Abstract
Conducting epidemiological and risk assessment research that considers the exposome concept, as in the case of HEALS project, requires the acquisition of higher dimension data sets of an increased complexity. In this context, new methods that provide accurate and interpretable data summary on relevant environmental factors are of major importance. In this work, a questionnaire was developed to collect harmonized data on potential pollutant sources to air in the indoor environment where children spend an important part of their early life. The questionnaire was designed in a user friendly checklist format to be filled out at the maternity in ten European cities. This paper presents and discusses the rationale for the selection of the questionnaire contents and the results obtained from its application in the households of 309 HEALS-enrolled families with babies recently born in Porto, Portugal. The tool was very effective in providing data on the putative air pollution sources in homes, with special focus on the bedroom of the newborns. The data collected is part of a wider effort to build the databases and risk assessment models of the HEALS project. The results of the analysis of the collected data suggest that, for the population under study, the main concerns on early life exposures at home can be related to emissions from the use of household solid fuels, indoor tobacco, household cleaning products, fragranced consumer products (e.g. air fresheners, incense and candles), moisture-related pathologies and traffic-related outdoor pollution. Furthermore, it is anticipated that the tool can be a valuable means to empower citizens to actively participate in the control of their own exposures at home. Within this context, the application of the checklist will also allow local stakeholders to identify buildings presenting most evident IAQ problems for sampling or intervention as well as to guide them in preparing evidence-based educational/awareness campaigns to promote public health through creating healthy households.

Abstract
Evaluation of lung function is central to the management of chronic obstructive respiratory diseases. It is typically evaluated with a spirometer by a specialized health professional, who ensures the correct execution of a forced expiratory manoeuvre (FEM). Audio recording of a FEM using a smart device embedded microphone can be used to self-monitor lung function between clinical visits. The challenge of microphone spirometry is to ensure the validity and reliability of the FEM, in the absence of a health professional. In particular, the absence of a mouthpiece may allow excessive mouth closure, leading to an incorrect manoeuvre. In this work, a strategy to automatically assess the correct execution of the FEM is proposed and validated. Using 498 FEM recordings, both specificity and sensitivity attained were above 90%. This method provides immediate feedback to the user, by grading the manoeuvre in a visual scale, promoting the repetition of the FEM when needed.

Abstract
Research and development in mobile robotics are continuously growing. The ability of a human-made machine to navigate safely in a given environment is a challenging task. In agricultural environments, robot navigation can achieve high levels of complexity due to the harsh conditions that they present. Thus, the presence of a reliable map where the robot can localize itself is crucial, and feature extraction becomes a vital step of the navigation process. In this work, the feature extraction issue in the vineyard context is solved using Deep Learning to detect high-level features - the vine trunks. An experimental performance benchmark between two devices is performed: NVIDIA's Jetson Nano and Google's USB Accelerator. Several models were retrained and deployed on both devices, using a Transfer Learning approach. Specifically, MobileNets, Inception, and lite version of You Only Look Once are used to detect vine trunks in real-time. The models were retrained in a built in-house dataset, that is publicly available. The training dataset contains approximately 1600 annotated vine trunks in 336 different images. Results show that NVIDIA's Jetson Nano provides compatibility with a wider variety of Deep Learning architectures, while Google's USB Accelerator is limited to a unique family of architectures to perform object detection. On the other hand, the Google device showed an overall Average precision higher than Jetson Nano, with a better runtime performance. The best result obtained in this work was an average precision of 52.98% with a runtime performance of 23.14 ms per image, for MobileNet-V2. Recent experiments showed that the detectors are suitable for the use in the Localization and Mapping context.

Abstract
When encountering a dubious diagnostic case, radiologists typically search in public or internal databases for similar cases that would help them in their decision-making process. This search represents a massive burden to their workflow, as it considerably reduces their time to diagnose new cases. It is, therefore, of utter importance to replace this manual intensive search with an automatic content-based image retrieval system. However, general content-based image retrieval systems are often not helpful in the context of medical imaging since they do not consider the fact that relevant information in medical images is typically spatially constricted. In this work, we explore the use of interpretability methods to localize relevant regions of images, leading to more focused feature representations, and, therefore, to improved medical image retrieval. As a proof-of-concept, experiments were conducted using a publicly available Chest X-ray dataset, with results showing that the proposed interpretability-guided image retrieval translates better the similarity measure of an experienced radiologist than state-of-the-art image retrieval methods. Furthermore, it also improves the class-consistency of top retrieved results, and enhances the interpretability of the whole system, by accompanying the retrieval with visual explanations. © Springer Nature Switzerland AG 2020.

Abstract
The impact of somatic structural variants (SVs) on gene expression in cancer is largely unknown. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data and RNA sequencing from a common set of 1220 cancer cases, we report hundreds of genes for which the presence within 100 kb of an SV breakpoint associates with altered expression. For the majority of these genes, expression increases rather than decreases with corresponding breakpoint events. Up-regulated cancer-associated genes impacted by this phenomenon include TERT, MDM2, CDK4, ERBB2, CD274, PDCD1LG2, and IGF2. TERT-associated breakpoints involve ~3% of cases, most frequently in liver biliary, melanoma, sarcoma, stomach, and kidney cancers. SVs associated with up-regulation of PD1 and PDL1 genes involve ~1% of non-amplified cases. For many genes, SVs are significantly associated with increased numbers or greater proximity of enhancer regulatory elements near the gene. DNA methylation near the promoter is often increased with nearby SV breakpoint, which may involve inactivation of repressor elements. © 2020, The Author(s).

Abstract
In 2015, to reverse a rise in unemployment, a decline in economic growth and the population aging, the Municipality of Fundão designed the Strategic Plan for Innovation. The plan was thought to attract companies and people to Fundão and to encourage families and younger generations already in Fundão to live and work in their home territory. This would be accomplished by attracting investments and new businesses based on new technologies. The strategy had a significant positive impact on the number of jobs created in the city, its economic growth, and attraction of businesses and population. In effect, the municipality started from scratch and built a new ICT industry cluster to transform the economy for a globalized, digital age, addressing needs in the areas of software development, robotics, and technology-based solutions for traditional sectors. The consortium supporting the plan includes governmental organizations, universities, schools, civic associations, businesses, financial institutions, and innovation centers. After four years, Fundão hosts 14 new companies, including four multinationals. Those companies have created over 500 highly qualified jobs. The municipality also has 70 new startups and over 200 privately funded innovative projects. The training of young students on digital technologies, namely on programming, as well as the reskilling of adults, was an essential first step in the plan’s implementation. This success was recognized with an award from the European Community-and it motivated the extension of the initiative into elementary schools, with the goal of covering all students of the municipality. This chapter examines the case within the lens of technopolis development and it includes interview insights from those involved in Fundão’s sustainability plan. © Springer Nature Switzerland AG 2020.