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Fusarium head blight (FHB) is a fungal disease of worldwide importance to small grain cereals that may lead to severe losses in both yield and quality. The development of resistant varieties is the most effective approach for managing the disease. Genetic variation for FHB resistance is large, including 'exotic' and 'native' wheat germplasm. Methods for selecting improved lines include: 1) phenotypic selection with direct symptom evaluation; 2) marker-assisted selection for well-characterized QTL and 3) genomic selection employing genome-wide prediction models. Breeding programs need to find the optimal deployment of the complementary approaches according to their available facilities, resources and requirements. This review aims to summarize recent advances in FHB resistance breeding, thereby discussing the importance of morphological traits like the extent of retained anthers after flowering, its suitability for indirect selection and the pronounced association of the semi-dwarfing allele Rht-D1b with increased anther retention and FHB severity. Markerassisted selection is successfully applied to select for largeeffect QTL, especially for the most prominent resistance QTL Fhb1 in bread wheat, as well as in durum wheat as recently demonstrated. The resistance locus Fhb1 has been partly elucidated, a pore-forming toxin-like gene confers resistance against fungal spread. Genomic selection for FHB resistance appears promising especially for breeding programs deploying 'native' resistance sources with many small-effect QTL.

Online communities have become popular for publishing and searching content, as well as for finding and connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. These items can be annotated and rated by different users, and these social tags and derived user-specific scores can be leveraged for searching relevant content and discovering subjectively interesting items. Moreover, the relationships among users can also be taken into consideration for ranking search results, the intuition being that you trust the recommendations of your close friends more than those of your casual acquaintances.Queries for tag or keyword combinations that compute and rank the top-k results thus face a large variety of options that complicate the query processing and pose efficiency challenges. This paper addresses these issues by developing an incremental top-k algorithm with two-dimensional expansions: social expansion considers the strength of relations among users, and semantic expansion considers the relatedness of different tags. It presents a new algorithm, based on principles of threshold algorithms, by folding friends and related tags into the search space in an incremental on-demand manner. The excellent performance of the method is demonstrated by an experimental evaluation on three real-world datasets, crawled from deli.cio.us, Flickr, and LibraryThing.

five independent breeding cycles and assessed the bias of within-cycle cross-validation. We investigated the influence of outliers on the prediction accuracy and predicted protein yield by its components traits. A high average heritability was estimated for protein content, followed by grain yield and protein yield. The bias of the prediction accuracy using populations from individual cycles using fivefold cross-validation was accordingly substantial for protein yield (17-712 %) and less pronounced for protein content (8-86 %). Cross-validation using the cycles as folds aimed to avoid this bias and reached a maximum prediction accuracy of r GS = 0.51 for protein content, r GS = 0.38 for grain yield and r GS = 0.16 for protein yield. Dropping outlier cycles increased the prediction accuracy of grain yield to r GS = 0.41 as estimated by cross-validation, while dropping outlier environments did not have a significant effect on the prediction accuracy. Independent validation suggests, on the other hand, that careful consideration is necessary before an outlier correction is undertaken, which removes lines from the training population. Predicting protein yield by multiplying genomic estimated breeding values of grain yield and protein content raised the prediction accuracy to r GS = 0.19 for this derived trait.

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