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Abstract Uncertainties in ocean-mixing parameterizations are primary sources for ocean and climate modeling biases. Due to lack of process understanding, traditional physics-driven parameterizations perform unsatisfactorily in the tropics. Recent advances in the deep-learning method and the new availability of long-term turbulence measurements provide an opportunity to explore data-driven approaches to parameterizing oceanic vertical-mixing processes. Here, we describe a novel parameterization based on an artificial neural network trained using a decadal-long time record of hydrographic and turbulence observations in the tropical Pacific. This data-driven parameterization achieves higher accuracy than current parameterizations, demonstrating good generalization ability under physical constraints. When integrated into an ocean model, our parameterization facilitates improved simulations in both ocean-only and coupled modeling. As a novel application of machine learning to the geophysical fluid, these results show the feasibility of using limited observations and well-understood physical constraints to construct a physics-informed deep-learning parameterization for improved climate simulations.

Abstract Studies of molecular mixtures containing hydrogen sulfide (H 2 S) could open up new routes towards hydrogen-rich high-temperature superconductors under pressure. H 2 S and ammonia (NH 3 ) form hydrogen-bonded molecular mixtures at ambient conditions, but their phase behavior and propensity towards mixing under pressure is not well understood. Here, we show stable phases in the H 2 S–NH 3 system under extreme pressure conditions to 4 Mbar from first-principles crystal structure prediction methods. We identify four stable compositions, two of which, (H 2 S) (NH 3 ) and (H 2 S) (NH 3 ) 4 , are stable in a sequence of structures to the Mbar regime. A re-entrant stabilization of (H 2 S) (NH 3 ) 4 above 300 GPa is driven by a marked reversal of sulfur-hydrogen chemistry. Several stable phases exhibit metallic character. Electron–phonon coupling calculations predict superconducting temperatures up to 50 K, in the Cmma phase of (H 2 S) (NH 3 ) at 150 GPa. The present findings shed light on how sulfur hydride bonding and superconductivity are affected in molecular mixtures. They also suggest a reservoir for hydrogen sulfide in the upper mantle regions of icy planets in a potentially metallic mixture, which couldmore »have implications for their magnetic field formation.« less

Toll/Toll-like receptors (TLRs) are key regulators of the innate immune system in both invertebrates and vertebrates. However, while mammalian TLRs directly recognize pathogen-associated molecular patterns, the insect Toll pathway is thought to be primarily activated by binding Spätzle cytokines that are processed from inactive precursors in response to microbial infection. Phylogenetic and structural data generated in this study supported earlier results showing that Toll9 members differ from other insect Tolls by clustering with the mammalian TLR4 group, which recognizes lipopolysaccharide (LPS) through interaction with myeloid differentiation-2 (MD-2)–like proteins. Functional experiments showed that BmToll9 from the silkmothBombyx morialso recognized LPS through interaction with two MD-2–like proteins, previously named BmEsr16 and BmPP, that we refer to in this study as BmMD-2A and BmMD-2B, respectively. A chimeric BmToll9–TLR4 receptor consisting of the BmToll9 ectodomain and mouse TLR4 transmembrane and Toll/interleukin-1 (TIR) domains also activated LPS-induced release of inflammatory factors in murine cells but only in the presence of BmMD-2A or BmMD-2B. Overall, our results indicate that BmToll9 is a pattern recognition receptor for LPS that shares conserved features with the mammalian TLR4–MD-2–LPS pathway.