Detecting Relevance during Decision-Making from Eye Movements for UI Adaptation

This paper proposes an approach to detect information relevance during decision-making from eye movements in order to enable user interface adaptation. This is a challenging task because gaze behavior varies greatly across individual users and tasks and groundtruth data is difficult to obtain. Thus, prior work has mostly focused on simpler target-search tasks or on establishing general interest, where gaze behavior is less complex. From the literature, we identify six metrics that capture different aspects of the gaze behavior during decision-making and combine them in a voting scheme. We empirically show, that this accounts for the large variations in gaze behavior and out-performs standalone metrics. Importantly, it offers an intuitive way to control the amount of detected information, which is crucial for different UI adaptation schemes to succeed. We show the applicability of our approach by developing a room-search application that changes the visual saliency of content detected as relevant. In an empirical study, we show that it detects up to 97% of relevant elements with respect to user self-reporting, which allows us to meaningfully adapt the interface, as confirmed by participants. Our approach is fast, does not need any explicit user input and can be applied independent of task and user.Comment: The first two authors contributed equally to this wor

Inferring relevance from eye movements with wrong models

Statistical inference forms the backbone of modern science. It is often viewed as giving an objective validation for hypotheses or models. Perhaps for this reason the theory of statistical inference is often derived with the assumption that the "truth" is within the model family. However, in many real-world applications the applied statistical models are incorrect. A more appropriate probabilistic model may be computationally too complex, or the problem to be modelled may be so new that there is little prior information to be incorporated. However, in statistical theory the theoretical and practical implications of the incorrectness of the model family are to a large extent unexplored. This thesis focusses on conditional statistical inference, that is, modeling of classes of future observations given observed data, under the assumption that the model is incorrect. Conditional inference or prediction is one of the main application areas of statistical models which is still lacking a conclusive theoretical justification of Bayesian inference. The main result of the thesis is an axiomatic derivation where, given an incorrect model and assuming that the utility is conditional likelihood, a discriminative posterior yields a distribution on model parameters which best agrees with the utility. The devised discriminative posterior outperforms the classical Bayesian joint likelihood-based approach in conditional inference. Additionally, a theoretically justified expectation maximization-type algorithm is presented for obtaining conditional maximum likelihood point estimates for conditional inference tasks. The convergence of the algorithm is shown to be more stable than in earlier partly heuristic variants. The practical application field of the thesis is inference of relevance from eye movement signals in an information retrieval setup. It is shown that relevance can be predicted to some extent, and that this information can be exploited in a new kind of task, proactive information retrieval. Besides making it possible to design new kinds of engineering applications, statistical modeling of eye tracking data can also be applied in basic psychological research to make hypotheses of cognitive processes affecting eye movements, which is the second application area of the thesis

Genotype-Specific Expression and NLR Repertoire Contribute to Phenotypic Resistance Diversity in Plantago lanceolata

Publisher Copyright: © Copyright © 2021 Safdari, Höckerstedt, Brosche, Salojärvi and Laine.High levels of phenotypic variation in resistance appears to be nearly ubiquitous across natural host populations. Molecular processes contributing to this variation in nature are still poorly known, although theory predicts resistance to evolve at specific loci driven by pathogen-imposed selection. Nucleotide-binding leucine-rich repeat (NLR) genes play an important role in pathogen recognition, downstream defense responses and defense signaling. Identifying the natural variation in NLRs has the potential to increase our understanding of how NLR diversity is generated and maintained, and how to manage disease resistance. Here, we sequenced the transcriptomes of five different Plantago lanceolata genotypes when inoculated by the same strain of obligate fungal pathogen Podosphaera plantaginis. A de novo transcriptome assembly of RNA-sequencing data yielded 24,332 gene models with N50 value of 1,329 base pairs and gene space completeness of 66.5%. The gene expression data showed highly varying responses where each plant genotype demonstrated a unique expression profile in response to the pathogen, regardless of the resistance phenotype. Analysis on the conserved NB-ARC domain demonstrated a diverse NLR repertoire in P. lanceolata consistent with the high phenotypic resistance diversity in this species. We find evidence of selection generating diversity at some of the NLR loci. Jointly, our results demonstrate that phenotypic resistance diversity results from a crosstalk between different defense mechanisms. In conclusion, characterizing the architecture of resistance in natural host populations may shed unprecedented light on the potential of evolution to generate variation.Peer reviewe

Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks

We propose an image-classification method to predict the perceived-relevance of text documents from eye-movements. An eye-tracking study was conducted where participants read short news articles, and rated them as relevant or irrelevant for answering a trigger question. We encode participants' eye-movement scanpaths as images, and then train a convolutional neural network classifier using these scanpath images. The trained classifier is used to predict participants' perceived-relevance of news articles from the corresponding scanpath images. This method is content-independent, as the classifier does not require knowledge of the screen-content, or the user's information-task. Even with little data, the image classifier can predict perceived-relevance with up to 80% accuracy. When compared to similar eye-tracking studies from the literature, this scanpath image classification method outperforms previously reported metrics by appreciable margins. We also attempt to interpret how the image classifier differentiates between scanpaths on relevant and irrelevant documents

High-throughput sequencing data and the impact of plant gene annotation quality

The use of draft genomes of different species and re-sequencing of accessions and populations are now a common tool for plant biology research. The de novo assembled draft genomes make it possible to identify pivotal divergence points in the plant lineage and provide an opportunity to investigate the genomic basis and timing of biological innovations by inferring orthologs between species. Furthermore, re-sequencing facilitates the mapping and subsequent molecular characterization of causative loci for traits including plant stress tolerance or development. In both cases high quality gene annotation, the identification of protein-coding regions, gene promoters and 5’ and 3’ untranslated regions, is critical for investigation of gene function. Annotations are constantly improving but automated gene annotations still require manual curation and experimental validation. This is particularly important for genes with large introns, genes located in regions rich with transposable elements or repeats, large gene families and segmentally duplicated genes. In this opinion paper we highlight the impact of annotation quality on evolutionary analyses, genome-wide association studies and the identification of orthologous genes in plants. Furthermore, we predict that incorporating the accurate information from manual curation into databases will dramatically improve the performance of automated gene predictors.Peer reviewe

Photosystem II Repair and Plant Immunity : Lessons Learned from Arabidopsis Mutant Lacking the THYLAKOID LUMEN PROTEIN 18.3

Chloroplasts play an important role in the cellular sensing of abiotic and biotic stress. Signals originating from photosynthetic light reactions, in the form of redox and pH changes, accumulation of reactive oxygen and electrophile species or stromal metabolites are of key importance in chloroplast retrograde signaling. These signals initiate plant acclimation responses to both abiotic and biotic stresses. To reveal the molecular responses activated by rapid fluctuations in growth light intensity, gene expression analysis was performed with Arabidopsis thaliana wild type and the tlp18.3 mutant plants, the latter showing a stunted growth phenotype under fluctuating light conditions (Biochem. J, 406, 415-425). Expression pattern of genes encoding components of the photosynthetic electron transfer chain did not differ between fluctuating and constant light conditions, neither in wild type nor in tlp18.3 plants, and the composition of the thylakoid membrane protein complexes likewise remained unchanged. Nevertheless, the fluctuating light conditions repressed in wild-type plants a broad spectrum of genes involved in immune responses, which likely resulted from shade-avoidance responses and their intermixing with hormonal signaling. On the contrary, in the tlp18.3 mutant plants there was an imperfect repression of defense-related transcripts upon growth under fluctuating light, possibly by signals originating from minor malfunction of the photosystem II (PSII) repair cycle, which directly or indirectly modulated the transcript abundances of genes related to light perception via phytochromes. Consequently, a strong allocation of resources to defense reactions in the tlp18.3 mutant plants presumably results in the stunted growth phenotype under fluctuating light.Peer reviewe

Colonic Mucosal Microbiota and Association of Bacterial Taxa with the Expression of Host Antimicrobial Peptides in Pediatric Ulcerative Colitis

Inflammatory bowel diseases (IBD), ulcerative colitis (UC) and Crohn’s disease (CD), are chronic debilitating disorders of unknown etiology. Over 200 genetic risk loci are associated with IBD, highlighting a key role for immunological and epithelial barrier functions. Environmental factors account for the growing incidence of IBD, and microbiota are considered as an important contributor. Microbiota dysbiosis can lead to a loss of tolerogenic immune effects and initiate or exacerbate inflammation. We aimed to study colonic mucosal microbiota and the expression of selected host genes in pediatric UC. We used high-throughput 16S rDNA sequencing to profile microbiota in colonic biopsies of pediatric UC patients (n = 26) and non-IBD controls (n = 27). The expression of 13 genes, including five for antimicrobial peptides, in parallel biopsies was assessed with qRT-PCR. The composition of microbiota between UC and non-IBD differed significantly (PCoA, p = 0.001). UC children had a decrease in Bacteroidetes and an increase in several family-level taxa including Peptostreptococcaceae and Enterobacteriaceae, which correlated negatively with the expression of antimicrobial peptides REG3G and DEFB1, respectively. Enterobacteriaceae correlated positively with the expression siderophore binding protein LCN2 and Betaproteobacteria negatively with DEFB4A expression. The results indicate that reciprocal interaction of epithelial microbiota and defense mechanisms play a role in UC

Colonic Mucosal Microbiota and Association of Bacterial Taxa with the Expression of Host Antimicrobial Peptides in Pediatric Ulcerative Colitis

Inflammatory bowel diseases (IBD), ulcerative colitis (UC) and Crohn’s disease (CD), are chronic debilitating disorders of unknown etiology. Over 200 genetic risk loci are associated with IBD, highlighting a key role for immunological and epithelial barrier functions. Environmental factors account for the growing incidence of IBD, and microbiota are considered as an important contributor. Microbiota dysbiosis can lead to a loss of tolerogenic immune effects and initiate or exacerbate inflammation. We aimed to study colonic mucosal microbiota and the expression of selected host genes in pediatric UC. We used high-throughput 16S rDNA sequencing to profile microbiota in colonic biopsies of pediatric UC patients (n = 26) and non-IBD controls (n = 27). The expression of 13 genes, including five for antimicrobial peptides, in parallel biopsies was assessed with qRT-PCR. The composition of microbiota between UC and non-IBD differed significantly (PCoA, p = 0.001). UC children had a decrease in Bacteroidetes and an increase in several family-level taxa including Peptostreptococcaceae and Enterobacteriaceae, which correlated negatively with the expression of antimicrobial peptides REG3G and DEFB1, respectively. Enterobacteriaceae correlated positively with the expression siderophore binding protein LCN2 and Betaproteobacteria negatively with DEFB4A expression. The results indicate that reciprocal interaction of epithelial microbiota and defense mechanisms play a role in UC

Assembly and Analysis of Unmapped Genome Sequence Reads Reveal Novel Sequence and Variation in Dogs

Correction Volume: 8, Article Number: 11853 DOI: 10.1038/s41598-018-30169-3 Published:AUG 2 2018Dogs are excellent animal models for human disease. They have extensive veterinary histories, pedigrees, and a unique genetic system due to breeding practices. Despite these advantages, one factor limiting their usefulness is the canine genome reference (CGR) which was assembled using a single purebred Boxer. Although a common practice, this results in many high-quality reads remaining unmapped. To address this whole-genome sequence data from three breeds, Border Collie (n = 26), Bearded Collie (n = 7), and Entlebucher Sennenhund (n = 8), were analyzed to identify novel, non-CGR genomic contigs using the previously validated pseudo-de novo assembly pipeline. We identified 256,957 novel contigs and paired-end relationships together with BLAT scores provided 126,555 (49%) high-quality contigs with genomic coordinates containing 4.6 Mb of novel sequence absent from the CGR. These contigs close 12,503 known gaps, including 2.4 Mb containing partially missing sequences for 11.5% of Ensembl, 16.4% of RefSeq and 12.2% of canFam3.1+ CGR annotated genes and 1,748 unmapped contigs containing 2,366 novel gene variants. Examples for six disease-associated genes (SCARF2, RD3, COL9A3, FAM161A, RASGRP1 and DLX6) containing gaps or alternate splice variants missing from the CGR are also presented. These findings from non-reference breeds support the need for improvement of the current Boxer-only CGR to avoid missing important biological information. The inclusion of the missing gene sequences into the CGR will facilitate identification of putative disease mutations across diverse breeds and phenotypes.Peer reviewe