance plan participants.

Abstract: The invention provides methods of identifying compounds that inhibit specific tRNA:34A deaminases encoded by yfhC genes, compounds that inhibit such deaminases and methods of using the deaminases in a variety of in vitro and in vivo contexts, such as in the treatment and prevention of bacterial infections.