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Three high resolution multicore records have been collected at three sites in the western Mediterranean with a MC400-Multicorer system during the MedSeA cruise (Mediterranean Sea Acidification in a changing climate) on 2 May to 2 June 2013 onboard the R/V Angeles Álvarino. Core MedSeA-S3-c1 was retrieved in the Alboran basin (Lat. 36.0746° N, Long. 04.11040° W) at a water depth of 1137 m, with a core length of 33 cm. Core MedSeA-S23-c1 was recovered at a water depth of 1156 m in the Balearic basin offshore Barcelona (Lat. 41.1121° N, Long. 2.38200° E) with a core length of 43 cm. MedSeA-S7-c2 was collected at the Strait of Sicily (Lat. 37.7080° N, Long. 12.40553° E) at a water depth of 263 m, with a core length of 46.5 cm. All three cores have been analyzed for changes in size normalized weight (SNW) and stable carbon isotopes (δ13C), measured in planktic foraminiferal clacite shells of the two species Globigerina bulloides and Globigerinoides elongatus. Boron (δ11B) isotopes have been measured in tests of Globigerinoides elongatus at the Alboran site, and in Globigerinoides ruber albus at the Strait of Sicily. Complementary data for the Strait of Sicily record has been obtained, including a 210Pb based age depth model, sea surface temperatures (SST), alkenone concentrations and planktic foraminiferal assemblage changes. The Strait of Sicily record (MedSeA-S7-c2) covers around the last 200 a, describing environmental changes throughout the Industrial Era (IE) at high temporal resolution. The Alboran (MedSeA-S3-c1) and Balearic Sea (MedSeA-S23-c1) records spanning the last about 1 ka at lower temporal resolution, displaying oceanographic changes throughout the transition from the pre-industrial era to present, as discussed in (Pallacks et al., 2021; doi:10.1016/j.gloplacha.2021.103549). Data has been collected to investigate the response of marine calcifiers to the combined effects of climate change stressors on decadal to centennial timescales, caused by anthropogenic CO2 emissions.

This scoring sheet was developed to collect data on the larval rearing methodology which is followed by commercial finfish hatcheries. It was used by Kourkouta et al. [Sci Rep. 2022 Oct 25;12(1):17896. doi: 10.1038/s41598-022-23008-z] to examine the link between the variability in the rearing conditions, and the variability in skeletal abnormalities incidence.

Gilthead sea bream (Sparus aurata) has been reported to be susceptible to a reassortant betanodavirus strain (RGNNV/SJNNV), posing a new threat for sea bream industry (Volpe et al. 2020) and raising the attention to selective breeding as a plausible disease prevention action. Genomic selection might be beneficial for traits, such as disease resistance, characterized by difficult, expensive and time-consuming routine individual phenotyping. Genomic models are trained firstly using a reference population of full- and half-sibs of the future breeding candidates, but, in a long-term view, the prediction of the genetic merit of future breeding candidates should be satisfactory even when the reference population consists of distant relatives of the animals to be predicted. In this sense, the genomic predictive accuracy provided by random k-fold cross-validations might be unrealistic. In this study, we assessed the accuracy of a genomic prediction model for VNN symptomatology pseudo-phenotypes (estimated breeding values, EBV) in gilthead sea bream in three different validation settings: 1) a random cross-validation; 2) a cross-validation based on genomic clustering; 3) a leave-one-family-out (LOFO) validation focused on the parents of the fish subjected to the VNN challenge test.

Viral nervous necrosis (VNN) otherwise known as viral encephalopathy and retinopathy (VER), is the most threatening infectious disease in Mediterranean aquaculture. VNN is caused by the nervous necrosis virus (NNV) a bisegmented ssRNA+ virus included in the genus Betanodavirus, family Nodaviridae. NNV genome consists of two molecules named RNA1 and RNA2, which encodes for the RNA-dependent RNA polymerase and the coat protein, respectively. Four genotypes of NNV have been so far described: RGNNV, SJNNV, BFNNV, TPNNV (Sahul Hameed et al., 2019). Furthermore, reassortant strains have emerged from the reassortment between the RGNNV and the SJNNV genotypes and named RGNNV/SJNNV and SJNNV/RGNNV according to the RNA1 and RNA2 origin. Currently, several NNV strains are co-circulating in the Mediterranean Basin with a high prevalence of the RGNNV genotype and the RGNNV/SJNNV reassortant strain and a more limited diffusion of the SJNNV genotype and the SJNNV/RGNNV reassortant (Bandin and Souto, 2020; Volpe et al., 2020). So far, identification of NNV genotype and reassortant strains is based on amplification, sequencing and phylogenetic analysis of both viral genome RNA molecules requiring time and expertise. As viral identification is an essential information for the management of the disease in the field (Toffan et al., 2021), the development of an easy and affordable method to genotype betanodaviruses was developed in the framework of the H2020 Peformfish Project, an industry-driven project targeting industry defined priorities.

Prevention is one of the most relevant aspects to provide efficient tools for improving health management in aquaculture. However, establishing useful guidelines for this purpose is not an easy task since the development of diseases in fish are usually multifactorial. The combination and impact of different factors, such as the characteristics of the infectious agents and fish host, environmental conditions, geographical parameters, and farming systems amongst others represents the main elements that determine the risk or likelihood to be impacted by a certain disease. The knowledge of these factors allows to establish biosecurity and prophylactic measures to reduce the risks of emergence of pathogens and the susceptibility to infectious diseases. Thus, the aim of this study is to assess the relevance of these factors in order to identify the main risk factors for the most relevant diseases in gilthead seabream and European seabass in Europe

Seed germination and seedling establishment are the main factors for a successful start of a crop, determining crop health, weed suppression and overall crop performance. They are critical, but sensitive phases in crop production. High seed vigour and appropriate seed microbiota significantly contribute to the resilience of seedlings. This opinion paper is based on a review of relevant literature, two case studies with wheat and carrot and a subsequent stakeholder consultation in the framework of the European project LIVESEED. It presents a seed health strategy for organic farming systems that accounts for dynamic processes behind seed and plant health, taking advantage of the contributions of seed vigour and seed microbiota. Seed and plant health are understood as a continuum. Benefits of seed vigour and microbiota are showcased. Recommendations are given for organic seed production, seed processing and storage, as well as organic plant breeding and future research. In conclusion, organic farming systems, in particular, have the opportunity, both to develop this perspective on seed and plant health and to benefit from it. Adopting this perspective may require a shift in how crop plants and their seeds are cared for, as well as adapted regulatory frameworks.

Computer Vision has played a major role in Intelligent Transportation Systems (ITS) and traffic surveillance. Along with the rapidly growing automated vehicles and crowded cities, the automated and advanced traffic management systems (ATMS) using video surveillance infrastructures have been evolved by the implementation of Deep Neural Networks. In this research, we provide a practical platform for real-time traffic monitoring, including 3D vehicle/pedestrian detection, speed detection, trajectory estimation, congestion detection, as well as monitoring of the interaction of vehicles and pedestrians, all using a single CCTV traffic camera. We adapt a custom YOLOv5 deep neural network model for vehicle/pedestrian detection and an enhanced SORT tracking algorithm. For the first time, a hybrid satellite-ground based inverse perspective mapping (SG-IPM) method for camera auto-calibration is also developed which leads to an accurate 3D object detection and visualisation. We also develop a hierarchical traffic modelling solution based on short- and long-term temporal video data streams to understand the traffic flow, bottlenecks, and risky spots for vulnerable road users. Several experiments on real-world scenarios and comparisons with state-of-the-art are conducted using various traffic monitoring datasets, including MIO-TCD, UA-DETRAC and GRAM-RTM collected from highways, intersections, and urban areas under different lighting and weather conditions.Computer Vision has played a major role in Intelligent Transportation Systems (ITS) and traffic surveillance. Along with the rapidly growing automated vehicles and crowded cities, the automated and advanced traffic management systems (ATMS) using video surveillance infrastructures have been evolved by the implementation of Deep Neural Networks. In this research, we provide a practical platform for real-time traffic monitoring, including 3D vehicle/pedestrian detection, speed detection, trajectory estimation, congestion detection, as well as monitoring the interaction of vehicles and pedestrians, all using a single CCTV traffic camera. We adapt a custom YOLOv5 deep neural network model for vehicle/pedestrian detection and an enhanced SORT tracking algorithm. For the first time, a hybrid satellite-ground based inverse perspective mapping (SG-IPM) method for camera auto-calibration is also developed which leads to an accurate 3D object detection and visualisation. We also develop a hierarchical traffic modelling solution based on short- and long-term temporal video data streams to understand the traffic flow, bottlenecks, and risky spots for vulnerable road users. Several experiments on real-world scenarios and comparisons with state-of-the-art are conducted using various traffic monitoring datasets, including MIO-TCD, UA-DETRAC and GRAM-RTM collected from highways, intersections, and urban areas under different lighting and weather conditions.

Enteromyxum leei is an enteric Myxozoan parasite causing the so-called “razor blade syndrome” in gilthead sea bream (Sparus aurata), a chronic condition characterized by a progressive weight loss up to emaciation in adult fish. Currently no treatments are found to be effective against this parasite, but a recent experimental study showed promising results using a functional feed for mitigating enteromyxosis (Palenzuela et al., 2020. Dis. Aquat. Org., 138, 111–120). During a field trial aimed to confirm the efficacy of this functional feed in an Italian sea bream facility where enteromyxosis was already known to be present, different methods for the detection of E. leei were compared to establish the more reliable diagnostic test also in relation to a non-lethal approach.

The documents in these folders represent part of the qualitative data collection documentation. Research has been performed in Flanders (Belgium) in 2016 and 2017. Involved stakehodlers were flemish sugar beet farmers, processors as well as other value chain members. Though, the main stakeholders involved were farmers. The raw data cannot be published. Anonymized interview transcripts and focus group transcripts exist. However, as indicated in the informed consent, farmers did not agree to the raw data being published. The codes that resulted from data analysis are in this folder. Interview questions differed slightly from farmer to farmer as follow up questions may have been posed if needed. First interviews were performed, then focus groups were conducted and finally a workshop was organized. The qualitative reserach followed the research strategy and plan determined by the SUFISA project. On the project webpage (https://www.sufisa.eu/) more information can be found.