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Marc Vaudel

Publications and source records attributed to Marc Vaudel.

4 recordsLinked to original sources

Emotional Engagement in Narrative Medical Visualization: An Electrodermal Activity and Eye-Tracking Study

Narrative visualization embeds data in visual stories to make medical information more relatable for non-experts. Despite the growing use of character elements in health communication, evidence on whether individual characters support affective responses remains inconclusive. Physiological evidence independent of verbal self-report is especially scarce, although such measures should complement participants' self-reported experiences. We present a mixed-methods study using electrodermal activity (EDA), eye tracking, and questionnaires to compare two medical data stories: an individual, character-based version and a general, population-level version without an individual protagonist. We examine how this framing influences physiological arousal, visual attention, viewing behavior, and self-reported affective response. Story-level EDA comparisons provide only limited support for stronger arousal in the character-based story. Stronger evidence comes from eye-tracking-based peak classification, where character illustrations were robustly associated with EDA peaks, and from questionnaire responses showing more negative empathy-related emotions for the individual story. The general story elicited more awe and joy, suggesting that individual and population-level framings may support different emotional qualities. We also observed a preliminary story-order effect: participants who first saw the individual story showed higher peak-based physiological arousal, although this effect cannot be fully disentangled from fatigue, novelty, or learning effects. By combining physiological, gaze-based, questionnaire, and lightweight qualitative evidence, our work advances time-resolved assessment of narrative medical visualization and highlights the need to interpret arousal, attention, curiosity, and self-reported emotions together.

cs.HC

A graph-based approach for modification site assignment in proteomics

Background In proteomics, the most probable localizations of post-translational modifications are assessed by localization scores evaluating the likelihood of a given modification to occupy a site on a peptide sequence. When identifying highly modified peptides, localization scores for different modifications can return conflicting results, stacking modifications on the same amino acid. Here, we propose a graph-based approach that assigns modifications to sites in a way that maximizes localization scores while avoiding conflicting assignments. Results The algorithm is implemented as both a standalone Python program and in the compomics-utilities Java library. Our graph-based approach showed the ability to match complex combinations of modifications and acceptor sites, allowing the processing of thousands of peptides in a few seconds. Conclusions Our graph-based approach to modification site assignment allows distributing multiple modifications in a way that maximizes individual localization scores. Having an optimal modification site assignment is important for spectrum annotation and biological interpretation.

q-bio.QM

ProHap Explorer: Visualizing Haplotypes in Proteogenomic Datasets

In mass spectrometry-based proteomics, experts usually project data onto a single set of reference sequences, overlooking the influence of common haplotypes (combinations of genetic variants inherited together from a parent). We recently introduced ProHap, a tool for generating customized protein haplotype databases. Here, we present ProHap Explorer, a visualization interface designed to investigate the influence of common haplotypes on the human proteome. It enables users to explore haplotypes, their effects on protein sequences, and the identification of non-canonical peptides in public mass spectrometry datasets. The design builds on well-established representations in biological sequence analysis, ensuring familiarity for domain experts while integrating novel interactive elements tailored to proteogenomic data exploration. User interviews with proteomics experts confirmed the tool's utility, highlighting its ability to reveal whether haplotypes affect proteins of interest. By facilitating the intuitive exploration of proteogenomic variation, ProHap Explorer supports research in personalized medicine and the development of targeted therapies.

q-bio.GN

On the importance of block randomisation when designing proteomics experiments

Randomisation is used in experimental design to reduce the prevalence of unanticipated confounders. Complete randomisation can however create unbalanced designs, for example, grouping all samples of the same condition in the same batch. Block randomisation is an approach that can prevent severe imbalances in sample allocation with respect to both known and unknown confounders. This feature provides the reader with an introduction to blocking and randomisation, insights into how to effectively organise samples during experimental design, with special considerations with respect to proteomics.

q-bio.QM