Abstract
Biofouling represents a major economic, environmental and health concern for which new eco-friendly solutions are needed. International legislation has restricted the use of biocidal-based antifouling coatings, and increasing efforts have been applied in the search for environmentally friendly antifouling agents. This research work deals with the assessment of the interest of a series of synthetic chalcone derivatives for antifouling applications. Sixteen chalcone derivatives were synthesized with moderate yields (38-85%). Antifouling bioactivity of these compounds was assessed at different levels of biological organization using both anti-macrofouling and anti-microfouling bioassays, namely an anti-settlement assay using mussel (Mytilus galloprovincialis) larvae, as well as marine bacteria and microalgal biofilms growth inhibition bioassays. Results showed that three compounds (11, 12, and 16) were particularly active against the settlement of mussel larvae (EC50 7.24-34.63 mu M), being compounds 12 and 16 also able to inhibit the growth of microfouling species (EC50 4.09-20.31 mu M). Moreover, the most potent compounds 12 and 16 were found to be non-toxic to the non-target species Artemia salina (<10% mortality at 25 mu M). A quantitative structure-activity relationship model predicted that descriptors describing the ability of molecules to form hydrogen bonds and encoding the shape, branching ratio and constitutional diversity of the molecule were implied in the antifouling activity against the settlement of mussel larvae. This work elucidates for the first time the relevance of synthesizing chalcone derivatives to generate new nontoxic products to prevent marine biofouling.

Abstract
The diversity of research topics and resulting datasets in the field of Ecology (the scientific study of ecological systems and their biodiversity) has grown in parallel with developments in research data management. Based on a meta-analysis performed on 93 scientific references, this paper presents a comprehensive overview of the use of metadata tools in the Ecology domain through time. Overall, 40 metadata tools were found to be either referred or used by the research community from 1997 to 2018. In the same period, 50 different initiatives in ecology and biodiversity research were conceptualized and implemented to promote effective data sharing in the community. A relevant concern that stems from this analysis is the need to establish simple methods to promote data interoperability and reuse, so far limited by the production of metadata according to different standards. With this study, we also highlight challenges and perspectives in research data management in the domain of Ecology towards best practice guidelines.

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Abstract
Evaluating scientists based on their scientific production is often a controversial topic. Nevertheless, bibliometrics and algorithmic approaches can assist traditional peer review in numerous tasks, such as attributing research grants, deciding scientific committees, or choosing faculty promotions. Traditional bibliometrics focus on individual measures, disregarding the whole data (i.e., the whole network). Here we put forward OTARIOS, a graph-ranking method which combines multiple publication/citation criteria to rank authors. OTARIOS divides the original network in two subnetworks, insiders and outsiders, which is an adequate representation of citation networks with missing information. We evaluate OTARIOS on a set of five real networks, each with publications in distinct areas of Computer Science. When matching a metric’s produced ranking with best papers awards received, we observe that OTARIOS is >20 more accurate than traditional bibliometrics. We obtain the best results when OTARIOS considers (i) the author’s publication volume and publication recency, (ii) how recently his work is being cited by outsiders, and (iii) how recently his work is being cited by insiders and how individual he his. © 2019, Springer Nature Switzerland AG.

Abstract
Health facilities produce an increasing and vast amount of data that must be efficiently analyzed. New approaches for healthcare monitoring are being developed every day and the Internet of Things (IoT) came to fill the still existing void on real-time monitoring. A new generation of mechanisms and techniques are being used to facilitate the practice of medicine, promoting faster diagnosis and prevention of diseases. We proposed a system that relies on IoT for storing and monitoring medical sensors data with analytic capabilities. To this end, we chose two approaches for storing this data which were thoroughly evaluated. Apache HBase presents a higher rate of data ingestion, when collaborating with the Kaa IoT platform, than Apache Cassandra, exhibiting good performance storing unstructured data, as presented in a healthcare environment. The outcome of this system has shown the possibility of a large number of medical sensors being simultaneously connected to the same platform (6000 records sent by the second or 48 ECG sensors with a frequency of 125Hz). The results presented in this paper are promising and should be further investigated as a comprehensive system would benefit the patient's diagnosis but also the physicians. © 2018 IEEE.

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