Search for: All records

Abstract Insect colouration mediated by melanization can assist in dealing with environmental temperatures. However, melanin synthesis can be costly and depends on the ability of insects to acquire enough energy and nutrients from their diets. Due to the increased plant C:N ratio associated with elevated CO₂concentrations, insect herbivores' melanization could be limited by the amount of nitrogen they acquire from their host plants.To investigate how diet C:N impacts the potential colour response to temperature, we usedManduca sextacaterpillars reared at different combinations of temperatures and diet C:N ratios, and measured pupal mass and development time (performance metrics) and colour morphology.The high‐temperature treatment (27°C) had a positive impact on larval performance, whereas a nitrogen‐poor diet was related to lower performance. Using a fitness metric that considers both pupal mass and development time, we found a positive effect of both high temperature and nitrogen‐rich diet treatments on larval fitness.We found that diet and temperature affected the colouration of larvae, in which larvae reared at the low‐temperature treatment (18°C) and fed a nitrogen‐rich diet were darker than their counterparts.Our results provide experimental evidence of the impact of diet on melanization and suggest that CO₂‐related changes in plant quality could be associated with changes in insect herbivore performance and colouration. 

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.