| 1. |
- Hao, Qian, et al.
(författare)
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Silicon Affects Plant Stoichiometry and Accumulation of C, N, and P in Grasslands
- 2020
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Ingår i: Frontiers in Plant Science. - : Frontiers Media S.A.. - 1664-462X. ; 11, s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- Silicon (Si) plays an important role in improving soil nutrient availability and plant carbon (C) accumulation and may therefore impact the biogeochemical cycles of C, nitrogen (N), and phosphorus (P) in terrestrial ecosystems profoundly. However, research on this process in grassland ecosystems is scarce, despite the fact that these ecosystems are one of the most significant accumulators of biogenic Si (BSi). In this study, we collected the aboveground parts of four widespread grasses and soil profile samples in northern China and assessed the correlations between Si concentrations and stoichiometry and accumulation of C, N, and P in grasses at the landscape scale. Our results showed that Si concentrations in plants were significantly negatively correlated (p< 0.01) with associated C concentrations. There was no significant correlation between Si and N concentrations. It is worth noting that since the Si concentration increased, the P concentration increased from less than 0.10% to more than 0.20% and therefore C:P and N:P ratios decreased concomitantly. Besides, the soil noncrystalline Si played more important role in C, N, and P accumulation than other environmental factors (e.g., MAT, MAP, and altitude). These findings indicate that Si may facilitate grasses in adjusting the utilization of nutrients (C, N, and P) and may particularly alleviate P deficiency in grasslands. We conclude that Si positively alters the concentrations and accumulation of C, N, and P likely resulting in the variation of ecological stoichiometry in both vegetation and litter decomposition in soils. This study further suggests that the physiological function of Si is an important but overlooked factor in influencing biogeochemical cycles of C and P in grassland ecosystems.
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| 2. |
- Li, Zichuan, et al.
(författare)
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Impacts of silicon on biogeochemical cycles of carbon and nutrients in croplands
- 2018
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Ingår i: Journal of Integrative Agriculture. - : Elsevier. - 2095-3119. ; 17:10, s. 2182-2195
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Forskningsöversikt (refereegranskat)abstract
- Crop harvesting and residue removal from croplands often result in imbalanced biogeochemical cycles of carbon and nutrients in croplands, putting forward an austere challenge to sustainable agricultural production. As a beneficial element, silicon(Si) has multiple eco-physiological functions, which could help crops to acclimatize their unfavorable habitats. Although many studies have reported that the application of Si can alleviate multiple abiotic and biotic stresses and increase biomass accumulation, the effects of Si on carbon immobilization and nutrients uptake into plants in croplands have not yet been explored. This review focused on Si-associated regulation of plant carbon accumulation, lignin biosynthesis, and nutrients uptake, which are important for biogeochemical cycles of carbon and nutrients in croplands. The tradeoff analysis the supply of bioavailable Si can enhance plant net photosynthetic rate and biomass carbon production (especially root biomass input to soil organic carbon pool), but reduce shoot lignin biosynthesis. Besides, the application of Si could improve uptake of most nutrients under deficient conditions, but restricts excess uptake when they are supplied in surplus amounts. Nevertheless, Si application to crops may enhance the uptake of nitrogen and iron when they are supplied in deficient to luxurious amounts, while potassium uptake enhanced by Si application is often involved in alleviating salt stress and inhibiting excess sodium uptake in plants. More importantly, the amount of Si accumulated in plant positively correlates with nutrients release during the decay of crop biomass, but negatively correlates with straw decomposability due to the reduced lignin synthesis. The Si-mediated plant growth and litter decomposition collectively suggest that Si cycling in croplands plays important roles in biogeochemical cycles of carbon and nutrients. Hence, scientific Si management in croplands will be helpful for maintaining sustainable development of agriculture.
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| 3. |
- Yu, Congyu, et al.
(författare)
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Artificial intelligence in paleontology
- 2024
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Ingår i: Earth-Science Reviews. - 0012-8252 .- 1872-6828. ; 252
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Forskningsöversikt (refereegranskat)abstract
- The accumulation of large datasets and increasing data availability have led to the emergence of data-driven paleontological studies, which reveal an unprecedented picture of evolutionary history. However, the fast-growing quantity and complication of data modalities make data processing laborious and inconsistent, while also lacking clear benchmarks to evaluate data collection and generation, and the performances of different methods on similar tasks. Recently, artificial intelligence (AI) has become widely practiced across scientific disciplines, but not so much to date in paleontology where traditionally manual workflows have been more usual. In this study, we review >70 paleontological AI studies since the 1980s, covering major tasks including micro- and macrofossil classification, image segmentation, and prediction. These studies feature a wide range of techniques such as Knowledge-Based Systems (KBS), neural networks, transfer learning, and many other machine learning methods to automate a variety of paleontological research workflows. Here, we discuss their methods, datasets, and performance and compare them with more conventional AI studies. We attribute the recent increase in paleontological AI studies most to the lowering of the entry bar in training and deployment of AI models rather than innovations in fossil data compilation and methods. We also present recently developed AI implementations such as diffusion model content generation and Large Language Models (LLMs) that may interface with paleontological research in the future. Even though AI has not yet been a significant part of the paleontologist's toolkit, successful implementation of AI is growing and shows promise for paradigm-transformative effects on paleontological research in the years to come.
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