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Despite of important functions of strigolactones (SLs) and karrikins (KARs) in plant development, plant–parasite and plant–fungi interactions, their roles in soybean–rhizobia interaction remain elusive. SL/KAR signaling genesGmMAX2a, GmD14s,andGmKAIsare activated by rhizobia infection. GmMAX2a restoredatmax2root hair defects and soybean root hairs were changed inGmMAX2aoverexpression (GmMAX2a‐OE) or knockdown (GmMAX2a‐KD) mutants.GmMAX2a‐KDgave fewer, whereasGmMAX2a‐OEproduced more nodules than GUS hairy roots. Mutation ofGmMAX2ain itsKDorOEtransgenic hairy roots affected the rhizobia infection‐induced increases in early nodulation gene expression. Both mutant hairy roots also displayed the altered auxin, jasmonate and abscisic acid levels, as further verified by transcriptomic analyses of their synthetic genes. Overexpression of an auxin synthetic geneGmYUC2aalso affected SL and KAR signaling genes. GmMAX2a physically interacted with SL/KAR receptors GmD14s, GmKAIs, and GmD14Ls with different binding affinities, depending on variations in the critical amino acids, forming active D14/KAI‐SCF^MAX2complexes. The knockdown mutant roots of the nodule‐specifically expressingGmKAIs andGmD14Ls gave fewer nodules, with altered expression of several early nodulation genes. The expression levels ofGmKAIs, andGmD14Ls were markedly changed inGmMAX2amutant roots, so did their target repressor genesGmD53s andGmSMAX1s. Thus, SL and KAR signaling were involved in soybean–rhizobia interaction and nodulation partly through interactions with hormones, and this may explain the different effects of MXA2 orthologs on legume determinate and indeterminate nodulation. The study provides fresh insights into the roles of GmMAX2‐mediated SL/KAR signaling in soybean root hair and nodule formation.

 

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.