ABSTRACT
Machine learning algorithms can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network in comparison to a Random Forest model to detect induced changes in microbial communities, in order to support environmental monitoring efforts of contamination events. Models were trained on taxon count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140â¯days to determine the effects of glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the taxa primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species for glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data.
Subject(s)
Glycine/analogs & derivatives , Microbiota/drug effects , Microbiota/genetics , Neural Networks, Computer , RNA, Ribosomal, 16S/genetics , Water Pollutants, Chemical/toxicity , Algorithms , Environmental Monitoring , Glycine/toxicity , High-Throughput Nucleotide Sequencing , Machine Learning , Random Allocation , Water Microbiology , GlyphosateABSTRACT
We use graphene bubbles to study the Raman spectrum of graphene under biaxial (e.g., isotropic) strain. Our Gruneisen parameters are in excellent agreement with the theoretical values. Discrepancy in the previously reported values is attributed to the interaction of graphene with the substrate. Bilayer balloons (intentionally pressurized membranes) have been used to avoid the effect of the substrate and to study the dependence of strain on the interlayer interactions.
