Designing for Online Collaborative Consumption: A Study of Sociotechnical Gaps and Social Capital

This study attempts to investigate sociotechnical gaps in online collaborative consumption (OCC) to improve user experience andprovide better design requirements. A new approach is proposed to evaluate usability and sociability of the OCC communities. The formation of social capital within OCC will also be studied to gain insights into design requirements. Due to its features as a community where OCC takes place, ETSY will be the focus of this study

Fijimycins A-C, three antibacterial etamycin-class depsipetides from a marine-derived Streptomyces sp

Three new depsipeptides, fijimycins A–C (1–3), together with the known etamycin A (4), were isolated and identified from the fermentation broth of strain CNS-575, a Streptomyces sp. cultured from a marine sediment sample collected off Nasese, Fiji. The planar structures of the new fijimycins were assigned by combined interpretation of NMR and MS/MS spectroscopic data. These assignments were complicated by the fact that 1–3 occurred as complex amide conformational mixtures. The absolute configurations of the component amino acids were established using the Marfey’s method. Fijimycins A–C, and etamycin A, were shown to possess significant in vitro antibacterial activity against three methicillin-resistant Staphylococcus aureus (MRSA) strains with MIC100 values between 4 and 16 μg mL−1

Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC–MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples. © 2020, The Author(s), under exclusive licence to Springer Nature America, Inc