Nature Communications最新文献

The composition and thickness of the venusian crust and their dependence on thermal gradients and geodynamic setting are not well constrained. Here, we use metamorphic phase transitions and the onset of melting to determine the maximum crustal thickness of basaltic plains in different tectonic settings. Crustal thickness is limited to ~40 km in a stagnant lid regime with a low thermal gradient of 5 °C/km due to density overturn and delamination. In contrast, the maximum crustal thickness in a mobile lid regime with a high thermal gradient of 25 °C/km is restricted to ~20 km due to the onset of crustal melting. The thickest the crust can be is ~65 km for a basaltic crust with a thermal gradient of 10 °C/km. Our models show that a venusian basaltic crust cannot be thicker than 20–65 km without either causing delamination and crustal recycling or melting and producing volcanic eruptions.

AI-empowered autonomous vehicles must sense the fast-changing three-dimensional environments with high speed and precision. However, the tradeoff between acquisition rate and non-ambiguity range prevents most LiDARs from achieving high-speed absolute distance measurement. Here we demonstrate a lithium niobate electro-optic comb-enabled ultrafast absolute distance measurement method — repetition rate-modulated frequency comb (RRMFC). We achieved an integrated lithium-niobate phase modulator with a half-wave voltage of 1.47 V, leading to over 50 sidebands and a repetition rate can be tuned over 12 GHz in 4 μs. Leveraging these unique features, RRMFC can coherently measure the distance by detecting interference peaks in the time domain, leading to acquisition rates up to 1.79 GHz and a large non-ambiguity range. This single-channel acquisition rate is over 4 orders of magnitude higher than the state-of-the-art absolute distance measurement system. Thus, RRMFC-based LiDAR allows autonomous vehicles to sense the fine details of a fast-changing environment using a single laser.

Piezoceramics for high-power applications require both high piezoelectric coefficient (d₃₃) and mechanical quality factor (Q_m). However, the trade-off between them poses a significant challenge in achieving high values simultaneously, which is more prominent in lead-free piezoceramics. Here, we propose a new strategy, local Cu-acceptor defect dipoles embedded orthorhombic-tetragonal phase boundary engineering (O-T PBE), to balance d₃₃ and Q_m in potassium sodium niobate piezoceramics. This is validated in 0.95(K_0.48Na_0.52)NbO₃-0.05(Bi_0.5Na_0.5)HfO₃-0.2%molFe₂O₃-xmol%CuO ceramics. Our strategy simultaneously maintains the O-T PBE and introduces local dimeric ({({{Cu}}_{{Nb}}^{{prime} {prime} {prime} }-{V}_{O}^{bullet bullet })}^{{prime} }) and trimeric ({left({V}_{O}^{bullet bullet }-{{Cu}}_{{Nb}}^{{prime} {prime} {prime} }-{V}_{O}^{bullet bullet }right)}^{bullet }) defects. The dimeric defects form defect dipole polarization that pins domain wall motion, while the trimeric ones introduce the local structural heterogeneity that leads to nano-scale multi-phase coexistence and abundant nano-domains. Encouragingly, for the Cu-doped sample with x = 1, Q_m increases by a factor of 4, but d₃₃ only decreases by 1/5 (i.e., achieving a d₃₃ of 340 pC/N and a Q_m of 256). Our research provides a new paradigm for balancing d₃₃ and Q_m in lead-free piezoceramics, which holds promise for high-power applications.

Immunity by vaccination can protect human against heterologous viruses. However, protective abilities of artificial vaccines are still weaker than natural infections. Here we develop a kinetically engineered vaccine (KE-VAC) that mimics the multidimensional immunomodulation in natural infections via dynamic activation of antigen presenting cells with masked TLR7/8 agonist and sustained supplies of antigens and adjuvants to lymph nodes, leading to follicular helper T and germinal centre B cell activation in vaccinated mice. KE-VAC demonstrates superior efficacy than traditional alum and mRNA vaccines, achieving a 100% survival rate with increased neutralizing antibodies titers and polyfunctional CD8⁺ T cells, recognizing heterologous SARS-CoV-2 variants, and inducing broad and long-term protection against multiple strains of influenza viruses. Prime/boost vaccination with KE-VAC also protect aged ferrets from severe fever with thrombocytopenia syndrome virus infection, with no virus detected in any organs at day 6 p.i. The efficacy of KE-VAC across various pathogens thus highlights its potential as an effective vaccine against emerging infectious risks.

Enhancer RNAs (eRNAs) are a pivotal class of enhancer-derived non-coding RNAs that drive gene expression. Here we identify the SNAI1 enhancer RNA (SNAI1e; SCREEM2) as a key activator of SNAI1 expression and a potent enforcer of transforming growth factor-β (TGF-β)/SMAD signaling in cancer cells. SNAI1e depletion impairs TGF-β-induced epithelial-mesenchymal transition (EMT), migration, in vivo extravasation, stemness, and chemotherapy resistance in breast cancer cells. SNAI1e functions as an eRNA to cis-regulate SNAI1 enhancer activity by binding to and strengthening the enrichment of the transcriptional co-activator bromodomain containing protein 4 (BRD4) at the local enhancer. SNAI1e selectively promotes the expression of SNAI1, which encodes the EMT transcription factor SNAI1. Furthermore, we reveal that SNAI1 interacts with and anchors the inhibitory SMAD7 in the nucleus, and thereby prevents TGF-β type I receptor (TβRI) polyubiquitination and proteasomal degradation. Our findings establish SNAI1e as a critical driver of SNAI1 expression and TGF-β-induced cell plasticity.

Renewable biomass serves as a cost-effective source of carbon matrix to carry single-atom catalysts (SACs). However, the natural abundant oxygen in these materials hinders the sufficient dispersion of element with high oxygen affinity such iron (Fe). The lowered-density and oxidized SACs greatly limits their catalytic applications. Here we develop a facile continuous activation (CA) approach for synthesizing robust biomass-derived Fe-SACs. Comparing to the traditional pyrolysis method, the CA approach significantly increases the Fe loading density from 1.13 atoms nm⁻² to 4.70 atoms nm⁻². Simultaneously, the CA approach induces a distinct coordination tuning from dominated Fe-O to Fe-N moieties. We observe a pH-universal oxygen reduction reaction (ORR) performance over the CA-derived Fe-SACs with a half-wave potential of 0.93 V and 0.78 V vs. RHE in alkaline and acidic electrolyte, respectively. Density functional theory calculations further reveal that the increased Fe-N coordination effectively reduces the energy barriers for the ORR, thus enhancing the catalytic activity. The Fe-SACs-based zinc-air batteries show a specific capacity of 792 mA·h·g_Zn⁻¹ and ultra-long life span of over 650 h at 5 mA cm⁻².

Partitioning precipitation into rain and snow with near-surface meteorology is a well-known challenge. However, whether a limit exists to its potential performance remains unknown. Here, we evaluate this possibility by applying a set of benchmark precipitation phase partitioning methods plus three machine learning (ML) models (an artificial neural network, random forest, and XGBoost) to two independent datasets: 38.5 thousand crowdsourced observations and 17.8 million synoptic meteorology reports. The ML methods provide negligible improvements over the best benchmarks, increasing accuracy only by up to 0.6% and reducing rain and snow biases by up to -4.7%. ML methods fail to identify mixed precipitation and sub-freezing rainfall events, while expressing their worst accuracy values from 1.0 °C–2.5 °C. A potential cause of these shortcomings is the air temperature overlap in rain and snow distributions (peaking between 1.0 °C–1.6 °C), which expresses a significant negative relationship (p < 0.0005) with partitioning accuracy. Thus, the meteorological characteristics of rain and snow are similar at air temperatures slightly above freezing with increasing overlap associated with decreasing performance. We suggest researchers switch their focus from marginally improving inherently limited precipitation phase partitioning methods using near-surface meteorology to creating new methods that assimilate novel data sources—e.g., crowdsourced precipitation phase observations.

Methane (CH₄) is a potent and short-lived climate pollutant, with the oil and gas sectors emerging as an important contributor. China exhibited a substantial expansion of oil and gas infrastructures over recent years, but the CH₄ emission accounting tends to be incomplete and uncertain. Here, we construct a CH₄ emission database of China’s oil and gas systems from 1990–2022 with 80% of emissions tracked as refineries, facilities, pipelines, and field sources. Results show that China’s oil and gas CH₄ emissions have risen from 0.5[0.5–0.6] TgCH₄ yr⁻¹ in 1990 to 4.0[3.7–4.4] TgCH₄ yr⁻¹ in 2022, primarily driven by the growing demand for natural gas during the energy transition. The spatial details provided are critical for characterizing emission hotspots, especially in unconventional gas production fields and densely populated eastern regions. This long-time series and spatially explicit CH₄ emission database can contribute to informed policy decisions and swift climate action.

Submarine groundwater discharge (SGD) is a nutrient source to coastal waters. However, most SGD estimates are restricted to a local scale and hardly distinguish contributions from fresh (FSGD) and recirculated (RSGD) SGD. Here, we compiled data on radium/radon of groundwater (n ~ 2000) and seawater (n ~ 10,000) samples along ~18,000 km of China’s coastal seas to resolve large scale FSGD and RSGD and their associated nutrient loads. Nearshore-scale FSGD ( ~ 3.56 × 10⁸ m³ d⁻¹) was only 2% of the total SGD but comparable to RSGD in terms of nutrient loads. Despite large uncertainties quantified via Monte Carlo simulations, SGD was a dominant contributor to China’s coastal nutrient budgets, with dissolved inorganic nitrogen, phosphorus and silicate fluxes of ~395, 2.9, and 581 Gmol a⁻¹, respectively. Total SGD accounted for 19–54% of nutrient inputs, exceeding inputs from atmospheric deposition and rivers. Overall, SGD helps sustaining primary production along one of the most human-impacted marginal seas on Earth.

Real-time dynamics of strongly correlated systems, in particular its critical dynamics near phase transitions, have been always on the cutting edge of studies in diverse fields of physics, e.g., high energy physics, condensed matter, holography, etc. In this work, we investigate the critical damping of collective modes associated with spontaneous breaking of approximate symmetries, which are called pseudo-Goldstone modes, in strongly correlated systems. Using the Schwinger-Keldysh field theory, we find a universal pseudo-Goldstone damping via the critical O(N) model that has never been found before by other approaches. Different from the conventional damping found in holography and hydrodynamics, the new one is controlled by critical fluctuations, hence is invisible in mean-field systems or strongly correlated systems with classical gravity duals. Since the critical damping depends solely on the universalities of the critical point, irrespective of the microscopic details, our conclusion should be applicable to a wide class of interacting systems.