Influence of temperature on the composition and polymerization of gluten proteins during grain filling in spring wheat (Triticum aestivum L.)

One Norwegian and one UK spring wheat cultivar, Bjarne and Cadenza, respectively, were grown in climate chambers to investigate the effects of lower to moderate temperatures during grain filling on the gluten quality. Two experiments were carried out with weekly fertilization until anthesis, while post-anthesis fertilization was applied in a third experiment. The proportions of different gluten proteins were affected by temperature in a similar manner for both cultivars when grown without post-anthesis fertilization. However, whereas low temperature strongly decreased %UPP for Cadenza, Bjarne had high %UPP at all temperature regimes. The results indicated that the assembly of glutenin polymers in Bjarne was less sensitive to variation in temperature than in Cadenza. Thus, our results suggested that the temperature influenced the proportion of different gluten proteins in both cultivars, while its effects on the assembly of the glutenin polymers were cultivar dependent. The duration of grain filling was longer at the lower temperatures, and this was associated with increased grain weight. Temperature had little effect on the amount of protein accumulated per grain, thus the proportion of proteins was strongly decreased at lower temperatures. This was to some extent, but not fully counteracted by post-anthesis fertilization.

Low-grade inflammation, diet composition and health: current research evidence and its translation

The importance of chronic low-grade inflammation in the pathology of numerous age-related chronic conditions is now clear. An unresolved inflammatory response is likely to be involved from the early stages of disease development. The present position paper is the most recent in a series produced by the International Life Sciences Institute's European Branch (ILSI Europe). It is co-authored by the speakers from a 2013 workshop led by the Obesity and Diabetes Task Force entitled ‘Low-grade inflammation, a high-grade challenge: biomarkers and modulation by dietary strategies’. The latest research in the areas of acute and chronic inflammation and cardiometabolic, gut and cognitive health is presented along with the cellular and molecular mechanisms underlying inflammation–health/disease associations. The evidence relating diet composition and early-life nutrition to inflammatory status is reviewed. Human epidemiological and intervention data are thus far heavily reliant on the measurement of inflammatory markers in the circulation, and in particular cytokines in the fasting state, which are recognised as an insensitive and highly variable index of tissue inflammation. Potential novel kinetic and integrated approaches to capture inflammatory status in humans are discussed. Such approaches are likely to provide a more discriminating means of quantifying inflammation–health/disease associations, and the ability of diet to positively modulate inflammation and provide the much needed evidence to develop research portfolios that will inform new product development and associated health claims.

High-throughput monitoring of wild bee diversity and abundance via mitogenomics

1. Bee populations and other pollinators face multiple, synergistically acting threats, which have led to population declines, loss of local species richness and pollination services, and extinctions. However, our understanding of the degree, distribution and causes of declines is patchy, in part due to inadequate monitoring systems, with the challenge of taxonomic identification posing a major logistical barrier. Pollinator conservation would benefit from a high-throughput identification pipeline. 2. We show that the metagenomic mining and resequencing of mitochondrial genomes (mitogenomics) can be applied successfully to bulk samples of wild bees. We assembled the mitogenomes of 48 UK bee species and then shotgun-sequenced total DNA extracted from 204 whole bees that had been collected in 10 pan-trap samples from farms in England and been identified morphologically to 33 species. Each sample data set was mapped against the 48 reference mitogenomes. 3. The morphological and mitogenomic data sets were highly congruent. Out of 63 total species detections in the morphological data set, the mitogenomic data set made 59 correct detections (93�7% detection rate) and detected six more species (putative false positives). Direct inspection and an analysis with species-specific primers suggested that these putative false positives were most likely due to incorrect morphological IDs. Read frequency significantly predicted species biomass frequency (R2 = 24�9%). Species lists, biomass frequencies, extrapolated species richness and community structure were recovered with less error than in a metabarcoding pipeline. 4. Mitogenomics automates the onerous task of taxonomic identification, even for cryptic species, allowing the tracking of changes in species richness and istributions. A mitogenomic pipeline should thus be able to contain costs, maintain consistently high-quality data over long time series, incorporate retrospective taxonomic revisions and provide an auditable evidence trail. Mitogenomic data sets also provide estimates of species counts within samples and thus have potential for tracking population trajectories.

Bayesian model comparison with un-normalised likelihoods

Models for which the likelihood function can be evaluated only up to a parameter-dependent unknown normalizing constant, such as Markov random field models, are used widely in computer science, statistical physics, spatial statistics, and network analysis. However, Bayesian analysis of these models using standard Monte Carlo methods is not possible due to the intractability of their likelihood functions. Several methods that permit exact, or close to exact, simulation from the posterior distribution have recently been developed. However, estimating the evidence and Bayes’ factors for these models remains challenging in general. This paper describes new random weight importance sampling and sequential Monte Carlo methods for estimating BFs that use simulation to circumvent the evaluation of the intractable likelihood, and compares them to existing methods. In some cases we observe an advantage in the use of biased weight estimates. An initial investigation into the theoretical and empirical properties of this class of methods is presented. Some support for the use of biased estimates is presented, but we advocate caution in the use of such estimates.

Shadow lines in the arithmetic of elliptic curves

Let E/Q be an elliptic curve and p a rational prime of good ordinary reduction. For every imaginary quadratic field K/Q satisfying the Heegner hypothesis for E we have a corresponding line in E(K)\otimes Q_p, known as a shadow line. When E/Q has analytic rank 2 and E/K has analytic rank 3, shadow lines are expected to lie in E(Q)\otimes Qp. If, in addition, p splits in K/Q, then shadow lines can be determined using the anticyclotomic p-adic height pairing. We develop an algorithm to compute anticyclotomic p-adic heights which we then use to provide an algorithm to compute shadow lines. We conclude by illustrating these algorithms in a collection of examples.