Real-time inference of word relevance from electroencephalogram and eye gaze.

OBJECTIVE: Brain-computer interfaces can potentially map the subjective relevance of the visual surroundings, based on neural activity and eye movements, in order to infer the interest of a person in real-time. APPROACH: Readers looked for words belonging to one out of five semantic categories, while a stream of words passed at different locations on the screen. It was estimated in real-time which words and thus which semantic category interested each reader based on the electroencephalogram (EEG) and the eye gaze. MAIN RESULTS: Words that were subjectively relevant could be decoded online from the signals. The estimation resulted in an average rank of 1.62 for the category of interest among the five categories after a hundred words had been read. SIGNIFICANCE: It was demonstrated that the interest of a reader can be inferred online from EEG and eye tracking signals, which can potentially be used in novel types of adaptive software, which enrich the interaction by adding implicit information about the interest of the user to the explicit interaction. The study is characterised by the following novelties. Interpretation with respect to the word meaning was necessary in contrast to the usual practice in brain-computer interfacing where stimulus recognition is sufficient. The typical counting task was avoided because it would not be sensible for implicit relevance detection. Several words were displayed at the same time, in contrast to the typical sequences of single stimuli. Neural activity was related with eye tracking to the words, which were scanned without restrictions on the eye movements.

Digging for gold nuggets: uncovering novel candidate genes for variation in gastrointestinal nematode burden in a wild bird species.

The extent to which genotypic variation at a priori identified candidate genes can explain variation in complex phenotypes is a major debate in evolutionary biology. Whereas some high-profile genes such as the MHC or MC1R clearly do account for variation in ecologically relevant characters, many complex phenotypes such as response to parasite infection may well be underpinned by a large number of genes, each of small and effectively undetectable effect. Here, we characterize a suite of novel candidate genes for variation in gastrointestinal nematode (Trichostrongylus tenuis) burden among red grouse (Lagopus lagopus scotica) individuals across a network of moors in north-east Scotland. We test for associations between parasite load and genotypic variation in twelve genes previously identified to be differentially expressed in experimentally infected red grouse or genetically differentiated among red grouse populations with overall different parasite loads. These genes are associated with a broad physiological response including immune system processes. Based on individual-level generalized linear models, genotypic variants in nine genes were significantly associated with parasite load, with effect sizes accounting for differences of 514-666 worms per bird. All but one of these variants were synonymous or untranslated, suggesting that these may be linked to protein-coding variants or affect regulatory processes. In contrast, population-level analyses revealed few and inconsistent associations with parasite load, and little evidence of signatures of natural selection. We discuss the broader significance of these contrasting results in the context of the utility of population genomics and landscape genomics approaches in detecting adaptive genomic signatures.