Identification and Characterization of Stress-Responsive TAS3-Derived TasiRNAs in Melon.

Small interfering RNAs (siRNA) are key regulators of gene expression that play essential roles in diverse biological processes. Trans-acting siRNAs (tasiRNAs) are a class of plant-endogenous siRNAs that lead the cleavage of nonidentical transcripts. TasiRNAs are usually involved in fine-tuning development. However, increasing evidence supports that tasiRNAs may be involved in stress response. Melon is a crop of great economic importance extensively cultivated in semiarid regions frequently exposed to changing environmental conditions that limit its productivity. However, knowledge of the precise role of siRNAs in general, and of tasiRNAs in particular, in regulating the response to adverse environmental conditions is limited. Here, we provide the first comprehensive analysis of computationally inferred melon-tasiRNAs responsive to two biotic (viroid-infection) and abiotic (cold treatment) stress conditions. We identify two TAS3-loci encoding to length (TAS3-L) and short (TAS3-S) transcripts. The TAS candidates predicted from small RNA-sequencing data were characterized according to their chromosome localization and expression pattern in response to stress. The functional activity of cmTAS genes was validated by transcript quantification and degradome assays of the tasiRNA precursors and their predicted targets. Finally, the functionality of a representative cmTAS3-derived tasiRNA (TAS3-S) was confirmed by transient assays showing the cleavage of ARF target transcripts.

Non-identifiability of parameters for a class of shear-thinning rheological models, with implications for haematological fluid dynamics.

Choosing a suitable model and determining its associated parameters from fitting to experimental data is fundamental for many problems in biomechanics. Models of shear-thinning complex fluids, dating from the work of Bird, Carreau, Cross and Yasuda, have been applied in highly-cited computational studies of hemodynamics for several decades. In this manuscript we revisit these models, first to highlight a degree of uncertainty in the naming conventions in the literature, but more importantly to address the problem of inferring model parameters by fitting to rheology experiments. By refitting published data, and also by simulation, we find large, flat regions in likelihood surfaces that yield families of parameter sets which fit the data equally well. Despite having almost indistinguishable fits to experimental data these varying parameter sets can predict very different flow profiles, and as such these parameters cannot be used to draw conclusions about physical properties of the fluids, such as zero-shear viscosity or relaxation time of the fluid, or indeed flow behaviours. We verify that these features are not a consequence of the experimental data sets through simulations; by sampling points from the rheological models and adding a small amount of noise we create a synthetic data set which reveals that the problem of parameter identifiability is intrinsic to these models.