Estimating transpiration as an individual component of canopy evapotranspiration using a theoretical approach is extremely useful as it eliminates the complexity involved in partitioning evapotranspiration. A model to predict transpiration based on radiation intercepted at various levels of canopy leaf area index (LAI) was developed in a controlled environment using a pasture species, tall fescue (Festuca arundinacea var. Demeter). The canopy was assumed to be a composite of two indistinct layers defined as sunlit and shaded; the proportion of which was calculated by utilizing a weighted model (W model). The radiation energy utilized by each layer was calculated from the PAR at the top of the canopy and the fraction of absorbed photosynthetically active radiation (fAPAR) corresponding to the LAI of the sunlit and shaded layers. A relationship between LAI and fAPAR was also established for this specific canopy to aid the calculation of energy interception. Canopy conductance was estimated from scaling up of stomatal conductance measured at the individual leaf level. Other environmental factors that drive transpiration were monitored accordingly for each individual layer. The Penman–Monteith and Jarvis evapotranspiration models were used as the basis to construct a modified transpiration model suitable for controlled environment conditions. Specially, constructed self-watering tubs were used to measure actual transpiration to validate the model output. The model provided good agreement of measured transpiration (actual transpiration = 0.96 × calculated transpiration, R2 = 0.98; p < 0.001) with the predicted values. This was particularly so at lower LAIs. Probable reasons for the discrepancy at higher LAI are explained. Both the predicted and experimental transpiration varied from 0.21 to 0.56 mm h−1 for the range of available LAIs. The physical proportion of the shaded layer exceeded that of the sunlit layer near LAI of 3.0, however, the contribution of the sunlit layer to the total transpiration remains higher throughout the entire growing season.
CD4+ T helper subsets, in particular Th1 and Th17, produce cytokines that play critical roles in inflammatory diseases. TNF-α, produced largely by innate immune cells, is also potently pro-inflammatory. Here we asked if TNF-α contributes to effector CD4+ Th generation, and found that in the presence of TGF-β TNF-α signals via its non-death receptor TNFR2 to generate Th17 cells expressing inflammatory mediators such as GM-CSF, CXCL3, and IFN-γ. Single cell RNA-Seq of CNS-infiltrating CD4+ T cells in EAE identified an IL-17-positive cluster with an inflammatory gene signature that required TNFR2 expression. Notably, TNFR2-deficient CD4+ T cells produced fewer inflammatory mediators and were relatively nonpathogenic in EAE and colitis. Thus, TNF-α is not just an effector but also an initiator of Th differentiation, raising the possibility that in diseases in which both TNF-α and IL-17 contribute, targeting TNF-α may diminish the generation of pathogenic Th17 cells.
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