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Background and aims While the coupled effects of root exudates and microbial feedbacks on soil processes are well-recognized, we still lack an understanding of differences in root exudate fluxes and the associated ecological consequences among tree growth forms. Methods Two deciduous tree species (i.e., Cercidiphyllum japonicum and Larix kaempferi ) and two evergreen tree species (i.e., Pinus armandi and Pinus tabulaeformis ) were selected to perform an in-situ collection of root exudates during the growing season in 2016. The net N mineralization rates and associated microbial enzyme activities were measured in rhizosphere and bulk soils to evaluate rhizosphere effects. Moreover, we compiled the dataset related to root exudation and their associated biological traits and the soil chemical properties for 21 tree species from temperate forests. Results The root exudation rates and the annual root exudate carbon (C) fluxes of two deciduous tree species were significantly higher than those of the two evergreen tree species. Correspondingly, the rhizosphere effects of deciduous tree species on the microbial biomass, enzyme activity and net N mineralization rate were approximately 1.9, 1.6 and 2.4 times greater than those of the evergreen tree species, respectively. Rhizosphere effects were positively correlated with the root exudation rate. The compiled dataset also suggest that deciduous tree species tend to have higher exudation rates than evergreen tree species in temperate forests. Conclusions Collectively, these results suggest that the two tree growth forms exhibit different patterns in root exudate inputs and associated rhizosphere microbial processes. Generally, deciduous tree species tend to exude more C into the soil and consequently induce greater microbial feedback on soil N transformations during the growing season in temperate regions, implying that deciduous tree species induced a greater effect on the C and nutrient cycling in rhizosphere soil than evergreen tree species.

In the post-COVID era, the rise of domestic motorcycle culture and platforms like Douyin have given motorcycles stronger social communication attributes, reducing their function as mere tools. More ordinary people have access to motorcycles, and motorcycle-related topics on the Douyin platform will be more discussed and controversial. This paper will analyze the current public opinion environment related to motorcycle topics on the Douyin platform, and discuss the role played by the social media platform Douyin in spreading motorcycle riding culture and shaping the image of motorcycle enthusiasts. This research will adopt the method of quantitative analysis questionnaire to study the usage habits of motorcycle enthusiasts and the motivation for sharing motorcycle-related content on Douyin and analyze the current status and problems of Douyin’s news reports on motorcycles. Douyin has played an active role in promoting motorcycle culture and shaping the image of motorcycle enthusiasts, while constantly influencing and promoting the revision and improvement of relevant laws and regulations towards motorcycle riding. However, due to more negative news reports, society’s aversion to motorcycle riding culture and motorcycle enthusiasts will lead to an antagonistic relationship between car drivers and motorcycle riders, which is not conducive to the positive image-building of motorcycle enthusiasts.

Background and aims Plant nitrogen (N) acquisition plays an important role in regulating plant growth and ecosystem functions. However, the seasonal variations in the relative contributions of different N sources to plant N uptake and how plants modify their N absorption preferences, especially in ectomycorrhizal forests, are not well understood. Methods We used the in situ 15 N-labeling method to quantitatively estimate the relative uptake contributions of plants for three different soil N sources (nitrate, ammonium and amino acids) and plant N acquisition preferences in an ectomycorrhizal alpine forest (a 70-year-old spruce plantation on the eastern Tibetan Plateau, China) during the growing season and the nongrowing season. Results Across the two seasons, plants in the spruce plantation showed a greater preference for acquiring soil NH 4 + -N, with soil NH 4 + contributing more than 50% to the total N uptake of plants (57.88% during the growing season and 52.72% during the non-growing season). Moreover, amino acids exhibited a considerable contribution to the total plant N uptake, and their contribution was significantly higher during the non-growing season (33.47%) than that during the growing season (9.86%). Accordingly, plants showed a greater preference for taking up amino acids over NO 3 − -N in the soil as the season changed from the growing season to the non-growing season. Conclusions Collectively, our data demonstrate that soil inorganic N is the predominant N source for plants in alpine forests, irrespective of seasonal variations. However, soil amino acids could also be an important supplementary N source for the plant N economy, especially during the non-growing season, when inorganic N availability is constrained. Our findings also suggest that plants in ectomycorrhizal alpine forests modify their nutrient absorption preference in response to seasonal changes.

Background and aims While large differences in microbial biomass and extracellular enzyme activities (EEAs) between rhizosphere and bulk soils have been demonstrated, the potentially different response of microbes and their EEAs in rhizosphere and bulk soils to nitrogen (N) deposition is still not elucidated. Methods We analyzed the microbial biomass and EEAs in the rhizosphere and bulk soils of Sibiraea angustata in an alpine shrubland on the eastern Qinghai-Tibet Plateau after chronic N application. We also analyzed the stoichiometric linkages between plants, microbes, enzymes and soils to clarify the coupled responses of aboveground plants and belowground ecological processes. Results Microbial nutrient concentrations and activities of EAAs responded differently to N addition in the rhizosphere and bulk soils. In the rhizosphere, N addition caused a significant increase in microbial biomass carbon (C), N and phosphorus (P) concentrations and greater P-degrading enzyme activity (relative to the activities of C- and N-degrading enzymes), which induced a significant reduction in enzyme C:P and N:P ratios. The rhizosphere enzyme N:P ratio was negatively correlated with the N:P ratios of plant, soil and microbe, implying that increased plant and microbial P uptake under N addition may gradually aggravate rhizosphere P limitation. However, for the bulk soil, N addition did not affect microbial biomass but significantly enhanced C-degrading enzyme activity and decreased the enzyme C:N ratio. Meanwhile, the bulk-soil enzyme C:N ratio was negatively correlated with the soil C:N ratio but independent of the plant C:N ratio, implying that N addition may enhance bulk-soil microbial C limitation. Conclusions Our study suggests that elevated N deposition may induce differential microbial nutrient limitation between the rhizosphere and bulk soils due to the plant-microbe-soil interactions in the rhizosphere. This study highlights the importance of incorporating rhizosphere microbial processes into biogeochemical models describing environmental changes.

In view of the complicated pathophysiological process of vascular dementia (VD), drugs for the clinical treatment of VD mainly target related risk factors, while drugs with excellent efficacy in cognitive function are still relatively lacking. Imperatorin (IMP), an active constituent extracted from angelica dahuricae and notopterygium Notopterygii, which has anti-inflammatory, vasodilator, anticoagulant, block calcium channel, anticonvulsant, and anti oxygen free radical injury properties. Therefore,the present study examined its effects on VD rats and the underlying molecular mechanisms, in order to provide promising therapeutic methods. VD was established by modified ligation of perpetual two-vessel occlusion (2VO). After 2VO surgery, IMP (2.5, 5, and 10 mg/kg) was administered by intraperitoneal injection for 12 consecutive weeks to evaluate therapeutic effects. Cognitive function was verified by the Morris water maze. The neuronal morphological changes were examined via Hematoxylin–Eosin staining. Real-Time PCR and Western blot were used for detecting pro- and antiapoptotic biomarkers, and the hippocampus synaptic damage was examined by Transmission electron microscope. We revealed that 2VO-induced cognitive impairment, hippocampus CA1 neuron damage, apoptosis and synaptic damage. IMP-treatment significantly improved 2VO-induced cognitive deficits and hippocampus neuron damage. Molecular analysis revealed that IMP inhibited apoptosis through the down regulation of Bax, Caspase-3 and upregulation of Bcl-2. Meanwhile, IMP-treatment markedly improved synaptic ultrastructure morphology, increased the SAZ length, PSD thickness and up-regulated PSD-95 expression. Collectively, our findings demonstrated that IMP was effective in the treatment of 2VO-induced VD via inhibiting apoptosis of hippocampus neurons and reducing the synaptic plasticity destroy.

Imperatorin, an active ingredient extracted from Angelica and Qianghuo, has anti-inflammatory, anti-oxidative stress damage, blocking calcium channels, and other properties. Our preliminary findings revealed the protective role of imperatorin in the treatment of vascular dementia, we further explored the underlying mechanisms concerning the neuroprotection function of imperatorin in vascular dementia. The cobalt chloride (C O Cl2)-induced chemical hypoxia and hypoglycemia of hippocampal neuronal cells was applied as in vitro vascular dementia model. Primary neuronal cells was isolated from the hippocampal tissue of SD suckling rats within 24 h of birth. Hippocampal neurons were identified by immunofluorescence staining of microtubule-associated protein 2. Silencing or overexpression of Nrf2 was conducted by transfection of corresponding plasmids in hippocampal neuronal cells. Cell viability was detected by MTT assay to determine the optimal modeling concentration of CoCl2. Mitochondrial membrane potential, intracellular reactive oxygen species and apoptosis rate was measured by flow cytometry. The expression of anti-oxidative proteins was detected by quantitative real-time PCR and western blot, including Nrf2, NQO-1 and HO-1. Nrf2 nuclear translocation was detected using laser confocal microscopy. The modeling concentration of CoCl2 was 150umol/l, and the best interventional concentration of imperatorin was 7.5umol/l. Significantly, imperatorin facilitated the nuclear localization of Nrf2, promoted the expressions of Nrf2, NQO-1, and HO-1 relative to the model-control group. Moreover, imperatorin reduced the mitochondrial membrane potential and ameliorated CoCl2-induced hypoxic apoptosis in hippocampal neurons. On the contrary, silencing Nrf2 completely abrogated the protective effects of imperatorin. Imperatorin might be an effective drug for preventing and treating vascular dementia.

Electrocatalytic CO2 reduction reaction shows great potential for converting CO2 into high-value chemicals and fuels at normal temperature and pressure, combating climate change and achieving carbon neutrality goals. However, the complex reaction pathways involve the transfer of multiple electrons and protons, resulting in poor product selectivity, and the existence of competitive hydrogen evolution reactions further increases the associated difficulties. This review illustrates the research progress on the micro mechanism of electrocatalytic CO2 reduction reaction in the electrolyte environment in recent years. The reaction pathways of the products, pH effects, cation effects and anion effects were systematically summarized. Additionally, further challenges and difficulties were also pointed out. Thus, this review provides a theoretical basis and future research direction for improving the efficiency and selectivity of electrocatalytic CO2 reduction reaction.

The Low Energy X-ray telescope (LE) is one of the three main instruments of the Insight-Hard X-ray Modulation Telescope ( Insight- HXMT) . It is equipped with Swept Charge Device (SCD) sensor arrays with a total geometrical area of 384 cm and an energy band from 0.7 to 13 keV. In order to evaluate the particle induced X-ray background and the cosmic X-ray background simultaneously, LE adopts collimators to define four types of Field Of Views (FOVs), i.e., 1.6°×6°, 4°×6°, 50°-60°×2°-6° and the blocked ones which block the X-ray by an aluminum cover. LE is constituted of three detector boxes (LEDs) and an electric control box (LEB) and achieves a good energy resolution of 140 eV@5.9 keV, an excellent time resolution of 0.98 ms, as well as an extremely low pileup (<1%@18000 cts/s). Detailed performance tests and calibration on the ground have been performed, including energy-channel relation, energy response, detection efficiency and time response.

Wind farms situated in proximity to residential areas present environmental challenges, primarily due to noise emissions. Rectangular and parallelogram layouts are commonly employed in current wind farm designs owing to their simplicity and visual appeal. However, such configurations often experience significant power loss under certain wind directions because of intense wake interactions. This paper proposes a layout fine-tuning strategy for low-noise wind farm design. Within a reinforcement learning framework integrated with an engineering wake model and a noise propagation model, the positions of two turbines (controlled by two variables) are optimized. The noise propagation model was validated for idealized long-range sound propagation over flat terrain with acoustically soft surfaces. A case study was conducted on a 12-turbine wind farm located on a flat plain in China, with a noise threshold of 45 dB(A) used to assess the noise impact area. Optimization results demonstrate that the proposed method achieves a balance between power output and noise reduction compared to the original regular layout: Annual Energy Production (AEP) increased slightly by 0.16%, while the noise impact area was reduced by 6.0%. Although these improvements appear modest, the potential of the proposed methodology warrants further investigation.

Traditional single-prediction models often exhibit limitations in meeting wind power prediction requirements in complex operational scenarios. Furthermore, the inherent “black-box” nature of deep learning models leads to limited interpretability of predictions, hindering effective support for grid dispatch planning. To address these issues, this study proposes a novel day-ahead wind power prediction method, referred to as SHapley Additive exPlanations (SHAP)–Mixture of Experts (MoE), which integrates SHAP into an MoE framework. Here, SHAP is employed for interpretability purposes. This study innovatively transforms SHAP analysis into prior knowledge to guide the decision-making of the MoE gating network and proposes a two-layer dynamic interpretation mechanism based on the collaborative analysis of gating weights and SHAP values. This approach clarifies key meteorological factors and the model’s advantageous scenarios, while quantifying the uncertainty among multiple expert decisions. Firstly, each expert model was pre-trained, and its parameters were frozen to construct a candidate expert pool. Secondly, the SHAP vectors for each pre-trained expert were computed over all sample features to characterize their decision-making logic under varying scenarios. Thirdly, an augmented feature set was constructed by fusing the original meteorological features with SHAP attribution matrices from all experts; this set was used to train the gating network within the MoE framework. Finally, for new input samples, each frozen expert model generates a prediction along with its corresponding SHAP vector, and the gating network aggregates these predictions to produce the final forecast. The proposed method was validated using operational data from an offshore wind farm located in southeastern China. Compared with the best individual expert model and traditional ensemble forecasting models, the proposed method reduces the Root Mean Square Error (RMSE) by 0.23% to 4.92%. Furthermore, the method elucidates the influence of key features on each expert’s decisions, offering insights into how the gating network adaptively selects experts based on the input features and expert-specific characteristics across different scenarios.