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Animal and plant microRNAs (miRNAs) are essential for the spatio-temporal regulation of development. Together with this role, plant miRNAs have been proposed to target transposable elements (TEs) and stimulate the production of epigenetically active small interfering RNAs. This activity is evident in the plant male gamete containing structure, the male gametophyte or pollen grain. How the dual role of plant miRNAs, regulating both genes and TEs, is integrated during pollen development and which mRNAs are regulated by miRNAs in this cell type at a genome-wide scale are unknown. Here, we provide a detailed analysis of miRNA dynamics and activity during pollen development in Arabidopsis thaliana using small RNA and degradome parallel analysis of RNA end high-throughput sequencing. Furthermore, we uncover miRNAs loaded into the two main active Argonaute (AGO) proteins in the uninuclear and mature pollen grain, AGO1 and AGO5. Our results indicate that the developmental progression from microspore to mature pollen grain is characterized by a transition from miRNAs targeting developmental genes to miRNAs regulating TE activity.

 

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.