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Biology subjects

O'Brien, A.

Publications and source records attributed to O'Brien, A..

2 recordsLinked to original sources

Resilience to multiple stressors in an aquatic plant and its microbiome

PremiseEnvironments are changing rapidly, and outcomes of species interactions, especially mutualisms, are notoriously dependent on the environment. A growing number of studies have investigated responses of mutualisms to anthropogenic changes, yet most studies have focused on nutrient pollution or climate change, and tested single stressors. Relatively little is known about impacts of simultaneous chemical contaminants, which may differ fundamentally from nutrient or climate stressors, and are especially widespread in aquatic habitats.\n\nMethodsWe investigated the impacts of two common contaminants on interactions between the common duckweed Lemna minor and its microbiome. Sodium chloride (salt) and benzotriazole (a corrosion inhibitor) negatively affect aquatic organisms individually, yet commonly co-occur in runoff to duckweed-inhabited sites. We tested three L. minor genotypes with and without the culturable portion of their microbiome across field realistic gradients of salt (3 levels) and benzotriazole (4 levels) in a fully factorial experiment (72 treatments), and measured plant and microbial growth.\n\nKey ResultsWe found that stressors had conditional effects. Salt decreased both plant and microbial growth, but decreased plant survival more as benzotriazole concentrations increased. In contrast, benzotriazole did not affect microbial abundance, and benefited plants when salt and microbes were absent, perhaps due to the biotrans-formation we observed without salt. Microbes did not ameliorate duckweed stressors, as microbial inoculation increased plant growth, but not at high salt concentrations.\n\nConclusionsOur results suggest that multistressor effects matter when predicting responses of mutualisms to global change, but that mutualisms may not buffer organisms from stressors.

plant biology

VariantSpark, A Random Forest Machine Learning Implementation for Ultra High Dimensional Data

The demands on machine learning methods to cater for ultra high dimensional datasets, datasets with millions of features, have been increasing in domains like life sciences and the Internet of Things (IoT). While Random Forests are suitable for \"wide\" datasets, current implementations such as Googles PLANET lack the ability to scale to such dimensions. Recent improvements by Yggdrasil begin to address these limitations but do not extend to Random Forest. This paper introduces CursedForest, a novel Random Forest implementation on top of Apache Spark and part of the VariantSpark platform, which parallelises processing of all nodes over the entire forest. CursedForest is 9 and up to 89 times faster than Googles PLANET and Yggdrasil, respectively, and is the first method capable of scaling to millions of features.

bioinformatics