The aim of this project is to move the production of human milk oligosaccharides (HMOs) to the next level by producing complex, branched HMOs. More than 150 HMO structures are known in human milk. They act as prebiotics and antimicrobial agents, promote infant immune system maturation, and help cognition development. Industrial biotechnology-based production of HMOs is a recent and growing commercial field, but new technologies are required to broaden the product portfolio. HMOs are key ingredients in top-brand infant formulas and utilized as dietary supplements for other age groups. Lacto-N-hexaose (LNH) represents a core, branched elongated HMO structure and disialyllacto-N-tetraose (DSLNT) is another branched HMO structure. DSLNT has been associated with protection from necrotizing enterocolitis in preterm infants. Both LNH and DSLNT are in high demand but are currently not possible to manufacture industrially. This project targets exactly these structures using a range of technologies combining enzyme technology and cell factories. The project builds on the combined core competences of DSM-Glycom and DTU Bioengineering: cell factory development and enzyme technology. Our combined efforts create value in healthier formula-fed infants including preterm infants and expansion in the Danish knowledge-based industry (>150 jobs). Estimated market value creation is 2bn euros/year. DSM-Glycom are world-leading HMO producers making result implementation straightforward.

Danish and European green energy goals and the geopolitical situation makes energy storage diversification ever more crucial. However, the Danish energy storage is dominated by externally acquired chemical/electrochemical batteries. These batteries have limited lifetimes and charging-discharging cycles making recycling problematic and expensive. In this context, we explore an alternative solution to utility scale storage: Flywheel Energy Storage Systems (FESS). With theoretically unlimited number of charging/discharging cycles, reduced costs and manufacturing in Denmark FESS need 25% increase in energy density to compete against the battery solutions. Current market solutions cannot address this issue due to their limits set by design and material concepts. HyFly gathers a sole Danish FESS producer and provider, an advanced composite manufacturer, carbon nanomaterial manufacturer, the material and technology developer and the academia to explore and commercialize graded hybrid composite materials and optimized structural and material design of the FESS rotor. The aim is to increase the speed of the rotor through synergistic developments of production techniques and material and structural designs. This project will map the effects of each component on the final energy density. With the expected energy density increase of min. 25% funding received will be equally spent between the research and development stages, from the material nano- and microscale, up to market solution.

Hyperspectral imaging using broadband light sources is very important to textiles and meat. Textile recycling is a growing industry due to its immense pollution of microplastics and chemicals, and its large carbon footprint. The EU will impose mandatory textile waste collection by 2025, and recycling processes will be ramped up after that. Hyperspectral imaging will most likely be a crucial tool for the cost-effective processing of complex modern garments at the high volumes that are consumed today. The meat industry is continuously working on upgrading sidestream products such as offal, which are not readily used for human consumption today despite containing high-value proteins. The challenges with processing offal are its mixed nature (muscles, glands, fat etc.) and short shelf-life, which calls for rapid quality sorting that can handle the heterogeneous matrix. Again, hyperspectral imaging appears to be an ideal solution. In HyperSort, we will develop a hyperspectral optical engine based on a supercontinuum light source. Tomographic spectroscopic imaging will be explored and expert partners in machine-learning-based chemometrics will tailor a partner-developed open-source software to applications first in textile sorting, then exploring sidestream meats. To achieve state-of-the-art performance, a novel supercontinuum light source and low-cost camera will be developed. Research indicates this will yield >100x lower sampling time to allow high-volume processing.

The hospital and caregiving sectors lack a coherent solution to effectively support patients with multimorbidity and their relatives on integrated care pathways. The primary challenges are poor organization and inefficiency in communication and coordination between patients, relatives, and healthcare professionals. Consequently, patients and relatives are burdened with the responsibility of managing tasks, sharing knowledge, repeating information, and ensuring coordination. In response, the I-PARD project has been initiated to improve integrated care pathways for patients with chronic illnesses and their relatives through the implementation of a digital platform solution. The objective is to empower patients and relatives to better handle their own health, leading to improved quality of life for patients with multimorbidity and their relatives. The project develops, tests, and validates a digital solution that, to begin with, will be accessible to patients with Parkinson's disease, acquired brain or spinal cord injuries, their relatives, and healthcare professionals. By enabling coordination, communication, and collaboration across the hospital and caregiving sectors, the project seeks to facilitate improved healthcare experiences for patients and their relatives. Moreover, the project engages stakeholders from various domains, including the formal research community and different sectors, to ensure the most beneficial advancements in integrated care pathways.

Healthcare systems worldwide face significant challenges due to shortages of healthcare workers (HCWs), and burnout due to bad work environments. While new hospitals are being built with single-bed patient rooms to ensure privacy and reduce patient infection risks, this architectural design poses additional problems. The physical and mental distance between HCWs and patients increases, and HCWs must oversee more rooms, resulting in fewer patient check-ins. Consequently, patients feel uninformed and isolated, and the risk of adverse events such as falls increases. iAware is an AI-trained workflow technology that prioritizes the well-being of HCWs AND patients. It introduces a virtual "window" that allows patients to stay informed about relevant HCW activities and vice versa. By virtually removing the barriers between patients and HCWs, iAware reduces the need for frequent calls from patients and colleagues, enabling HCWs to work more efficiently. The two-way exchange of information is facilitated by using existing tablets and screens in hospitals. iAware's integrable solution incorporates predictive ward rounds to ensure timely patient visits, patient movement advice, and automated registrations of cleaned and available rooms for optimized room utilization. This approach enables real-time workflow optimization intending to improve the efficiency and the satisfaction of patients and HCWs. See the concept video: rb.gy/pxknf