We have determined the crystal structure of a spliceosomal RNP complex comprising the 15.5kD protein of the human U4/U6.U5 tri-snRNP and the 5' stem-loop of U4 snRNA. The protein interacts almost exclusively with a purine-rich (5+2) internal loop within the 5' stem-loop, giving an unusual RNA fold characterized by two tandem sheared G-A base pairs, a high degree of purine stacking, and the accommodation of a single RNA base, rotated out of the RNA chain, in a pocket of the protein. Apart from yielding the structure of an important entity in the pre-mRNA splicing apparatus, this work also implies a model for the complex of the 15.5kD protein with box C/D snoRNAs. It additionally suggests a general recognition principle in a novel family of RNA binding proteins.
The condition of railway infrastructure is currently assessed by track recording cars, wayside equipment, onboard monitoring techniques and visual inspections. These data sources deliver valuable information for infrastructure managers on the asset’s condition but are mostly carried out in time-based intervals. This paper examines the potential of fibre optic cables, which are already installed in cable troughs alongside railway tracks, to monitor railway infrastructure conditions. The sensing technique, known as distributed acoustic/vibration sensing (DAS/DVS), relies on the effect of Rayleigh scattering and transforms the optical fibre into an array of “virtual microphones” in the thousands. This sensing method has the ability to be used over long distances and thus provide information about the events taking place in the proximity of the monitored asset in real-time. This study outlines the potential of DAS for the identification of different track conditions and isolated track defects. The results are linked to asset data of the infrastructure manager to identify the root cause of the detected signal anomalies and pattern. A methodology such as this allows for condition-based and component-specific maintenance planning and execution and avoids the installation of additional sensors. DAS can pave the way toward a permanent and holistic assessment of railway tracks.
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