Enhancing Acceptance and Trust in Automated Driving trough Virtual Experience on a Driving Simulator

As vehicle driving evolves from human-controlled to autonomous, human–machine interaction ensures intuitive usage as well as the feedback from vehicle occupants to the machine for optimising controls. The feedback also improves understanding of the user satisfaction with the system behaviour, which is crucial for determining user trust and, hence, the acceptance of the new functionalities that aim to improve mobility solutions and increase road safety. Trust and acceptance are potentially the crucial parameters for determining the success of autonomous driving deployment in wider society. Hence, there is a need to define appropriate and measurable parameters to be able to quantify trust and acceptance in a physically safe environment using dependable methods. This study seeks to support technical developments and data gathering with psychology to determine the degree to which humans trust automated driving functionalities. The primary aim is to define if the usage of an advanced driving simulator can improve consumer trust and acceptance of driving automation through tailor-made studies. We also seek to measure significant differences in responses from different demographic groups. The study employs tailor-made driving scenarios to gather feedback on trust, usability and user workload of 55 participants monitoring the vehicle behaviour and environment during the automated drive. Participants’ subjective ratings are gathered before and after the simulator session. Results show a significant increase in trust ensuing the exposure to the driving automation functionalities. We quantify this increase resulting from the usage of the driving simulator. Those less experienced with driving automation show a higher increase in trust and, therefore, profit more from the exercise. This appears to be linked to the demanded participant workload, as we establish a link between workload and trust. The findings provide a noteworthy contribution to quantifying the method of evaluating and ensuring user acceptance of driving automation. It is only through the increase of trust and consequent improvement of user acceptance that the introduction of the driving automation into wider society will be a guaranteed success

Molecular profiling of soft-tissue sarcomas with FoundationOne® Heme identifies potential targets for sarcoma therapy: a single-centre experience

Background: Molecular diagnosis has become an established tool in the characterisation of adult soft-tissue sarcomas (STS). FoundationOne ® Heme analyses somatic gene alterations in sarcomas via DNA and RNA-hotspot sequencing of tumour-associated genes. Methods: We evaluated FoundationOne ® Heme testing in 81 localised STS including 35 translocation-associated and 46 complex-karyotyped cases from a single institution. Results: Although FoundationOne ® Heme achieved broad patient coverage and identified at least five genetic alterations in each sample, the sensitivity for fusion detection was rather low, at 42.4%. Nevertheless, potential targets for STS treatment were detected using the FoundationOne ® Heme assay: complex-karyotyped sarcomas frequently displayed copy-number alterations of common tumour-suppressor genes, particularly deletions in TP53 , NF1 , ATRX , and CDKN2A . A subset of myxofibrosarcomas (MFS) was amplified for HGF ( n  = 3) and MET ( n  = 1). PIK3CA was mutated in 7/15 cases of myxoid liposarcoma (MLS; 46.7%). Epigenetic regulators (e.g. MLL2 and MLL3 ) were frequently mutated. Conclusions: In summary, FoundationOne ® Heme detected a broad range of genetic alterations and potential therapeutic targets in STS (e.g. HGF/MET in a subset of MFS, or PIK3CA in MLS). The assay’s sensitivity for fusion detection was low in our sample and needs to be re-evaluated in a larger cohort

Super-Resolution Imaging Strategies for Cell Biologists Using a Spinning Disk Microscope

In this study we use a spinning disk confocal microscope (SD) to generate super-resolution images of multiple cellular features from any plane in the cell. We obtain super-resolution images by using stochastic intensity fluctuations of biological probes, combining Photoactivation Light-Microscopy (PALM)/Stochastic Optical Reconstruction Microscopy (STORM) methodologies. We compared different image analysis algorithms for processing super-resolution data to identify the most suitable for analysis of particular cell structures. SOFI was chosen for X and Y and was able to achieve a resolution of ca. 80 nm; however higher resolution was possible >30 nm, dependant on the super-resolution image analysis algorithm used. Our method uses low laser power and fluorescent probes which are available either commercially or through the scientific community, and therefore it is gentle enough for biological imaging. Through comparative studies with structured illumination microscopy (SIM) and widefield epifluorescence imaging we identified that our methodology was advantageous for imaging cellular structures which are not immediately at the cell-substrate interface, which include the nuclear architecture and mitochondria. We have shown that it was possible to obtain two coloured images, which highlights the potential this technique has for high-content screening, imaging of multiple epitopes and live cell imaging