Arbuscular mycorrhizal (AM) fungi play an important role in soil organic matter (SOM) formation and stabilization. Previous studies have emphasized organic compounds produced by AM fungi as persistent binding agents for aggregate formation and SOM storage. This concept overlooks the multiple biogeochemical processes mediated by AM fungal activities, which drive SOM generation, reprocessing, reorganization, and stabilization. Here, we propose an updated conceptual framework to facilitate a mechanistic understanding of the role of AM fungi in SOM dynamics. In this framework, four pathways for AM fungi-mediated SOM dynamics are included: 'Generating', AM fungal exudates and biomass serve as key sources of SOM chemodiversity; 'Reprocessing', hyphosphere microorganisms drive SOM decomposition and resynthesis; 'Reorganizing', AM fungi mediate soil physical changes and influence SOM transport, redistribution, transformation, and storage; and 'Stabilizing', AM fungi drive mineral weathering and organo-mineral interactions for SOM stabilization. Moreover, we discuss the AM fungal role in SOM dynamics at different scales, especially when translating results from small scales to complex larger scales. We believe that working with this conceptual framework can allow a better understanding of AM fungal role in SOM dynamics, therefore facilitating the development of mycorrhiza-based technologies toward soil health and global change mitigation.
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http://dx.doi.org/10.1111/nph.19178 | DOI Listing |
Int J Environ Res Public Health
December 2024
Department of Innovation & Technology Management, Management Center Innsbruck, 6020 Innsbruck, Austria.
Current work environments, driven by globalization, demographic changes, and digitalization, demand substantial adaptation, which leads to decreased employee well-being. While occupational psychology research has identified supportive mechanisms, it often lacks a deepened understanding of how interventions function. This study aims to analyze the impacts of VUCA contexts and leadership behavior on job crafting, focusing on white-collar workers.
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January 2025
Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
Thawing Arctic permafrost can induce hydrologic change and alter redox conditions, shifting the balance of soil organic matter (SOM) decomposition. There remains uncertainty about how soil saturation and redox transitions impact dissolved and gas phase carbon fluxes, and efforts to link hydrobiogeochemical processes to ecosystem-scale models are limited. This study evaluates SOM decomposition of Arctic tundra soils using column experiments, water chemistry measurements, microbial community analysis, and a PFLOTRAN reactive transport model.
View Article and Find Full Text PDFJ Neurosci
January 2025
Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
The human hippocampus, essential for learning and memory, is implicated in numerous neurological and psychiatric disorders, each linked to specific neuronal subpopulations. Advancing our understanding of hippocampal function requires computational models grounded in precise quantitative neuronal data. While extensive data exist on the neuronal composition and synaptic architecture of the rodent hippocampus, analogous quantitative data for the human hippocampus remain very limited.
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January 2025
Department of Radiation Oncology, Henry Ford Hospital, Detroit, USA.
Best current practice in the analysis of dynamic contrast enhanced (DCE)-MRI is to employ a voxel-by-voxel model selection from a hierarchy of nested models. This nested model selection (NMS) assumes that the observed time-trace of contrast-agent (CA) concentration within a voxel, corresponds to a singular physiologically nested model. However, admixtures of different models may exist within a voxel's CA time-trace.
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January 2025
Civil and Transportation College, Beihua University, Jilin, China.
An improved concrete structure health monitoring method based on G-S-G is proposed, which fully combines an optimized Gray-Level Co-occurrence Matrix (GLCM) with an improved Self-Organizing Map (SOM) neural network to achieve accurate and real-time concrete structure health monitoring. First of all, in order to obtain a dynamic image of the crack damage region of interest (ROI) with clear contrast and obvious target, the image acquisition system and image optimization method are used to process the damaged image. Moreover, in order to realize the accurate location of crack damage, crack damage identification research based on GLCM-SOM effectively eliminates the interference of honeycomb and pothole damage on crack damage.
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