With the advancement of second generation sequencing techniques, our ability to detect and quantify RNA editing on a global scale has been vastly improved. As a result, RNA editing is now being studied under a growing number of biological conditions so that its biochemical mechanisms and functional roles can be further understood. However, a major barrier that prevents RNA editing from being a routine RNA-seq analysis, similar to gene expression and splicing analysis, for example, is the lack of user-friendly and effective computational tools. Based on years of experience of analyzing RNA editing using diverse RNA-seq datasets, we have developed a software tool, RED-ML: RNA Editing Detection based on Machine learning (pronounced as “red ML”). The input to RED-ML can be as simple as a single BAM file, while it can also take advantage of matched genomic variant information when available. The output not only contains detected RNA editing sites, but also a confidence score to facilitate downstream filtering. We have carefully designed validation experiments and performed extensive comparison and analysis to show the efficiency and effectiveness of RED-ML under different conditions, and it can accurately detect novel RNA editing sites without relying on curated RNA editing databases. We have also made this tool freely available via GitHub . We have developed a highly accurate, speedy and general-purpose tool for RNA editing detection using RNA-seq data. With the availability of RED-ML, it is now possible to conveniently make RNA editing a routine analysis of RNA-seq. We believe this can greatly benefit the RNA editing research community and has profound impact to accelerate our understanding of this intriguing posttranscriptional modification process.

Intensified human activities have generated a large amount of phosphorus-containing waste (P waste). Unrecycled P waste is lost to the environment and causes eutrophication, while the increasing phosphate consumption risks the depletion of phosphorus resources. The management of P waste is critical to solving these problems. In this study, we quantified the historic trends of P waste generation and recycling in China. From 1900 to 2015, the annual generation of P waste increased from 1 Mt P to 12 Mt P. Crop farming was the largest P waste source in most years, while P waste from phosphate mining and phosphorus chemical production increased the fastest. The total recycled P waste increased 5-fold, but phosphorus loss increased 26fold. In 2015, 28% of the P waste was lost on cultivated land, and 21% was lost on nonarable land. The largest phosphorus contributor to inland water changed from crop farming to aquaculture. The full recycling of P waste would have reduced phosphate consumption by more than one-third in 2015. The results of a scenario analysis showed that a healthier diet would greatly increase the generation and loss of P waste, but balanced fertilization could reduce the generation of P waste by 17% and promoting waste recycling could reduce the phosphorus loss by 35%.

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Traditional neural networks usually concentrate on temporal data in system simulation, and lack of capabilities to reason inner logic relations between different dimensions of data collected from embedded sensors. This paper proposes a graph neural network-based modeling approach for IoT equipment (called GNNM-IoT), which considers both temporal and inner logic relations of data, in which vertices denote sensor data and edges denote relationships between vertices. The GNNM-IoT model's relationships between sensors with neural networks to produce nonlinear complex relationships. We have evaluated the GNNM-IoT using air-conditioner data from a world leading IoT company, which demonstrates that it is effective and outperforms ARIMA and LSTM methods. INDEX TERMS Graph neural networks, deep learning, simulation, time series prediction, IoT.