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The extracellular matrix (ECM) is a complex 3D framework of macromolecules, which regulate cell bioactivity via chemical and physical properties. The ECM's physical properties, including stiffness and physical constraints to cell shape, regulate actomyosin cytoskeleton contractions, which induce signaling cascades influencing gene expression and cell fate. Engineering such bioactivity, a.k.a., mechanotransduction, has been mainly achieved by 2D platforms such as micropatterns. These platforms cause cytoskeletal contractions with apico‐basal polarity and can induce mechanotransduction that is unnatural to most cells in native ECMs. An effective method to engineer mechanotransduction in 3D is needed. This work creates FiberGel, a 3D artificial ECM comprised of sub‐cellular scale fibers. These microfibers can crosslink into defined microstructures with the fibers' diameter, stiffness, and alignment independently tuned. Most importantly, cells are blended amongst the fibers prior to crosslinking, leading to homogeneously cellularized scaffolds. Studies using mesenchymal stem cells showed that the microfibers' diameter, stiffness, and alignment regulate 3D cell shape and the nuclei translocation of transcriptional coactivators YAP/TAZ (yes‐associated protein/transcriptional coactivator), which enables the control of cell differentiation and tissue formation. A novel technology based on repeated stretching and folding is created to synthesize FiberGel. This 3D platform can significantly contribute to mechanotransduction research and applications.

 

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.