The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry techniques are well-suited to high-throughput characterization of natural products, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social molecular networking (GNPS, http://gnps.ucsd.edu), an open-access knowledge base for community wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of ‘living data’ through continuous reanalysis of deposited data.

X. (2020). Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors.

Microorganisms such as bacteria and fungi produce a variety of specialized metabolites that are invaluable for agriculture, biological research, and drug discovery. However, the screening of microbial metabolic output is usually a time intensive task. Here we utilize a liquid micro-junction surface sampling probe for electrospray ionization mass spectrometry to extract and ionize metabolite mixtures directly from living microbial colonies grown on soft nutrient agar in Petri-dishes without any sample pre-treatment. To demonstrate the method is robust, this technique was applied to observe the metabolic output of more than 30 microorganisms, including yeast, filamentous fungi, pathogens, and marine-derived bacteria, that were collected worldwide. Diverse natural products produced from different microbes, including Streptomyces coelicolor, Bacillus subtilis, and Pseudomonas aeruginosa are further characterized.

With the rapid evolution of network traffic diversity, the understanding of network traffic has become more pivotal and more formidable. Previously, traffic classification and intrusion detection require a burdensome analyzing of various traffic features and attack-related characteristics by experts, and even, private information might be required. However, due to the outdated features labeling and privacy protocols, the existing approaches may not fit with the characteristics of the changing network environment anymore. In this paper, we present a lightweight framework with the aid of deep learning for encrypted traffic classification and intrusion detection, termed as deep-full-range (DFR). Thanks to deep learning, DFR is able to learn from raw traffic without manual intervention and private information. In such a framework, our proposed algorithms are compared with other state-of-the-art methods using two public datasets. The experimental results show that our framework not only can outperform the state-of-the-art methods by averaging 13.49% on encrypted traffic classification's F1 score and by averaging 12.15% on intrusion detection's F1 score but also require much lesser storage resource requirement. INDEX TERMS Encrypted traffic classification, network intrusion detection, deep learning, end-to-end.

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