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Improvement of crop climate resilience will require an understanding of whole-plant adaptation to specific local environments. This review places features of plant form and function related to photosynthetic productivity, as well as associated gene-expression patterns, into the context of the adaptation of Arabidopsis thaliana ecotypes to local environments with different climates in Sweden and Italy. The growth of plants under common cool conditions resulted in a proportionally greater emphasis on the maintenance of photosynthetic activity in the Swedish ecotype. This is compared to a greater emphasis on downregulation of light-harvesting antenna size and upregulation of a host of antioxidant enzymes in the Italian ecotype under these conditions. This differential response is discussed in the context of the climatic patterns of the ecotypes’ native habitats with substantial opportunity for photosynthetic productivity under mild temperatures in Italy but not in Sweden. The Swedish ecotype’s response is likened to pushing forward at full speed with productivity under low temperature versus the Italian ecotype’s response of staying safe from harm (maintaining redox homeostasis) while letting productivity decline when temperatures are transiently cold. It is concluded that either strategy can offer directions for the development of climate-resilient crops for specific locations of cultivation. 

This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. This approach identifies stiff parameter combinations strongly affecting the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily by the data or are also substantively influenced by the priors. We focus on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated, and showcase the benefits of this technique for applications in biochemistry, ecology, and cardiac electrophysiology. We also show how stiff parameter combinations, once identified, uncover controlling mechanisms underlying the system being modeled and inform which of the model parameters need to be prioritized in future experiments for improved parameter inference from collective model-data fitting. 

Biological and pharmaceutical analytes like liposomes, therapeutic proteins, nanoparticles, and drug-delivery systems are utilized in applications, such as pharmaceutical formulations or biomimetic models, in which controlling their size is often critical. Many of the common techniques for sizing these analytes require method development, significant sample preparation, large sample quantities, and lengthy analysis times. In other cases, such as DLS, sizing can be biased towards the largest constituents in a mixture. Therefore, there is a need for more rapid, sensitive, accurate, and straightforward analytical methods for sizing macromolecules, especially those of biological origin which may be sample-limited. Taylor dispersion analysis (TDA) is a sizing technique that requires no calibration and consumes only nL to pL sample volumes. In TDA, average diffusion coefficients are determined via the Taylor–Aris equation by characterizing band broadening of an analyte plug under well-controlled laminar flow conditions. Diffusion coefficient can then be interpreted as hydrodynamic radius ( R H ) via the Stokes–Einstein equation. Here, we offer a tutorial review of TDA, intended to make the method better understood and more widely accessible to a community of analytical chemists and separations scientists who may benefit from the unique advantages of this versatile sizing method. We first provide a tutorial on the fundamental principles that allow TDA to achieve calibration-free sizing of analytes across a wide range of R H , with an emphasis on the reduced sample consumption and analysis times that result from utilizing fused silica capillaries. We continue by highlighting relationships between operating parameters and critically important flow conditions. Our discussion continues by looking at methods for applying TDA to sample mixtures via algorithmic approaches and integration of capillary electrophoresis and TDA. Finally, we present a selection of reports that demonstrate TDA applied to complex challenges in bioanalysis and materials science. 

Amidst global shifts in the distribution and abundance of wildlife and livestock, we have only a rudimentary understanding of ungulate parasite communities and parasite-sharing patterns. We used qPCR and DNA metabarcoding of fecal samples to characterize gastrointestinal nematode (Strongylida) community composition and sharing among 17 sympatric species of wild and domestic large mammalian herbivore in central Kenya. We tested a suite of hypothesis-driven predictions about the role of host traits and phylogenetic relatedness in describing parasite infections. Host species identity explained 27–53% of individual variation in parasite prevalence, richness, community composition and phylogenetic diversity. Host and parasite phylogenies were congruent, host gut morphology predicted parasite community composition and prevalence, and hosts with low evolutionary distinctiveness were centrally positioned in the parasite-sharing network. We found no evidence that host body size, social-group size or feeding height were correlated with parasite composition. Our results highlight the interwoven evolutionary and ecological histories of large herbivores and their gastrointestinal nematodes and suggest that host identity, phylogeny and gut architecture—a phylogenetically conserved trait related to parasite habitat—are the overriding influences on parasite communities. These findings have implications for wildlife management and conservation as wild herbivores are increasingly replaced by livestock.