Abstract: Generating a predictive mapping of an educational journey of a student. The method includes receiving, over a computer network, from a database of student records, unstructured data about a particular student of the educational institution. The unstructured data is normalized to classify the unstructured data consistent with a machine learning classification model to classify the unstructured data into a plurality of classifications. Based on the classifications of the unstructured data, A plurality of friction points that hinder a particular student's progress in the educational journey and a plurality of achievement points that promote the particular student's progress in the educational journey are identified. Using the friction points and achievement points, prediction information is generated of the particular student's progress in the educational journey. The prediction information is consistent with a machine learning prediction model.

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.

Abstract: The present invention provides methods of putatively identifying, based on presence of rare codon usage, cellular components involved in virulence. Also included are methods of verifying putative virulence genes and methods of attenuating such virulence, e.g., through identification and modification of genes/gene products that modulate translation of gene subsets involved in pathogen virulence. The methods include examining the codon usage and frequency employed in the organism, and identifying and structurally characterizing, e.g., tRNA molecules associated with over-represented or under-represented codons. By targeting the cell's ability to decode specific sets of genes, the virulence of a pathogen can be modulated.