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Interpreting function and fitness effects in diverse plant genomes requires transferable models. Language models (LMs) pretrained on large-scale biological sequences can capture evolutionary conservation and offer cross-species prediction better than supervised models through fine-tuning limited labeled data. We introduce PlantCaduceus, a plant DNA LM that learns evolutionary conservation patterns in 16 angiosperm genomes by modeling both DNA strands simultaneously. When fine-tuned on a small set of labeledArabidopsisdata for tasks such as predicting translation initiation/termination sites and splice donor/acceptor sites, PlantCaduceus demonstrated remarkable transferability to maize, which diverged 160 Mya. The model outperformed the best existing DNA language model by 1.45-fold in maize splice donor prediction and 7.23-fold in maize translation initiation site prediction. In variant effect prediction, PlantCaduceus showed performance comparative to state-of-the-art protein LMs. Mutations predicted to be deleterious by PlantCaduceus showed threefold lower average minor allele frequencies compared to those identified by multiple sequence alignment-based methods. Additionally, PlantCaduceus successfully identifies well-known causal variants in bothArabidopsisand maize. Overall, PlantCaduceus is a versatile DNA LM that can accelerate plant genomics and crop breeding applications. 

Continuous greenhouse gas monitoring at sub-zero temperatures is needed for monitoring greenhouse gas emission in cold environments such as the Arctic tundra. This work reports a single-frequency electrochemical impedance sensing (SF-EIS) method for real-time continuous monitoring of carbon dioxide (CO2) at a wide range of temperatures (−15 to 40 °C) by using robust ionic liquid (IL) sensing materials and noninvasive, low-power, and low-cost impedance readout mechanisms since they cause minimal changes in the sensing interface, avoiding the baseline change for long-term continuous sensing. In addition, a miniaturized planar electrochemical sensor was fabricated that incorporates a hydrophobic 1-butyl-1-methylpyrrolidinium bis(trifluromethylsulfonyl)imide ([Bmpy][NTf2]) IL electrolyte and Pt black electrode materials. The high viscosity of the ILs facilitates the formation of thin, ordered, and concentrated layers of ionic charges, and the inverse relationship of IL viscosity with temperature makes them especially suited for impedance sensing at low temperatures. The unique low-temperature properties of ILs together with EIS transduction mechanisms are shown to be sensitive and selective for continuously monitoring CO2 at a −15 to 40 °C temperature range via impedance changes at a specifically selected frequency at the open circuit potential (OCP). Molecular dynamics simulations revealed insights into the structure and dynamics of the IL at varying temperatures in the presence of methane and CO2 and provided potential explanations for the observed sensing results. The miniaturized and flexible planar electrochemical sensor with the [Bmpy][NTf2] electrolyte was tested repeatedly at subzero temperatures over a 58-day period, during which good stability and repeatability were obtained. The CO2 impedance sensor was further tested for sensing CO2 from soil samples and shows promising results for their use in real-time monitoring of greenhouse gas emissions in cold temperatures such as permafrost soils.