35% of the total annual emissions of micro-/nano-plastics (MNPs) into the environment comes from the washing of synthetic textiles. This, as well as the fact that contaminants of emerging concerns (CECs) from garments can be absorbed by MNPs, makes the discharge of MNPs from industrial laundry wastewater an environmental problem. It can lead to aquatic pollution through inadequately treated wastewater and terrestrial contamination due to the use of wastewater sludge in agriculture. It results in chronic exposure as MNPs work their way up through the food chain, ending up in human food products. As society moves toward a circular economy, by increasing the use of multi-use garments, there is an urgent need to remove the MNPs from industrial laundry wastewater before discharge into the sewage. However, the current industrial laundries are not capable of removing MNPs. An estimated 90% of industrial laundry wastewater discharges to sewage without adequate treatment, raising risks for the natural environment and human health. SusBrane consortium delivers a solution to this problem by capturing MNPs in a membrane filtration system followed by degradation of nano-plastics (NPs) and CECs in a catalytic reactor. The implementation will enable the industrial laundries to achieve zero-harmful discharge, reduce consumption of freshwater by 90% and energy by 20%, enabling significant cost reduction (Return on Investment = 55%), circular economy solutions and CO2 reductions.

Skin cancers pose significant diagnostic challenges, particularly in telemedicine where physical examination cannot be performed. Dermatologists are compelled to practise defensive medicine due to the fear of overlooking potentially deadly melanomas. This defensive approach results in costly overtreatment of benign lesions and dangerous delays in diagnosing cancers. SynTIA seeks to develop an artificial intelligence (AI) product that provides synergistic, trustworthy and interpretable diagnostic feedback for tele-dermatologists. SynTIA will guide the dermatologist towards a cost-effective and safe treatment strategy. More specifically, SynTIA will suggest the likely diagnoses, accompanied by the reasoning and confidence behind this analysis. Additionally, SynTIA will show the tumor's potential evolution over the next 3-6 months if left untreated. Finally, SynTIA will offer an analysis of the clinician's bias and likelihood of misdiagnosis, supported by examples of previous similar cases. The development of SynTIA will rely on data from the AISC-database, which is a large repository of skin lesion images and over 400,000 annotations from the educational Dermloop Learn application. We will use both established and cutting-edge machine learning methodologies, such as Concept Bottleneck and Diffusion Models, to achieve the desired outcomes. The resulting solution will be commercialized within Dermloop, an existing and rapidly scaling telemedical platform for skin diagnostics.