Storm surges from tropical and extratropical storms frequently impact coastal communities globally. While the potential of natural and nature‐based features for coastal defenses has gained increased attention as a viable option for coastal flood protection, the lack of in situ measurements of storm surge attenuation has delayed their widespread utilization. We present the findings of a 3‐yr water level monitoring campaign that resulted in a large collection (52 flood events) of attenuation rates from marsh transects located in two natural preserves in the U.S. mid‐Atlantic region. Results show that the overall marsh attenuated water levels, exhibiting values up to 0.02 cm/m at Eastern Shore of Virginia National Wildlife Refuge (ES) and 0.03 cm/m at Magothy Bay Natural Preserve (MGB). In general, the greatest attenuation rates were observed at the marsh edge section. The reach close to the coastline revealed an amplification of the water level followed by water level attenuation toward the backside of the marsh. However, analyses of five major storms at ES demonstrated that, within each event, the ability of the upper marsh to attenuate water level decreased with higher inundation heights. Additionally, small spatial scales of the marsh platform, geomorphological features such as channels, elevated surrounding forests and levees seem to play a major role in reducing the attenuation rates provided by the marshes. These results indicate that, while this type of marshland would provide storm surge attenuation during low inundation heights, these ecosystems would be less effective attenuating higher water depths from extreme events.

Summary: Genome-scale metabolic network reconstructions (GENREs) are valuable for understanding cellular metabolism in silico. Several tools exist for automatic GENRE generation. However, these tools frequently (1) do not readily integrate with some of the widely-used suites of packaged methods available for network analysis, (2) lack effective network curation tools, and (3) are not sufficiently user-friendly. Here, we present Reconstructor, a user-friendly COBRApy compatible tool with ModelSEED namespace compatibility and a pFBA-based gap- filling technique. We demonstrate how Reconstructor readily generates high-quality GENRES that are useful for further biological discovery. Availability and Implementation: The Reconstructor package is freely available for download via pip in the command line (pip install reconstructor). Usage instructions and benchmarking data are available at http://github.com/emmamglass/reconstructor. Contact: Jason Papin: papin@virginia.edu

Nanoparticles (NP) are being increasingly explored as vehicles for targeted drug delivery because they can overcome free therapeutic limitations by drug encapsulation, thereby increasing solubility and transport across cell membranes. However, a translational gap exists from animal to human studies resulting in only several NP having FDA approval. Because of this, researchers have begun to turn toward physiologically based pharmacokinetic (PBPK) models to guide in vivo NP experimentation. However, typical PBPK models use an empirically derived framework that cannot be universally applied to varying NP constructs and experimental settings. The purpose of this study was to develop a physics-based multiscale PBPK compartmental model for determining continuous NP biodistribution. We successfully developed two versions of a physics-based compartmental model, models A and B, and validated the models with experimental data. The more physiologically relevant model (model B) had an output that more closely resembled experimental data as determined by normalized root mean squared deviation (NRMSD) analysis. A branched model was developed to enable the model to account for varying NP sizes. With the help of the branched model, we were able to show that branching in vasculature causes enhanced uptake of NP in the organ tissue. The models were solved using two of the most popular computational platforms, MATLAB and Julia. Our experimentation with the two suggests the highly optimized ODE solver package DifferentialEquations.jl in Julia outperforms MATLAB when solving a stiff system of ordinary differential equations (ODEs). We experimented with solving our PBPK model with a neural network using Julia's Flux.jl package. We were able to demonstrate that a neural network can learn to solve a system of ODEs when the system can be made non-stiff via quasi-steady-state approximation (QSSA). Our model incorporates modules that account for varying NP surface chemistries, multiscale vascular hydrodynamic effects, and effects of the immune system to create a more comprehensive and modular model for predicting NP biodistribution in a variety of NP constructs.