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Climate changes are known to have been a key regulator of the biodiversity in Earth's history. However, the dry‐humid degrees and alternating patterns throughout the Phanerozoic remain largely unconstrained. In this study, we report high contents (2.4 ± 3.8 mg N kg−1) and 17O anomalies (11.0 ± 7.4‰) of nitrate (NO3−) in the early Cambrian black shale from South China, likely caused by atmospheric NO3−intrusion under dry climates that followed tectonic uplift. By developing new methods to quantify aridity indices (AI, 0.06 ± 0.08) in combination with observational data with paleoclimate models, we reconstructed the historical AI variations. Our analyses revealed three significant dry‐to‐humid transitions which include Cambrian‐Ordovician to Silurian‐Permian, Permian‐Triassic boundary to middle Triassic‐early Jurassic, and Jurassic‐Paleogene to Neogene. This study quantitatively unlocked the Phanerozoic climate changes in South China, offering important evidence for understanding the co‐evolution of ecological and climatic systems in Earth's history. Climate changes have played a critical role in shaping the biological evolution during the Phanerozoic, but the precise degree of dry‐humid fluctuations and their patterns remain unclear. This study observed high contents and 17O anomalies of nitrate in Cambrian black shale from South China, attributed to atmospheric deposition during dry periods following tectonic uplift. By integrating isotopic and sedimentological data with climate models, we quantified historical aridity and reconstructed its variations in South China. We identified three major dry‐to‐humid transitions in the Phanerozoic. These findings improve our understanding of how climate dynamics interacted with biological and geological changes. High 17O anomaly (11.0 ± 7.4‰) of NO3−in Cambrian black shale was not explained by early diagenesis and modern surface processesA new framework using the ∆17O of sedimentary NO3−to quantify the deep‐time aridity was developedThe data suggest three alternations of dry‐humid climates in the Phanerozoic were recorded by sedimentary geochemistry in South China High 17O anomaly (11.0 ± 7.4‰) of NO3−in Cambrian black shale was not explained by early diagenesis and modern surface processes A new framework using the ∆17O of sedimentary NO3−to quantify the deep‐time aridity was developed The data suggest three alternations of dry‐humid climates in the Phanerozoic were recorded by sedimentary geochemistry in South China

Sterol regulatory element binding protein (SREBP) 1 is considered to be a crucial regulator for lipid synthesis in vertebrates. However, whether SREBP1 could regulate hepatic gluconeogenesis under high-fat diet (HFD) condition is still unknown, and the underlying mechanism is also unclear.

Data‐driven methods have been extensively applied to predict atmospheric compositions. Here, we explore the capability of a deep learning (DL) model to make ozone (O3) predictions across continents in China, the United States (US) and Europe. The DL model was trained and validated with surface O3observations in China and the US in 2015–2018. The DL model was applied to predict hourly surface O3over three continents in 2015–2022. Compared to baseline simulations using GEOS‐Chem (GC) model, our analysis exhibits mean biases of 2.6 and 4.8 μg/m3with correlation coefficients of 0.94 and 0.93 (DL); and mean biases of 3.7 and 5.4 μg/m3with correlation coefficients of 0.95 and 0.92 (GC) in Europe in 2015–2018 and 2019–2022, respectively. The comparable performances between DL and GC indicate the potential of DL to make reliable predictions over spatial and temporal domains where a wealth of local observations for training is not available. Machine learning techniques have been extensively applied in the field of atmospheric science. It provides an efficient way of integrating data and predicting atmospheric compositions. Here, we explore the capability of a deep learning (DL) model to make ozone (O3) predictions across continents in China, the United States (US) and Europe. The DL model was trained and validated with surface O3observations in China and the US in 2015–2018. We then applied the DL model to predict surface O3concentrations in China, the US and Europe in 2015–2022. Our analysis exhibits comparable performances between DL and chemical transport models for surface O3predictions in Europe. This indicates the potential of DL models to extend predictions across spatial and temporal domains. Deep learning exhibits acceptable capabilities of spatial and temporal extrapolations for surface O3predictionsComparable performances between deep learning and GEOS‐Chem models with respect to independent O3observationsGood performance of deep learning for rapid hourly O3predictions Deep learning exhibits acceptable capabilities of spatial and temporal extrapolations for surface O3predictions Comparable performances between deep learning and GEOS‐Chem models with respect to independent O3observations Good performance of deep learning for rapid hourly O3predictions

Urban air pollution continues to pose a significant health threat, despite regulations to control emissions. Here we present a comparative analysis of urban ozone (O3) responses to nitrogen oxide (NOx) changes in China and the United States (US) over 2015–2020 by integrating various data‐ and model‐based methods. The data‐based deep learning (DL) model exhibited good performance in simulating urban air quality: the correlation coefficients (R) of O3daily variabilities with respect to independent O3observations are 0.88 and 0.79 over N. China, 0.87 and 0.90 over S. China, and 0.87 and 0.49 over E. United States by the DL and GEOS‐Chem chemical transport models, respectively. Furthermore, the data‐based methods suggest volatile organic compound (VOC)‐limited regimes in urban areas over northern inland China and transitional regimes over eastern US urban areas; in contrast, GEOS‐Chem model suggests strong NOx‐limited regimes. Sensitivity analysis indicates that the inconsistent O3responses are partially caused by the inaccurate representation of O3precursor concentrations at the locations of urban air quality stations in the simulations, while the data‐based methods are driven by the variabilities in local O3precursor concentrations and meteorological conditions. The O3responses to NOxchanges reported here provide a better understanding of urban O3pollution; for example, reductions in NOxemissions are suggested to have resulted in an increase in surface O3by approximately 7 ppb in the Sichuan Basin in 2014–2020. Ozone (O3) at the surface level is an important pollutant with complicated photochemistry. Here we analyze the responses of urban O3concentrations to nitrogen oxide (NOx) changes in China and the United States (US) over 2015–2020. The data‐based methods suggest that NOxemission declines have resulted in increases in urban O3concentrations over northern inland China, while urban O3concentrations over eastern US are less sensitive to NOxchanges. In contrast, GEOS‐Chem simulation suggests that NOxemission declines can result in dramatic decreases in urban O3concentrations over both China and the US. The derived O3responses in this work are helpful for making effective regulatory policies to control urban O3pollution. Data‐based methods suggest VOC‐limited regimes over northern inland China and transitional regimes over eastern United States urban areasGEOS‐Chem simulation suggests strong NOx‐limited regimesData‐based methods have the potential to provide new insights for air quality regulations as a supplement to Chemical transport models Data‐based methods suggest VOC‐limited regimes over northern inland China and transitional regimes over eastern United States urban areas GEOS‐Chem simulation suggests strong NOx‐limited regimes Data‐based methods have the potential to provide new insights for air quality regulations as a supplement to Chemical transport models

Accumulating evidence indicates that early and essential events for receptor-like kinase (RLK) function involve both autophosphorylation and substrate phosphorylation. However, the structural and biochemical basis for these events is largely unclear. Here, we used RLK FERONIA (FER) as a model and crystallized its core kinase domain (FER-KD) and two FER-KD mutants (K565R, S525A) in complexes with ATP/ADP and Mg2+in the unphosphorylated state. Unphosphorylated FER-KD was found to adopt an unexpected active conformation in its crystal structure. Moreover, unphosphorylated FER-KD mutants with reduced (S525A) or no catalytic activity (K565R) also adopt similar active conformations. Biochemical studies revealed that FER-KD is a dual-specificity kinase, and its autophosphorylation is accomplished via an intermolecular mechanism. Further investigations confirmed that initiating substrate phosphorylation requires autophosphorylation of the activation segment on T696, S701, and Y704. This study reveals the structural and biochemical basis for the activation and regulatory mechanism of FER, providing a paradigm for the early steps in RLK signaling initiation.