**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** EVERY TIME THAT WE USE ANTIBIOTICS IN ANIMALS OR PEOPLE, WE MAY CONTRIBUTE TO THE RISE OF ANTIMICROBIAL-RESISTANT BACTERIA. THESE BACTERIA CAN SURVIVE ANTIBIOTIC TREATMENT, THREATENING HUMAN AND ANIMAL HEALTH. NATIONAL SURVEILLANCE SYSTEMS ARE ESSENTIAL TOOLS FOR DETECTING AND RESPONDING TO RESISTANT BACTERIA. SURVEILLANCE CAN ALSO SHOW US WHEN ANTIBIOTIC USE POLICIES ARE SUCCESSFUL IN REDUCING RESISTANCE. HOWEVER, VARIATION IN LABORATORY PROCESSES ACROSS TIME AND INSTITUTIONS MAKES IT DIFFICULT TO ASSESS TRENDS IN RESISTANCE OVER MANY YEARS. IN ADDITION, WE HAVE LIMITED TOOLS FOR ANALYZING MULTIDRUG RESISTANCE.THIS SEED GRANT SUPPORTS THE DEVELOPMENT OF NEW ANALYTIC TOOLS FOR ANTIMICROBIAL RESISTANCE SURVEILLANCE. WE WILL EXAMINE ESCHERICHIA COLI IN CATTLE AND THE RESTRICTIONS ON CEPHALOSPORIN USE. RESISTANT E. COLI CAN BE TRANSMITTED FROM CATTLE TO HUMANS THROUGH BEEF PRODUCTS, DIRECT CONTACT, OR THE ENVIRONMENT. FIRST, WE WILL FILL IN GAPS THAT ARISE FROM VARIED LABORATORY PROCESSES. RANDOM FORESTS, A MACHINE LEARNING METHOD, WILL PREDICT MISSING RESISTANCE DATA (I.E., MINIMUM INHIBITORY CONCENTRATIONS, MIC). THEN, STATISTICAL MODELS CAN IDENTIFY PREVIOUSLY HIDDEN TRENDS IN RESISTANCE DATA OVER TIME. WE WILL ALSO INVESTIGATE THE IMPACT OF UNCERTAINTY IN RESISTANCE TESTING ON OUR CONCLUSIONS. ASSOCIATION MINING, ANOTHER MACHINE LEARNING TOOL, WILL ANALYZE MICS AND GENOMIC SEQUENCING DATA TO REVEAL CHANGES IN MULTIDRUG RESISTANCE. OVERALL, OUR RESEARCH WILL IMPROVE ANTIMICROBIAL RESISTANCE SURVEILLANCE AND HELP IDENTIFY SUCCESSFUL RESISTANCE MITIGATION STRATEGIES.