Wheat amylase/trypsin inhibitors (ATIs) have gained significant relevance as inducers of intestinal and extra-intestinal inflammation. In this study, we present a novel hybrid dataindependent acquisition (DIA) liquid chromatography−mass spectrometry (LC-MS) approach, combining QconCAT technology with short microflow LC gradients and DIA and apply the method toward the quantitative proteome analysis of ATI extracts. The presented method is fast, robust, and reproducible and provides precise QconCAT-based absolute quantification of major ATI proteins while simultaneously quantifying the proteome by label-free quantification (LFQ). We analyzed extracts of 60 varieties of common wheat grown in replication and evaluated the reproducibility and precision of the workflow for the quantification of ATIs. Applying the method to analyze different wheat species (i.e., common wheat, spelt, durum wheat, emmer, and einkorn) and comparing the results to published data, we validated inter-laboratory and cross-methodology reproducibility of ATI quantification, which is essential in the context of large-scale breeding projects. Additionally, we applied our workflow to assess environmental effects on ATI expression, analyzing ATI content and proteome of same varieties grown at different locations. Finally, we explored the potential of combining QconCAT-based absolute quantification with DIA-based LFQ proteome analysis for the generation of new hypotheses or assay development.

Recently, social media have been used by researchers to detect depressive symptoms in individuals using linguistic data from users’ posts. In this study, we propose a framework to identify social information as a significant predictor of depression. Using the proposed framework, we develop an application called the Socially Mediated Patient Portal (SMPP), which detects depression-related markers in Facebook users by applying a data-driven approach with machine learning classification techniques. We examined a data set of 4350 users who were evaluated for depression using the Center for Epidemiological Studies Depression (CES-D) scale. From this analysis, we identified a set of features that can distinguish between individuals with and without depression. Finally, we identified the dominant features that adequately assess individuals with and without depression on social media. The model trained on these features will be helpful to physicians in diagnosing mental diseases and psychiatrists in analysing patient behaviour.

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Wheat consumption can trigger celiac disease, allergic reactions and non-celiac wheat sensitivity (NCWS) in humans. Some people with NCWS symptoms claim a better tolerability of spelt compared to bread wheat products. We therefore investigated potential differences in the proteomes of spelt and bread wheat flour using nano LC–ESI–MS/MS on a set of 15 representative varieties for each of the two species. Based on the bread wheat reference, we detected 3,050 proteins in total and for most of them the expression was mainly affected by the environment. By contrast, 274 and 409 proteins in spelt and bread wheat, respectively, had a heritability ≥ 0.4 highlighting the potential to influence their expression level by varietal choice. We found 84 and 193 unique proteins for spelt and bread wheat, respectively, and 396 joint proteins, which expression differed significantly (p ≤ 0.05) when comparing both species. Thus, about one third of proteins differed significantly between spelt and bread wheat. Of them, we identified 81 proteins with high heritability, which therefore might be interesting candidates for future research on wheat hypersensitivities.

The rapid growth of GPS-enabled mobile devices has popularized many location-based applications. Spatial keyword search which finds objects of interest by considering both spatial locations and textual descriptions has become very useful in these applications. The recent integration of social data with spatial keyword search opens a new service horizon for users. Few previous studies have proposed methods to combine spatial keyword queries with social data in Euclidean space. However, most real-world applications constrain the distance between query location and data objects by a road network, where distance between two points is defined by the shortest connecting path. This paper proposes geo-social top-k keyword queries and geo-social skyline keyword queries on road networks. Both queries enrich traditional spatial keyword query semantics by incorporating social relevance component. We formalize the proposed query types and appropriate indexing frameworks and algorithms to efficiently process them. The effectiveness and efficiency of the proposed approaches are evaluated using real datasets.