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Cycloneuralians are ecdysozoans with a fossil record extending to the Early Cambrian Fortunian Age and represented mostly by cuticular integuments. However, internal anatomies of Fortunian cycloneuralians are virtually unknown, hampering our understanding of their functional morphology and phylogenetic relationships. Here we report the exceptional preservation of cycloneuralian introvert musculature in Fortunian rocks of South China. The musculature consists of an introvert body-wall muscular grid of four circular and 36 radially arranged longitudinal muscle bundles, as well as an introvert circular muscle associated with 19 roughly radially arranged, short retractors. Collectively, these features support at least a scalidophoran affinity, and the absence of muscles associated with a mouth cone and scalids further indicates a priapulan affinity. As in modern scalidophorans, the fossil musculature, and particularly the introvert circular muscle retractors, may have controlled introvert inversion and facilitated locomotion and feeding. This work supports the evolution of scalidophoran-like or priapulan-like introvert musculature in cycloneuralians at the beginning of the Cambrian Period. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.