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Plastics make up a great portion of solid waste that constitutes an emerging threat to the natural environment. Among plastics, polyethylene is the most widely used in the world. In this research, polyethylene materials with different densities designed for packaging applications are the subject of a degradation study. The materials were exposed to simulated solar exposure and were characterized by a combination of analytical techniques to compare both chemical and physical changes, including attenuated total reflection‐Fourier transform infrared spectroscopy (ATR‐FTIR), water contact angle measurements, Raman spectroscopy, grazing incidence X‐ray Diffraction (GIXRD) and nanoindentation tests. The experimental data were systematically analyzed using statistic methods. It was found that the extent of the increase of polar groups, hydrophilicity, crystallinity, and elastic modulus depends on both the density of polyethylene and the duration of UV aging. These changes result from photooxidation and the subsequent structure reorganization occurring in amorphous chains. The implementation of various analytical techniques and statistical analysis provides a holistic understanding of polyethylene degradation behaviors. The knowledge of increased hydrophilicity/polarity in polyethylene is crucial for understanding the fate of plastics in the environment because such polyethylene changes also facilitate the environmental biodegradation on initially non‐polar polyethylene. [ABSTRACT FROM AUTHOR]

Viruses of microbes are ubiquitous biological entities that reprogram their hosts' metabolisms during infection in order to produce viral progeny, impacting the ecology and evolution of microbiomes with broad implications for human and environmental health. Advances in genome sequencing have led to the discovery of millions of novel viruses and an appreciation for the great diversity of viruses on Earth. Yet, with knowledge of only "who is there?" we fall short in our ability to infer the impacts of viruses on microbes at population, community, and ecosystem-scales. To do this, we need a more explicit understanding "who do they infect?" Here, we developed a novel machine learning model (ML), Virus-Host Interaction Predictor (VHIP), to predict virus-host interactions (infection/non-infection) from input virus and host genomes. This ML model was trained and tested on a high-value manually curated set of 8849 virus-host pairs and their corresponding sequence data. The resulting dataset, 'Virus Host Range network' (VHRnet), is core to VHIP functionality. Each data point that underlies the VHIP training and testing represents a lab-tested virus-host pair in VHRnet, from which meaningful signals of viral adaptation to host were computed from genomic sequences. VHIP departs from existing virus-host prediction models in its ability to predict multiple interactions rather than predicting a single most likely host or host clade. As a result, VHIP is able to infer the complexity of virus-host networks in natural systems. VHIP has an 87.8% accuracy rate at predicting interactions between virus-host pairs at the species level and can be applied to novel viral and host population genomes reconstructed from metagenomic datasets. Author summary: The ecology and evolution of microbial communities are deeply influenced by viruses. Metagenomics analysis, the non-targeted sequencing of community genomes, has led to the discovery of millions of novel viruses. Yet, through the sequencing process, only DNA sequences are recovered, begging the question: which microbial hosts do those novel viruses infect? To address this question, we developed a computational tool to allow researchers to predict virus-host interactions from such sequence data. The power of this tool is its use of a high-value, manually curated set of 8849 lab-verified virus-host pairs and their corresponding sequence data. For each pair, we computed signals of coevolution to use as the predictive features in a machine learning model designed to predict interactions between viruses and hosts. The resulting model, Virus-Host Interaction Predictor (VHIP), has an accuracy of 87.8% and can be applied to novel viral and host genomes reconstructed from metagenomic datasets. Because the model considers all possible virus-host pairs, it can resolve complete virus-host interaction networks and supports a new avenue to apply network thinking to viral ecology. [ABSTRACT FROM AUTHOR]