Real-time reaction monitoring with Nuclear Magnetic Resonance (NMR) spectroscopy is an emerging and increasingly important analytical technique. In this work, we develop and implement software to identify molecules and monitor a chemical reaction in real time using a low-field benchtop NMR operating at 24.35 MHz. The approach was validated using a simple acid-catalyzed elimination reaction, the dehydration of cyclohexanol to cyclohexene, and enabled both qualitative and semi-quantitative assessment of the reaction progress. Automated peak integration and yield calculation were performed at each time step, and the results were benchmarked against a high-field 400 MHz Bruker NMR instrument.

Objectives: The biotransformation of primary to secondary bile acids via 7α-dehydroxylation is key in the onset of C. difficile-associated disease, due to their role in the germination and outgrowth of spores in the gut. Clostridium scindens, a gut commensal, has been implicated in bile acid-mediated colonisation resistance against C. difficile, but research has been hindered by its genetic intractability. Here we demonstrate the use of genetic tools in an alternative 7α-dehydroxylating species, Peptacetobacter hiranonis, for the generation of bile acid associated (bai) gene loss-of-function mutations. Methods: An appropriate vector backbone was first identified for successful conjugation into P. hiranonis, and the inducible CRISPR-Cas9 system RiboCas used to generate a baiCD deletion. Biotransformations of cholic acid (CA) by the ΔbaiCD strain were assessed using UHPLC-HRMS. Bile acid-mediated colonisation resistance against C. difficile was then assayed using supernatant challenge and co-culture studies. Results: The ΔbaiCD strain was shown to be incapable of 7α-dehydroxylation, with no conversion of CA to deoxycholic acid (DCA). Supernatant challenge assays demonstrated that when incubated with CA the ΔbaiCD strain did not result in the delayed germination of C. difficile spores observed with the wildtype strain. Co-culture assays also demonstrated that the reduction in vegetative growth of C. difficile when incubated with wildtype P. hiranonis in the presence of CA was not observed in the ΔbaiCD strain. Conclusions: These results illustrate bile-acid mediated resistance to C. difficile by P. hiranonis, furthering our understanding of colonisation resistance by 7α-dehydroxylating bacteria. Moreover, as the first null mutant in any classical 7α-dehydroxylating species, the ΔbaiCD strain could be utilised as a control strain in future bile acid studies.

Dynamic (kinetic) models track time-varying metabolite concentrations, fluxes, and enzyme levels, quantifying responses to genetic and environmental perturbations. Yet building these models at scale is hindered by scarce enzyme kinetic parameters. Generative neural networks can rapidly parameterize near-genome-scale kinetic models, but their representations are hard to interpret and often require new training to move across species or physiological states. Here we introduce a latent-space exploration framework that repurposes a trained generative network to produce models with targeted dynamics in new regimes without additional training. We show in Escherichia coli that latent inputs tune aerobic response speed, identify rate-limiting enzymes, and retarget the generative network to anaerobic dynamics. We extend our approach to Saccharomyces cerevisiae, demonstrating robust control of metabolic dynamics across training stages and diverse latent inputs. Latent variables thus become practical control knobs for kinetic model behavior, accelerating cell-factory design and enabling personalized metabolic modeling.

Stimulator of interferon genes (STING) is an evolutionary conserved immune signalling protein with key roles in host defence, cancer, senescence and inflammation1–3. Downstream of STING, type I interferon, inflammatory cytokine signalling and non-canonical autophagy are governed by a multilayered mechanism integrating ligand-induced structural transitions, protein–protein interactions and coordinated intracellular trafficking4–13. Despite its central role in immunity and relevance as therapeutic target14, the sequence elements that govern STING (in)activation in cells remain incompletely understood. Here we developed a massively parallel assay to systematically chart the sequence-function landscape of STING. Profiling thousands of single amino-acid variants, we identified structural and functional determinants that shape the immunostimulatory capacity of STING and its ability to translate ligand recognition into distinct signalling outputs. Cryogenic-electron microscopy structures of select STING hyperactive variants revealed new regulatory principles dictating conformational transition from inactive to signalling-competent states of STING. Mutational effects are widespread across the functional landscape and can sensitize STING towards the natural ligand 2′3′-cGAMP15–18 or decouple interferon induction from non-canonical autophagy, demonstrating a diversity of possible responses that can be accessed through single point substitutions. Finally, our data showed the clinical and evolutionary relevance of naturally occurring STING protein variants. Collectively, these findings define molecular principles that tune STING activity and chart the landscape of its functional potential across immune contexts. A massively parallel assay systematically charts the sequence-function landscape of the STING signalling protein, and the findings define molecular principles that tune STING activity and show its functional potential across immune contexts.

Authors and Affiliations Center for Convergent Technologies, Laboratory for Polymers and Biomaterials, Fondazione Istituto Italiano di Tecnologia (IIT), Genova, Italy Silvia Rancati, Matilde Campolungo, Gerusa Duarte Dalmolin & Nicola Tirelli Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT), Genova, Italy Matilde Campolungo Center for Human Technologies, Non-coding RNAs and RNA-based therapeutics, Fondazione Istituto Italiano di Tecnologia (IIT), Genova, Italy Pierre Lau & Stefano Gustincich Center for Clinical and Computational Genomics, Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia (IIT), Aosta, Italy Federica Furia, Alessandro Coppe, Fabio Landuzzi & Andrea Cavalli Center for Clinical and Computational Genomics, Non-coding RNAs and RNA-based therapeutics, Fondazione Istituto Italiano di Tecnologia (IIT), Aosta, Italy Manuela Vecchi & Stefano Gustincich Center for Human Technologies, Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia (IIT), Genvoa, Italy Andrea Cavalli Centre Européen de Calcul Atomique et Moléculaire (CECAM), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Andrea Cavalli Authors Silvia Rancati View author publications Search author on: PubMed Google Scholar Matilde Campolungo View author publications Search author on: PubMed Google Scholar Gerusa Duarte Dalmolin View author publications Search author on: PubMed Google Scholar Pierre Lau View author publications Search author on: PubMed Google Scholar Federica Furia View author publications Search author on: PubMed Google Scholar Alessandro Coppe View author publications Search author on: PubMed Google Scholar Fabio Landuzzi View author publications Search author on: PubMed Google Scholar Manuela Vecchi View author publications Search author on: PubMed Google Scholar Andrea Cavalli View author publications Search author on: PubMed Google Scholar Stefano Gustincich View author publications Search author on: PubMed Google Scholar Nicola Tirelli View author publications Search author on: PubMed Google Scholar Corresponding author Correspondence to Nicola Tirelli .

Lung adenocarcinoma (LUAD) progression is accompanied by changes in epithelial cell state, but how adult and developmental lung epithelial programs are retained, lost, or remodeled across disease stages remains incompletely defined. Here, we analyzed a harmonized single-cell RNA-sequencing cohort of twelve LUAD datasets and used adult and fetal lung atlases as complementary references to quantify epithelial program activity in cancer cells.

LUAD progression was associated with selective loss of distal alveolar and transitional epithelial programs. Positivity for the AT2/late-tip module decreased from 17.1% of cancer cells in early tumors to 5.4% in stage IV metastatic tumors, and the pre-terminal bronchiolar/AT0 module decreased from 12.1% to 2.7%. In contrast, the club/proximal-secretory module increased in advanced disease, but gene-level analyses indicated a shift away from canonical club markers toward stress-associated and alternative secretory genes. Smoking history modified the early-stage baseline of the AT2/late-tip program, although all smoking groups converged toward low prevalence in advanced disease.

Direct scoring against fetal-reference epithelial signatures showed that LUAD progression was not characterized by broad fetal-like reactivation. Most fetal-reference signatures, including fetal AT1, AT2, club, late-tip, and late-stalk programs, were depleted in advanced tumors. By contrast, early-tip and, to a lesser extent, early-stalk signatures showed selective enrichment, with early-tip positivity increasing despite reduced per-cell score intensity.

Together, these results indicate that epithelial remodeling during LUAD progression is not a simple process of global dedifferentiation. Instead, LUAD progression involves selective loss of alveolar and transitional distal epithelial programs, divergence of secretory trajectories toward stress-associated states, and restricted reactivation of early developmental-reference programs. This atlas-guided framework provides interpretable benchmarks for quantifying cancer cell plasticity during LUAD progression.

Urbanization has profoundly transformed the living conditions of a large proportion of the global population and continues to expand worldwide. Urban environments relate to human health through multiple pathways, including lifestyle factors, social conditions, and environmental exposures. These associations reflect not only urban living itself but also the geographic context and spatial organization of cities. Urbanicity may therefore influence physical health, mental health and cognitive functioning. In this context, the brain represents a potential biological pathway linking urban environments to behavioral outcomes. Despite increasing research on environmental exposures, brain structure, mental health, and cognition, integrative approaches that combine these domains while accounting for geographic context remain limited. This thesis aims to address these gaps by developing an analytical framework that integrates georeferenced environmental exposures and neurobehavioral data at the individual level. It combines geospatial epidemiology, neuroscience, and outcome-wide analysis within urban exposome frameworks to examine how environmental conditions in cities relate to brain structure and behavioral health. The thesis is structured around three case studies. The first two use data from the CoLaus|PsyCoLaus and BrainLaus cohorts in Lausanne, Switzerland. In these, we applied spatially explicit approaches to investigate neurobehavioral outcomes in an urban setting. The analyses identified geographic patterns of brain structure, mental health and cognitive performance across the city and characterized the environmental exposures associated with these patterns. Spatial modeling further revealed localized associations operating at different spatial scales, highlighting the importance of considering the geographic context when studying environmental determinants of neurobehavioral health. In the third study, we extended the analysis to maternal mental health during pregnancy using data from the Barcelona Life Study Cohort (BiSC) in Spain. We applied an outcome-wide exposome framework to enable the simultaneous assessment of multiple urban environmental exposures and multiple mental health outcomes. The findings indicated that certain environmental exposures showed consistent associations with general psychological distress, illustrating the potential of many-to-many analytical approaches to identify environmental factors related to multiple dimensions of mental health. Overall, this thesis demonstrates the value of combining spatially explicit analytical methods with exposome approaches to study brain, mental, and cognitive health in urban environments across different populations. By identifying geographic patterns of these outcomes and modifiable environmental exposures associated with them, this work contributes to bridging environmental health research and urban policy, and highlights potential targets for public health and urban planning interventions.

Laser Powder Bed Fusion (LPBF) enables the fabrication of geometrically complex metal components, yet the process remains limited by insufficient understanding and control of local heat accumulation. Spatially varying thermal conditions induce fluctuations in melt pool geometry, promote defect formation, and degrade microstructural and mechanical consistency. This thesis investigates intra-layer heat build-up as a critical but largely unexplored contributor to LPBF process variability.

Through high-speed infrared thermography, the work experimentally quantifies the magnitude and spatial distribution of preheating within individual layers under varied scan strategies, laser powers, and scan speeds. The measurements reveal substantial and highly localized temperature elevations that strongly correlate with scan vector length and temporal laser scan patterns. To unify these observations, the Hatch Return Time (HRT) is introduced as a physically grounded metric that links laser kinematics to local thermal history and predicts preheating behavior across different scanning strategies and process parameters.

Building on these insights, the thesis develops numerical models that quantify the influence of initial temperature on melt pool morphology. Coupling these models with Design of Experiments (DoE) approaches enables efficient identification of dominant parameters governing melt pool dimensions, absorptivity, and lack-of-fusion defect formation in Ti-6Al-4V and 316L stainless steel. This chapter particularly highlights the severe influence of the preheating temperature on the aforementioned metrics. Finally, a multiscale feedforward optimization framework is proposed to mitigate geometry-induced heat accumulation. By adaptively adjusting laser power based on predicted thermal fields near complex features, the method reduces melt pool variability and improves dimensional accuracy without requiring in-situ feedback.

Taken together, this thesis demonstrates that intra-layer heat accumulation, in the vicinity of geometric features and in their absence, is both more severe and more spatially heterogeneous than commonly accounted for, and that it constitutes a major source of systematic process variability in LPBF. The proposed HRT metric, predictive models, and compensation strategies provide a practical foundation for more stable, thermally informed LPBF processing and represent a step toward intelligent, predictive thermal control in metal additive manufacturing.

Computational methods are central to modern physics, yet many problems, especially those involving Quantum Many-Body Systems (QMBSs), remain challenging at scale. The core difficulty of simulating QMBSs is the exponential growth of the solution space with system size. This renders exact treatments intractable and pushes approximate methods to their limits when tackling two- and higher-dimensional systems and long-time dynamics. Quantum computers offer a quantum-native platform for representing and manipulating such systems. Recent algorithmic and technological advancements have enabled experiments on quantum processors in the quantum utility regime, a scale at which exact classical reproduction is no longer feasible. This represents the first step towards realizing quantum advantage, where quantum computers solve specific, practically relevant problems more efficiently than any classical approach.

While advancing towards the goal of quantum advantage, useful computations on quantum processors are expected to be realised through hybrid quantum-classical algorithms, which combine quantum experiments and classical computational resources. A prominent example are variational quantum algorithms, where a Parametrized Quantum Circuit (PQC) prepares a trial quantum state whose parameters are iteratively updated by a classical optimisation routine to minimize a target cost function (e.g., the energy of a target Hamiltonian). However, the optimisation of such PQCs is affected by trainability issues, such as barren plateaus in the parameter landscape, which can render the optimisation increasingly difficult for larger system sizes. This motivates the development of novel hybrid approaches overcoming the limits of PQCs. In this thesis, we introduce a new class of approaches based on hybrid Tensor Networks (TNs). A hybrid TN is obtained by integrating quantum states prepared on a quantum processor within TNs -- one of the state-of-the-art approaches for simulating quantum systems. We first show how hybrid TNs can be used for ground-state searches within a variational setting. Then, we further extend the framework to non-variational hybrid TN ansätze for which the quantum tensors are prepared via quantum annealing and subsequently post-processed by classical TNs. In the context of quantum machine learning, which also relies on PQCs, we further investigate heuristic best practices that mitigate the same exponential concentration effects in kernel-based models for anomaly detection.

Another class of hybrid algorithms are quantum error-mitigation workflows, where classical post-processing is used to reconstruct expectation values from noise-affected circuit executions. Such methods generally feature a trade-off between the bias and the variance: the price to pay for mitigating noise in a bias-free fashion is that the number of measurements usually grows exponentially with the depth of the circuit in order to achieve a fixed accuracy. In this thesis, we develop a low-resource error-mitigation strategy that reduces the measurement overhead in practical digital Hamiltonian-simulation workflows. We apply this method to Trotterized dynamics in a lattice-gauge-theory model, enabling the extraction of expectation values with improved accuracy at time-scales that allow the observation of post-collision scattering dynamics.

Electrodeposition has been widely used to develop micro-mechanical devices because it offers high dimensional accuracy, flexibility, and relatively low cost. In devices such as mechanical watches, micro-components are required to be made from non-magnetic, hard, and wear-resistant materials.

Copper is a common non-magnetic material used in the electrodeposition process, but its intrinsic low strength and soft mechanical behavior limit its use in such applications. These limitations can be overcome by developing copper matrix composite materials via electro-co-deposition, which improves copper's mechanical properties by incorporating particle reinforcements. However, electro-co-deposition is inherently complex, and the mechanisms governing the co-deposition process, as well as the associated hardening mechanisms, are not sufficiently understood.

This thesis addresses these existing scientific gaps by elucidating the influence of key process parameters governing particle incorporation into a copper matrix during the electro-co-deposition process, and assessing the consequent influence on the enhancement of hardness by analysing the different governing hardening mechanisms. To this end, thick (200 micrometer) electro-co-deposited composite coatings are produced with different particle reinforcements (graphite, alumina, cubic boron nitride) using a standard three-electrode setup with a rotating disc electrode (RDE).

A systematic experimental approach was used to investigate how particle mass transport (RDE rotation speed), metal ion reduction kinetics (current density and waveform), particle characteristics (type and concentration), and other process parameters affect particle co-deposition in the metal matrix. Scanning electron microscopy (SEM) and focused ion beam (FIB) cross-sections were performed to determine particle incorporation and grain size. Nanoindentation hardness measurements were performed to determine the hardness of the developed composite material.

The co-deposition mechanism of the particles was governed by the small grain size, where the increase in the number of grain boundaries and triple junctions served as anchoring points, facilitating their incorporation into the metal matrix. The smaller grain size (around 100 nm), increased RDE rotation rate (1600-2500 rpm), and higher concentration of particles in the electrolyte (80 g/L), collectively led to a substantial increase in the incorporation of alumina particles (up to 15% by volume), resulting in a hardness of 304 ± 20 HV, for a 200-micrometer Cu-Al2O3 composite.

Thus, the co-deposition of particles was described as a synergistic coupling between grain boundary pinning/anchoring and particle incorporation. The small grain size achieved with high peak pulse current density (-2 A/cm2), the use of graphite, or tribological sliding not only enabled the incorporation of a large number of particles but also helped understand the hardness of the developed composite, typically through Hall-Petch and dispersion strengthening of the electrodeposited composite. These findings highlight the technological potential of the electro-co-deposition process for the development of high-performance composite materials with refined microstructures and enhanced particle incorporation.