A Steering Wheel Reversal Rate Metric for Assessing Effects of Visual and Cognitive Secondary Task Load

This paper presents a steering wheel reversal rate metric intended for assessment of the effects of secondary tasks, such as interacting with in-vehicle information systems, on vehicle lateral control performance. The metric was compared to a number of other common steering wheel metrics with respect to the sensitivity to visual and cognitive secondary task load. It was shown that the proposed reversal rate metric, together with the existing steering entropy metric, was the most sensitive across experimental conditions. Different parameter settings for the metric were systematically investigated and suitable values for capturing the effects of visual and cognitive secondary task load recommended

A genetic evaluation of male reproductive fitness at early and late age in Drosophila melanogaster treated with a mutagen

International audienc

Ancora: a web resource for exploring highly conserved noncoding elements and their association with developmental regulatory genes

Ancora is a web resource that provides data and tools for exploring genomic organization of highly conserved noncoding elements for multiple genomes

The pattern of genetic and environmental variation in relation to ageing in laying hens

International audienc

Search for age-dependent as compared to mutagen-induced mutations on the X-chromosome affecting viability in Drosophila melanogaster males

International audienc

Considering Polymorphism in Change-Based Test Suite Reduction

With the increasing popularity of continuous integration, algorithms for selecting the minimal test-suite to cover a given set of changes are in order. This paper reports on how polymorphism can handle false negatives in a previous algorithm which uses method-level changes in the base-code to deduce which tests need to be rerun. We compare the approach with and without polymorphism on two distinct cases ---PMD and CruiseControl--- and discovered an interesting trade-off: incorporating polymorphism results in more relevant tests to be included in the test suite (hence improves accuracy), however comes at the cost of a larger test suite (hence increases the time to run the minimal test-suite).Comment: The final publication is available at link.springer.co

Evasion of autophagy mediated by Rickettsia surface protein OmpB is critical for virulence.

Rickettsia are obligate intracellular bacteria that evade antimicrobial autophagy in the host cell cytosol by unknown mechanisms. Other cytosolic pathogens block different steps of autophagy targeting, including the initial step of polyubiquitin-coat formation. One mechanism of evasion is to mobilize actin to the bacterial surface. Here, we show that actin mobilization is insufficient to block autophagy recognition of the pathogen Rickettsia parkeri. Instead, R. parkeri employs outer membrane protein B (OmpB) to block ubiquitylation of the bacterial surface proteins, including OmpA, and subsequent recognition by autophagy receptors. OmpB is also required for the formation of a capsule-like layer. Although OmpB is dispensable for bacterial growth in endothelial cells, it is essential for R. parkeri to block autophagy in macrophages and to colonize mice because of its ability to promote autophagy evasion in immune cells. Our results indicate that OmpB acts as a protective shield to obstruct autophagy recognition, thereby revealing a distinctive bacterial mechanism to evade antimicrobial autophagy

ECG-based estimation of respiratory modulation of AV nodal conduction during atrial fibrillation

Information about autonomic nervous system (ANS) activity may be valuable for personalized atrial fibrillation (AF) treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in AV nodal refractory period and conduction delay. A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1-minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where a ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis. We demonstrated using synthetic data that the 1D-CNN can predict the respiratory modulation from RR series alone ($\rho$ = 0.805) and that the addition of either respiration signal ($\rho$ = 0.830), AFR ($\rho$ = 0.837), or both ($\rho$ = 0.855) improves the prediction. Results from analysis of clinical ECG data of 20 patients with sufficient signal quality suggest that respiratory modulation decreased in response to deep breathing for five patients, increased for five patients, and remained similar for ten patients, indicating a large inter-patient variability.Comment: 20 pages, 7 figures, 5 table