Cancer development involves the co-evolution of cancer cells and their surrounding microenvironment, yet the dynamics of this interaction within the physical architecture remains poorly understood. Here, we present a spatial transcriptomic map at single-cell resolution, encompassing 127 multi-stage fields of view from 43 patients, to chart the evolutionary trajectories of human esophageal squamous cell carcinoma (ESCC). By analyzing 6.4 million cells, we reveal that ESCC progression is driven by a proliferative epithelial cell subpopulation that acquires dedifferentiated and invasive characteristics. At the late precancerous stage, these cells disrupt the epithelial-stromal interface and recruit normal fibroblasts via JAG1-NOTCH1 signaling, transforming them into cancer-associated fibroblasts (CAFs). This interaction leads to the formation of a "CAF-Epi" (CAF and epithelial cell) niche at the tumor edge that shields the tumor from immune surveillance. The CAF-Epi niche formation is a key indicator of progression in ESCC and other squamous cell carcinomas and patient outcomes.

Cloud is an important factor that affects weather and climate, and the vertical distribution of cloud determines its role in the atmospheric radiation transfer process. In this paper, the characteristics of different cloud types and their vertical cloud base height distributions over Eastern China are investigated with a four-year 2B-CLDCLASS-LIDAR product. The intercomparison of cloud base height distribution from ground-based lidar, CloudSat and CALIPSO measurements was studied with observations over the Hefei and Jinhua areas. The 2B-CLDCLASS-LIDAR product has the potential to uncover geographical and seasonal changes in cloud base height distribution over the Hefei area and Jinhua area, which may be beneficial for local climate models, although the CPR on CloudSat suffers from surface clutter or blind-zones. The results show that for non-precipitation cloud over the defined region (Eastern China), the occurrence frequencies of altocumulus, stratocumulus, and cirrus clouds are 29.4%, 21.0%, and 18.9%, respectively. The vertical occurrence frequencies of their cloud base heights are 0.5–8.5 km, below 3.5 km, and 5.5–17.0 km. The precipitation clouds are dominated by nimbostratus (48.4%), cumulus (17.9%), and deep convective clouds (24.2%), and their cloud base heights are all below 3.0 km. The cloud base height distributions have large differences below 3 km between the satellite measurement and ground-based measurement over Hefei site. Between the Hefei site and Jinhua site, the difference in cloud base height distribution measured by ground-based lidar is in good agreement with that measured by satellite over their matched grid boxes. Over the Hefei site, the vertical occurrence frequencies of cloud base height measured by ground-based lidar are higher than the satellite measurement within 0–0.5 km during all the seasons. It is suggested that more cloudy days may result from the sufficient water vapor environment in Hefei. In summer, the occurrence frequency of the cloud base height distribution at a height of 0–2.0 km is lower than other seasons over Jinhua city, which may be associated with the local weather system. Over the Jinhua site, the difference in seasonal cloud base height distribution based on satellite is in good agreement with that based on ground-based lidar. However, it does not appear over Hefei site. Thus, a multi-platform observation of cloud base height seems to be one of the essential ways for improvement in the observation of cloud macroscopic properties.

The annual increase in total methane emissions in China has decelerated, largely due to the continued implementation of effective emission reduction policies. Tracking methane emission hotspots and characterizing their emission profiles are critical for driving further reductions. However, the specific role of strong methane emissions in the annual trend changes of China’s methane emissions, as well as seasonal emission profiles, has not been clarified. In this study, we use TROPOMI satellite observations to estimate methane emissions in China for 2019-2023 and comprehensively analyze the driving mechanisms of the trend changes and seasonal variations of methane emissions in China by combining the emissions structural properties with the emission regions’ signatures. Our results reveal that methane emission trends in China over the five years, based on top-down estimations, have significantly moderated. Emission intensity has declined, stability has improved, and the geographic distribution exhibits a pronounced north-heavy and south-light pattern. While the proportion of unstable strong emissions has remained small and consistent over the years, their contribution to total emissions remains significant. Moreover, the influence of stable strong emissions and unstable weak emissions on overall methane levels has grown over time. Unstable strong emissions, primarily linked to energy-related activities, play a pivotal role in shaping seasonal methane emission trends in China. These findings highlight the complex interplay of emission sources and provide valuable insights for refining methane reduction strategies.

The tracking of carbon and aerosol co-emissions is essential for environmental management. Satellite-based atmospheric synoptic observation networks provide large-scale and multifaceted data to help resolve emission behaviors. This study employs a comprehensive analysis of atmospheric dynamics, combustion byproducts, and aerosol characteristics around power plants. Strong correlations between Aerosol Optical Depth (AOD) at 500 nm and the column-averaged dry-air mole fraction of carbon dioxide (XCO₂) were observed, revealing synchronous peaks in their emission patterns. The investigation into combustion completeness utilized metrics such as the ratio of carbon monoxide (CO)/XCO₂ and Black Carbon Extinction (BCEXT)/Total Aerosol Extinction (TOTEXT). Discrepancies in these ratios across cases suggest variations in combustion efficiency and aerosol characteristics. Nitrogen dioxide (NO₂) distributions closely mirrored XCO₂, indicating consistent emission patterns, while variations in sulfur dioxide (SO₂) distributions implied differences in sulfide content in the coal used. The influence of coal composition on AOD/XCO₂ ratios was evident, with sulfide content contributing to variations besides combustion efficiency. This multifactorial analysis underscores the complex interplay of combustion completeness, aerosol composition, and coal components in shaping the air quality around power stations. The findings highlight the need for a nuanced understanding of these factors for effective air quality management.