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ABSTRACT EnteroaggregativeEscherichia coli(EAEC) is a common cause of diarrhea worldwide and is associated with growth faltering in developing countries. EAEC are defined by a characteristic adherence pattern mediated by the aggregative adherence fimbriae (AAFs). Despite the critical role of AAF in the definition of the EAEC pathotype, it is not known what host molecules mediate adherence and EAEC pathogenesis during infection of the human gastrointestinal tract. Multiple receptor candidates have been proposed based onin vitroexperimentation. We propose that AAFs interact with multiple receptors during colonization of the human gastrointestinal mucosa, and we hypothesize that structural features of the AafA protein (the major subunit of AAF variant II produced by EAEC strain 042) promote these diverse interactions. In this study, we utilize a panel of AafA variants encoding single amino acid substitutions to understand the role of individual residues in biofilm formation as well as adherence to mucin, fibronectin, and human intestinal cells. We identify both charged and uncharged residues that participate in these interactions, and these residues cluster in two regions of the protein that may define a binding pocket at the junction of polymerized subunits. Although both bovine submaxillary mucin and human fibronectin are sialylated molecules, adherence to mucin is diminished by the removal of sialic acid residues while adherence to fibronectin is not, suggesting that the mechanisms of adherence to these molecules are distinct. Overall, our data provide insight into the structural features that determine AAF/II binding to mucin, sialic acid, and human intestinal cells. 

Evaluating whether hydrological models are right for the right reasons demands reproducible model benchmarking and diagnostics that evaluate not just statistical predictive model performance but also internal processes. Such model benchmarking and diagnostic efforts will benefit from standardized methods and ready-to-use toolkits. Using the Jupyter platform, this work presents HydroBench, a model-agnostic benchmarking tool consisting of three sets of metrics: 1) common statistical predictive measures, 2) hydrological signature-based process metrics, including a new time-linked flow duration curve and 3) information-theoretic diagnostics that measure the flow of information among model variables. As a test case, HydroBench was applied to compare two model products (calibrated and uncalibrated) of the National Hydrologic Model - Precipitation Runoff Modeling System (NHM-PRMS) at the Cedar River watershed, WA, United States. Although the uncalibrated model has the highest predictive performance, particularly for high flows, the signature-based diagnostics showed that the model overestimates low flows and poorly represents the recession processes. Elucidating why low flows may have been overestimated, the information-theoretic diagnostics indicated a higher flow of information from precipitation to snowmelt to streamflow in the uncalibrated model compared to the calibrated model, where information flowed more directly from precipitation to streamflow. This test case demonstrated the capability of HydroBench in process diagnostics and model predictive and functional performance evaluations, along with their tradeoffs. Having such a model benchmarking tool not only provides modelers with a comprehensive model evaluation system but also provides an open-source tool that can further be developed by the hydrological community. 

The growth in scientific data volumes has resulted in a need to scale up processing and analysis pipelines using High Performance Computing (HPC) systems. These workflows need interactive, reproducible analytics at scale. The Jupyter platform provides core capabilities for interactivity but was not designed for HPC systems. In this paper, we outline our efforts that bring together core technologies based on the Jupyter Platform to create interactive, reproducible analytics at scale on HPC systems. Our work is grounded in a real world science use case - applying geophysical simulations and inversions for imaging the subsurface. Our core platform addresses three key areas of the scientific analysis workflow - reproducibility, scalability, and interactivity. We describe our implemention of a system, using Binder, Science Capsule, and Dask software. We demonstrate the use of this software to run our use case and interactively visualize real-time streams of HDF5 data.