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Conformational dynamics play essential roles in RNA function. However, detailed structural characterization of excited states of RNA remains challenging. Here, we apply high hydrostatic pressure (HP) to populate excited conformational states of tRNA Lys3 , and structurally characterize them using a combination of HP 2D-NMR, HP-SAXS (HP-small-angle X-ray scattering), and computational modeling. HP-NMR revealed that pressure disrupts the interactions of the imino protons of the uridine and guanosine U–A and G–C base pairs of tRNA Lys3 . HP-SAXS profiles showed a change in shape, but no change in overall extension of the transfer RNA (tRNA) at HP. Configurations extracted from computational ensemble modeling of HP-SAXS profiles were consistent with the NMR results, exhibiting significant disruptions to the acceptor stem, the anticodon stem, and the D-stem regions at HP. We propose that initiation of reverse transcription of HIV RNA could make use of one or more of these excited states. 

Concurrency bugs are extremely difficult to detect. Recently, several dynamic techniques achieve sound analysis. M2 is even complete for two threads. It is designed to decide whether two events can occur consecutively. However, real-world concurrency bugs can involve more events and threads. Some can occur when the order of two or more events can be exchanged even if they occur not consecutively. We propose a new technique SeqCheck to soundly decide whether a sequence of events can occur in a specified order. The ordered sequence represents a potential concurrency bug. And several known forms of concurrency bugs can be easily encoded into event sequences where each represents a way that the bug can occur. To achieve it, SeqCheck explicitly analyzes branch events and includes a set of efficient algorithms. We show that SeqCheck is sound; and it is also complete on traces of two threads. We have implemented SeqCheck to detect three types of concurrency bugs and evaluated it on 51 Java benchmarks producing up to billions of events. Compared with M2 and other three recent sound race detectors, SeqCheck detected 333 races in ~30 minutes; while others detected from 130 to 285 races in ~6 to ~12 hours. SeqCheck detected 20 deadlocks in ~6 seconds. This is only one less than Dirk; but Dirk spent more than one hour. SeqCheck also detected 30 atomicity violations in ~20 minutes. The evaluation shows SeqCheck can significantly outperform existing concurrency bug detectors.