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A 'pitch–catch' low frequency ultrasonic technique has been developed that can be used for detection and depth gauging of transverse cracks in the rail head, such as the gauge corner cracking identified as a contributory factor in recent UK rail failures. The electromagnetic acoustic transducers (EMATs) used in this technique employ a guided wave mode with very similar characteristics to classical Rayleigh surface waves. These waves propagate along the surface of the rail, penetrating to a depth of approximately 2–15 mm. The surface wave generated is wideband, containing a range of frequencies in one single pulse. Crack depth can be estimated by comparing the surface wave at a particular frequency that has passed underneath the crack with a wave propagated through a defect-free region. Preliminary measurements indicate that the ultrasonic system has the potential to assess the microstructure–stress state around the rail head, thus identifying precursors to crack formation. [ABSTRACT FROM AUTHOR]

The metabolomic profile of aging is complex. Here, we analyse 325 nuclear magnetic resonance (NMR) biomarkers from 250,341 UK Biobank participants, identifying 54 representative aging-related biomarkers associated with all-cause mortality. We conduct genome-wide association studies (GWAS) for these 325 biomarkers using whole-genome sequencing (WGS) data from 95,372 individuals and perform multivariable Mendelian randomization (MVMR) analyses, discovering 439 candidate "biomarker - disease" causal pairs at the nominal significance level. We develop a metabolomic aging score that outperforms other aging metrics in predicting short-term mortality risk and exhibits strong potential for discriminating aging-accelerated populations and improving disease risk prediction. A longitudinal analysis of 13,263 individuals enables us to calculate a metabolomic aging rate which provides more refined aging assessments and to identify candidate anti-aging and pro-aging NMR biomarkers. Taken together, our study has presented a comprehensive aging-related metabolomic profile and highlighted its potential for personalized aging monitoring and early disease intervention., (© 2024. The Author(s).)