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As an indispensable component in human beings, the acetylcholine system regulates multiple physiological processes not only in neuronal tissues but also in nonneuronal tissues. However, since the concept of the “Nonneuronal cholinergic system (NNCS)” has been proposed, the role of the acetylcholine system in nonneuronal tissues has received increasing attention. A growing body of research shows that the acetylcholine system also participates in modulating inflammatory responses, regulating contraction and mucus secretion of respiratory tracts, and influencing the metastasis and invasion of lung cancer. In addition, the susceptibility and severity of respiratory tract infections caused by pathogens such as Mycobacterium Tuberculosis and the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) can also correlate with the regulation of the acetylcholine system. In this review, we summarized the major roles of the acetylcholine system in respiratory diseases. Despite existing achievements in the field of the acetylcholine system, we hope that more in-depth investigations on this topic will be conducted to unearth more possible pharmaceutical applications for the treatment of diverse respiratory diseases.

Hematologic malignancies (HMs), which originate from hematopoietic or lymphoid tissues, pose a significant therapeutic challenge due to issues such as drug resistance, relapse, and treatment-related toxicity. The tumor microenvironment (TME), especially within the bone marrow niche, is now widely recognized as a critical determinant of disease progression and treatment response. A central mechanism within this specialized niche is the extensive metabolic reprogramming of key stromal and immune cells, including tumor-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), cancer-associated fibroblasts (CAFs), and bone marrow adipocytes (BMAds). This review systematically elaborates on the alterations in glucose, lipid, and amino acid metabolism within these cellular compartments of the HM-TME. We detail how metabolites such as lactate, fatty acids, and itaconate function not merely as metabolic byproducts but as active signaling molecules that drive critical processes like immune cell polarization, stromal remodeling, and intricate metabolic crosstalk. This comprehensive reprogramming collectively fosters a profoundly immunosuppressive milieu, promotes tumor cell survival and proliferation, and confers resistance to conventional and novel therapies. Furthermore, we explore emerging therapeutic strategies designed to target these metabolic vulnerabilities. These include inhibitors of specific metabolic pathways, modulators of metabolite-driven signaling, and innovative approaches such as nanomedicine and metabolically enhanced immunotherapy. Finally, we outline the current challenges in the field—such as intra-tumoral metabolic heterogeneity and the pressing need for targeted delivery systems—and discuss future perspectives involving advanced technologies like single-cell metabolomics and rational combination strategies. In summary, this synthesis aims to provide a comprehensive and rational foundation for developing novel immunometabolic interventions against HMs, highlighting the therapeutic potential of disrupting the metabolic dialogue within the TME.

Chronic obstructive pulmonary disease (COPD) is characterized by persistent airway inflammation and immune dysfunction. However, the molecular alterations and precise origins of immune cells in COPD airways remain poorly understood. Here, CD45+ immune cells in bronchoalveolar lavage fluid and peripheral blood mononuclear cells were collected from four COPD patients and four healthy smokers to provide a comprehensive single-cell transcriptomic atlas of immune cells in COPD airways. Notably, CD8+ T cells exhibited increased exhaustion, reduced cytotoxicity, and decreased TCR diversity in COPD airways. Especially, we identified two distinct exhausted CD8+ T cell clusters (CD8Tex_PDCD1 and CD8Trm_LAG3) originating from different developmental trajectories. Regulatory T cells had a reduced proportion and regulatory capacity in COPD airways, while CD4+ tissue-resident memory T cells displayed excessive Th2 responses and diminished Th1 responses. Additionally, monocyte-derived alveolar macrophages (Macro_SPP1) underwent lipid metabolic reprogramming and exhibited a shift to an anti-inflammatory phenotype with reduced phagocytosis and protease-antiprotease imbalance in COPD airways. Furthermore, macrophages (particularly Macro_SPP1) showed increased interactions with T cells via SPP1 and GALECTIN signaling, likely contributing to T cell suppression in COPD airways. Together, these findings elucidate the dysregulated immune responses in COPD airways and provide a valuable resource for identifying potential therapeutic targets to restore immune homeostasis in COPD.

In this study, the chemical fractions (CFs) of trace metal (TMs) and multiple magnetic parameters were analysed in the sedimentary column from the centre of Lake Taihu. The sedimentary column, measuring 53 cm in length, was dated using 210Pb and 137Cs to be 124 years old. Surface layers of the column were found to contain significantly higher concentrations of Cd, Co, Cu, Pb, Sb, Ti, and Zn than the middle and bottom layers. The sedimentary core contained a substantial amount of ferrimagnetic minerals. Most of the TMs were present in the residual state, except for Mn and Pb. The chemical fractions of Cd exhibited the most significant variation with depth. The pollution load index (PLI) indicated moderate TMs pollution levels in the region, whereas the risk assessment code (RAC) classified Mn as being heavily polluted. Multiple linear regression (MLR) and random forest (RF), support vector machine (SVM), and XGBoost (1.7.7.1) machine learning models were used to simulate the RAC and total concentration of TMs, using physical and chemical indicators and magnetic parameters of the sediments as input variables. The MLR model outperformed RF, SVM, and XGBoost in simulating the CFs and total concentrations of most TMs in the sedimentary column, with R2 up to 0.668 and 0.87. The SHapley Additive exPlanations (SHAP) method reveals that χarm/χ is the dominant factor influencing the RAC of As in the XGBoost models. For the RAC of Co and Cu in RF models, C% and N% exhibit greater contributions.

This study accounted for and analyzed the carbon emissions of 13 cities in Jiangsu Province from 1999 to 2021. We compared the simulation effects of four models—STIRPAT, random forest, extreme gradient boosting, and support vector regression—on carbon emissions and performed model optimization. The random forest model demonstrated the best simulation performance. Using this model, we predicted the carbon emission paths for the 13 cities in Jiangsu Province under various scenarios from 2022 to 2040. The results show that Xuzhou has already achieved its peak carbon target. Under the high-speed development scenario, half of the cities can achieve their peak carbon target, while the remaining cities face significant challenges in reaching their peak carbon target. To further understand the factors influencing carbon emissions, we used the machine learning interpretation method SHAP and the features importance ranking method. Our analysis indicates that electricity consumption, population size, and energy intensity have a greater influence on overall carbon emissions, with electricity consumption being the most influential variable, although the importance of the factors varies considerably across different regions. Results suggest the need to tailor carbon reduction measures to the differences between cities and develop more accurate forecasting models.

The rise of Multimodal Large Language Models (MLLMs), renowned for their advanced instruction-following and reasoning capabilities, has significantly propelled the field of visual reasoning. However, due to limitations in their image tokenization processes, most MLLMs struggle to capture fine details of text and objects in images, especially in high-resolution samples. To overcome this limitation, we introduce P2G, a novel framework for plug-and-play grounding in MLLMs. P2G utilizes the tool-usage potential of MLLMs to employ expert agents for on-the-fly grounding of reasoning into critical visual and textual elements in images, thereby enabling deliberate reasoning through multimodal prompting. Additionally, we develop P2GB, a benchmark designed to evaluate MLLMs' proficiency in understanding inter-object relationships and textual content in challenging high-resolution images. Extensive experiments on visual reasoning tasks demonstrate the superiority of P2G, achieving performance comparable to GPT-4V on P2GB with a 7B backbone. Our work underscores the potential of grounding reasoning with external agents in MLLMs, presenting a promising alternative to mere model scaling.

Medical image segmentation plays an important role in disease diagnosis and surgical guidance. There are two problems in the current field of medical image segmentation. First, due to the inherent locality of convolution operations, it is difficult for convolutional neural network models to capture global context information. Second, the data set is usually small and the model is at risk of overfitting. To solve the above problems, we innovatively introduced Transformer and information bottlenecks based on the UNet model (IB-TransUNet). Transformer can capture global context information. Information bottleneck can compress redundant features and reduce the risk of overfitting in medical image segmentation tasks. Furthermore, we add a multi-resolution fusion mechanism to skip connections, which helps high-resolution feature maps to have both spatial texture information and semantic information. Finally, a channel attention block with residuals is added to the decoder to help the model learn relevant features. To verify the performance and efficiency of the proposed model, we conduct ablation experiments on two public datasets and compare them with those state-of-the-art models. Experimental results demonstrate the advantages of the proposed model.

Functional nanostructures are exploited for a variety of cutting-edge fields including plasmonics, metasurfaces, and biosensors, just to name a few. Some applications require nanostructures with uniform feature sizes while others rely on spatially varying morphologies. However, fine manipulation of the feature size over a large area remains a substantial challenge because mainstream approaches to precise nanopatterning are based on low-throughput pixel-by-pixel processing, such as those utilizing focused beams of photons, electrons, or ions. In this work, we provide a solution toward wafer-scale, arbitrary modulation of feature size distribution by introducing a lithographic portfolio combining interference lithography (IL) and grayscale-patterned secondary exposure (SE). Employed after the high-throughput IL, a SE with patterned intensity distribution spatially modulates the dimensions of photoresist nanostructures. Based on this approach, we successfully fabricated 4-inch wafer-scale nanogratings with uniform linewidths of <5% variation, using grayscale-patterned SE to compensate for the linewidth difference caused by the Gaussian distribution of the laser beams in the IL. Besides, we also demonstrated a wafer-scale structural color painting by spatially modulating the filling ratio to achieve gradient grayscale color using SE.Wafer-scale fabrication of periodic nanostructures with spatially modulated feature sizes is achieved by combining interference lithography and grayscale-patterned secondary exposure, opening new pathways toward large-area nanophotonic devices.

Sustained nutrient supply and dry matter accumulation during the late field growth stage are crucial for the final yield and quality of flue-cured tobacco. However, the slow nutrient release of conventional organic fertilizers often restricts plant growth during this critical period. This study evaluated the comprehensive effects of supplementing conventional organic fertilizer with a carbon polymer water-soluble fertilizer alone (T2), or in further combination with an anti-continuous cropping agent (T3) and a microbial inoculant (T4). The results demonstrated that, compared to the sole application of organic fertilizer, both the T3 and T4 combined treatments effectively promoted late-stage growth. Specifically, the T4 and T3 treatments significantly increased leaf dry matter accumulation by 39.84% and 29.62%, respectively. Regarding nutrient uptake, the T4 treatment significantly enhanced whole-plant nitrogen and phosphorus accumulation by 41.76% and 154.28%, respectively. Meanwhile, the T3 treatment significantly boosted whole-plant and leaf potassium accumulation by 62.43% and 124.59%, respectively. Crucially, multivariate analysis confirmed that the T4 treatment maximized dry matter by alleviating soil compaction (reducing bulk density), increasing soil available nutrients, and significantly activating glutamine synthetase and invertase in the leaves while maintaining the stable abundance of core dominant bacterial taxa. In contrast, the T3 treatment significantly altered soil acid-base conditions and selectively enriched specific taxa, including and , thereby improving the root zone microenvironment to facilitate a unique source-sink nutrient allocation. In conclusion, supplementing the organic and carbon polymer fertilizer base with either an anti-continuous cropping agent or a microbial inoculant effectively overcomes late-stage growth bottlenecks. Specifically, the microbial inoculant combination (T4) demonstrated the optimal overall performance in maximizing dry matter accumulation and nitrogen/phosphorus uptake, while the anti-continuous cropping agent combination (T3) was optimal for enhancing leaf potassium accumulation. These combined applications achieve this by ameliorating soil physicochemical properties, improving the rhizosphere microenvironment, promoting carbon and nitrogen metabolism, and optimizing source-sink nutrient allocation. Ultimately, these findings provide practical fertilization strategies to alleviate late-stage premature senescence and optimize field nutrient management in flue-cured tobacco.

Key messageOverexpression of K2-NhaD in transgenic cotton resulted in phenotypes with strong salinity and drought tolerance in greenhouse and field experiments, increased expression of stress-related genes, and improved regulation of metabolic pathways, such as the SOS pathway.Drought and salinity are major abiotic stressors which negatively impact cotton yield under field conditions. Here, a plasma membrane Na+/H+ antiporter gene, K2-NhaD, was introduced into upland cotton R15 using an Agrobacterium tumefaciens-mediated transformation system. Homozygous transgenic lines K9, K17, and K22 were identified by PCR and glyphosate-resistance. TAIL-PCR confirmed that T-DNA carrying the K2-NhaD gene in transgenic lines K9, K17 and K22 was inserted into chromosome 3, 19 and 12 of the cotton genome, respectively. Overexpression of K2-NhaD in transgenic cotton plants grown in greenhouse conditions and subjected to drought and salinity stress resulted in significantly higher relative water content, chlorophyll, soluble sugar, proline levels, and SOD, CAT, and POD activity, relative to non-transgenic plants. The expression of stress-related genes was significantly upregulated, and this resulted in improved regulation of metabolic pathways, such as the salt overly sensitive pathway. K2-NhaD transgenic plants growing under field conditions displayed strong salinity and drought tolerance, especially at high levels of soil salinity and drought. Seed cotton yields in transgenic line were significantly higher than in wild-type plants. In conclusion, the data indicate that K2-NhaD transgenic lines have great potential for the production of stress-tolerant cotton under field conditions.