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Large-scale hydrologic models are increasingly being developed for operational use in the forecasting and planning of water resources. However, the predictive strength of such models depends on how well they resolve various functions of catchment hydrology, which are influenced by gradients in climate, topography, soils, and land use. Most assessments of hydrologic model uncertainty have been limited to traditional statistical methods. Here, we present a proof-of-concept approach that uses interpretable machine learning techniques to provide post hoc assessment of model sensitivity and process deficiency in hydrologic models. We train a random forest model to predict the Kling–Gupta efficiency (KGE) of National Water Model (NWM) and National Hydrologic Model (NHM) streamflow predictions for 4383 stream gauges in the conterminous United States. Thereafter, we explain the local and global controls that 48 catchment attributes exert on KGE prediction using interpretable Shapley values. Overall, we find that soil water content is the most impactful feature controlling successful model performance, suggesting that soil water storage is difficult for hydrologic models to resolve, particularly for arid locations. We identify nonlinear thresholds beyond which predictive performance decreases for NWM and NHM. For example, soil water content less than 210 mm, precipitation less than 900 mm yr−1, road density greater than 5 km km−2, and lake area percent greater than 10 % contributed to lower KGE values. These results suggest that improvements in how these influential processes are represented could result in the largest increases in NWM and NHM predictive performance. This study demonstrates the utility of interrogating process-based models using data-driven techniques, which has broad applicability and potential for improving the next generation of large-scale hydrologic models. 

Abstract Seismic and magnetotelluric studies suggest hydrous silicate melts atop the 410 km discontinuity form 30–100 km thick layers. Importantly, in some regions, two layers are observed. These stagnant layers are related to their comparable density to the surrounding mantle, but their formation mechanisms and detailed structures remain unclear. Here we report a large decrease of silicate melt viscosity at ~14 GPa, from 96(5) to 11.7(6) mPa⋅s, as water content increases from 15.5 to 31.8 mol% H₂O. Such low viscosities facilitate rapid segregation of melt, which would typically prevent thick layer accumulation. Our 1D finite element simulations show that continuous dehydration melting of upwelling mantle material produces a primary melt layer above 410 km and a secondary layer at the depth of equal mantle-melt densities. These layers can merge into a single thick layer under low density contrasts or high upwelling rates, explaining both melt doublets and thick single layers. 

Social isolation hampers immune system function, and the biological mechanisms driving this effect remain understudied. We hypothesized that oxytocin (OT), a key neuropeptide involved in social cognition, is a critical mediator of social context on immune function. In the California mouse (Peromyscus californicus), we examined how female and male immune function is influenced by (1) social isolation from same-sex peers, (2) social peer affiliation, and (3) exogenous OT. We evaluated immune function through wound size progression following a skin biopsy and proinflammatory cytokines in the wound fluid. Unexpectedly, social isolation alone did not influence wound healing, but isolation +OT increased wound size in a dose dependent manner. Wound size progression interacted with sex and OT in socially-housed mice, suggesting that OT increases inflammation in females, while decreasing inflammation in males in a social context-dependent manner. Inflammatory biomarker interleukin-6 (IL-6) mRNA expression correlated with wound size overall, supporting wound healing as an index of inflammatory response. However, isolation +OT mice did not have higher levels of IL-6, suggesting that the mechanism through which isolation +OT influences wound size is not through IL-6 activity. Behaviorally, higher levels of affiliation were negatively associated with wound size, and this effect was diminished by OT treatment. Our results highlight that the anti-inflammatory effects of OT are likely highly dependent on social context. 

Small molecule solutions to many contemporary societal challenges await discovery, but the artisanal and manual process via which this class of chemical matter is typically accessed limits the discovery of new functions. Automated assembly of (N‐methyl iminodiacetic acid) MIDA or (tetramethyl N‐methyl iminodiacetic acid) TIDA boronate building blocks via iterative C─C bond formation, an approach we call “block chemistry”, alternatively enables generalized and automated preparation of many different types of small molecules in a modular fashion. But in its current form, this engine cannot also leverage nitrogen atoms as iteration handles. Here, we disclose a new iteration‐enabling group, CbzT (p‐TIDA boronate‐substituted carboxybenzyl), that reversibly attenuates the reactivity of nitrogen atoms and enables generalized catch‐and‐release purification. CbzT is leveraged to achieve the automated modular synthesis of Imatinib (Gleevec), an archetypical clinically approved kinase inhibitor, in which building blocks are iteratively linked by both N─C and C─C bonds. This work substantially expands the types of small molecules that can be iteratively assembled in an automated modular fashion. It also advances the concept of intentionally developing chemistry that machines can do. 

Metal and nutrient loads were calculated from 2019-2024 from the inflow stream to Falling Creek Reservoir (FCR), a drinking water reservoir located in Vinton, Virginia, USA. The reservoir is owned and operated by the Western Virginia Water Authority and is managed as a secondary drinking-water source for the city of Roanoke, VA. Only Fe, Mn, and nutrients (TN and TP) were analyzed and calculated in 2019. The full suite of metals (Li, Na, Mg, Al, K, Ca, Fe, Mn, Cu, Sr, Ba) and nutrients were analyzed from 2020-2024. The loads that were collected using an ISCO automated sampler located at the main inflow tributary to FCR. Sampling frequency was approximately fortnightly from spring to fall (March - November). Load calculations were performed using the calculated cumulative flow over the sampling period from the ISCO and the analyzed total metal and nutrient concentrations. Please note we are publishing this data package before the nutrient samples have been analyzed, but will be included in later versions. 

Mercury (Hg) is a global pollutant with substantial human health impacts. While most studies focus on atmospheric total Hg (THg) deposition, contributions of methylated Hg (MeHg), including monomethylmercury (MMHg) and dimethylmercury (DMHg), remain poorly understood. To examine this, we use rain and aerosol Hg speciation data and high-resolution surface DMHg measurements, collected on a transect from Alaskan coastal waters to the Bering and Chukchi Seas. We observed a significant fivefold increase in the MeHg:THg fraction in rain and a 10-fold increase for aerosols, closely linked to elevated surface DMHg and the highest DMHg evasion (~9.4 picomoles per square meter per hour) found in upwelling waters near the Aleutian Islands. These data highlight a previously underexplored aspect of MeHg air-sea exchange and its importance to Hg cycling and human health concerns. Our findings emphasize the importance of DMHg evasion by demonstrating that atmospheric MeHg can be transported long distances (~1700 kilometers) in the Arctic, posing risks to human health and ecosystems. 

Whiteface Mountain (WFM) in northern NY State is the site of a historic mountaintop atmospheric observatory with an ongoing cloud water chemistry monitoring program that has been operating every summer (June through September) since 1994. Though long-term chemical analysis has been conducted, no analysis on the microbiome has been completed at WFM. Over the years, a new chemical regime has been reported in the cloudwater with missing analytes. Knowing how microbes can interact with chemicals, we hypothesize microbes are partially responsible for this shift and are crucial in understanding the chemical background of clouds. To start this study, cloudwater filters have been analyzed both chemically and microbially. Chemically, weighted averages have been calculated for each cloudwater filter based on the chemical composition of the clouds. Microbially, we have begun DNA extractions and subsequent metagenomic analysis using the Oxford Nanopore MinION using a select number of cloud water filters from 2024. Overall, this study aims to build upon microbial work accomplished by the Puy de Dôme groups and discuss the collection, storage, and analysis of cloudwater filters to connect the chemical to the microbial at WFM.