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Multidrug-resistant (MDR) bacteria cause severe clinical infections and a high mortality rate of over 40% in patients with immunodeficiencies. Therefore, more effective, broad-spectrum, and accurate treatment for severe cases of infection is urgently needed. Here, we present an adoptive transfer of macrophages loaded with a near-infrared photosensitizer ( Lyso700D ) in lysosomes to boost innate immunity and capture and eliminate bacteria through a photodynamic effect. In this design, the macrophages can track and capture bacteria into the lysosomes through innate immunity, thereby delivering the photosensitizer to the bacteria within a single lysosome, maximizing the photodynamic effect and minimizing the side effects. Our results demonstrate that this therapeutic strategy eliminated MDR Staphylococcus aureus (MRSA) and Acinetobacter baumannii (AB) efficiently and cured infected mice in both two models with 100% survival compared to 10% in the control groups. Promisingly, in a rat model of central nervous system bacterial infection, we performed the therapy using bone marrow-divided macrophages and implanted glass fiber to conduct light irradiation through the lumbar cistern. 100% of infected rats survived while none of the control group survived. Our work proposes an efaficient and safe strategy to cure MDR bacterial infections, which may benefit the future clinical treatment of infection. There is increased demand for effective, broad-spectrum treatment options against severe, multi-drug resistant bacterial infections. Here, Wang et al describe an effective photodynamic therapy based on the adoptive transfer of macrophages loaded with a lysosomal photosensitiser.

The use of wireless sensor networks (WSNs) enables multidimensional and high-precision forest environment monitoring around the clock. However, the limited energy supply of sensor nodes using solely batteries is insufficient to support long-term data collection. Furthermore, since the complex terrain, dense vegetation, and variable weather in forests present unique challenges, relying on a single energy source is insufficient to ensure a stable energy supply for sensor nodes. Combining multiple energy sources is a promising way which has not been well studied. In this paper, to effectively utilize multiple energy sources, we propose a novel dynamic clustering routing protocol which considers the inherent diversity and intermittency of energy sources of the WSN in the forest. First, to address the inconsistency in residual energy caused by uneven energy harvesting among sensor nodes, a cluster head selection weight function is developed, and a dynamic weight-based cluster head election algorithm is proposed. This mechanism effectively prevents low-energy nodes from being selected as cluster heads, thereby maximizing the utilization of harvested energy. Second, a Q-learning-based adaptive hybrid transmission scheme is introduced, integrating both single-hop and multi-hop communication. The scheme dynamically optimizes intra-cluster transmission paths based on the current network state, reducing energy consumption during data transmission. The simulation results show that the proposed routing algorithm significantly outperforms existing methods in total network energy consumption, network lifetime, and energy balance. These advantages make it particularly suitable for forest environments characterized by strong fluctuations in harvested energy. In summary, this work provides an energy-efficient and adaptive routing solution suitable for forest environments with fluctuating energy availability.

Group 3 innate lymphoid cells (ILC3s), a novel subpopulation of lymphocytes enriched in the intestinal mucosa, are currently considered as key sentinels in maintaining intestinal immune homeostasis. ILC3s can secrete a series of cytokines such as IL-22 to eliminate intestinal luminal antigens, promote epithelial tissue repair and mucosal barrier integrity, and regulate intestinal immunity by integrating multiple signals from the environment and the host. However, ILC3 dysfunction may be associated with the development and progression of various diseases in the gut. Therefore, in this review, we will discuss the role of ILC3 in intestinal diseases such as enteric infectious diseases, intestinal inflammation, and tumors, with a focus on recent research advances and discoveries to explore potential therapeutic targets.

Generating new drug-like molecules is an essential aspect of drug discovery, and deep learning models significantly accelerate this process. Language models have demonstrated great potential in generating novel and realistic SMILES representations of molecules. Molecular scaffolds, which serve as the key structural foundation, can facilitate language models in discovering chemically feasible and biologically relevant molecules. However, directly using scaffolds as prior inputs can introduce bias, thereby limiting the exploration of novel molecules. To combine the above advantages and address the limitation, we incorporate molecular scaffold information into language models via an Online knowledge distillation framework for the unconditional Molecule Generation task (OMG), which consists of a GPT model that generates SMILES strings of molecules from scratch and a Transformer model that generate SMILES strings of molecules from scaffolds. The knowledge of scaffolds and complete molecular structures is deeply integrated through the mutual learning of the two models. Experimental results on two well-known molecule generation benchmarks show that the OMG framework enhances both the validity and novelty of the GPT-based unconditional molecule generation model. Furthermore, comprehensive property-specific evaluation results indicate that the generated molecules achieve a favorable balance across multiple chemical properties and biological activity, demonstrating the potential of our method in discovering viable drug candidates.

In the context of the implementation of the rural revitalization strategy in China, returning rural migrant workers are bound to have a certain impact on the rural economy, and land is a very important factor in the agricultural economy. Using data from the 2018 China Labor Dynamic Survey (CLDS), this study examines how migrant workers’ return behaviors influence farmland transfer-in. To address potential endogeneity, the analysis employs the Probit model, instrumental variable methods, and propensity score matching. The findings reveal that returning migrant workers significantly promote farmland transfer-in. Households with returning migrant workers exhibit stronger demands for land transfer-in and tend to operate farmland on a larger scale. Furthermore, returning migrant workers drive farmland expansion through mechanization labor substitution, enhanced access to agricultural loans, and reduced non-farm participation. Additionally, returning migrant workers who are highly educated and younger play a particularly influential role, underscoring the heterogeneous impacts across different migrant groups. This study provides empirical evidence for rural revitalization policies in China by systematically analyzing the effect of returning migrant workers in promoting land transfer-in and the path of influence on farmland scale.

As a region with functions such as energy production and as an ecological barrier, the Loess Plateau plays a vital role in China. This study examines the spatiotemporal changes in water storage and vegetation cover and their correlations. The changes in water storage were calculated using GRACE data and the GLDAS-NOAH model, while vegetation changes were derived from MODIS data. The results showed that the groundwater inventory decreased by 7.80 mm/a and the land inventory decreased by 9.72 mm/a. Surface water storage capacity increased by 1.92 mm/a. From west to east, terrestrial and groundwater storage decrease, reflecting overall losses, but surface water storage remains positive. By analyzing the FVC, it can be observed that since 2006, vegetation coverage has shown an overall increasing trend, with the highest value occurring in 2018. There has been a remarkably increase in vegetation coverage in most areas, while there was a decrease in vegetation coverage along the borders of Qinghai Province and northern Shaanxi Province. By conducting a correlation analysis, it can be found that the correlation coefficients between terrestrial water storage, surface water storage, and groundwater storage changes and vegetation coverage are −0.85, 0.60, and −0.93, respectively, indicating that increased vegetation coverage leads to reduced groundwater and terrestrial water storage. The results also indicate that there are significant spatial differences in the monthly correlations and maximum lag months between water storage and vegetation coverage. In addition, through discussing the driving factors of water storage changes in the Loess Plateau, we consider that the Grain for Green Project and mining activities may be the two major drivers of these changes. This study is highly important and valuable to the study of changes in water reserves in the Loess Plateau, as well as ecological protection and environmental assessment in the Loess Plateau.

Groundwater safety in coastal tourism regions is increasingly compromised by the conflict between rigorous quality standards and diffuse agricultural pollution. Traditional linear assessment methods often mask specific high-risk indicators through mathematical averaging. To address this challenge, this study applies the Extension Cloud Model using data from 18 monitoring wells in the Jiaodong Peninsula. To robustly quantify uncertainty despite the limited sample size, the framework is integrated with the Multi-step Backward Cloud Transformation based on Sampling with Replacement algorithm. While hydrochemical analysis indicates that rock weathering controls the regional baseline, water quality is significantly degraded by where potassium shows a strong correlation coefficient of 0.90 with chemical oxygen demand. The assessment results indicate that 38.9% of the groundwater samples fall into Class IV or V which are unsuitable for drinking purposes. A comparative analysis confirms that the Extension Cloud Model successfully identified critical contamination risks masked by the eclipsing effect in the Entropy Weighted Water Quality Index. Furthermore, the resampling algorithm calculated a regional expectation value of 0.3609 quantifying that the overall water quality is in a critical transition state between Class III and IV. This framework offers a scientifically robust tool for diagnosing groundwater safety in data-scarce coastal zones.

Breast cancer represents the most prevalent malignancy in women globally. While UDP-glucose pyrophosphorylase 2 (UGP2) has been implicated in tumor biology, its pathophysiological role in breast carcinogenesis remains undefined. This study systematically investigates the clinical relevance and functional mechanisms of UGP2 in breast cancer progression. Multi-omics analysis was conducted using GEPIA2, TIMER2, and TCGA databases to evaluate UGP2 expression patterns, clinicopathological correlations, and prognostic significance across breast cancer subtypes. Findings were validated through immunohistochemical analysis of 118 clinical specimens. Single-cell RNA sequencing data from GEO were employed to resolve UGP2 spatial distribution within tumor microenvironments. Functional enrichment analysis elucidated UGP2-associated pathways, while loss-of-function experiments using siRNA-mediated knockdown assessed UGP2’s impact on malignant phenotypes in breast cancer cell lines. Our research found that the expression level of UGP2 in the TNBC subtype was significantly higher than that in other subtypes. The elevated expression level of UGP2 suggests a poor prognosis for breast cancer patients. The high expression level of UGP2 is positively correlated with the level of CNV. UGP2 is mainly located in epithelial cells and CAFs subsets and can participate in biological processes such as collagen synthesis. Knockdown of UGP2 in vitro can inhibit the proliferation, migration and invasion abilities of breast cancer cells. This study demonstrates that elevated UGP2 expression is clinically associated with aggressive tumor behavior and unfavorable prognosis in breast cancer patients, particularly in the triple-negative subtype (TNBC). Mechanistic analyses reveal UGP2’s functional involvement in both tumor cell proliferation and stromal remodeling through cancer-associated fibroblast (CAF) interactions. The robust association between UGP2 overexpression and aggressive clinicopathological features positions it as both a promising prognostic biomarker and potential therapeutic target, particularly in TNBC management.

Water inrush from confined aquifers in coal seam floor poses a great threat to the safety production of coal mines, while the aquiclude in coal seam floor is a key factor to avoid water inrush disasters. Therefore, an objective and accurate evaluation on the water-resisting ability of the aquiclude can effectively identify potential risk areas of water inrush and plays an important role in preventing such disasters. In this study, Longfeng coal mine was taken as an example. And based on the availability of data and the representativeness of factors, thickness of effective aquiclude, brittle rock thickness of aquiclude, rock quality designation of aquiclude, consumption of drilling fluid, mudstone ratio of aquiclude and fault fracture zone were selected as evaluation factors for water-resisting ability. Besides, the non-linear mathematical method—entropy weight method was combined to construct a comprehensive evaluation model of the water-resisting ability of aquiclude in coal seam floor. On this basis, the information processing and spatial display functions of GIS technology were used to make a thematic map of influencing factors and the thematic map was compositely superimposed. Finally, the evaluation on the water-resisting ability of aquiclude in coal seam floor in Longfeng coal mine was realized. The multi-source information fusion evaluation method based on GIS and entropy weight can comprehensively and objectively reflect the feature that the water-resisting ability of aquiclude in coal seam floor was controlled by multiple factors and had complex formation mechanisms. Also, the evaluation results were displayed intuitively and visually and can provide a scientific basis for the prevention and control of water inrush disasters from coal seam floor.

Free-standing reduced graphene oxide (rGO) papers were prepared by chemical reduction, thermal reduction and combined chemical-thermal reduction, respectively. Four-point probe and nanoindentation experiments were conducted to investigate the electrical and mechanical properties of rGO papers. The rGO paper prepared by soaking in L-ascorbic acid and thermal annealing in argon at 1000 °C (labeled rGO-AsA-T) showed superior electrical and mechanical properties when compared with rGO papers prepared merely by chemical reduction or thermal reduction. The as-prepared rGO-AsA-T paper exhibited an electrical conductivity of 5.7 × 10 4 S/m, showing an increase of 90% compared to that in the thermally annealed rGO paper and nearly 40 times that of rGO paper reduced by L-ascorbic acid. It was also found that the rGO-AsA-T paper had the lowest elastic modulus of 288 MPa, showing enhanced flexibility. The nearly free voids in rGO-AsA-T paper proved by scanning electron microscopy (SEM) were due to the capillary action in chemical reduction and were significant in improving the electrical conductivity and flexibility of the paper. The rGO-AsA-T paper with high conductivity and flexibility has a promising application in flexible electronics.