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Lab-on-a-chip model of mucociliary clearance for the treatment of Inflammatory Lung Diseases – MucOnChip
The Global Burden of Disease Study reports around 4 million deaths each year worldwide for inflammatory lung diseases, the large proportion of which occurring in low- and middle-income countries. Inflammatory lung diseases include asthma, chronic obstructive pulmonary disease (COPD), pulmonary fibro
Deep Learning meets Numerical Analysis – DeepNuM
OBJECTIVE: DeepNuM aims at developing the interplay between two families of computational approaches, Deep Neural Networks (DNNs) and Partial Differential Equations (PDEs), with the goal of modeling complex dynamical systems arising from the observation of natural phenomena. Three central questions
An innovative non-mutant neoepitopes-based immunotherapy targeting immune-escaped tumors – NEOCOMBO
Although promising, immunotherapies targeting PD-1 are effective in only a fraction of cancer patients and the mechanisms associated with resistance are poorly elucidated. Indeed, tumors use additional pathways to evade CD8 T-cell recognition in particular alterations in peptide transporters TAP. We
Gene therapies approaches for Charcot-Marie-Tooth diseases: targeting the right cell type and compartment – GENERATE
Charcot-Marie-Tooth disease (CMT) is a hereditary disorder that affects the nerves outside the brain and spinal cord. It affects about 300,000 people in Europe. The disease progresses slowly and causes muscle weakness, especially in the legs, as well as sensory problems, often detected in childhood
Data, geometry and curvature – DataGC
Riemannian data are ubiquitous in modern statistics and machine learning: Low-rank matrix completion, dictionary learning, matrix factorization, computer vision, shape statistics, optimal transport, etc. Moreover, in an era where extraordinarily rich, yet also complex and heterogeneous datasets b
Compositional functions networks: adaptive learning for high-dimensional approximation and uncertainty quantification – COFNET
High-dimensional approximation tasks are ubiquitous in all areas of scientific computing and data science, including the solution of partial differential equations (PDEs), machine learning and uncertainty quantification (UQ). With the recent success of deep Neural Networks (NNs) and tree Tensor Netw
Electrically-Excited Chiral Plasmonic NanoCavities – ChiC
Chiral structures, whose initial and mirror structural images cannot be superimposed, interact differently with left-handed and right-handed circularly polarized light. Thus a structure of a certain “handedness” preferentially scatters or absorbs circularly polarized light of the same handedness, le
Multimodal Transit for Accessibility and Sustainability – MuTAS
Transit has a crucial role in the economics and the society of urban conurbations. To drive its evolution toward environmental sustainability and better user-centric performance, we envision optimally integrating new (Auto- mated) Mobility on Demand and classic Fixed Route Transit. This calls for a
Adaptive Microfluidic Networks for Optimal Transport – AMNOT
Flow transport in complex networks is abundant in biology and engineering, from the vasculature of animals, to the hyphal networks of fungi, to the random porous media making up batteries. It has long been thought that biological network morphologies were optimised to minimise the energetic cost ass
Operando investigation of chemo-mechanical degradation in sulfide-based solid electrolyte – OpInSolid
All solid-state batteries based on thiosulfate solid electrolyte hold the promise of safer and more energetic batteries, especially once coupled to Li metal anode and high voltage cathodes. Unfortunately, it was demonstrated in the literature that their electrochemical stability window is far from o
