Magnetically Directed Two-Dimensional Crystallization of OmpF Membrane Proteins in Block Copolymers

Two-dimensional (2D) alignment and crystallization of membrane proteins (MPs) is increasingly important in characterizing their three-dimensional (3D) structure, in designing pharmacological agents, and in leveraging MPs for biomimetic devices. Large, highly ordered MP 2D crystals in block copolymer (BCP) matrices are challenging to fabricate, but a facile and scalable technique for aligning and crystallizing MPs in thin-film geometries would rapidly translate into applications. This work introduces a novel method to grow larger and potentially better ordered 2D crystals by performing the crystallization process in the presence of a strong magnetic field. We demonstrate the efficacy of this approach using a \u3b2-barrel MP, outer membrane protein F (OmpF), in short-chain polybutadiene-poly(ethylene oxide) (PB-PEO) membranes. Crystals grown in a magnetic field were up to 5 times larger than conventionally grown crystals, and a signal-to-noise (SNR) analysis of diffraction peaks in Fourier transforms of specimens imaged by negative-stain electron microscopy (EM) and cryo-EM showed twice as many high-SNR diffraction peaks, indicating that the magnetic field also improves crystal order

Effects of meteorological and land surface modeling uncertainty on errors in winegrape ET calculated with SIMS

Characterization of model errors is important when applying satellite-driven evapotranspiration (ET) models to water resource management problems. This study examines how uncertainty in meteorological forcing data and land surface modeling propagate through to errors in final ET data calculated using the Satellite Irrigation Management Support (SIMS) model, a computationally efficient ET model driven with satellite surface reflectance values. The model is applied to three instrumented winegrape vineyards over the 2017-2020 time period and the spatial and temporal variation in errors are analyzed. We illustrate how meteorological data inputs can introduce biases that vary in space and at seasonal timescales, but that can persist from year to year. We also observe that errors in SIMS estimates of land surface conductance can have a particularly strong dependence on time of year. Overall, meteorological inputs introduced RMSE of 0.33-0.65 mm/day (7-27%) across sites, while SIMS introduced RMSE of 0.55-0.83 mm/day (19-24%). The relative error contribution from meteorological inputs versus SIMS varied across sites; errors from SIMS were larger at one site, errors from meteorological inputs were larger at a second site, and the error contributions were of equal magnitude at the third site. The similar magnitude of error contributions is significant given that many satellite-driven ET models differ in their approaches to estimating land surface conductance, but often rely on similar or identical meteorological forcing data. The finding is particularly notable given that SIMS makes assumptions about the land surface (no soil evaporation or plant water stress) that do not always hold in practice. The results of this study show that improving SIMS by eliminating these assumptions would result in meteorological inputs dominating the error budget of the model on the whole. This finding underscores the need for further work on characterizing spatial uncertainty in the meteorological forcing of ET

Desalination for a circular water economy

Advancing a circular water economy through autonomous, precise, resilient, intensified, modular, and electrified technologies enabling distributed desalination and fit-for-purpose reuse

High-Resolution Carbon Accounting Framework for Urban Water Supply Systems

Decarbonization of urban infrastructure systems is imperative to meeting global climate goals. Urban water supply systems (UWSSs) account for 1–3% of urban electricity consumption in the U.S., a value expected to increase, as municipalities tap nontraditional water supplies that are either more distant or require more energy-intensive treatment. Reducing the carbon intensity of UWSSs will require a combination of infrastructure upgrades, operational modifications, and behavioral interventions, but urban water planners, water treatment system operators, and consumers lack transparent tools for quantifying the carbon emission implications of these decisions. We propose a high-resolution carbon accounting framework that allows for attribution of carbon emissions to individual water sources, water system components, or individual consumers in a UWSS. The high temporal resolution of this framework also enables rapid assessment of the potential for operational and behavioral interventions to reduce the carbon intensity of UWSSs. We demonstrate this carbon accounting framework on a real-world UWSS serving a city of roughly 100 000 residents. The high spatial and temporal resolution, coupled with the scalability of this approach, makes it a valuable tool for consulting engineers, operators, and consumers seeking to deliver Net Zero water supplies

Multi-scale planning model for robust urban drought response

Increasingly severe droughts are straining municipal water resources and jeopardizing urban water security, but uncertainty in their duration, frequency, and intensity challenges drought planning and response. We develop the Drought Resilient Interscale Portfolio Planning model (DRIPP) to generate optimal planning responses to urban drought. DRIPP is a generalizable multi-scale framework for optimizing dynamic planning strategies of long-term infrastructure deployment and short-term drought response. It integrates climate and hydrological variability with high-fidelity representations of urban water distribution, available technology options, and demand reduction measures to yield robust and cost-effective water supply portfolios that are location-specific. We apply DRIPP in Santa Barbara, California to assess how least cost water supply portfolios vary under different drought scenarios and identify portfolios that are robust across drought scenarios. In Santa Barbara, we find that drought intensity, not duration or frequency, drives cost increases, reliability risk, and regret of overbuilding infrastructure. Under uncertain drought conditions, a diversified technology portfolio that includes both rapidly deployable, decentralized technologies alongside larger centralized technologies minimizes water supply cost while maintaining high robustness to climate uncertainty

Quantifying uncertainty in groundwater depth from sparse well data in the California Central Valley

Groundwater is a critical freshwater resource for irrigation in the California Central Valley, particularly in times of drought. Groundwater depth has dropped rapidly in California’s overdrafted basins, but irregular monitoring across space and time limits the accuracy of the groundwater depth projections in the Groundwater Sustainability Plans required by the California Sustainable Groundwater Management Act (SGMA). This work constructs a Bayesian hierarchical model for predicting groundwater depth from sparse monitoring data in three Central Valley counties. We apply this model to generate 300 m resolution monthly groundwater depth estimates for drought years 2013–2015, and compare our smoothed groundwater depth map to smoothed rasterized maps published by the CA Department of Water Resources. Finally, we quantify uncertainty in groundwater depth predictions that are made by imputing missing well data and interpolating predictions across the study domain, which is helpful in directing future sampling efforts towards areas with high uncertainty. The BHM model accurately captures the spatiotemporal pattern in groundwater depth, as evidenced by 94.54% of withheld test samples’ true depth being covered by the 95% prediction interval drawn from the BHM posterior distribution. The model converged despite a very sparse dataset, demonstrating broad applicability for evaluating changes in regional groundwater depth as required by SGMA. Depth prediction intervals can also help prioritize future groundwater depth sampling activity and increase the utility of groundwater depth maps in total storage predictions by enabling sensitivity analysis