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As an important tropospheric trace gas and precursor of photochemical smog, the accumulation of NO2 will cause serious air pollution. China, as the largest developing country in the world, has experienced a large amount of NO2 emissions in recent decades due to the rapid economic growth. Compared with the traditional air pollution monitoring technology, the rapid development of the remote sensing monitoring method of atmospheric satellite has gradually become the critical technical means of global atmospheric environmental monitoring. To reveal the NO2 pollution situation in China, based on the latest NO2 products from Sentinel-5P TROPOMI, the spatial–temporal characteristics and impact factors of troposphere NO2 column concentration of mainland China in the past year (February 2018 to January 2019) were analyzed on two administrative levels for the first time. Results show that the monthly fluctuation of tropospheric NO2 column concentration has obvious characteristics of “high in winter and low in summer”, while the spatial distribution forms a “high in East and low in west” pattern, bounded by Hu Line. The comparison of Coefficient of Variation (CV) and spatial autocorrelation models at two kinds of administrative scales indicates that although the spatial heterogeneity of NO2 column concentration is less affected by the observed scale, there is a “delayed effect” of about one month in the process of NO2 column concentration fluctuation. Besides, the impact factors analysis based on Spatial Lag Model (SLM) and Geographic Weighted Regression (GWR) reveals that there is a positive correlation between nighttime light intensity, the secondary and tertiary industries proportion and NO2 column concentration. Furthermore, for regions with serious NO2 pollution in North China Plain, the whole society electricity consumption and vehicle ownership also play a positive role in increasing the NO2 column concentration. This study will enlighten the government and policy makers to formulate policies tailored to local conditions, to more effectively implement NO2 emission reduction and air pollution prevention.

The development of mechanochemistry substantially expands the traditional synthetic realm at the molecular level. Here, we extend the concept of mechanochemistry from atomic/molecular solids to the nanoparticle solids, and show how the macroscopic grinding is being capable of generating chirality in self-assembled nanorod (NR) assemblies. Specifically, the weak van der Waals interaction is dominated in self-assembled NR assemblies when their surface is coated with aliphatic chains, which can be overwhelmed by a press-and-rotate mechanic force macroscopically. The chiral sign of the NR assemblies can be well-controlled by the rotating directions, where the clockwise and counter-clockwise rotation leads to the positive and negative Cotton effect in circular dichroism and circularly polarized luminescence spectra, respectively. Importantly, we show that the present approach can be applied to NRs of diverse inorganic materials, including CdSe, CdSe/CdS, and TiO 2 . Equally important, the as-prepared chiral NR assemblies could be served as porous yet robust chiral substrates, which enable to host other molecular materials and induce the chirality transfer from substrate to the molecular system. Chiroptic materials made of self-assembled nanomaterials are essential for advanced optical applications. Here, the authors show that macroscopic grinding can break the symmetry in achiral superlattices of inorganic nanorods, generating chiral superstructures.

With the development of artificial intelligence, the ability to capture the background characteristics of hyperspectral imagery (HSI) has improved, showing promising performance in hyperspectral anomaly detection (HAD) tasks. However, existing methods proposed in recent years still suffer from certain limitations: (1) Constraints are lacking in the deep feature learning process in terms of the issue of the absence of prior background and anomaly information. (2) Hyperspectral anomaly detectors with traditional self-supervised deep learning methods fail to ensure prioritized reconstruction of the background. (3) The architecture of fully connected deep networks in hyperspectral anomaly detectors leads to low utilization of spatial information and the destruction of the original spatial relationship in hyperspectral imagery and disregards the spectral correlation between adjacent pixels. (4) Hypotheses or assumptions for background and anomaly distributions restrict the performance of many hyperspectral anomaly detectors because the distributions of background land covers are usually complex and not assumable in real-world hyperspectral imagery. In consideration of the above problems, in this paper, we propose a novel fully convolutional auto-encoder based on dual clustering and latent feature adversarial consistency (FCAE-DCAC) for HAD, which is carried out with self-supervised learning-based processing. Firstly, density-based spatial clustering of applications with a noise algorithm and connected component analysis are utilized for successive spectral and spatial clustering to obtain more precise prior background and anomaly information, which facilitates the separation between background and anomaly samples during the training of our method. Subsequently, a novel fully convolutional auto-encoder (FCAE) integrated with a spatial–spectral joint attention (SSJA) mechanism is proposed to enhance the utilization of spatial information and augment feature expression. In addition, a latent feature adversarial consistency network with the ability to learn actual background distribution in hyperspectral imagery is proposed to achieve pure background reconstruction. Finally, a triplet loss is introduced to enhance the separability between background and anomaly, and the reconstruction residual serves as the anomaly detection result. We evaluate the proposed method based on seven groups of real-world hyperspectral datasets, and the experimental results confirm the effectiveness and superior performance of the proposed method versus nine state-of-the-art methods.

Global warming accelerates water cycle, causing more droughts globally that challenge monitoring and forecasting. The Standardized Precipitation Evapotranspiration Index (SPEI) is used to assess drought characteristics and response time of natural and economic systems at various timescales. However, existing SPEI datasets have coarse spatial or temporal resolution or limited spatial extent, restricting their ability to accurately identify the start or end dates or the extent of drought at the global scale. To narrow these gaps, we developed a global daily SPEI dataset (SPEI-GD), with a 0.25° spatial resolution from 1982 to 2021 at multiple timescales (5, 30, 90, 180 and 360 days), based on the precipitation from European Center for Medium Weather Forecasting Reanalysis V5 (ERA5) dataset and the potential evapotranspiration from Singer’s dataset. Compared to widely used SPEIbase dataset, the SPEI-GD can improve the spatial-temporal resolution and the accuracy of SPEI in areas where meteorological sites are lacking. The SPEI-GD significantly correlates with site-based SPEI and soil moisture. Our dataset solidly supports sub-seasonal and daily-scale global and regional drought research.

Extreme heat events intensified by climate change and urbanization are heightening population exposure to heat-stress (heat-stress exposure). Although urban green spaces (UGS) offer an effective countermeasure, most planning ignores planning-efficiency, i.e. heat-stress exposure covered per unit area of additional-UGS (UGS planned for construction). We introduced an efficiency-oriented UGS planning framework that reconstructed hourly thermal comfort data (Universal Thermal Climate Index, UTCI), coupled these data with dynamic population data to map heat-stress exposure, linked extracted cooling-zones to UGS size to simulate the cooling performance of additional-UGS, and ranked additional-UGS by planning-efficiency (Lorenz curve). Applied to Kunming’s urban core, the framework identified 783 additional-UGS patches whose cooling-zones intersected 9.14 × 10 6 person·°C of heat-stress exposure (23.7% of the total). Ranking additional-UGS by planning-efficiency and segmenting the Lorenz curve produced five priority levels: planning in only Priority 1 (around 10% of additional-UGS area) would cover nearly 40% of coverable heat-stress exposure, while Priority 1 + 2 (around 32%) would cover over 86%. These findings demonstrate that accurate augmentation, phased expansion of high planning-efficiency UGS, can achieve greater cooling benefits with limited land, offering a transferable tool for urban heat resilience planning.

In face of the everlasting battle toward COVID-19 and the rapid evolution of SARS-CoV-2, no specific and effective drugs for treating this disease have been reported until today. Angiotensin-converting enzyme 2 (ACE2), a receptor of SARS-CoV-2, mediates the virus infection by binding to spike protein. Although ACE2 is expressed in the lung, kidney, and intestine, its expressing levels are rather low, especially in the lung. Considering the great infectivity of COVID-19, we speculate that SARS-CoV-2 may depend on other routes to facilitate its infection. Here, we first discover an interaction between host cell receptor CD147 and SARS-CoV-2 spike protein. The loss of CD147 or blocking CD147 in Vero E6 and BEAS-2B cell lines by anti-CD147 antibody, Meplazumab, inhibits SARS-CoV-2 amplification. Expression of human CD147 allows virus entry into non-susceptible BHK-21 cells, which can be neutralized by CD147 extracellular fragment. Viral loads are detectable in the lungs of human CD147 (hCD147) mice infected with SARS-CoV-2, but not in those of virus-infected wild type mice. Interestingly, virions are observed in lymphocytes of lung tissue from a COVID-19 patient. Human T cells with a property of ACE2 natural deficiency can be infected with SARS-CoV-2 pseudovirus in a dose-dependent manner, which is specifically inhibited by Meplazumab. Furthermore, CD147 mediates virus entering host cells by endocytosis. Together, our study reveals a novel virus entry route, CD147-spike protein, which provides an important target for developing specific and effective drug against COVID-19.

Existing methods of hyperspectral anomaly detection still face several challenges: (1) Due to the limitations of self-supervision, avoiding the identity mapping of anomalies remains difficult; (2) the ineffective interaction between spatial and spectral features leads to the insufficient utilization of spatial information; and (3) current methods are not adaptable to the detection of multi-scale anomaly targets. To address the aforementioned challenges, we proposed a blind-spot network based on multi-scale blind-spot convolution for HAD. The multi-scale mask convolution module is employed to adapt to diverse scales of anomaly targets, while the dynamic fusion module is introduced to integrate the advantages of mask convolutions at different scales. The proposed approach includes a spatial–spectral joint module and a background feature attention mechanism to enhance the interaction between spatial–spectral features, with a specific emphasis on highlighting the significance of background features within the network. Furthermore, we propose a preprocessing technique that combines pixel shuffle down-sampling (PD) with spatial spectral joint screening. This approach addresses anomalous identity mapping and enables finite-scale mask convolution for better detection of targets at various scales. The proposed approach was assessed on four real hyperspectral datasets comprising anomaly targets of different scales. The experimental results demonstrate the effectiveness and superior performance of the proposed methodology compared with nine state-of-the-art methods.

Global heat stress is intensifying under climate change, yet the relative roles of natural and anthropogenic forcing remain insufficiently quantified. Here, we show that global heat stress trend, assessed with the Universal Thermal Climate Index, increases markedly over the past four decades, with 52% of land area experiencing rises in mean heat stress intensity and 67% showing increases in extreme heat stress days. We find that anthropogenic climate change overwhelmingly dominates these trends, with the land area it dominates nearly twice as large as that dominated by natural climate change. Anthropogenic climate change also results in pronounced spatial inequality in heat stress trends across different economies, with low-income economies experiencing a growth rate two to three times higher than that of high-income economies. These findings demonstrate that human-induced climate change is amplifying global heat stress while deepening existing spatial inequalities, underscoring the urgency of equitable climate change adaptation. Global heat stress has increased, with 52% of land area showing rising mean intensity and 67% showing more extreme heat stress days. Anthropogenic climate change dominates these trends and widens inequalities across economies.

Background Cage subsidence may occur following transforaminal lumbar interbody fusion (TLIF) and lead to nonunion, foraminal height loss and other complications. Low bone quality may be a risk factor for cage subsidence. Assessing bone quality through Hounsfield units (HU) from computed tomography has been proposed in recent years. However, there is a lack of literature evaluating the correlation between HU and cage subsidence after TLIF. Methods Two hundred and seventy-nine patients suffering from lumbar degenerative diseases from April, 2016 to August, 2018 were enrolled. All underwent one-level TLIF with a minimum of 1-year follow-up. Cage subsidence was defined as > 2 mm loss of disc height at the fusion level. The participants were divided into 2 groups: cage subsidence group (CS) and non-cage subsidence group (non-CS). Bone quality was determined by HU, bone mineral density of lumbar (BMD-l) and femoral (BMD-f) from dual-emission X-ray absorptiometry (DXA). HU of each vertebra from L1 to L4 (e.g., HU1 for HU of L1) and mean value of the four vertebrae (HUm) were calculated. Visual analog scale (VAS) of back/leg pain and Oswestry disability index (ODI) were used to report clinical outcomes. Results Cage subsidence occurred in 82 (29.4%) cases at follow-ups. Mean age was 50.8 ± 9.0 years with a median follow-up of 18 months (range from 12 to 40 months). A total of 90.3% patients presented fusion with similar fusion rate between the two groups. ODI and VAS in leg were better in non-CS group at last follow-ups. Using receiver operating characteristic curves (ROCs) to predict cage subsidence, HUm provided a larger area under the curve (AUC) than BMD-l (Z = 3.83, P  <  0.01) and BMD-f (Z = 2.01, P  = 0.02). AUC for HU4 was larger than BMD-f and close to HUm (Z = 0.22, P  = 0.481). Conclusions Cage subsidence may indicate worse clinical outcomes. HU value could be a more effective predictor of lumbar cage subsidence compared with T-score of DXA after TLIF.