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Shifts in agricultural land use over the past 200 years have led to a loss of nearly 50% of existing wetlands in the USA, and agricultural activities contribute up to 65% of the nutrients that reach the Mississippi River Basin, directly contributing to biological disasters such as the hypoxic Gulf of Mexico “Dead” Zone. Federal efforts to construct and restore wetland habitats have been employed to mitigate the detrimental effects of eutrophication, with an emphasis on the restoration of ecosystem services such as nutrient cycling and retention. Soil microbial assemblages drive biogeochemical cycles and offer a unique and sensitive framework for the accurate evaluation, restoration, and management of ecosystem services. The purpose of this study was to elucidate patterns of soil bacteria within and among wetlands by developing diversity profiles from high-throughput sequencing data, link functional gene copy number of nitrogen cycling genes to measured nutrient flux rates collected from flow-through incubation cores, and predict nutrient flux using microbial assemblage composition. Soil microbial assemblages showed fine-scale turnover in soil cores collected across the topsoil horizon (0–5 cm; top vs bottom partitions) and were structured by restoration practices on the easements (tree planting, shallow water, remnant forest). Connections between soil assemblage composition, functional gene copy number, and nutrient flux rates show the potential for soil bacterial assemblages to be used as bioindicators for nutrient cycling on the landscape. In addition, the predictive accuracy of flux rates was improved when implementing deep learning models that paired connected samples across time. 

Abstract An internationally collaborative airborne campaign in July 2023 – led by the University of Bergen (Norway) and NASA, with contributions from many other institutions – discovered that thunderstorms near Florida and Central America produce gamma rays far more frequently than previously thought. The campaign was called Airborne Lightning Observatory for Fly’s Eye Geostationary Lightning Mapper (GLM) Simulator (FEGS) and Terrestrial Gamma-ray Flashes (TGFs), which shortens to ALOFT. The campaign employed a unique sampling strategy with NASA’s high-altitude ER-2 aircraft, equipped with gamma-ray and lightning sensors, flying near ground-based lightning sensors. Realtime updates from instruments, downlinked to mission scientists on the ground, enabled immediate return to thunderstorm cells found to be producing gamma rays. This maximized the observations of radiation created by strong electric fields in clouds, and showed how gamma-ray production may be physically linked to thunderstorm lifecycle. ALOFT also sampled storms entirely within the stereo-viewing region of the GLM instruments on GOES-16/18 and performed multiple underflights of the International Space Station Lightning Imaging Sensor (ISS LIS), while using an upgraded FEGS instrument that demonstrated the operational value of observing multiple wavelengths (including ultraviolet) with future spaceborne lightning mappers. In addition, a robust complement of airborne active and passive microwave sensors – including X- and W-band Doppler radars, as well as radiometers spanning 10-684 GHz – sampled some of the most intense convection ever overflown by the ER-2. These observations will benefit planned convection-focused NASA spaceborne missions. ALOFT is an exemplar of a high-risk, high-reward field campaign that achieved results far beyond original expectations. 

Abstract Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex human traits, but only a fraction of variants identified in discovery studies achieve significance in replication studies. Replication in genome-wide association studies has been well-studied in the context of Winner’s Curse, which is the inflation of effect size estimates for significant variants due to statistical chance. However, Winner’s Curse is often not sufficient to explain lack of replication. Another reason why studies fail to replicate is that there are fundamental differences between the discovery and replication studies. A confounding factor can create the appearance of a significant finding while actually being an artifact that will not replicate in future studies. We propose a statistical framework that utilizes genome-wide association studies and replication studies to jointly model Winner’s Curse and study-specific heterogeneity due to confounding factors. We apply this framework to 100 genome-wide association studies from the Human Genome-Wide Association Studies Catalog and observe that there is a large range in the level of estimated confounding. We demonstrate how this framework can be used to distinguish when studies fail to replicate due to statistical noise and when they fail due to confounding.