Abstract Climate change is profoundly altering hydrological processes in alpine regions, which are acutely sensitive to warming; however, accurate projections of hydrological fluxes, notably runoff, remain challenging. In this study, six hydrological conceptual models and their combinations were first evaluated through comparative experiments for daily runoff simulation. Downscaled CMIP6 climatic variables were then used to drive the selected model/combination for future runoff variations using the Datong River Basin on the northeast Tibetan Plateau, China as a case study. The results indicate that: (1) among the single and combined models, the IHACRES-CMD and GR4JSG combination has shown satisfactory performance for runoff simulation in alpine basins; (2) future projections of climate and hydrological variables based on the selected model driven by CMIP6 outputs reveal a consistent warming-wetting tendency, with mean annual temperature, precipitation and runoff increasing by 1.6°C–3.8°C, 25.5–55.8 mm, and 0.8 – 1.4 billion m 3 , respectively, under various Shared Socioeconomic Pathways (SSP) scenarios between 2015 and 2100 relative to the historical period; and (3) runoff changes also demonstrate significant future hydrological shifts, showing increased interannual runoff but a more uniform intra-annual distribution, manifested through reduced runoff in July but increase in June. In addition, results also show larger runoff variability with increased occurrence of above-normal runoff in the future. These changes imply an elevated flood-risk potential during early summer, which poses a significant challenge to existing reservoir operation rules, necessitating adaptive strategies and climate-resilient allocation frameworks for water resource systems in alpine basins.
Observed differences between paddy and upland croplands in soil organic carbon (SOC) and soil inorganic carbon (SIC) may reflect environmental and data-provenance imbalances rather than land-use effects. We reconstructed independent SOC and SIC profile datasets across China, harmonized depths, and separated fullsample comparisons from a prespecified comparable subset. Among all topsoil SOC profiles retained after applying the inclusion criteria (334 paddy, 347 upland), the median Upland-minus-Paddy contrast was -2.12 g kg- 1; within the shared subset (97/95), it attenuated to -0.35 g kg- 1 (95% interval, -1.74 to 1.55). For 97 paddy soils, changing only the land-use indicator to upland while holding observed soil and environment states fixed yielded a mean SOC contrast of -0.58 g kg- 1 (joint 95% interval, -1.02 to -0.15), with 78.4% negative. A layer-resolved Extra Trees S-learner supported this scenario with out-of-fold R2 = 0.72 and mean R2 = 0.72 across ten spatial partitions. The shared topsoil SIC subset retained only 9 paddy and 7 upland profiles, so adjusted SIC could not be estimated. Aridity index (AI) stratification showed the strongest negative SOC contrast in more humid high-AI croplands (-1.08 g kg- 1; -1.62 to -0.56), whereas low- and middle-AI strata crossed zero; the high-minus-middle difference was -1.29 g kg- 1 (-2.09 to -0.60). Among comparable croplands, observed topsoil SOC differed little between paddy and upland fields, but model predictions indicated lower SOC under upland land use, particularly in humid regions. These results suggest a modest SOC advantage of paddy croplands, while SIC differences remain unresolved.
Pig respiratory diseases present a major health challenge to the global pig farming industry, and early identification of pig cough is crucial for disease prevention, control, and minimizing economic losses. However, existing deep learning methods based on a single audio modality exhibited limited generalization ability in acoustically complex farming environments. Moreover, conventional static multimodal audio fusion methods failed to fully exploit complementary cross-modal information and lacked adaptability to dynamic pig production conditions. To tackle the above challenges, a novel multimodal audio fusion (MAF) attention mechanism was proposed, enabling dynamic fusion of multimodal audio by adaptively leveraging complementary information between audio modalities. Based on the MAF, a pig cough recognition model tailored for practical farming environments was developed. The results demonstrated that the proposed MAF-based dynamic fusion significantly improved the pig cough recognition compared to static fusion methods. The best performance was achieved by dynamically fusing the logarithmic Mel (log-mel) and Constant-Q Transform (CQT) spectrograms using the MAF (Mel + CQT_MAF), reaching an accuracy of 96.55% and consistently outperforming existing stateof-the-art pig cough recognition methods. Both the position and number of the MAF blocks significantly affected model performance. Optimal results were obtained with MAF-based classifier-wise fusion, which improved recognition accuracy by approximately 2.5% compared to early-stage fusion approaches. Increasing the number of MAF blocks did not necessarily lead to improved performance. Similarly, incorporating more audio modalities did not inherently improve recognition accuracy. Instead, the use of high-quality and complementary audio modalities proved critical for effective multimodal audio fusion. This study indicates that the proposed method is a powerful tool for pig cough recognition in farming environments.
Microplastics (MPs) are recognized as emerging contaminants that facilitate the dissemination of antibiotic resistance genes (ARGs). However, the ecological mechanisms underlying MP-induced changes soil antibiotic resistance, particularly those mediated by the soil food web, remain largely unexplored. To address this knowledge gap, we conducted a microcosm experiment to investigate how biodegradable MPs (bio-MPs) of different particle sizes influence soil antibiotic resistance through their effects on the soil micro-food web. The results showed that the abundances of functional genes associated with bio-MPs biodegradation were significantly higher in soils amended with 20 μm bio-MPs than in those amended with 300 μm bio-MPs. The biodegradation of 20 μm bio-MP significantly increased the diversity and abundance of ARGs while altering their composition, particularly through the enrichment of genes conferring resistance to multidrug, tetracycline, and aminoglycoside. The tetracycline resistance gene tetA(48), the multidrug resistance gene ompR, and the unclassified resistance gene VanR were the three most abundant ARG subtypes and were significantly enriched in soils amended with 20 μm bio-MPs. Furthermore, amendment with 20 μm bio-MPs significantly increased the total abundance of mobile genetic elements (MGEs), which was positively correlated with ARG abundance. Additionally, amendment with 20 μm bio-MPs enriched Psedomonadota, the predominant hosts of ARGs. Furthermore, bio-MPs destabilized the soil micro-food web, and its stability was negatively associated with ARG abundance. Collectively, these findings suggest that multi-trophic interactions within the soil micro-food web play a previously underappreciated role in regulating antibiotic resistance responses to plastic pollution in soil ecosystems.
Starch retrogradation, driven by reassociation and recrystallization of gelatinized amylose and amylopectin chains, induces undesirable firming, syneresis, and sensory deterioration, ultimately shortening the shelf-life of starch-based foods. Enzymatic modification has emerged as a promising route to retard retrogradation, but its efficacy critically depends on the structural features targeted by the enzyme, the susceptibility of the starch substrate, and the accessibility of catalytic sites. This review systematically interprets the structure-dependent mechanisms underlying enzymatic retrogradation regulation, covering the differential regulatory patterns of various enzyme systems. This review further clarifies the essential distinction between substrate structural susceptibility and enzyme catalytic accessibility, and summarizes advanced modification strategies based on porous starch construction and controlled starch swelling to optimize enzyme-substrate interfacial contact efficiency. In addition, mainstream detection techniques are compared based on the different levels of the studied starch structure. Finally, the practical applicability of enzymatic anti-retrogradation technologies in diverse staple and processed starch-based food systems are also summarized. We propose that the key design principle is controlled structural remodeling enabled by enzyme action within a defined processing window, rather than indiscriminate starch degradation. This work provides systematic theoretical insights and practical guidance for the quality stabilization and shelf-life extension of starch-based food products.
