University of Zaragoza

The immune system consists of a collection of cells with a high ability to migrate that work together to remove harmful foreign material from the body. Each immune cell can migrate between tissues, fulfilling specific functions in different microenvironments. However, this immune-surveillance response is not very effective in those tissues with a high non-physiological stiffness and a significant level of residual stresses, which are characteristics of solid tumors. Understanding the mechanisms that govern the cellular immune response to solid tumors is crucial to strengthen the development of novel immunotherapies. ICoMICS aims to develop a novel predictive modeling platform to investigate how therapeutic immune cells (TICs) sense, migrate and interact with cancerous cells and with the tumor microenvironment (TME). This platform will be built on two key pillars: in-vitro 3D tumor organoids and multicellular simulations, which will be combined and integrated by means of Bayesian optimization and machine learning techniques. On the one hand, cell culture microfluidic chips will be microfabricated, allowing continuous perfusion of chemical modulators through hydrogels (including decellularized matrices from murine stroma) inhabited by human tumor cells arranged to recreate 3D solid tumor organoids. On the other hand, an agent-based model will be developed to simulate cells as deformable objects, including cell-cell and cell-matrix interactions, combined with a continuum approach to model matrix mechanics and chemical reactions of cells, such as reactive oxygen species (ROS) and nutrients diffusion. Finally, ICoMICS will originally develop two innovative mechanistic-based immunotherapies. First, TICs will be subjected to high strains in micro-channels to induce them higher migration capacity. Second, TICs will be clustered as bio-bots, to ensure that they have improved functionality. All this research will be applied to 3 main solid tumors: lung, liver and pancreas.

Computer-generated imagery is now ubiquitous in our society, spanning fields such as games and movies, architecture, engineering, or virtual prototyping, while also helping create novel ones such as computational materials. With the increase in computational power and the improvement of acquisition techniques, there has been a paradigm shift in the field towards data-driven techniques, which has yielded an unprecedented level of realism in visual appearance. Unfortunately, this leads to a series of problems, identified in this proposal: First, there is a disconnect between the mathematical representation of the data and any meaningful parameters that humans understand; the captured data is machine-friendly, but not human friendly. Second, the many different acquisition systems lead to heterogeneous formats and very large datasets. And third, real-world appearance functions are usually nonlinear and high-dimensional. As a result, visual appearance datasets are increasingly unfit to editing operations, which limits the creative process for scientists, engineers, artists and practitioners in general. There is an immense gap between the complexity, realism and richness of the captured data, and the flexibility to edit such data. We believe that the current research path leads to a fragmented space of isolated solutions, each tailored to a particular dataset and problem. We propose a research plan at the theoretical, algorithmic and application levels, putting the user at the core. We will learn key relevant appearance features in terms humans understand, from which intuitive, predictable editing spaces, algorithms, and workflows will be defined. In order to ensure usability and foster creativity, we will also extend our research to efficient simulation of visual appearance, exploiting the extra dimensionality of the captured datasets. Achieving our goals will finally enable us to reach the true potential of real-world captured datasets in many aspects of society.

The nature of the Dark Matter (DM) is one of major open questions in modern physics with two interesting candidates: axions and Weakly Interacting Massive Particles (WIMPs). Axions arise from extensions of the Standard Model (SM) implementing the Peccei-Quinn mechanism. On the other hand, WIMPs appear in well-motivated extensions of the SM, like supersymmetry. Axions and WIMPs could have been produced in an early stage of the Universe and have been extensively searched during last decades. The use of Micromegas (MICROMesh GAs Structure) readouts has been recently introduced for axion and WIMPs searches and proposed for different experiments: TREX-DM and IAXO. TREX-DM is located in the Canfranc Underground Laboratory and is looking for low-mass WIMPs, in a mass range that is attracting the interest of the experiments. On the other hand, IAXO is a proposed new generation solar axion helioscope with enhanced sensitivity. The low counting rates expected in these experiments require to bring down the experimental background to the lowest levels possible. Another important aspect to enhance the physics potential is to reduce the low energy threshold, that would increase TREX-DM sensitivity to even lower WIMP masses. Moreover, it would make IAXO sensitive to axion-electron coupling values. The goal of this proposal is to reduce the background levels in order to reach sensitivity scenarios with discovery potential for axions and low-mass WIMPs and secondarily to reduce the low energy threshold. These objectives can be achieved by the use of some innovative aspects like the introduction of new Micromegas architectures and the use of new radiopure electronics. If the objectives are fulfilled, the advancements in the field would be tremendous, it will set the baseline for the design and construction of ultra-low background detectors for many different applications. Moreover, these new developments could be extrapolated to different kind of particle detectors.