Nicotinamide mononucleotide (NMN) has gained significant attention as a promising anti-aging and health-promoting supplement, driving the development of efficient and sustainable production methods. Among these methods, enzymatic synthesis has emerged as a highly specific and environmentally friendly approach. This study presents a comprehensive evaluation of NMN synthesized via two key enzymatic pathways-nicotinamide riboside kinase (NRK)-mediated and nicotinamide phosphoribosyltransferase (NAMPT)-mediated routes-along with commercially available raw NMN materials. Key quality attributes, including purity, content, moisture levels, thermal stability, and biological activity, were systematically assessed. Results show that NAMPT-synthesized NMN consistently achieves 100% purity, matching pharmaceutical-grade standards, while NRK-synthesized NMN displays batch-dependent variability, with purity ranging from 92.13% to 96.19%. Stability analysis reveals that NAMPT-derived NMN maintains high thermal stability, whereas NRK-derived NMN undergoes significant physical and chemical degradation at elevated temperatures, with nicotinamide as the primary degradation product. Additionally, bioactivity testing demonstrated that NAMPT-derived NMN retains significantly higher biological activity compared with NRK-synthesized and commercial NMN. These findings provide essential data for optimizing NMN production processes and support the establishment of industry-wide quality control standards.
Elucidating shrub ecohydrological adaptation is critical for optimizing vegetation-restoration strategies in arid regions and maintaining regional ecological stability. This study examined typical desert shrubs at the northern edge of the Mu Us Sand Land. During the growth peak season (July–September), we measured understory-soil δ18O, soil water content (SWC), leaf δ13Cp, stem δ18O, and gas-exchange rates, and evaluated shrub drought resistance and water-use efficiency using Mantel tests and principal component analysis (PCA). Based on the VPDB standard, the δ13Cp values of leaves ranked as follows: Caragana microphylla (−27.21‰) > Salix psammophila (−27.80‰) > Artemisia ordosica (−28.48‰). The results indicate that leaf δ13Cp and water δ18O are effective indicators of shrub water-use efficiency, reflecting Cᵢ/Cₐ dynamics and water-transport pathways, respectively. The three shrubs exhibit distinct water-use strategies: Caragana microphylla follows a conservative strategy that relies on deep-water sources and tight stomatal regulation; Salix psammophila shows an opportunistic strategy, responding to precipitation pulses and drawing from multiple soil layers; Artemisia ordosica displays a vulnerable, shallow-water-dependent strategy with high drought susceptibility. SWC was the primary driver of higher Long Water Use Efficiency (WUE), whereas Mean Air Temperature (MMAT) and Mean Relative Humidity (MMRH) exerted short-term regulation by modulating the vapor-pressure deficit (VPD). We conclude that desert-shrub water-use strategies form a complementary functional portfolio at the community scale. Vegetation restoration should prioritize high-WUE conservative species, complement them with opportunistic species, and use vulnerable species cautiously to optimize community water-use efficiency and ecosystem stability.
Based on the manual observation of hail records in Inner Mongolia,China,from 1959 to 2021,the spa-tial and temporal characteristics of hail distribution are analyzed,and a hail recognition method is constructed based on machine learning algorithms.The results are as follows:(1)Regarding temporal distribution,the num-ber of hail days and affected stations in Inner Mongolia shows a decreasing trend.In terms of spatial distribution,hail events are predominantly concentrated in the Yinshan Mountains and the Greater Hinggan Mountains,with hail-prone areas extending along these mountain ranges.(2)Hail exhibits distinct seasonal and diurnal characteris-tics.The peak hail months in Inner Mongolia are from May to September,accounting for 91.79%of the annual hail days.The most frequent period for hail occurrences is between 12:00 BST and 19:00 BST.(3)Four machine learning algorithms(random forest,LightGBM,K-proximity,and decision tree)are used to model and evaluate hail events in Inner Mongolia through data preprocessing,predictor selection,model training,and tuning.Verifi-cation results indicate that machine learning methods effectively identify hail events,with the threat score of each model exceeding 0.83 and hit rates surpassing 92%.Among these,the random forest algorithm demonstrates the best recognition performance on the test set.These findings provide useful references for hail forecasting and arti-ficial hail prevention in Inner Mongolia.
Objective: To analyze the application value of artificial intelligence (AI) in the comprehensive service pathway for rational clinical use of western medicines. Methods: A sample of 73 patients undergoing western medicine treatment from December 2024 to March 2025 was selected and divided into groups by lottery. Group A received AI-assisted management for rational clinical use of western medicines, while Group B received conventional management. Thirty-six patients taking oral western medicines were included in Group B. Results: Group A outperformed Group B in terms of the irrationality rate of western medicine usage, drug management scores, adverse reaction rates, complaint rates, and satisfaction levels, with p < 0.05. Conclusion: AI-assisted rational clinical use of western medicines enhances drug management quality, reduces the irrationality rate of medication usage, and decreases complaint rates related to western medicine usage.
The Lenghu site has emerged as a prominent candidate for future large telescopes, making a detailed understanding of its meteorological environment essential as it directly determines telescope performance, observational efficiency, and data quality. Based on multi-year observations spanning nearly two decades (2005–2024) from six meteorological stations, we conducted a comprehensive analysis of temperature, humidity, atmospheric pressure, wind speed, and wind direction, which directly affect key observing conditions such as atmospheric seeing, infrared transparency, and telescope pointing stability, in order to characterize the site’s meteorological conditions. The diurnal temperature range is approximately 9 °C, with 90% of hourly nighttime variations within 0–1°C. Relative humidity exceeds 90% less than 1% of the time, with a median below 31%. Atmospheric pressure becomes increasingly stable with altitude. Winds are predominantly westerly at night and weaker than during the day, with median speeds of 2–4 m s−1. Strong wind events (≥15 m s−1) occur occasionally at mid-mountain levels, while the maximum recorded wind speed at the summit is 21.0 m s−1. We further calculated the nighttime dew-point temperature using ambient temperature and relative humidity, and inferred nighttime weather stability by comparing observed and theoretical pressures. The meteorological characteristics revealed by long-term, multi-station observations exhibit stable interannual and seasonal variations, indicating that the Lenghu site offers long-term, stable, and favorable conditions for astronomical observations. These results also provide valuable guidance for the construction, operation, and observational planning of future large telescopes.
