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Tandem mass spectrometry (MS2) has been employed to elucidate the topology and branching architecture of star-branched polyethers. The polymers were ionized by matrix-assisted laser desorption/ionization (MALDI) to positive ions and dissociated after leaving the ion source via laser-induced fragmentation. The bond scissions caused under MALDI-MS2 conditions occur preferentially near the core-branch joining points due to energetically favorable homolytic and heterolytic bond cleavages near the core and release of steric strain and/or reduction of crowding. This unique fragmentation mode detaches complete arms from the core generating fragment ion series at the expected molecular weight of each branch. The number of fragment ion distributions observed combined with their mass-to-charge ratios permit conclusive determination of the degree of branching and the corresponding branch lengths, as demonstrated for differently branched homo- and mikto-arm polyether stars synthesized via azide-alkyne click chemistry. The results of this study underscore the utility of MS2 for the characterization of branching architecture and branch lengths of (co)polymers with two or more linear chains attached to a functionalized central core.

The transition toward exascale computing will be accompanied by a performance dichotomy. Computational peak performance will rapidly increase; I/O performance will either grow slowly or be completely stagnant. Essentially, the rate at which data are generated will grow much faster than the rate at which data can be read from and written to the disk. MD simulations will soon face the I/O problem of efficiently writing to and reading from disk on the next generation of supercomputers. This article targets MD simulations at the exascale and proposes a novel technique forin situdata analysis and indexing of MD trajectories. Our technique maps individual trajectories' substructures (i.e.,α‐helices,β‐strands) to metadata frame by frame. The metadata captures the conformational properties of the substructures. The ensemble of metadata can be used for automatic, strategic analysis within a trajectory or across trajectories, without manually identify those portions of trajectories in which critical changes take place. We demonstrate our technique's effectiveness by applying it to 26.3k helices and 31.2k strands from 9917 PDB proteins and by providing three empirical case studies. © 2017 Wiley Periodicals, Inc.