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Background Chemoresistance is a major factor contributing to the poor prognosis of patients with pancreatic cancer, and cancer stemness is one of the most crucial factors associated with chemoresistance and a very promising direction for cancer treatment. However, the exact molecular mechanisms of cancer stemness have not been completely elucidated. Methods m 6 A-RNA immunoprecipitation and sequencing were used to screen m 6 A-related mRNAs and lncRNAs. qRT-PCR and FISH were utilized to analyse DDIT4-AS1 expression. Spheroid formation, colony formation, Western blot and flow cytometry assays were performed to analyse the cancer stemness and chemosensitivity of PDAC cells. Xenograft experiments were conducted to analyse the tumour formation ratio and growth in vivo. RNA sequencing, Western blot and bioinformatics analyses were used to identify the downstream pathway of DDIT4-AS1. IP, RIP and RNA pulldown assays were performed to test the interaction between DDIT4-AS1, DDIT4 and UPF1. Patient-derived xenograft (PDX) mouse models were generated to evaluate chemosensitivities to GEM. Results DDIT4-AS1 was identified as one of the downstream targets of ALKBH5, and recruitment of HuR onto m 6 A-modified sites is essential for DDIT4-AS1 stabilization. DDIT4-AS1 was upregulated in PDAC and positively correlated with a poor prognosis. DDIT4-AS1 silencing inhibited stemness and enhanced chemosensitivity to GEM (Gemcitabine). Mechanistically, DDIT4-AS1 promoted the phosphorylation of UPF1 by preventing the binding of SMG5 and PP2A to UPF1, which decreased the stability of the DDIT4 mRNA and activated the mTOR pathway. Furthermore, suppression of DDIT4-AS1 in a PDX-derived model enhanced the antitumour effects of GEM on PDAC. Conclusions The ALKBH5-mediated m 6 A modification led to DDIT4-AS1 overexpression in PDAC, and DDIT-AS1 increased cancer stemness and suppressed chemosensitivity to GEM by destabilizing DDIT4 and activating the mTOR pathway. Approaches targeting DDIT4-AS1 and its pathway may be an effective strategy for the treatment of chemoresistance in PDAC.

Alterations in the composition and function of the gut microbiota have been reported in patients with type 2 diabetes mellitus (T2DM). Emerging studies show that prescribed antidiabetic drugs distort the gut microbiota signature associated with T2DM. Even more importantly, accumulated evidence provides support for the notion that gut microbiota, in turn, mediates the efficacy and safety of antidiabetic drugs. In this review, we highlight the current state-of-the-art knowledge on the crosstalk and interactions between gut microbiota and antidiabetic drugs, including metformin, α-glucosidase inhibitors, glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, sodium-glucose cotransporter 2 inhibitors, traditional Chinese medicines and other antidiabetic drugs, as well as address corresponding microbial-based therapeutics, aiming to provide novel preventative strategies and personalized therapeutic targets in T2DM.

Ensemble learning is widely applied in various real-world settings, with Bagging and Boosting being two core algorithms. Although these techniques have been extensively investigated through experimental comparisons of their performance in various scenarios, few studies have analyzed and quantified their benefits, costs, and complexities to support algorithm-aware decision making. In this study, we develop a theoretical model to compare Bagging and Boosting in terms of performance, computational costs, and ensemble complexity, and validate it through experiments on four datasets (MNIST, CIFAR-10, CIFAR-100, IMDB) with varying data complexity and computational environments. The results show that, for MNIST, as ensemble complexity increases (e.g., from 20 to 200), Bagging’s performance improves from 0.932 to 0.933 before plateauing, while Boosting improves from 0.930 to 0.961 before showing signs of overfitting. At the same ensemble complexity, such as 200 base learners, Boosting requires approximately 14 times more computational time than Bagging, indicating substantially higher computational costs. Similar patterns are observed across the other three datasets, confirming the generality of our findings and revealing consistent trade-offs between performance and computational costs. Taken together, these results confirm the robustness of our theoretical predictions and provide a foundation for practical guidance. Specifically, decision-makers prioritizing cost-efficiency may prefer Bagging, whereas those focusing on maximizing performance might find Boosting more beneficial. For simpler datasets on average-performing devices, Boosting can be effective, whereas Bagging is more suitable for complex datasets on high-performing devices. Overall, this study contributes by integrating analytical modeling with empirical validation across multiple datasets to provide theoretical insights and practical guidance. It systematically compares Bagging and Boosting in terms of performance, computational costs, and ensemble complexity, thereby enabling practitioners to choose the most appropriate method under varying data complexities, performance needs, and resource constraints.

Unmanned Aerial Vehicles (UAVs) consistently encounter complex operational environments during task execution. To enhance UAV adaptability in such environments, improve rapid and efficient path planning capabilities, and reduce operational costs, this paper proposes a 3D UAV path planning algorithm based on an improved Dwarf Mongoose Optimization (DMO) algorithm enhanced with multiple strategies. Initially, a chaos mapping-based opposition-based learning strategy is introduced to ensure a uniform distribution of the initial population in the solution space, thereby enhancing diversity and improving global search performance. Then, a golden sine function based on nonlinear weights is employed to help dwarf mongooses avoid getting trapped in local optima and to balance global exploration with local exploitation. In addition, a differential mutation strategy is incorporated, which uses difference information between individuals to guide the evolutionary process, further improving diversity and enhancing the ability to escape local optima. The efficacy of the improved algorithm, in terms of convergence precision, the ability to escape local optima, and a balanced exploration and exploitation capability, is demonstrated through ablation experiments and the Wilcoxon rank-sum test. Comparative evaluations on benchmark test functions demonstrate that the improved algorithm (CDMOS) outperforms the original DMO in optimization performance, convergence precision, and overall stability, achieving an average improvement of 53.5% in convergence accuracy and 35.1% in solution stability across 29 benchmark functions. Finally, the improved algorithm is applied to 3D map path planning simulations involving multiple nodes and obstacles, confirming its capability to enhance UAV robustness, adaptability, and real-time performance. In these simulations, CDMOS reduced path length by 46.0%, smoothness cost by 93.4%, and maintained a low obstacle cost, thus generating shorter, smoother, and safer flight paths. The generated flight paths are optimized in terms of both stability and efficiency, making the algorithm suitable for complex mission scenarios.

Biological nitrogen fixation (BNF) plays a crucial role in nitrogen utilization in agroecosystems. Functional characteristics of plants (grasses vs. legumes) affect BNF. However, little is still known about how ecological zones and cropping patterns affect legume nitrogen fixation. This study's objective was to assess the effects of different cropping systems on aboveground dry matter, interspecific relationships, nodulation characteristics, root conformation, soil physicochemistry, BNF, and nitrogen transfer in three ecological zones and determine the main factors affecting nitrogen derived from the atmosphere (Ndfa) and nitrogen transferred (Ntransfer). The N labeling method was applied. Oats ( L.), forage peas ( L.), common vetch ( L.), and fava beans ( L.) were grown in monocultures and mixtures (YS: oats and forage peas; YJ: oats and common vetch; YC: oats and fava beans) in three ecological regions (HZ: Huangshui Valley; GN: Sanjiangyuan District; MY: Qilian Mountains Basin) in a split-plot design. The results showed that mixing significantly promoted legume nodulation, optimized the configuration of the root system, increased aboveground dry matter, and enhanced nitrogen fixation in different ecological regions. The percentage of nitrogen derived from the atmosphere (%Ndfa) and percentage of nitrogen transferred (%Ntransfer) of legumes grown with different legume types and in different ecological zones were significantly different, but mixed cropping significantly increased the %Ndfa of the legumes. Factors affecting Ndfa included the cropping pattern, the ecological zone (R), the root nodule number, pH, ammonium-nitrogen, nitrate-nitrogen, microbial nitrogen mass (MBN), plant nitrogen content (N%), and aboveground dry biomass. Factors affecting Ntransfer included R, temperature, altitude, root surface area, nitrogen-fixing enzyme activity, organic matter, total soil nitrogen, MBN, and N%. We concluded that mixed cropping is beneficial for BNF and that mixed cropping of legumes is a sustainable and effective forage management practice on the Tibetan Plateau.

With the gradual industrial development of the Fuyu oil formation in the X oilfield and the frequent occurrence of set losses in the stratigraphic parts of the Nengjiang Formation, the importance of the overall characterisation of the fracture system has increased significantly. By establishing an integrated model of the fracture system from the Nengjiang Formation to the Quantui Formation, research work such as batch extraction of fracture elements and comparative analysis of the characteristics of the fracture system in the X Oilfield was carried out. The study shows that the Yaojia Formation to Quantou Formation fault system, vertically, has a multi-phase fault inheritance relationship; when the faults formed at a later stage develop, the faults formed at an earlier stage are revived again and grow together with the later faults, and the development pattern of the present-day faults is the result of multiple phases of tectonic movements and the cumulative development of faults.

Despite the known influence of continuous cropping on soil microorganisms, little is known about the associated difference in the effects of continuous cropping on the community compositions of soil bacteria and fungi. Here, we assessed soil physicochemical property, as well as bacterial and fungal compositions across different years (Uncropped control, 1, 6, 11, 16, and 21 years) and in the watermelon system of a gravel mulch field in the Loess Plateau of China. Our results showed that long-term continuous cropping led to substantial shifts in soil bacterial and fungal compositions. The relative abundances of dominant bacterial and fungal genera (average relative abundance > 1.0%) significantly varied among different continuous cropping years ( P  < 0.05). Structural equation models demonstrated that continuous cropping alter soil bacterial and fungal compositions mainly by causing substantial variations in soil attributes. Variations in soil pH, nutrient, salinity, and moisture content jointly explained 73% and 64% of the variation in soil bacterial and fungal compositions, respectively. Variations in soil moisture content and pH caused by continuous cropping drove the shifts in soil bacterial and fungal compositions, respectively (Mantel R  = 0.74 and 0.54, P  < 0.01). Furthermore, the variation in soil bacterial and fungal composition showed significant correlation with watermelon yield reduction ( P  < 0.01). Together, long-term continuous cropping can alter soil microbial composition, and thereby influencing watermelon yield. Our findings are useful for alleviating continuous cropping obstacles and guiding agricultural production.

Kentucky bluegrass ( Poa pratensis L.), a native grass species of the Qinghai-Tibetan Plateau, is widely used for ecological restoration due to its high growth rate and strong adaptability. However, monocultures of Poa pratensis are prone to rapid degradation and low productivity, limiting their suitability for animal husbandry. To address these challenges, this study evaluated the production performance and interspecific relationships of different mixed-sown and monoculture grasslands to identify optimal cultivation strategies. Field experiments were conducted over a six-year period, with three mixed-sown treatments— Poa pratensis combined with Siberian wildrye ( Elymus sibiricus L.), Chinese fescue ( Festuca sinensis Engler ex S.L.Lu), and alkali grass ( Puccinellia tenuiflora (Griseb.) Scribn. & Merr.)—alongside their respective monocultures. LASSO regression (Least Absolute Shrinkage and Selection Operator Regression) and ROC curve analysis (Receiver Operating Characteristic Curve Analysis) were applied to identify key factors influencing production performance. The results indicated that the mixed-sown grassland of Elymus sibiricus and Poa pratensis significantly boosted forage yield by 216.88% to 323.06% in comparison with monoculture Poa pratensis . Additionally, the comprehensive evaluation index, which integrates forage yield and nutritional quality, was 16.41% higher for the Elymus sibiricus and Poa pratensis mixture than for the monoculture Poa pratensis grassland. These findings imply that the mixed-sown grassland of Elymus sibiricus and Poa pratensis effectively addresses the low productivity issue often seen in monoculture Poa pratensis grasslands. However, in terms of yield stability and interspecific compatibility, the mixed-sown grassland of Puccinellia tenuiflora and Poa pratensis demonstrated superior performance. Its relative total yield (RTY) consistently exceeded 1.0 from the third to the sixth year, reflecting higher interspecific compatibility and stable productivity over time. And the Poa pratensis and Puccinellia tenuiflora mixture showed the best performance, achieving the highest stability value of 3.12. Therefore, the combination of Poa pratensis and Puccinellia tenuiflora is recommended as the optimal strategy for achieving long-term yield stability and high productivity in cultivated grasslands.

Fluorescence polarization microscopy images both the intensity and orientation of fluorescent dipoles and plays a vital role in studying molecular structures and dynamics of bio-complexes. However, current techniques remain difficult to resolve the dipole assemblies on subcellular structures and their dynamics in living cells at super-resolution level. Here we report polarized structured illumination microscopy (pSIM), which achieves super-resolution imaging of dipoles by interpreting the dipoles in spatio-angular hyperspace. We demonstrate the application of pSIM on a series of biological filamentous systems, such as cytoskeleton networks and λ-DNA, and report the dynamics of short actin sliding across a myosin-coated surface. Further, pSIM reveals the side-by-side organization of the actin ring structures in the membrane-associated periodic skeleton of hippocampal neurons and images the dipole dynamics of green fluorescent protein-labeled microtubules in live U2OS cells. pSIM applies directly to a large variety of commercial and home-built SIM systems with various imaging modality. Polarization microscopy has been combined with single-molecule localization, but it’s often limited in either speed or resolution. Here the authors present polarized Structured Illumination Microscopy (pSIM), a method that uses polarized laser excitation to measure dye orientation during fast super-resolution live cell imaging.

Landslide susceptibility mapping (LSM) in mountain–basin transition zones remains challenging because conventional approaches rely mainly on historical inventories and static conditioning factors, whereas independent deformation evidence is seldom incorporated to refine susceptibility zonation. This study proposes an integrated LSM framework for the Xining Basin by coupling a Mamba-based model (Mamba-LSM) with SBAS-InSAR-based deformation-informed bidirectional reclassification, with the key innovation lying in the use of independent deformation evidence to refine susceptibility zonation after model prediction. Specifically, Mamba-LSM integrates six-channel neighborhood patches, CNN-based local spatial encoding, and Mamba-based latent feature transformation to improve the representation of local terrain context for landslide susceptibility assessment. Results show that Mamba-LSM achieved the highest AUC among the evaluated models, reaching 0.9011 with an F1-score of 0.7431. After deformation-informed bidirectional reclassification, the high- and very-high-susceptibility classes occupied only 25.31% of the study area but contained 69.84% of the mapped landslides, and were concentrated mainly in valley–mountain transition belts, river-incised slopes, and engineering-disturbed sectors where SBAS-InSAR deformation hotspots were also preferentially distributed. These findings demonstrate that integrating independent SBAS-InSAR deformation evidence can improve both the spatial concentration of landslides in high-susceptibility zones and the physical interpretability of susceptibility zonation.