Personalizing automated driving speed to enhance user experience and performance in intermediate-level automated driving.

In the context of high-level driving automation (SAE levels 4-5), several studies have shown that a personalized automated driving style, i.e., mimicking that of the human behind the wheel, can improve his experience. The objective of this simulator study was to examine the potential transfer of these benefits in the context of intermediate-level driving automation (SAE levels 2-3), focusing on driving speed personalization. In the first phase of the study, the driving speed of 52 participants was recorded. In the second phase, the same participants were driven by an automated car on a highway twice, and sometimes had to takeover during the drive because of a stationary vehicle on the lane. On these two drives, the automated car drove either at the same speed as them (personalized) or 20 km/h faster. The results showed that using a personalized speed driving style led to higher comfort, and that this effect was fully mediated by automated driving perceived safety. Although driving speed predicted automated driving perceived safety, this effect was actually moderated by trust in automated cars. Regarding takeover performance, the results showed that the brake use and maximum force were lower with the personalized speed driving style, leading to lower resulting maximum negative longitudinal acceleration and speed variability. Overall, the results of this study suggest that the benefits of automated driving style personalization in terms of speed extend to SAE levels 2-3. In addition to the experience benefits, this personalization approach could also improve traffic flow and safety.

Genome scale metabolic network modelling for metabolic profile predictions.

Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.

Should my automated car drive as I do? Investigating speed preferences of drivengers in various driving conditions.

Studies investigating the question of how automated cars (ACs) should drive converge to show that a personalized automated driving-style, i.e., mimicking the driving-style of the human behind the wheel, has a positive influence on various aspects of his experience (e.g., comfort). However, few studies have investigated the fact that these benefits might vary with respect to driver-related variables, such as trust in ACs, and contextual variables of the driving activity, such as weather conditions. Additionally, the context of intermediate levels of automation, such as SAE level 3, remains largely unexplored. The objective of this study was to investigate these points. In a scenario-based experimental protocol, participants were exposed to written scenarios in which a character is driven by a SAE level 3 AC in different combinations of conditions (i.e., types of roads, weather conditions and traffic congestion levels). For each condition, participants were asked to indicate how fast they would prefer their AC to drive and how fast they would manually drive in the same situation. Through analyses of variance and equivalence tests, results showed a tendency for participants to overall prefer a slightly lower AC speed than their own. However, a linear regression analysis showed that while participants with the lowest levels of trust preferred an AC speed lower than theirs, those with the highest levels preferred an AC speed nearly identical to theirs. Overall, the results of this study suggest that it would be more beneficial to implement a personalization approach for the design of automated driving-styles rather than a one for all approach.

Suggesting disease associations for overlooked metabolites using literature from metabolic neighbors.

In human health research, metabolic signatures extracted from metabolomics data have a strong added value for stratifying patients and identifying biomarkers. Nevertheless, one of the main challenges is to interpret and relate these lists of discriminant metabolites to pathological mechanisms. This task requires experts to combine their knowledge with information extracted from databases and the scientific literature. However, we show that most compounds (>99%) in the PubChem database lack annotated literature. This dearth of available information can have a direct impact on the interpretation of metabolic signatures, which is often restricted to a subset of significant metabolites. To suggest potential pathological phenotypes related to overlooked metabolites that lack annotated literature, we extend the "guilt-by-association" principle to literature information by using a Bayesian framework. The underlying assumption is that the literature associated with the metabolic neighbors of a compound can provide valuable insights, or an a priori, into its biomedical context. The metabolic neighborhood of a compound can be defined from a metabolic network and correspond to metabolites to which it is connected through biochemical reactions. With the proposed approach, we suggest more than 35,000 associations between 1,047 overlooked metabolites and 3,288 diseases (or disease families). All these newly inferred associations are freely available on the FORUM ftp server (see information at https://github.com/eMetaboHUB/Forum-LiteraturePropagation).

Searching in clutter: Visual behavior and performance of expert action video game players.

Searching for targets among distractors in visual scenes can be more difficult due to the presence of clutter. However, studies in various domains have shown differentiated effects according to the expertise of the searcher. The present study extended these findings to the domain of action video games expertise. 58 participants, split in 2 groups (action video game players and non-action video game players) searched for targets in visual scenes under two clutter conditions (uncluttered and high clutter). Reaction times and accuracy served as measures of performance, and the visual behavior was assessed using the number and duration of eye fixations. Our findings suggest that visual clutter has a negative influence on performance and alters the visual behavior during visual search in action video game scenes. Our results also suggest that expert action video game players might use different visual strategies to cope with clutter, leading however to no performance benefits.

FORUM: building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases.

MOTIVATION: Metabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories. RESULTS: The use of a Semantic Web framework on biological data allows us to apply ontological-based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries. AVAILABILITY AND IMPLEMENTATION: A web interface to browse and download the extracted relations, as well as a SPARQL endpoint to directly probe the whole FORUM KG, are available at https://forum-webapp.semantic-metabolomics.fr. The code needed to reproduce the triplestore is available at https://github.com/eMetaboHUB/Forum-DiseasesChem. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Circular ANRIL isoforms switch from repressors to activators of p15/CDKN2B expression during RAF1 oncogene-induced senescence.

Long non-coding RNAs (ncRNAs) are major regulators of gene expression and cell fate. The INK4 locus encodes the tumour suppressor proteins p15INK4b, p16INK4a and p14ARF required for cell cycle arrest and whose expression increases during senescence. ANRIL is a ncRNA antisense to the p15 gene. In proliferative cells, ANRIL prevents senescence by repressing INK4 genes through the recruitment of Polycomb-group proteins. In models of replicative and RASval12 oncogene-induced senescence (OIS), the expression of ANRIL and Polycomb proteins decreases, thus allowing INK4 derepression. Here, we found in a model of RAF1 OIS that ANRIL expression rather increases, due in particular to an increased stability. This led us to search for circular ANRIL isoforms, as circular RNAs are rather stable species. We found that the expression of two circular ANRIL increases in several OIS models (RAF1, MEK1 and BRAF). In proliferative cells, they repress p15 expression, while in RAF1 OIS, they promote full induction of p15, p16 and p14ARF expression. Further analysis of one of these circular ANRIL shows that it interacts with Polycomb proteins and decreases EZH2 Polycomb protein localization and H3K27me3 at the p15 and p16 promoters, respectively. We propose that changes in the ratio between Polycomb proteins and circular ANRIL isoforms allow these isoforms to switch from repressors of p15 gene to activators of all INK4 genes in RAF1 OIS. Our data reveal that regulation of ANRIL expression depends on the senescence inducer and underline the importance of circular ANRIL in the regulation of INK4 gene expression and senescence.