A catalogue of <Emphasis Type="Italic">Triticum monococcum</Emphasis> genes encoding toxic and immunogenic peptides for celiac disease patients

The celiac disease (CD) is an inflammatory condition characterized by injury to the lining of the small-intestine on exposure to the gluten of wheat, barley and rye. The involvement of gluten in the CD syndrome has been studied in detail in bread wheat, where a set of “toxic” and “immunogenic” peptides has been defined. For wheat diploid species, information on CD epitopes is poor. In the present paper, we have adopted a genomic approach in order to understand the potential CD danger represented by storage proteins in diploid wheat and sequenced a sufficiently large number of cDNA clones related to storage protein genes of Triticum monococcum. Four bona fide toxic peptides and 13 immunogenic peptides were found. All the classes of storage proteins were shown to contain harmful sequences. The major conclusion is that einkorn has the full potential to induce the CD syndrome, as already evident for polyploid wheats. In addition, a complete overview of the storage protein gene arsenal in T. monococcum is provided, including a full-length HMW x-type sequence and two partial HMW y-type sequences. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing

Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.