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ABSTRACT Labile carbon (C) inputs in soils are expected to increase in the future due to global change drivers such as elevated atmospheric CO₂concentrations or warming and potential increases in plant primary productivity. However, the role of mycorrhizal association in modulating microbial activity and soil organic matter (SOM) biogeochemistry responses to increasing below‐ground C inputs remains unclear. We employed¹⁸O–H₂O quantitative stable isotope probing to investigate the effects of synthetic root exudate addition (0, 250, 500, and 1000 μg C g soil⁻¹) on bacterial growth traits and SOM biogeochemistry in rhizosphere soils of trees associated with arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) fungi. Soil respiration increased proportionally to the amount of exudate addition in both AM and ECM soils. However, microbial biomass C (MBC) responses differed, increasing in AM and decreasing in ECM soils. In AM soils, exudate addition increased taxon‐specific and community‐wide relative growth rates of bacteria, leading to enhanced biomass production. Conversely, in ECM soils, relative growth rates were less responsive to exudate addition, and estimates of MBC mortality increased with increasing exudate addition. In the AM soils, aggregated bacterial growth traits were predictive of soil respiration, but this relationship was not observed in ECM soils, perhaps due to substantial MBC mortality. These findings highlight the distinct responses of bacterial communities in AM and ECM rhizosphere soils to exudate addition. Considering that microbial products contribute to the formation of stable soil organic carbon (SOC) pools, future increases in labile exudate release in response to global change may consequently lead to greater SOC gains in AM soils compared to ECM soils. 

Abstract Cosmic rays and solar energetic particles pose significant risks to satellites, space stations, and human space exploration. They also produce atmospheric radiocarbon (¹⁴C), which enters the carbon cycle and is recorded by paleoenvironmental proxies. Miyake events, rapid increases in atmospheric¹⁴C, first identified in annual tree rings and later confirmed through ice core¹⁰Be and³⁶Cl isotopes, are thought to result from extreme solar activity, are seven events identified over the last 14,300 years. However, uncertainty in annual¹⁴C measurements limits precise inferences about their timing and magnitude. This study examines uncertainties in¹⁴C during two Miyake events (774 CE and 993 CE) across trees with differing uptake, storage, and allocation of carbon. We hypothesize that tree species physiology affects tree‐ring Δ¹⁴C, with deciduous species recording lagged, attenuated tree‐ring Δ¹⁴C relative to evergreen species. Using Δ¹⁴C data from pine and larch in Mongolia and a larger multi‐species Northern Hemisphere data set, we employed a Bayesian framework to estimate the timing, duration, and magnitude of these two events. Our AMS results showed no differences in Δ¹⁴C between evergreen and deciduous species growing at similar sites during the 774 CE event. The 993 CE event was variable, but parameter estimates were consistent between species. Northern Hemisphere comparisons indicated that annual series of Δ¹⁴C from evergreen and deciduous conifers yielded relatively more precise modeled estimates of start date and duration relative to deciduous broadleaf species. Future studies should consider the role of species‐specific carbon allocation strategies and storage dynamics in determining the radiocarbon response to Miyake events. 

Abstract Physics‐based models of the ionosphere‐thermosphere system have been touted as the next big thing in the context of drag modeling and space operations for decades. However, the computational complexity of such models have primarily kept them being used operationally. We recently demonstrated a proof‐of‐concept for developing what we call a reduced order probabilistic emulator (ROPE) for the thermosphere using the thermosphere ionosphere electrodynamics ‐ general circulation model (TIE‐GCM). The methodology uses a page out of dynamical systems theory to first reduce the order of the state using dimensionality reduction and then modeling the temporal dynamics in the reduced state space. The methodology uses an ensemble of temporal dynamic models to provide uncertainty estimates in the prediction. This work focuses on the dimensionality reduction step of the ROPE development process and addresses three limitations of the proof‐of‐concept: (a) extending the altitude upper boundary from 450 km to nearly 1000 km, (b) employing deep learning for nonlinear dimensionality reduction over principal component analysis (PCA) for improved performance during storm periods, and (c) maintaining the spatial resolution of the physical TIE‐GCM model, without down‐sampling, to preserve the spatial scales and variations. Results show overall performance boost over PCA for the high‐resolution and extrapolated data set as well as reduced reconstruction errors during storm‐time conditions. This work represents a major step toward operationalization. 

Abstract A stepwise copper‐catalyzed boracarboxylation then palladium‐catalyzed Suzuki‐Miyaura cross‐coupling methodology was developed to access 2,3‐diarylpropionic acid derivatives regioselectively by pre‐setting the position of the carboxylic acid in the boracarboxylation reaction. This method provides access to a wide range of aryl and heteroaryl products in up to 80% isolated yield. Pharmaceutical potential was demonstrated by synthesizing a glucagon receptor antagonist drug in three steps (31% overall yield) from commercially available 4‐tert‐butylstyrene. 

Abstract Soil is home to a multitude of microorganisms from all three domains of life. These organisms and their interactions are crucial in driving the cycling of soil carbon. One key indicator of this process is Microbial Carbon Use Efficiency (CUE), which shows how microbes influence soil carbon storage through their biomass production. Although CUE varies among different microorganisms, there have been few studies that directly examine how biotic factors influence CUE. One such factor could be body size, which can impact microbial growth rates and interactions in soil, thereby influencing CUE. Despite this, evidence demonstrating a direct causal connection between microbial biodiversity and CUE is still scarce. To address these knowledge gaps, we conducted an experiment where we manipulated microbial body size and biodiversity through size‐selective filtering. Our findings show that manipulating the structure of the microbial community can reduce CUE by approximately 65%. When we restricted the maximum body size of the microbial community, we observed a reduction in bacterial diversity and functional potential, which in turn lowered the community's CUE. Interestingly, when we included large body size micro‐eukarya in the soil, it shifted the soil carbon cycling, increasing CUE by approximately 50% and the soil carbon to nitrogen ratio by about 25%. Our metrics of microbial diversity and community structure were able to explain 36%–50% of the variation in CUE. This highlights the importance of microbial traits, community structure and trophic interactions in mediating soil carbon cycling.