PROTECTIVE is designed to improve an organisations ongoing awareness of the risk posed to its business by cyber security attacks. PROTECTIVE makes two key contributions to achieve this enhanced situational awareness. Firstly it increases the computer security incident response team’s (CSIRT) threat awareness through improved security monitoring and increased sharing of threat intelligence between organisations within a community. Secondly it ranks critical alerts based on the potential damage the attack can inflict on the threatened assets and hence to the organisations business. High impact alerts that target important hosts will have a higher priority than other alerts. Through the combination of these two measures organisations are better prepared to handle incoming attacks, malware outbreaks and other security problems and to guide the development of the prevention and remediation processes. The PROTECTIVE system is designed to provide solutions for public domain CSIRTs and SME’s who both have needs outside the mainstream of cyber security solution provision. Public CSIRTs needs arise in part because commercial tools do not address their unique requirements. This has created a shortfall, clearly articulated by ENISA, of tools with the required analytical and visualisation capabilities to enable public CSIRTs provide optimised services to their constituency. SME’s also are vulnerable to cybercrime as they have limited resources to protect themselves and often a limited understanding of what needs to be done. Two pilots will be conducted to evaluate and validate the PROTECTIVE outcomes with CSIRTs from 3 National Research and Educational Networks (NRENs) and with SMEs via a managed security service provider (MSSP). The PROTECTIVE consortium is constituted of 3 NRENs, 3 academic and four commercial partners from 8 countries so as to maximise the technical and commercial impact of the outputs and the dissemination and uptake of the results.

Haematological malignancies (HM), also known as blood cancers, are a heterogeneous and complex group of multicausal diseases that can’t be easily diagnosed nor treated. Nowadays most treatments are extremely complex, and advances in patient diagnosis and therapies slow due to the low number of patients per centre. Thus, there is a need to harmonise, store, and analyse the current HM information to speed-up and support the decision-making process for patients’ access to new therapies. HARMONY PLUS takes advantage of the capabilities of the HARMONY Big Data platform to match these unmet needs by expanding its scope to incorporate myeloproliferative neoplasms, including chronic myeloid leukemia, polycythaemia vera, essential thrombocythaemia, and myelofibrosis; and lymphoproliferative disorders, including Hodgkin’s lymphoma, Waldenström macroglobulinemia and all the other rare HMs not covered by HARMONY Project. In parallel, HARMONY PLUS will continue to refine and define the Core Outcome Sets (COS), especially for these new HMs to ensure the use by researchers of useful common outcomes relevant to all stakeholders. As previously accomplished in HARMONY, HARMONY PLUS is committed to pursue the maximum ethical and legal requirements, particularly to ensure patient’s right to privacy. Data-driven research within Europe will be enhanced by converting the current HARMONY platform into an Integrated Services Platform to serve as a valuable tool to support clinical trial design and use of available data as a control arm. This platform, combined with a HaemoDatabank repository with information about HMs patient biological samples across Europe, will facilitate a more efficient research and clinical trial design, and consequently will promote collaborations with recognised databases outside Europe. From the regulatory point of view, HARMONY PLUS will be a valuable technology tool during the evaluation of new treatments and drugs by also considering the patients’ needs.

Despite significant recent progress in the field of hematological malignancies (HMs), with increasing survival rates and improvement in quality of life, many children and adults with HMs still die from these disorders or experience disabling complications. Therefore, improvement of health care of HMs is an unmet medical need. Thus, it is important to define and align standard and efficient sets of HMs outcomes to measure and evaluate HM data for clinical decisions, long term risk/benefit profile, reimbursement, value analysis, and clinical trials design. Improving outcome measures and endpoint definitions by taking into account “real-life” data and differences in cross-national healthcare practice will undoubtedly result in an optimized, sustainable and effective treatment delivery, as well as in desirable and innovative accelerated pathways for novel drug availability. All these challenges will be addressed within a pan-EU perspective by HARMONY (Healthcare Alliance for Resourceful Medicines Offensive against Neoplasms in HematologY), a comprehensive public-private European consortium of excellence. HARMONY consortium is made up of 51 partners: 44 participants from 10 European countries and 7 pharmaceutical companies from the EFPIA. HARMONY aims to assemble, assess, connect, and analyze heterogeneous HM patient derived Big Data sets to define sets of outcome indicators that can be used for decision-making by key healthcare stakeholders. The consortium will orchestrate leading experts and working cooperative groups in HMs, European study alliances, pharmaceutical market leaders, patient advocacy groups, HTA and regulatory agencies, to: (i) optimize Europe-wide data collection and create a high-quality HM data repository for further explorative studies; (ii) establish a clinical data-sharing platform that empowers clinicians, patients and policy stakeholders to improve decision-making procedures and identify appropriate treatments to patients with HMs

Clinical experts make design decisions on treatments, interventions, or on devices. ICT empowers them with patient-specific simulation models that enable better-informed design decisions. But patient-specific computational medicine is currently cumbersome, slow, and unintuitive; it relies on complex processing by technical experts, and it is hence far from reaching its full potential on clinical design, and scarcely used. RAINBOW envisions next-generation biomechanics simulation and optimization tools for personalized clinical design that are rapidly setup for a particular patient, have a fast learning curve, are easy-to-use by clinical experts, and do not require intervention by a technical team. Research objectives entail automated processing of patient data; automated setup of representations and parameters, capability to manage variance across patients; robust and accurate simulation as a latent part of design tools; and fast optimization methods that allow intuitive exploration of the design space. Novel computational methods will be created to reach the objective of rapid biomechanics simulation. RAINBOW will apply research solutions for diagnosis, prognosis, monitoring, surgical training, planning, guidance, design of prosthetics, implants, and medical devices, and will address health conditions such as osteoarthritis, scoliosis, hearing impairment, cardiovascular diseases, obesity etc. RAINBOW has 5 excellent academic participants: UCPH (medical imagine, machine learning), URJC (data-driven modeling), UL (computational mechanics), CARDIFF (model reduction), AAU and one hospital HH (bone modeling) and 8 industries 3Shape (prosthesis), Kitware (imaging), Insimo (surgical simulation), GMV (eHealth), Simpleware (CAD/CAE), inuTech (numerics), Anatascope (Patient specific modeling) and Next-Limit (CFD). This combined expertise will ensure diverse impact and training of highly qualified individuals.