Search for: All records

Abstract The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived fromSorghum bicolorandS. propinquum. The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations. 

Biological soil crusts (biocrusts) and arbuscular mycorrhizal (AM) fungi are communities of soil organisms often targeted to assist in the achievement of multiple ecological restoration goals. In drylands, benefits conferred from biocrust and AM fungal inoculation, such as improved native plant establishment and soil stabilization, have primarily been studied separately. However, comparisons between these two types of soil inoculants and investigations into potential synergies between them, particularly at the plant community scale, are needed to inform on‐the‐ground management practices in drylands. We conducted two full‐factorial experiments—one in greenhouse mesocosms and one in field plots—to test the effects of AM fungal inoculation, biocrust inoculation, and their interaction on multiple measures of dryland restoration success. Biocrust inoculation promoted soil stabilization and plant drought tolerance, but had mixed effects on native plant diversity (positive in greenhouse, neutral in field) and productivity (negative in greenhouse, neutral in field). In greenhouse mesocosms, biocrust inoculation reduced plant biomass, which was antagonistic to % root length colonized by AM fungi. Inoculation with native or commercial AM fungi did not influence plant establishment, drought tolerance, or soil stabilization in either study, and few synergistic effects of simultaneous inoculation of AM fungi and biocrusts were observed. These results suggest that, depending on the condition of existing soil communities, inoculation with AM fungi may not be necessary to promote dryland restoration goals, while inoculation with salvaged biocrust inoculation may be beneficial in some contexts. 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.