The organization of genomic DNA into nucleosomes profoundly affects all DNA-related processes in eukaryotes. The histone chaperone ‘FAcilitates Chromatin Transcription’ (FACT, consisting of subunits SPT16 and SSRP1 1 ) promotes both disassembly and reassembly of nucleosomes during gene transcription, DNA replication, and repair 2 . The mechanism by which FACT causes these opposing outcomes is unknown. Here we report two cryo-EM structures of human FACT in complex with partially assembled ‘sub-nucleosomes’, with supporting biochemical and hydrogen-deuterium exchange (HDX) data. FACT is engaged in extensive interactions with nucleosomal DNA and all histones. The large DNA-binding surface on FACT appears to be protected by the C-terminal domains of both subunits, and this inhibition is released by interaction with H2A-H2B, allowing FACT-H2A-H2B to dock onto a (H3-H4) 2 -DNA complex 3 . SPT16 binds nucleosomal DNA and tethers H2A-H2B through its C-terminal domain by acting as a placeholder for DNA. SSRP1 also contributes to DNA binding, and can assume two conformations, depending on whether a second H2A-H2B dimer is present. Our data suggest a compelling mechanism for how FACT maintains chromatin integrity during polymerase passage, by facilitating H2A-H2B dimer removal, stabilizing intermediate ‘subnucleosomal’ states, and promoting nucleosome reassembly. Our findings reconcile discrepancies regarding the many roles of FACT and underscore the dynamic interactions between histone chaperones and nucleosomes.

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Proactive caching is an effective way to alleviate peak-hour traffic congestion by prefetching popular contents at the wireless network edge. To maximize the caching efficiency requires the knowledge of content popularity profile, which however is often unavailable in advance. In this paper, we first propose a new linear prediction model, named grouped linear model (GLM) to estimate the future content requests based on historical data. Unlike many existing works that assumed the static content popularity profile, our model can adapt to the temporal variation of the content popularity in practical systems due to the arrival of new contents and dynamics of user preference. Based on the predicted content requests, we then propose a reinforcement learning approach with model-free acceleration (RLMA) for online cache replacement by taking into account both the cache hits and replacement cost. This approach accelerates the learning process in non-stationary environment by generating imaginary samples for Q-value updates. Numerical results based on real-world traces show that the proposed prediction and learning based online caching policy outperform all considered existing schemes.

Summary Hydrogen–Deuterium eXchange coupled to mass spectrometry (HDX) is a powerful tool for the analysis of protein dynamics and interactions. Bottom-up experiments looking at deuterium uptake differences between various conditions are the most common. These produce multidimensional data that can be challenging to depict in a single visual format. Each user must also set significance thresholds to define meaningful differences and make these apparent in data presentation. To assist in this process, we have created HD-eXplosion, an open-source, web-based application for the generation of chiclet and volcano plots with statistical filters. HD-eXplosion fills a void in available software packages and produces customizable plots that are publication quality. Availability and implementation The HD-eXplosion application is available at http://hd-explosion.utdallas.edu. The source code can be found at https://github.com/HD-Explosion. Contact sheena.darcy@utdallas.edu

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