Deficiencies in monitoring practices of marine protected areas in southern European seas.

Worldwide, states are gazetting new Marine Protected Areas (MPAs) to meet the international commitment of protecting 30% of the seas by 2030. Yet, protection benefits only come into effect when an MPA is implemented with activated regulations and actively managed through continuous monitoring and adaptive management. To assess if actively managed MPAs are the rule or the exception, we used the Mediterranean and Black Seas as a case study, and retrieved information on monitoring activities for 878 designated MPAs in ten European Union (EU) countries.

Persistent Organic Pollutants (POPs) in three bathyal chondrichthyes from the North-Western Mediterranean Sea.

The deep-sea can act as a sink for legacy contaminants such as organochlorines (OCs), causing damages in its inhabitants for their persistence and their prolonged effects in the organisms. HCB, DDT and its isomers, and 28 PCBs congeners were detected in muscle and embryonic tissues of three deep-sea chondrichthyes Chimaera monstrosa (n = 16), Dalatias licha (n = 12) and Etmopterus spinax (n = 51) sampled in Ligurian and Tyrrhenian Sea (Mediterranean Sea). Contaminant distribution in E.

Development of vaccines for Chagas disease (CRUZIVAX): stakeholders' preferences and potential impacts on healthcare.

A vaccine for Chagas disease does not currently exist. This study aims to inform the development of two vaccines for the prevention and treatment of Trypanosoma cruzi infection, and guide their pre-clinical phase up to clinical phase I. The three main objectives are: 1) to explore patients' and policy makers' preferences on the candidate vaccines in Argentina and Spain; 2) to investigate health-related quality of life of patients affected by Chagas disease; and 3) to assess the potential health provider savings associated with the vaccines, in terms of resource use and health care costs.

Reframing control methods for parameters optimization in adversarial image generation.

Training procedures for deep networks require the setting of several hyper-parameters that strongly affect the obtained results. The problem is even worse in adversarial learning strategies used for image generation where a proper balancing of the discriminative and generative networks is fundamental for an effective training. In this work we propose a novel hyper-parameters optimization strategy based on the use of Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers.

Unsupervised Domain Adaptation of Deep Networks for ToF Depth Refinement.

Depth maps acquired with ToF cameras have a limited accuracy due to the high noise level and to the multi-path interference. Deep networks can be used for refining ToF depth, but their training requires real world acquisitions with ground truth, which is complex and expensive to collect. A possible workaround is to train networks on synthetic data, but the domain shift between the real and synthetic data reduces the performances.