The medial entorhinal cortex (MEC) and the adjacent pre- and parasubiculum are thought to create an internal map of self-position that animals may use for goal-directed navigation. This map uses a set of functionally specific and largely non-overlapping cell types: grid cells, border cells, speed cells, object-vector cells, and head-direction cells. The presence of multiple distinct functional cell types, matched in specificity only by cell populations in some of the sensory and motor cortices, allows us to examine input-output transformations and computational algorithms in association cortices with unprecedented power and detail. In order to examine these algorithms, however, an obvious and crucial first step is to map the division of function across cells in anatomical space. This requires recording of hundreds of cells at the same time in freely-behaving animals exploring open spatial environments. Unfortunately the absence of appropriate methods for neural recording at the population level has so far prevented a clear understanding of the broader organization of multi-cell-type and multi-layer networks of MEC, at both micro and macro scales. During my PhD, I invented a technique called “fast high-resolution miniaturized two-photon microscopy (FHIRM-TPM)”, which, through the use of a portable light-weight (2g) two-photon microscope, allows animals to move freely while large scale, single-cell-resolution calcium imaging is performed. In ANAT-MEC, I will refine this optical imaging method to study neural activity during spatial navigation in two-dimensional environments. I shall characterize in detail the anatomical organization of distinct cell types in MEC while mice engage in naturalistic, exploratory behavior in open spaces. Besides shedding light on this specific question, the project will – by developing a new technology - also open doors to unravel fundamental mechanisms of neural code formation in the mammalian space circuit.

Cardiovascular disease (CVD) remains the leading cause of death globally for men and women. State-of-the art clinical risk prediction models for CVD use conventional risk factors and polygenic scores (PGS)a measure of an individuals inherited CVD risk. However, the accuracy of current models is moderate and worse in women than men. Protein markers of CVD represent an individuals current health state and both inherited and environmental disease risk. Indeed, there is evidence that combining conventional risk factors, PGS, and protein markers of CVD may facilitate vast improvements in CVD risk prediction. Furthermore, by modelling inflammation-specific protein markers missing from sex-specific CVD risk models, ProtectHearts will specifically capture excess risk in women. ProtectHearts will use the worlds largest population-biobank-linked proteomics datasets to 1) develop a novel protein-based risk score (ProtRS); 2) develop an inflammation-specific ProtRS (i-ProtRS); and 3) estimate the clinical utility of these scores when modelled with clinical risk scores and PGS. Cutting-edge machine learning algorithms will be used to establish target proteins for CVD prediction in 100,000 individuals across four European biobanks. ProtectHearts brings together a physician-scientist Supervisor with proteomics expertise and a Fellow with experience modelling CVD risk prediction using PGS in biobanks. Through a secondment and non-academic placement, the Fellow will gain intersectoral experience. Activities of the training and dissemination work packages will build her competencies in machine learning, advanced statistics and science communication. ProtectHearts will engage clinicians in exploitation of the new sex-specific, comprehensive CVD risk prediction model. ProtectHearts has the potential to move CVD risk prediction beyond state-of-the-art by improving risk prediction accuracy, specifically in women, using protein-based markers for CVD and CVD-related inflammation.

Mycobacterium tuberculosis (Mtb) causes tuberculosis a leading infectious killer worldwide. Mtb primarily infects macrophages, leading to the death of some of these cells via distinct regulated cell death (RCD) mechanisms. While apoptosis facilitates Mtb elimination, necrotic cell deaths, like pyroptosis, necroptosis, and ferroptosis may enhance bacterial replication and spread. Mtb effectors, including the type VII secretion system ESX-1 and the cell wall lipid PDIM, induce host membrane damage, inducing necrotic cell death. Notably, the host lab has shown that inhibiting all RCD pathways does not prevent Mtb-induced cell lysis. However, depleting Ninjurin-1 (Ninj1), a membrane protein vital for plasma membrane (PM) rupture during various RCD pathways, mitigates Mtb-induced necrosis. Ninj1 oligomerization appears to occur after initial PM integrity disruption, yet the precise mechanisms governing Ninj1 activation and its role in Mtb-induced cell lysis remain unclear. I hypothesize that Ninj1 plays a crucial role in Mtb-induced lysis and that Mtb exploits this process through effector-mediated PM permeabilization. To investigate how Ninj1 oligomerization influences cell fate and the inflammatory response, I will leverage my expertise in super-resolution microscopy of pore-forming proteins alongside the host lab’s skills in Mtb infection biology. First, I will identify PM chemical and physical changes during RCD that correlate with Ninj1 oligomerization to elucidate the activation mechanism. Next, I will analyze Ninj1 dynamics and the functional impact of its macromolecular structures using DNA-PAINT super-resolution microscopy and long-term single-molecule tracking. Finally, I will determine whether Mtb activates Ninj1 via effector-induced PM damage and quantify the impact of this activation on the inflammatory response. This project aims to provide groundbreaking insights into cellular responses to Mtb and molecular inflammation research.

The goal of the TECTONIC project is to alleviate the challenging problem of hot-spots in 3D stacked chip-multiprocessors by employing a software-hardware based combined approach. With the stagnation in process technology scaling new emerging memory technologies are investigated. Promise of better scalability with reduced static leakage makes Non-volatile memories (NVM) as the potential candidates to replace conventional SRAM. However, many of the proposed NVM technologies are sensitive to heat, that raised up the issue of reliability. Considering heat dissipation as an exclusive issue of hardware will not be the appropriate approach towards finding out the solutions, as running-application has direct impacts on on-chip thermal imbalance. Hence, TECTONIC will manage the on-chip temperature and eliminate hot-spots by leveraging application specific knowledge extracted at compile time in combination with new hardware mechanisms for distributing computational work and memory accesses for even heat distribution while maintaining high performance.