Overview of the Role of Environmental Factors in Neurodevelopmental Disorders

Evidence implicates environmental factors in the pathogenesis of diverse complex neurodevelopmental disorders. However, the identity of specific environmental chemicals that confer risk for these disorders, and the mechanisms by which environmental chemicals interact with genetic susceptibilities to influence adverse neurodevelopmental outcomes remain significant gaps in our understanding of the etiology of most neurodevelopmental disorders. It is likely that many environmental chemicals contribute to the etiology of neurodevelopmental disorders but their influence depends on the genetic substrate of the individual. Research into the pathophysiology and genetics of neurodevelopmental disorders may inform the identification of environmental susceptibility factors that promote adverse outcomes in brain development. Conversely, understanding how low-level chemical exposures influence molecular, cellular, and behavioral outcomes relevant to neurodevelopmental disorders will provide insight regarding gene-environment interactions and possibly yield novel intervention strategies

Detecting Woody Plants in Southern Arizona Using Data from the National Ecological Observatory Network (NEON)

Land cover changes and conversions are occurring rapidly in response to human activities throughout the world. Woody plant encroachment (WPE) is a type of land cover conversion that involves the proliferation and/or densification of woody plants in an ecosystem. WPE is especially prevalent in drylands, where subtle changes in precipitation and disturbance regimes can have dramatic effects on vegetation structure and degrade ecosystem functions and services. Accurately determining the distribution of woody plants in drylands is critical for protecting human and natural resources through woody plant management strategies. Using an object-based approach, we have used novel open-source remote sensing and in situ data from Santa Rita Experimental Range (SRER), National Ecological Observatory Network (NEON), Arizona, USA with machine learning algorithms and tested each model’s efficacy for estimating fractional woody cover (FWC) to quantify woody plant extent. Model performance was compared using standard model assessment metrics such as accuracy, sensitivity, specificity, and runtime to assess model variables and hyperparameters. We found that decision tree-based models with a binary classification scheme performed best, with sequential models (Boosting) slightly outperforming independent models (Random Forest) for both object classification and FWC estimates. Mean canopy height and mean, median, and maximum statistics for all vegetation indices were found to have highest variable importance. Optimal model hyperparameters and potential limitations of the NEON dataset for classifying woody plants in dryland regions were also identified. Overall, this study lays the groundwork for developing machine learning models for dryland woody plant management using solely NEON data

Detecting Woody Plants in Southern Arizona Using Data from the National Ecological Observatory Network (NEON)

Land cover changes and conversions are occurring rapidly in response to human activities throughout the world. Woody plant encroachment (WPE) is a type of land cover conversion that involves the proliferation and/or densification of woody plants in an ecosystem. WPE is especially prevalent in drylands, where subtle changes in precipitation and disturbance regimes can have dramatic effects on vegetation structure and degrade ecosystem functions and services. Accurately determining the distribution of woody plants in drylands is critical for protecting human and natural resources through woody plant management strategies. Using an object-based approach, we have used novel open-source remote sensing and in situ data from Santa Rita Experimental Range (SRER), National Ecological Observatory Network (NEON), Arizona, USA with machine learning algorithms and tested each model’s efficacy for estimating fractional woody cover (FWC) to quantify woody plant extent. Model performance was compared using standard model assessment metrics such as accuracy, sensitivity, specificity, and runtime to assess model variables and hyperparameters. We found that decision tree-based models with a binary classification scheme performed best, with sequential models (Boosting) slightly outperforming independent models (Random Forest) for both object classification and FWC estimates. Mean canopy height and mean, median, and maximum statistics for all vegetation indices were found to have highest variable importance. Optimal model hyperparameters and potential limitations of the NEON dataset for classifying woody plants in dryland regions were also identified. Overall, this study lays the groundwork for developing machine learning models for dryland woody plant management using solely NEON data

Impaired synaptic development in a maternal immune activation mouse model of neurodevelopmental disorders

Both genetic and environmental factors are thought to contribute to neurodevelopmental and neuropsychiatric disorders with maternal immune activation (MIA) being a risk factor for both autism spectrum disorders and schizophrenia. Although MIA mouse offspring exhibit behavioral impairments, the synaptic alterations in vivo that mediate these behaviors are not known. Here we employed in vivo multiphoton imaging to determine that in the cortex of young MIA offspring there is a reduction in number and turnover rates of dendritic spines, sites of majority of excitatory synaptic inputs. Significantly, spine impairments persisted into adulthood and correlated with increased repetitive behavior, an ASD relevant behavioral phenotype. Structural analysis of synaptic inputs revealed a reorganization of presynaptic inputs with a larger proportion of spines being contacted by both excitatory and inhibitory presynaptic terminals. These structural impairments were accompanied by altered excitatory and inhibitory synaptic transmission. Finally, we report that a postnatal treatment of MIA offspring with the anti-inflammatory drug ibudilast, prevented both synaptic and behavioral impairments. Our results suggest that a possible altered inflammatory state associated with maternal immune activation results in impaired synaptic development that persists into adulthood but which can be prevented with early anti-inflammatory treatment. Includes supplementary materials