Abstract
Background/Aims: Personalised nutrition has potential to revolutionise dietary health promotion if accepted by the general public. We studied trust and preferences regarding personalised nutrition services, how they influence intention to adopt these services, and cultural and social differences therein. Methods: A total of 9,381 participants were quota-sampled to be representative of each of 9 EU countries (Germany, Greece, Ireland, Poland, Portugal, Spain, the Netherlands, the UK, and Norway) and surveyed by a questionnaire assessing their intention to adopt personalised nutrition, trust in service regulators and information sources, and preferences for service providers and information channels. Results: Trust and preferences significantly predicted intention to adopt personalised nutrition. Higher trust in the local department of health care was associated with lower intention to adopt personalised nutrition. General practitioners were the most trusted of service regulators, except in Portugal, where consumer organisations and universities were most trusted. In all countries, family doctors were the most trusted information providers. Trust in the National Health Service as service regulator and information source showed high variability across countries. Despite its highest variability across countries, personal meeting was the preferred communication channel, except in Spain, where an automated internet service was preferred. General practitioners were the preferred service providers, except in Poland, where dietitians and nutritionists were preferred. The preference for dietitians and nutritionists as service providers highly varied across countries. Conclusion: These results may assist in informing local initiatives to encourage acceptance and adoption of country-specific tailored personalised nutrition services, therefore benefiting individual and public health. (C) 2017 S. Karger AG, Basel

Abstract
Object tracking in a moving frame is becoming a common requirement in a lot of mobile robotic applications, such as search and rescue, monitoring and surveillance, and even in some scientific applications, such as robotic soccer. In all these applications, the robots must be capable of estimating the target position and, sometimes, velocity on their own. Depending on the application and on the current scene situation, the estimates must be more or less accurate, depending on the robot intention to interact with the target, whether to catch it, follow it, etc. The problem is that a robot moves along the working area, having some uncertainty in its pose estimation. This paper proposes an approach based on a Kalman Filter to estimate the object position and velocity, regardless the robot pose. As a testbed, a Middle-Size League soccer robot tracking a moving ball example will be used. This approach allows the agent to follow and interact with a moving object, even if its localization is not available. © Springer International Publishing AG 2018.

Abstract
Proportional, integrative and derivative (PID) controllers are among the most used in industrial control applications. Classical PID controller design methodologies can be significantly improved by incorporating recent computational intelligence techniques. Two techniques based on particle swarm optimization (PSO) algorithms are proposed to design PI-PID controllers. Both control design methodologies are directed to optimize PI-PID controller gains using two degrees-of-freedom control configurations, subjected to frequency domain robustness constraints. The first technique proposes a single-objective PSO algorithm, to sequentially design a two degrees-of-freedom control structure, considering the optimization of load disturbance rejection followed by set-point tracking optimization. The second technique proposes a many-objective PSO algorithm, to design a two degrees-of-freedom control structure, considering simultaneously, the optimization of four different design criteria. In the many-objective case, the control engineer may select the most adequate solution among the resulting optimal Pareto set. Simulation results are presented showing the effectiveness of the proposed PI-PID design techniques, in comparison with both classic and optimization based methods.

Abstract
AbstractDiscovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5’UTRs ofTP53, RFTN1, RNF34,andMTG2,in the 3’UTRs ofNFKBIZandTOB1,and in the non-coding RNARMRP.We provide evidence that the previously reported non-coding RNAsNEAT1andMALAT1may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyondTERT.

Abstract
Background: Positron Emission Tomography - Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods: In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results: The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions: After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response- to-treatment classes.

Abstract