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Background: The CDC considers methicillin-resistant Staphylococcus aureus (MRSA) one of the most important hospital-acquired infections (HAIs) in the United States. However, infection control departments (ICDs) often rely on subjective data to determine whether multiple MRSA cases are a true outbreak and whether the hospital is responsible (community- vs hospital-acquired). Objective: Our objective was to determine whether whole-genome sequencing (WGS) of MRSA provided new insights into on transmission dynamics at large, inner-city hospital in Jacksonville, Florida. Methods: Over a 4-month period, MRSA samples were obtained from 44 infected patients at 3 campuses within a single hospital system. Limited nonpatient identifying information was obtained, including date of collection, campus, unit, reason for admission, and days post admission (DPA) of MRSA diagnosis. Whole-genome sequences were generated using the Illumina platform. Raw reads were processed, and genetic distances were calculated and used to identify genetically linked bacterial infections using FoxSeq version 1.0 software. Results: Based on their length of stay, 10 patients were reported by the ICD as obtaining an HAI. Three distinct “episodes” were evident in which >5 MRSA cases were observed within a 3–5-day period. Genomic analysis identified 5 clusters of linked infections: 4 clusters contained 2 patients and another contained 3. Of these clusters, only 1 contained multiple cases that were reported as HAIs; however, because these case were separated by 2 weeks, it is unlikely that they came from a source in the hospital. The results suggest that HAIs were overreported and that most MRSA present in the hospital likely came from community sources. Conclusions: WGS provided clear evidence that temporally clustered MRSA cases do not imply an outbreak is occurring. Furthermore, ongoing detection of the same community-acquired infections over several months is indicative of a shared source outside of the hospital, which could be uncovered through examination of clinical records. Considering the implications of HAIs, best approaches to combat them should include identifying their sources. As molecular surveillance approaches to infection control are rapidly becoming easier and less expensive, the methods can be used to bring objective clarity to the ICD. Funding: None Disclosures: Susanna L. Lamers reports salary from BioInfoExperts and contract research for the NIH, the University of California - San Francisco, and UMASS Medical School. 

Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive understanding of droplet generation makes engineering a droplet-based platform an iterative and resource-intensive process. We present a web-based tool, DAFD, that predicts the performance and enables design automation of flow-focusing droplet generators. We capitalize on machine learning algorithms to predict the droplet diameter and rate with a mean absolute error of less than 10μm and 20 Hz. This tool delivers a user-specified performance within 4.2% and 11.5% of the desired diameter and rate. We demonstrate that DAFD can be extended by the community to support additional fluid combinations, without requiring extensive machine learning knowledge or large-scale data-sets. This tool will reduce the need for microfluidic expertise and design iterations and facilitate adoption of microfluidics in life sciences.