Biology subjects

Robbins, R.

Publications and source records attributed to Robbins, R..

4 recordsLinked to original sources

bioRxiv2022

Cycad phylogeny predicts host plant use of Eumaeus butterflies

Eumaeus butterflies are obligate herbivores of Zamia, the most diverse neotropical genus of cycads. Eumaeus-Zamia interactions have been mainly characterized for species distributed in North and Central America. However, host plant use by the southern Eumaeus clade remains largely unknown, precluding a comprehensive study of co-evolution between the genera. Here, we combine fieldwork with museum and literature surveys to expand herbivory records for Eumaeus from 21 to 38 Zamia species. We inferred a time-calibrated phylogeny of Eumaeus to test for distinct macroevolutionary scenarios of host plant conservatism and co-evolution. We found remarkable coincidence between Eumaeus and Zamia diversification, with the butterfly stem group diverging at the same time as the most recent radiation of Zamia in the Miocene. Cophylogenetic reconciliation analyses show a strong cophylogenetic signal between cycads and their butterfly herbivores. Bipartite model-based approaches indicate that this is because closely related Zamia species are used by the same Eumaeus species, suggesting host plant resource tracking by the butterfly herbivores. Our results highlight a case of tight evolution between Eumaeus butterflies and cycads, pointing to the generality of correlated evolution and phylogenetic tracking in plant-herbivore interactions across seed plants.

evolutionary biology↗

bioRxiv2020

Random Forest Regression for Optimizing Variable Planting Rates for Corn and Soybean Using High-Resolution Topographical and Soil Data

In recent years, planting machinery that enables precise control of the planting rates has become available for corn (Zea mays L.) and soybean (Glycine max L.). With increasingly available topographical and soil information, there is a growing interest in developing variable rate planting strategies to exploit variation in the agri-landscape in order to maximize production. A random forest regression-based approach was developed to model the interactions between planting rate, topography, and soil characteristics and their effects on yield based on on-farm variable rate planting trials for corn and soybean conducted at 27 sites in New York between 2014 and 2018 (57 site-years) in collaboration with the New York Corn and Soybean Growers Association. Planting rate ranked highly in terms of random forest regression variable importance while explaining relatively minimal yield variation in the linear context, indicating that yield response to planting rate likely depends on complex interactions with agri-landscape features. Models were moderately predictive of yield within site-years and across years at a particular site, while the ability to predict yield across sites was low. Relatedly, variable importance measures for the topographical and soil features varied considerably across sites. Together, these results suggest that local testing may provide the most accurate optimized planting rate designs due to the unique set of conditions at each site. The proposed method was extended to identify the optimal variable rate planting design for maximizing yield at each site given the topographical and soil data, and empirical validation of the resulting designs is currently underway.

plant biology↗

bioRxiv2019

Microenvironmental adaptations drive obesity-associated pancreatic cancer

Obesity is a major modifiable risk factor for pancreatic ductal adenocarcinoma (PDAC), yet how and when obesity contributes to PDAC progression is not well understood. Leveraging an autochthonous mouse model, we demonstrate a causal and reversible role for obesity in early PDAC progression, showing that obesity markedly enhances tumorigenesis, while genetic or dietary induction of weight loss intercepts cancer development. Bulk and single cell molecular analyses of human and murine samples define microenvironmental consequences of obesity that promote tumor development rather than new driver gene mutations. We observe increased inflammation and fibrosis and also provide evidence for significant pancreatic islet cell adaptation in obesity-associated tumors. Specifically, we identify aberrant islet beta cell expression of the peptide hormone cholecystokinin (CCK) in tumors as an adaptive response to obesity. Furthermore, beta cell CCK expression promotes oncogenic Kras-driven pancreatic ductal tumorigenesis. Our studies argue that PDAC progression is driven by local obesity-associated changes in the tumor microenvironment - rather than systemic effects - and implicate endocrine-exocrine signaling beyond insulin in PDAC development. Furthermore, our demonstration that these obesity-associated adaptations are reversible supports the use of anti-obesity strategies to intercept PDAC early during progression.

cancer biology↗

bioRxiv2019

Predicting neurodevelopmental outcomes in children with perinatal HIV using a novel machine learning algorithm

BackgroundA subset of children with perinatal HIV (pHIV) experience long-term neurocognitive symptoms despite treatment with antiretroviral therapy. However, predictors of neurocognitive outcomes remain elusive, particularly for children with pHIV residing in low-to-middle income countries. The present study utilized a novel data analytic approach to identify clinically-relevant predictors of neurocognitive development in children with pHIV.\n\nMethodsAnalyses were conducted on a large repository of longitudinal data from 285 children with pHIV in Thailand (n=170) and Cambodia (n=115). Participants were designated as neurocognitively resilient (i.e., positive slope; n=143) or at risk (i.e., negative slope; n=142) according to annual performances on the Beery-Buktenica Developmental Test of Visual-Motor Integration over an average of 5.4 years. Gradient-boosted multivariate regression (GBM) with 5-fold cross validation was utilized to identify the optimal combination of demographic, HIV disease, blood markers, and emotional health indices that predicted classification into the two neurocognitive subgroups. Model performance was assessed using Receiver Operator Curves and sensitivity/specificity.\n\nResultsThe analytic approach distinguished neurocognitive subgroups with high accuracy (93%; sensitivity and specificity each > 90%). Dynamic change indices and interactions between mental health and biological indices emerged as key predictors.\n\nConclusionMachine learning-based regression defined a unique explanatory model of neurocognitive outcomes among children with pHIV. The predictive algorithm included a combination of HIV, physical health, and mental health indices extracted from readily available clinical measures. Studies are needed to explore the clinical relevance of the data-driven explanatory model, including potential to inform targeted interventions aimed at modifiable risk factors.

neuroscience↗