Total transcriptome, proteome, and allergome of Johnson grass pollen, which is important for allergic rhinitis in subtropical regions

Background Genomic data are lacking for many allergen sources. To circumvent this limitation, we implemented a strategy to reveal the repertoire of pollen allergens of a grass with clinical importance in subtropical regions, where an increasing proportion of the world's population resides. Objective We sought to identify and immunologically characterize the allergenic components of the Panicoideae Johnson grass pollen (JGP; Sorghum halepense). Methods The total pollen transcriptome, proteome, and allergome of JGP were documented. Serum IgE reactivities with pollen and purified allergens were assessed in 64 patients with grass pollen allergy from a subtropical region. Results Purified Sor h 1 and Sor h 13 were identified as clinically important allergen components of JGP with serum IgE reactivity in 49 (76%) and 28 (43.8%), respectively, of patients with grass pollen allergy. Within whole JGP, multiple cDNA transcripts and peptide spectra belonging to grass pollen allergen families 1, 2, 4, 7, 11, 12, 13, and 25 were identified. Pollen allergens restricted to subtropical grasses (groups 22-24) were also present within the JGP transcriptome and proteome. Mass spectrometry confirmed the IgE-reactive components of JGP included isoforms of Sor h 1, Sor h 2, Sor h 13, and Sor h 23. Conclusion Our integrated molecular approach revealed qualitative differences between the allergenic components of JGP and temperate grass pollens. Knowledge of these newly identified allergens has the potential to improve specific diagnosis and allergen immunotherapy treatment for patients with grass pollen allergy in subtropical regions and reduce the burden of allergic respiratory disease globally.

Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic technique that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.