Infections during pregnancy may increase the risk of neuropsychiatric disorders in humans. In rodents, prenatal infections induce behavioural and cognitive deficits in the offspring. The mechanisms that contribute to these long-term neurocognitive defects are largely unknown, and potential protective interventions are not much studied. The aim of this project is to determine whether prenatal functional food additives can protect against cognitive impairment induced by maternal viral infection, and to study the role of the immune system therein. To mimic viral infection we will use the murine maternal poly-riboinosinic-polyribocytidilic acid (poly (I:C) model. Poly(I:C) exposure during pregnancy has long-lasting effects on behaviour, alters cytokine levels in the fetal brain, and increases the density of activated microglia in the offspring. The maternal dietary intervention we will use comprises non-digestible oligosaccharides. We and others have shown that this maternal diet has beneficial effects on immune functioning in mice. During this project we will: 1. Introduce and optimize the maternal poly(I:C) mouse model in our laboratory to induce robust behavioural and cognitive changes in the offspring. 2. Test the hypothesis that maternal diet aimed at restoring the balance between pro- and anti-inflammatory cytokines can protect against these cognitive deficits. 3. Characterize the effect of maternal diet on changes in the peripheral and central immune system induced by maternal poly(I:C) exposure. Together, the consortium partners have all the required expertise and techniques up and running to turn this project into a success.

Laser satellite communications is a promising technology to support worldwide access to telecommunications services. A major technological challenge is atmospheric turbulence impacting the propagation of the laser beams. Its effect can be mitigated by adaptive optics and geographic diversity. Our project aims to provide a map of the effective optical channel performance over Europe. This map is needed to design ground network technology and estimate communications service availability. Since our current understanding and ability to estimate the channel performance are limited, we will develop novel physics-informed machine learning algorithms to formulate the optical link performance map.

Microelectronics and light emitting diodes (LEDs) are often encapsulated in polymers to protect the embedded electric circuit from exposure to humidity, acids, UV lights and other contaminations. Water and other contaminations can penetrate into the packages, resulting in the deterioration of chemical and optical properties of the system. The absorption of UV light and other contaminations at the polymer surface provokes oxidation and browning; with both being major reliability risks in LED industry. In this project dedicated experimental techniques will be combined with reliability models to understand and predict the browning and hydrolysis susceptibility of LED packages in harsh and outdoor environments. Based on the obtained results, both the optical and chemical properties of the packaging materials will be optimized.