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Ozone concentrations in China have been rising since 2013 despite emission reductions, yet future episode frequencies remain poorly investigated. In this study, we employ extreme value theory (EVT) to project ozone episode frequency in China by 2060, integrating six emission pathways and four climate scenarios. Under the baseline (most polluted) scenario, annual population‐weighted ozone exceedances (>82 ppb, national standard) reach 65 days nationally and 112 days in northern China by 2060, with climate change driving >50% of the increases. In the 1.5°C (cleanest) scenario, reduced emissions considerably lower ozone levels nationally, yet northern China still experiences 56 annual exceedances of the WHO Interim Target 2 (>61 ppb). Across all scenarios, ozone episode frequency and its temperature sensitivity continuously decline with anthropogenic NOx emissions reduction, highlighting the critical role of NOx mitigation in curbing ozone episodes.

Under rapid urbanization and agglomeration of population, cities are facing various environmental challenges. As urban forests play a crucial role in mitigating native environmental problems and providing ecosystem services, cities might enhance their urban forest construction through multiple approaches, of which the introduction of exotic tree species could be an effective way. Under the background of constructing a high-quality forest city, Guangzhou was considering introducing a series of exotic tree species to improve the local urban greening, among which Tilia cordata Mill. and Tilia tomentosa Moench became the potential objects. As Guangzhou was reported to experience higher temperatures with less precipitation and face drought events with increasing frequency and intensity, whether the two tree species could survive in the dry environment required to be investigated profoundly. Thus, we launched a drought-simulation experiment and measured their above- and below-ground growth in 2020. In addition, their ecosystem services were also simulated and evaluated for their future adaption. Furthermore, a congeneric native tree species Tilia miqueliana Maxim was also measured in the same experiment as a comparison. Our results showed that Tilia miqueliana exhibited moderate patterns of growth and advantages in evapotranspiration and cooling. Besides, its investment in root development at horizontal level could account for its special strategy against drought stress. Tilia tomentosa ’s vigorous root growth could be the most positive behavior of coping with water deficit, which explained its maintenance of carbon fixation and implied a well adaption. Tilia cordata showed a complete decrease in above- and below-ground growth, especially for its fine root biomass. In addition, its ecosystem services were significantly reduced, reflecting a comprehensive failure when it faced a long-term scarcity of water. Therefore, it was necessary to supply sufficient water and under-ground space for their living in Guangzhou, especially for Tilia cordata . In the future, long-time observation of their growth under different stresses can be practical approaches to amplify their multiple ecosystem services.

The growth of plants is threatened by numerous diseases. Accurate and timely identification of these diseases is crucial to prevent disease spreading. Many deep learning-based methods have been proposed for identifying leaf diseases. However, these methods often combine plant, leaf disease, and severity into one category or treat them separately, resulting in a large number of categories or complex network structures. Given this, this paper proposes a novel leaf disease identification network (LDI-NET) using a multi-label method. It is quite special because it can identify plant type, leaf disease and severity simultaneously using a single straightforward branch model without increasing the number of categories and avoiding extra branches. It consists of three modules, i.e., a feature tokenizer module, a token encoder module and a multi-label decoder module. The LDI-NET works as follows: Firstly, the feature tokenizer module is designed to enhance the capability of extracting local and long-range global contextual features by leveraging the strengths of convolutional neural networks and transformers. Secondly, the token encoder module is utilized to obtain context-rich tokens that can establish relationships among the plant, leaf disease and severity. Thirdly, the multi-label decoder module combined with a residual structure is utilized to fuse shallow and deep contextual features for better utilization of different-level features. This allows the identification of plant type, leaf disease, and severity simultaneously. Experiments show that the proposed LDI-NET outperforms the prevalent methods using the publicly available AI challenger 2018 dataset.

Although the pathogenesis of seropositive rheumatoid arthritis (RA) remains unclear, studies suggest that pulmonary inflammation-related biological mechanisms play a significant role in its development. This review thoroughly explores the mechanisms underlying early pulmonary lesions in seropositive RA, focusing on the mucosal barrier hypothesis, neutrophil extracellular traps, pathogenic microbial infections like COVID-19, Vitamin D, the microbiome and gut-lung axis, inhalation exposures and chronic pulmonary diseases. This study seeks to provide novel insights and theoretical foundations for the prevention and treatment of seropositive rheumatoid arthritis.

Infrared–visible image matching is a prerequisite for environmental monitoring, military reconnaissance, and multisource geospatial analysis. However, pronounced texture disparities, intensity drift, and complex non-linear radiometric distortions in such cross-modal pairs mean that existing frameworks such as SuperPoint + SuperGlue (SP + SG) and LoFTR cannot reliably establish correspondences. To address this issue, we propose a dual-path architecture, the Environment-Adaptive Wavelet Enhancement and Radiation Priors Aided Matcher (EWAM). EWAM incorporates two synergistic branches: (1) an Environment-Adaptive Radiation Feature Extractor, which first classifies the scene according to radiation-intensity variations and then incorporates a physical radiation model into a learnable gating mechanism for selective feature propagation; (2) a Wavelet-Transform High-Frequency Enhancement Module, which recovers blurred edge structures by boosting wavelet coefficients under directional perceptual losses. The two branches collectively increase the number of tie points (reliable correspondences) and refine their spatial localization. A coarse-to-fine matcher subsequently refines the cross-modal correspondences. We benchmarked EWAM against SIFT, AKAZE, D2-Net, SP + SG, and LoFTR on a newly compiled dataset that fuses GF-7, Landsat-8, and Five-Billion-Pixels imagery. Across desert, mountain, gobi, urban and farmland scenes, EWAM reduced the average RMSE to 1.85 pixels and outperformed the best competing method by 2.7%, 2.6%, 2.0%, 2.3% and 1.8% in accuracy, respectively. These findings demonstrate that EWAM yields a robust and scalable framework for large-scale multi-sensor remote-sensing data fusion.

Coronaviruses (CoVs) are a family of the largest RNA viruses that typically cause respiratory, enteric, and hepatic diseases in animals and humans, imposing great threats to the public safety and animal health. Porcine deltacoronavirus (PDCoV), a newly emerging enteropathogenic coronavirus, causes severe diarrhea in suckling piglets all over the world and poses potential risks of cross-species transmission. Here, we use PDCoV as a model of CoVs to illustrate the reciprocal regulation between CoVs infection and host antiviral responses. In this study, downregulation of DNA polymerase delta interacting protein 3 (POLDIP3) was confirmed in PDCoV infected IPEC-J2 cells by isobaric tags for relative and absolute quantification (iTRAQ) and Western blotting analysis. Overexpression of POLDIP3 inhibits PDCoV infection, whereas POLDIP3 knockout (POLDIP3 -/- ) by CRISPR-Cas9 editing significantly promotes PDCoV infection, indicating POLDIP3 as a novel antiviral regulator against PDCoV infection. Surprisingly, an antagonistic strategy was revealed that PDCoV encoded nonstructural protein 5 (nsp5) was responsible for POLDIP3 reduction via its 3C-like protease cleavage of POLDIP3 at the glutamine acid 176 (Q176), facilitating PDCoV infection due to the loss of antiviral effects of the cleaved fragments. Consistent with the obtained data in IPEC-J2 cell model in vitro , POLDIP3 reduction by cleavage was also corroborated in PDCoV infected-SPF piglets in vivo . Collectively, we unveiled a new antagonistic strategy evolved by PDCoV to counteract antiviral innate immunity by nsp5-mediated POLDIP3 cleavage, eventually ensuring productive virus replication. Importantly, we further demonstrated that nsp5s from PEDV and TGEV harbor the conserved function to cleave porcine POLDIP3 at the Q176 to despair POLDIP3-mediated antiviral effects. In addition, nsp5 from SARS-CoV-2 also cleaves human POLDIP3. Therefore, we speculate that coronaviruses employ similar POLDIP3 cleavage mechanisms mediated by nsp5 to antagonize the host antiviral responses to sustain efficient virus infection.

Alternative splicing (AS), a crucial mechanism in post‐transcriptional regulation, has been implicated in diverse cancer processes. Several splicing variants of solute carrier (SLC) transporters reportedly play pivotal roles in tumorigenesis and tumor development. However, an in‐depth analysis of AS landscapes of SLCs in colon adenocarcinoma (COAD) is lacking. Herein, we analyzed data from The Cancer Genome Atlas and identified 1215 AS events across 243 SLC genes, including 109 differentially expressed AS (DEAS) events involving 62 SLC genes in COAD. Differentially spliced SLCs were enriched in biological processes, including transmembrane transporter activity, transporter activity, ferroptosis, and choline metabolism. In patients with COAD, tumor tissues exhibited higher expression of longer mitochondrial carrier SLC25A16 isoforms than adjacent normal tissues, consistent with bioinformatics analysis. Protein‐coding sequences and transmembrane helices of survival‐related DEAS were predicted, revealing that shifts in splicing sites altered the number and structure of their transmembrane proteins. We developed a prognostic risk model based on the screened 6‐SLC‐AS (SLC7A6_RI_37208 (SLC7A6‐RI), SLC11A2_AP_21724, SLC2A8_ES_87631, SLC35B1_AA_42317, SLC39A11_AD_43204, and SLC7A8_AP_26712). Knockdown of the intronic region of SLC7A6‐RI isoform enhanced colon cancer cell proliferation. In vivo, knockdown of the intronic region of SLC7A6‐RI isoform enhanced tumor growth in colon cancer. Mechanistically, si‐SLC7A6‐RI isoform exerted oncogenic effects by activating the PI3K‐Akt–mTOR signaling pathway and promoting cell proliferation, evidenced by increased expression of key regulators Phosphorylated Mammalian Target of Rapamycin (p‐mTOR) and a cell proliferation marker Proliferating Cell Nuclear Antigen (PCNA) using western blotting. Our study elucidated SLC‐AS in COAD, highlighting its potential as a prognostic and therapeutic target and emphasizing the suppressive influence of SLC7A6‐RI in colon cancer progression. Our study elucidated solute carrier alternative splicing in colon adenocarcinoma, highlighting its potential as a prognostic and therapeutic target and emphasizing the suppressive influence of SLC7A6‐RI in colon cancer progression.

Drought dramatically impacts the autumn phenology of vegetation. However, the underlying mechanisms of vegetation autumn phenology responses to drought in tropical and subtropical forests remain unclear. Here, we employed three fitting methods to extract the end‐of‐photosynthetic‐growing‐season (EOPS) dates and quantified their responses to drought intensity using ridge regression and correlation analysis. Our analysis revealed a general delay in the trend of EOPS at an average rate of 3.6 days per decade from 2001 to 2020 in southern China. The standardized precipitation evapotranspiration index (SPEI) emerged as the primary influencing predictor of EOPS processes, surpassing the impacts of temperature, precipitation, and radiation. Notably, our analysis highlighted a shift in the response of EOPS to drought from delay to advancement when drought intensity exceeded 0.38. Incorporating this reversal phenomenon into EOPS models is crucial for accurately predicting autumn phenology under future escalating drought conditions. Plain Language Summary Ongoing climate change is substantially altering autumn phenology, and subsequently impacting terrestrial carbon and water balance. Tropical and subtropical forests, being major reservoirs of global carbon, are experiencing notable effects due to escalating drought conditions. However, the underlying mechanisms of its autumn phenology responses to drought remain unclear. We employed three fitting methods to extract the end‐of‐photosynthetic‐growing‐season (EOPS) dates of subtropical and tropical forests and quantified their response to drought intensity through ridge regression and correlation analysis. Our analysis revealed a delay in EOPS across over 70% of the studied forests during the period 2001–2020, and we found that the standardized precipitation evapotranspiration index (SPEI) primarily determined the EOPS processes. Interestingly, our analysis demonstrated a shift in the response of EOPS to drought from delay to advancement when drought intensity exceeded 0.38. These findings underscore the existence of a critical threshold in drought severity, beyond which a reversal in the response of EOPS to drought is anticipated. This reversal effect may stem from vegetation's adaptive strategies and environmental constraints. Incorporating this reversal phenomenon into EOPS modeling is imperative for enhancing predictions of autumn phenology and advancing our comprehension of carbon and water balances in forest ecosystems. Key Points The standardized precipitation evapotranspiration index (SPEI) emerged as the primary influencing predictor factor on EOPS processes Increasing drought induces contrasting shifts in the autumnal photosynthetic phenology of tropical and subtropical forests The effect of drought on autumn photosynthetic vegetation phenology shifts from delay to advancement as drought intensity exceeds 0.38

Given the global challenge of increasing teacher attrition and turnover rates, the exploration of factors and mechanisms that improve teachers’ organizational commitment has become a pivotal topic in educational research. In this context, the present study examines the influence of teachers’ emotional intelligence on their organizational commitment, with a specific inquiry into the mediating role of teachers’ psychological well-being and the moderating role of principal transformational leadership, as informed by the broaden-and-build theory of positive emotions and the trait activation theory. We verified this study’s hypotheses based on 768 valid questionnaires collected from Chinese primary and secondary school teachers. The results reveal that teachers’ emotional intelligence can predict their organizational commitment both directly and indirectly through the mediating role of psychological well-being. Additionally, principal transformational leadership amplifies the positive effect of teachers’ emotional intelligence on psychological well-being and, subsequently, organizational commitment. These findings theoretically deepen our understanding of the psychological pathways and the boundary conditions linking teachers’ emotional intelligence to their organizational commitment, while also offering valuable practical implications for building a stable and effective teaching workforce.

It is widely recognized that post-seismic mass movements amplify and decay. Previous studies have found that most post-seismic mass movement is concentrated in the first few years following an earthquake. A major debris flow occurred in Wenchuan County in 2019, 11 years after the 2008 Wenchuan earthquake, showing that there might be a different temporal evolution pattern. In Wenchuan Country, the area struck by the 2019 disaster was investigated to explore whether a pattern existed. Remote sensing and field surveys investigate the initiation processes, sediment supply, and triggering rainfall. The result shows that most of the active landslides occurred in high-elevation areas where vegetation cover was lacking, and nearly half of them were reactivated landslides. The debris flows were mainly initiated by run-off erosion of debris in steep channels, and more than half of the sediment supply was from deposition along the channels in some catchments. The spatial and temporal evolution of debris flows was analyzed by combining our investigation results with historical events. More debris flows occurred in the northern part of the study area, where the relative annual rainfall and coseismic landslide density were low. The average values of debris-active catchments’ size and relief are increasing, while the average values of their coseismic landslide density and the annual rainfall received were decreasing from 2008 to 2019. Larger catchments receiving less annual rainfall tend to have a more prolonged and enhanced mass movement.