In many European countries, childbirth still includes a variety of routine medical interventions, such as labour-inducing drugs, the lithotomic position, epidurals, manoeuvres, episiotomy and an excess of surgical deliveries. These also happen for women with low-risk pregnancies, despite WHO recommendations and the findings of evidence-based medicine on the topic. Sometimes women can be coerced into accepting medical interventions, or these interventions are performed without their consent. In Latin America, over the past decade, the term “obstetric violence” has become part of the legal framework. The concept refers to acts in the context of labour and birth categorised as physically or psychologically violent due to unjustified use of medical interventions. Specific laws against obstetric violence – a type of gender-based violence – exist in Argentina (2009), Venezuela (2007) and Mexico (2014). In Europe, the issue is discussed by human rights organisations and social movements in order to fight for a more respectful birth, but no country has passed legislation on the matter yet. The project aims to reconstruct and analyse the historical, social and political processes that led to the legal recognition of obstetric violence in Latin America, focusing on the experience in Argentina. The impact that this recognition has had on birth care services and the training of health personnel will be analysed. A qualitative methodology based on case studies and social actors’ perspectives will be adopted. Medical anthropology – with contributions from disciplines such as community health, human rights and gender studies – will provide the theoretical framework for the project. Tools and best practices will be identified and transferred at the European level in order to contribute to the public debate on a respectful birth and to support a process of political recognition of obstetric violence in the European context. An Observatory on Obstetric Violence will be implemented.

The exponential growth of demand for data processing requires increasingly large computational resources and, consequently, prohibitively high energy consumption. To sustain this evolution, a paradigm switch from conventional computing architectures to data-centric platforms is needed. Neuromorphic computing aims at reaching this goal by realizing brain-inspired circuits based on artificial neurons and synapses, which are extremely energy efficient. The objective of this project is to explore a novel type of artificial synapse to be employed in neuromorphic chips. Among the technological options for solid state synapses, memories based on ferroelectric field-effect transistors (FeFET) are considered very promising due to their energy efficiency and to their compatibility with a Back-End-Of-Line implementation and thus a 3D integration. A FeFET is a field-effect transistors that employs a ferroelectric (FE) material as gate oxide. FE materials have a spontaneous electric polarization that can be reversed by the application of an electric field, and in conventional FeFETs this is used to modulate the threshold voltage and thus resistance in the channel region. This project will address an alternative physical mechanism to obtain a synaptic behavior in FeFET, namely a polarization-induced tuning of the resistance at source/drain Schottky contacts. In the Ferroelectric Schottky barrier FETs (Fe-SBFETs), the FE material overlaps the Schottky contact region, hence in this region the FE material is placed between two metals resulting in an effective and low voltage ferroelectric switching. For this reason, Fe-SBFETs are expected to operate as low energy synaptic devices. In this project, Fe-SBFETs will be extensively studied and modeled, by means of TCAD simulations. A design-space for optimal synaptic operation will be derived. Finally, a compact model for SPICE simulations of neuromorphic circuits based on Fe-SBFETs will be developed.

In recent years, a general decline has been documented in several agriculturally relevant woody plants, and numerous reports point towards a possible role of the plant-associated microbiome. This especially applies to grapevines (Vitis vinifera L.) and to the recent outbreak of grapevine trunk diseases (GTD). This cluster of fungal diseases is considered the greatest challenge in modern viticulture, due to the complexity of the different pathosystems and the lack of reliable control strategies. During the last >100 years, vineyards have been routinely treated with fungicides, insecticides, herbicides and fertilizers. Recent research have demonstrated that such treatments and other anthropic intervention (e.g., domestication, grafting, training) affect the plant-associated microbiome. These human-driven alterations, exacerbated by grapevines clonal propagation process, are believed to have led to an imbalance in grapevines endophytic microbiome, which may be a key explanation for the recent success of GTD-associated pathogens. The best candidates to investigate the composition of a balanced wood microbiome, unaffected by anthropic activities, are wild populations of V. vinifera subsp. sylvestris (VVS). In this multidisciplinary project, I will join the fields of plant pathology, epigenetics and microbial ecology to (1) unravel the endophytic microbiome diversity of wild populations of VVS, using DNA metabarcoding and in vitro isolation techniques; (2) re-introduce endophytes isolated from VVS in cultivated grapevines, by means of a synthetic microbiome transfer; (3) assess the efficacy of the synthetic microbiome in antagonizing GTD-associated pathogens, a next-generation approach to biological control. The outcomes of this project will not only contribute to advance our understanding of grapevine endophytes ecology, but have also the potential to provide a concrete solution to viticulture, for the benefit numerous European and non-European countries.

The PRINNEVOT project embarks on a mission to bridge the gap between computer vision and the primate visual system in the context of Visual Object Tracking (VOT). VOT is the task of maintaining focus The PRINNEVOT project embarks on a mission to bridge the gap between computer vision and the primate visual system in the context of Visual Object Tracking (VOT). VOT is the task of maintaining focus on a specific object amidst a dynamic visual environment. Our brains excel at it but replicating this ability in artificial vision systems remains a challenge. This project seeks to develop a novel class of VOT algorithms inspired by the primate visual system's prowess. Despite notable advancements in deep learning-based VOT over the past decade, these algorithms still fall short in emulating the robustness exhibited by primate vision. PRINNEVOT will address this gap through a multi-faceted approach. Firstly, PRINNEVOT will construct a comprehensive reference dataset, investigating both primate behavior and neural recordings. Secondly, among the existing artificial neural network (ANN)-based VOT methodologies, the project aims to identify those that align most closely with the primate brain's mechanisms. Lastly, PRINNEVOT will leverage the discovered inductive biases to develop a new ANN architecture for VOT that closely mirrors the primate's way of continuous object recognition and localization. By merging computer vision and computational neuroscience research, PRINNEVOT aspires to contribute to the development of more accurate and robust VOT algorithms. These algorithms, in alignment with the European Union's pursuit of safer and ethically grounded Artificial Intelligence, promise to enhance human-centric and trustworthy technologies. Furthermore, the project's outcomes will not only benefit AI and computer vision but also advance our understanding of the primate visual system, offering new empirical models of how the brain tracks objects in dynamic visual environments.