Search for: All records

Improved understanding of bacterial community responses to multiple environmental filters over long time periods is a fundamental step to develop mechanistic explanations of plant–bacterial interactions as environmental change progresses.

This is the first study to examine responses of grassland root‐associated bacterial communities to 15 years of experimental manipulations of plant species richness, functional group and factorial enrichment of atmospheric CO₂(eCO₂) and soil nitrogen (+N).

Across the experiment, plant species richness was the strongest predictor of rhizobacterial community composition, followed by +N, with no observed effect of eCO₂. Monocultures of C₃and C₄grasses and legumes all exhibited dissimilar rhizobacterial communities within and among those groups. Functional responses were also dependent on plant functional group, where N₂‐fixation genes, NO₃₋‐reducing genes and P‐solubilizing predicted gene abundances increased under resource‐enriched conditions for grasses, but generally declined for legumes. In diverse plots with 16 plant species, the interaction of eCO₂+N altered rhizobacterial composition, while +N increased the predicted abundance of nitrogenase‐encoding genes, and eCO₂+N increased the predicted abundance of bacterial P‐solubilizing genes.

Synthesis: Our findings suggest that rhizobacterial community structure and function will be affected by important global environmental change factors such as eCO₂, but these responses are primarily contingent on plant species richness and the selective influence of different plant functional groups.

 

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. 

In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression ofCR1(complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.