Prostate cancer (PC), the second most common male cancer, affects 1.3 mil men and accounts for +360,000 deaths annually worldwide. At an early stage, local treatments (i.e. surgery, chemical castration and chemotherapy) often cure PC initially. However, an estimated 10-30% progress to metastatic castration-resistant PC (mCRPC) with no curative options, and 70% of patients with mCRPC die within 5 years. This highlights the need for more effective treatments such as immunotherapy with check point inhibitors that has shown remarkable clinical success with other cancer types but minimal efficacy in mCRPC due to the immunologically non-inflammatory nature of PC, characterised by restricted immune cell infiltration. Our approach addresses this unmet need through a bispecific monoclonal antibody, EBM-001, with specificity to Prostate-specific Membrane Antigen (PSMA) and IFNy which: 1) captures and accumulates the patient's own circulating IFNy from the blood: followed by 2) their redistribution to the site of the tumor, igniting an immunological activation by attracting the patient's immune cells to the cancer site. The unique mode of action of EBM-001, will enable a safe long lasting immune infiltration that will improve the efficacy of current immunotherapies for mCRPC. Our consortium (Ebumab, Southern University of Denmark, Bioneer) will use this Grand Solutions project to advance EBM-001 from the current initial proof of concept, to provide in vivo PoC and CMC development.

Access to clean water is a major global issue. Saline and wastewater can be returned to high quality freshwater through reverse osmosis (RO) desalination. The main operational challenge for this process is scaling and biofouling, which decreases RO water recovery and increases energy- and chemical consumption. Conventional RO systems are operated in continuous mode with constant brine salinity. In such systems, fouling organisms adapt and form biofilms on the RO membrane. Operating RO systems in batch mode with fluctuating salinity and intermittent brine discharge can reduce water and energy consumption. However, few such installations exist, and it is not known if batch RO operation can be used to eradicate biofouling. We hypothesize that, with appropriate controls, batch operation of RO will minimize biofouling as alternating salinity hinders the biofilm-forming microorganism´s ability to adapt and proliferate. The aim of BE-SALT is to provide mechanistic insight into biofouling and scaling under fluctuating salt stress implement this knowledge into design and optimization of sensor-based operation of batch RO systems and reduce operational cost (water, energy, chemicals) by >15% compared to continuous RO systems, while enabling high water recovery rates (>75 %) demonstrate the concept in pilot scale and at an end-user Our vision is to lead the way for process design and water/energy efficient operation of RO systems for clean water production.

Wind turbines work in highly variable operational conditions and harsh environments, especially offshore, that can cause expensive operation and maintenance, reaching up to 30% of the overall energy generation cost. Lowering the levelized cost of electricity is the main challenge faced by the wind turbine industry: hence, condition monitoring is a key enabler to avoid shutdowns and reduce operational and maintenance costs, providing a high availability. The most “challenging” component of a wind turbine is the main bearing, responsible for up to 30% failures over 20-year lifetime. Furthermore, current roller main bearing technology faces challenges in scalability, as wind turbines increases in size. To address these challenges, SGRE continuous to develop and explore new technologies, for which its condition monitoring system likewise needs to evolve, as classical vibration monitoring no longer necessarily can be used. Our consortium (SGRE DK, SGRE I&T, AAU) will develop a first ever set of algorithms and methods (A&M) for condition monitoring of new disruptive main bearing technology, aimed for the next generation offshore wind turbines. We will use parametric & non-parametric anomaly detection models and recent advances in machine learning techniques. The condition monitoring system to be developed, which will be implemented in a new full-scale wind turbine prototype, is expected to increase availability, and significantly lower the operational and maintenance cost.