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In situ correlative cryo-electron tomography and spatial lipid mapping of Influenza A virus infected cells: Final report - DFG reference number: CH 2158/1-1, Project number: 437060729
No full textInfluenza A virus (IAV) is an important human pathogen that causes annual epidemics and occasional pandemics. Like many enveloped viruses, IAV interacts with host membranes and lipids at multiple stages of its replication cycle, frequently altering lipid homeostasis to facilitate viral replication. IAV utilizes Rab11a for the membrane trafficking of its segmented genome, which is divided into eight viral ribonucleoproteins (vRNPs). While vRNP sorting during traf-ficking has been demonstrated, the associated membrane remodeling has not been charac-terized in situ using cryo-electron tomography. Moreover, although IAV assembles at choles-terol-enriched plasma membrane domains, the lipid distribution in infected cells remains in-completely understood.
Here, we applied in situ cryo-electron tomography to study membrane remodeling during IAV assembly and budding. Our results reveal that HA- and NA-containing membranes mediate Rab11a-dependent, membrane-assisted vRNP clustering, indicating that genome assembly is guided by specific membrane contexts rather than by HA alone. The characteristic 7 + 1 vRNP bundle forms during budding, coordinated by M1 layer assembly that precedes plasma membrane attachment. Intracellular M1 forms multilayered helical assemblies of antiparallel dimers that likely serve as a reservoir for budding. These findings uncover a membrane-guided mechanism coordinating genome assembly and virion formation in IAV.
Together with our collaborators at the Luxembourg Institute of Science and Technology, we have established a novel cryo-secondary ion mass spectrometry (cryo-SIMS) technology. This method is now ready to map lipid distributions and membrane organelles in both uninfect-ed and IAV-infected cells. It will, for the first time, enable visualization of cholesterol distribution within the 3D context of cellular membranes under native conditions. Using this approach, we aim to elucidate the role of cholesterol distribution and nanodomains in membranes remodeled by IAV infection
Late-Life Relocation and Personality Development: An Exploration from Macro-Level Phenomena to Micro-Level Mechanisms
No full textAgainst the backdrop of accelerating global population aging, late-life relocation has become an increasingly common major life event. However, previous research has largely overlooked the profound association between this event and personality development. Older adults’ personality not only serves as a key predictor of successful adaptation to relocation, but a series of changes triggered by relocation, such as shifts in environment, social networks, and daily routines, may also drive changes in personality development. This dissertation addresses this research gap by systematically exploring the mechanisms associating late-life relocation and personality development. It comprises three empirical studies and one study protocol, employing a progressive research logic and diverse methodologies to enable an in-depth exploration spanning from macro phenomena and long-term developmental processes to micro mechanisms.
Chapter 2 examines the personality-related emotional responses of older adults following involuntary relocation. Focusing on a sample of Chinese older adults (N = 301) who moved from rural to urban areas due to policy-driven resettlement, this study found that participants widely reported clinically significant levels of anxiety. Although excessive reassurance-seeking was positively associated with anxiety, this association was primarily mediated by attention to negative information. Furthermore, the study revealed a paradoxical effect of resilience in the context of late-life relocation. Older adults with higher resilience, when experiencing stressful events such as relocation, were more likely to amplify negative attention bias in their external support-seeking behaviors (i.e., reassurance-seeking), thereby exacerbating their anxiety.
Chapter 3 extends the focus from post-relocation emotional responses to more stable personality traits. Using data from Wave 7 of the Survey of Health, Ageing and Retirement in Europe (SHARE) across 25 EU countries (N = 48,298), this study examined personality differences between nursing home and private home residents. The results showed only slight differences between the two groups. These differences were largely explained by a combination of individual-level sociodemographic and health-related factors, as well as macro-level contextual factors such as regional and national economic development and investment in long-term care.
Chapter 4 investigates the dynamic trajectory of personality development before and after relocation by analyzing 16 years of longitudinal data from the Health and Retirement Study (HRS; approximately 20,000 participants). Separate models were estimated for two relocation types: community relocation and nursing home admission. The results indicated that community relocation had mild effects, mainly reflecting the positive personality selection effect before the move. Such relocation did not change the overall trajectory of personality development and was even associated with higher life satisfaction after relocation. In contrast, nursing home admission represented a profound psychological turning point. It was characterized not only by a significant negative selection effect, but also by immediate shocks and complex, long-lasting nonlinear changes in personality, accompanied by a marked decrease in life satisfaction.
Chapter 5 presents a prospective cohort study protocol to address the limitations of existing panel data and traditional longitudinal methods in capturing personality development processes. The protocol employs a high-density longitudinal tracking design with daily and monthly assessments following relocation, aiming to accurately capture the complete dynamic process through which short-term fluctuations in post-relocation personality states, social interactions, and daily routines develop into trait-level personality changes.
Overall, this dissertation systematically reveals the significant association between late-life relocation and personality development, delineating the complex boundary conditions of personality plasticity in later life. The studies emphasize that the effects of relocation on personality are not uniform but are profoundly influenced by the combined moderating roles of relocation type, individual characteristics, and macro-level socioeconomic and policy factors. These findings extend theoretical understanding of late-life personality development and provide an empirical foundation and practical guidance for developing multi-phase, personalized intervention programs and psychosocial support strategies for older adults in different relocation contexts
Genomic landscape of resistance evolution
Full text linkAntimicrobial resistance (AMR) presents a major public health challenge threatening the foundations of modern medicine, with gaps in our understanding of the evolutionary pathways leading to AMR. Through genetic interactions with resistance genes, the genetic background offers a way to understand and modulate AMR evolution. To systematically assess how the genetic background affects the evolution of AMR, I established a high-throughput (HT) experimental evolution protocol that allowed me to grow and track thousands of lineages over time. I used this protocol to profile a genome-wide single-gene knockout (KO) collection of Escherichia coli K12 during treatment with the commonly used cephalosporin cefotaxime.
By quantifying the contribution of every non-essential gene to AMR evolvability, I uncovered several genes that delayed or promoted cefotaxime resistance evolution. While several mutants with decreased evolvability (evolvability genes) were involved in peptidoglycan (PG) remodeling and metabolism, mutants with increased evolvability were enriched for phospholipid transport and enterobacterial common antigen biosynthetic process. I uncovered two evolvability genes, dpaA, which cleaves linkages between PG and outer membrane (OM), and ivy, which folds periplasmic proteins and is required for DpaA protein presence. The absence of these evolvability genes blocks evolutionary trajectories towards cefotaxime AMR via epistatic genetic interactions with the known cefotaxime resistance genes marR and ompR.
Using HT microscopy, proteomics, and quantitative genetics, I uncovered that LPS biosynthesis and transport are deregulated in marR-resistant mutants, and these phenotypes are exacerbated in the absence of the evolvability gene dpaA. This negative genetic interaction is likely caused by the increased abundance of crosslinks between PG and OM in dpaA KO mutants, which could prevent the compensatory shedding of LPS via OM vesicles and blebs in marR mutants. This epistatic interaction can be relieved in mutants with reduced LPS biosynthesis or decreased abundance of PG-OM crosslinks.
Uncovering deregulated LPS biosynthesis and transport in marR mutants leading to a compromised OM barrier function allowed me to identify several drugs that inhibit AMR evolution against cefotaxime, potentially by selectively preventing the evolution of marR mutations. Furthermore, to assess the specificity of evolvability genes, I evolved nine evolvability gene mutants against 12 other antibiotics. I found that all of them delayed antibiotic resistance evolution of multiple antibiotics in a drug-, target- or resistance-dependent manner.
Furthermore, I developed a complementary approach using Clustered Regularly Interspaced Short Palindromic Repeats interference (CRISPRi) to measure the global epistasis of antibiotic targets or resistance genes with the KO collection in the absence or presence of an antibiotic. This approach is more scalable and can support the insights gained from the experimental evolution of a genome-wide KO library. I applied this method to probe genome-wide genetic interactions of the antibiotic target and resistance gene DNA topoisomerase IV (parC) and discovered that gene KOs with negative epistasis were enriched for binding DNA. The identified evolvability genes represent potential targets for AMR-inhibiting drugs against multiple antibiotics. They can guide combinatorial strategies with existing drugs to delay resistance evolution, resensitize resistant populations, and improve antimicrobial therapies
YKL-40 as a biomarker for long-term changes in innate immunity after COVID-19 onset
Full text linkIn the complex landscape of COVID-19 research, a particular area of interest is the study of individuals who have recovered from the virus, known as COVID-19 convalescents. Changes in trained innate immunity, especially within the monocyte-macrophage system, play a crucial role in the convalescent period following virus infection. As a differentiation and activation marker of macrophages, YKL-40 can be involved in innate immune reactions during virus infection. YKL-40 has been identified as a biomarker for the severity of COVID-19 in the acute infection period. In this study, we explore the potential biomarker role of YKL-40 in monocyte-differentiated macrophages from COVID-19 convalescent individuals. 39 COVID-19 convalescent plasma donors and 36 healthy plasma donors were included in the study as part of the CORE project. The donation at time point 1 (T1) occurred 44-447 days after COVID-19 diagnosis, donation at time point 2 (T2) was conducted 21-60 days after T1, followed by a third donation (T3) 25-62 days after T2. Donors' baseline characteristics, including COVID-19 symptoms, were documented through a questionnaire. For the study CD14+ selection of monocytes has been performed, and monocytes were differentiated towards homeostatic M(NS), inflammatory M(IFN-γ), and healing M(IL4) macrophages for 2 days. Challenge with LPS has been applied after 24 h of cultivation for next 24 hours. Expression of IL-1β, IL-1ra, IL-10 and YKL-40 was analyzed by RT-PCR. Compared to healthy individuals, a statistically significantly higher expression of YKL-40 was found in M(NS) (T1), M(NS)+LPS (T1), M(IL4) (T1). YKL-40 expression was further analyzed in monocyte-derived macrophages obtained at T2 and T3. The statistically significant higher expression of YKL-40 in COVID-19 convalescent donors was found in M(NS) (T2) and M(IL4)+LPS (T3). Correlation of YKL-40 expression level with clinical parameters of acute and post/long-COVID was further analyzed. A statistically significant negative correlation was found between the expression of YKL-40 and the total amount of acute symptoms in M(NS) (T1, T2). To explore the correlation between YKL-40 expression and specific symptoms, all symptoms were classified into respiratory, systemic and neurology symptoms. Donors who reported the presence of respiratory symptoms showed statistically lower expression of YKL-40 in M(NS) (T2, T3) and in M(IFNγ) (T3). The negative correlation with YKL-40 expression was also evident with the amount of respiratory symptoms in M(NS) (T1), M(NS)+LPS (T1), M(IFNγ) (T1), M(IFNγ)+LPS (T3), and M(IL4)+LPS (T2). Donors who reported the presence of the systemic symptoms showed statistically lower expression of YKL-40 in M(NS) (T2) and M(IL4) (T3), although the YKL-40 expression had no statistical significant correlation with the amount of systemic symptoms. For neurological symptoms, no statistically significant differences in the YKL-40 expression was found between donors who reported the presence or absence of the symptoms. The statistically significant negative correlation was found with neurological symptoms in M(NS) (T1, T3), M(IFNγ) (T3), and M(IFNγ)+LPS (T1, T2, T3). As post/long COVID is consider as a public health challenge, correlation with YKL-40 with post/long COVID was analyzed. Donors who reported the post/long COVID symptoms showed a statistically significantly higher expression of YKL-40 in M(NS) (T3). Among all donors with post/long COVID symptoms, people reporting the persistent difficulty in concentration showed statistically significant lower expression of YKL-40 in M(NS)+LPS (T1). In summary, our study reveals a long-term change in YKL-40 expression in monocyte-derived macrophages in COVID-19 convalescent individuals, where the strongest correlation is detected for neurological symptoms. Given the importance of monocytes/macrophages system, YKL-40 may become a potential marker for the long-term dysregulation of the innate immune system
Theoretical and Machine Learning Approaches to Beyond General Relativity: Stability of Generalized Proca Theories and Multi-Method Classification of Gravitational Wave Observables
Full text linkThis thesis explores two complementary approaches to testing and understanding grav- ity beyond General Relativity (GR). The first part focuses on Generalized Proca theo- ries—vector-tensor models that extend the Proca action through derivative self-interactions and non-minimal couplings, while maintaining second-order equations of motion and avoid- ing ghost instabilities. We analyze the quantum consistency of these theories in both flat Minkowski spacetime and weakly curved backgrounds. In flat space, we compute one-loop corrections and observe the emergence of gauge-invariant structures, suggesting a form of radiative stability. In curved spacetime, we develop a scalar-vector-tensor (SVT) decom- position to isolate physical modes and consistently integrate out non-dynamical fields. Our results show that the theories remain well-behaved under quantum corrections, supporting their viability as effective field theories.
The second part leverages gravitational wave (GW) observations as precision probes of strong-field gravity. Using convolutional neural networks (CNNs), we construct a ma- chine learning framework to classify GW signals as either consistent with GR or exhibiting beyond-GR (BGR) deviations. The dataset includes both artificial phase deformations and physically motivated waveforms derived using the parameterized post-Einsteinian (ppE) formalism. A key tool is the response function, which captures the sensitivity of the wave- form to small deformations. We show that training neural networks on response functions significantly improves classification accuracy and lowers detection thresholds. Applied to massive graviton models, this approach allows us to estimate the smallest graviton mass distinguishable from GR predictions.
Together, these investigations form a coherent program to study modified gravity from both theoretical and observational perspectives, contributing to the broader effort of devel- oping consistent and testable alternatives to Einstein’s theory
Simulation Studies for Measuring the Soft-Photon Spectrum with the ALICE 3 Forward Conversion Tracker
Full text linkThe soft-photon puzzle has been an open problem in high energy particle physics for many years
now. Experiments have been performed in the past to try to measure the soft-photon spectrum of
inelastic m → n scattering processes. Some measured an excess of a factor 4–8 in terms of the internal
bremsstrahlung spectrum predicted by Low’s theorem, but others measured no excess at all. This
work presents for the first time a full Monte Carlo simulation with generated internal bremsstrahlung
photons predicted by Low’s theorem propagated through the detector setup of ALICE 3. One of the
goals of the proposed ALICE 3 experiment is to measure the soft-photon spectrum with the use of
the Forward Conversion Tracker through the tracking of e+e− conversion pairs in a separate dipole
magnetic field. Through dedicated studies of the (spatial) origin of the various sources of background,
cuts and vetoes are implemented to minimize its impact on the measurement. These studies show
a significance of 5–10 in the regime of 0.5 < kT < 7.5 MeV/c for the case where the excess is 4
times the predicted internal bremsstrahlung spectrum. A conical modification of the beam pipe is
suggested to reduce the material budget in front of the FCT and as such the kinematic regime where
the significance exceeds 5 is extended to 0.5 < kT < 10 MeV/c for the case where the excess is 4 times
the predicted internal bremsstrahlung spectrum. In this thesis the theory, previous measurements and
the performance of the proposed Forward Conversion Tracker will be discussed
Development of a data-driven method to determine reconstruction efficiencies in the LHCb tracking detectors and study of the detector performance in Run 3
Full text linkThe LHCb experiment is a highly complex apparatus dedicated to measuring the decays of particles containing b- and c-quarks. During Run 3 of the Large Hadron Collider (LHC), protons collide at the interaction point of LHCb with an instantaneous luminosity of up to 2x10^33 cm^(-2)s^(-1) at a centre-of-mass energy of sqrt(s)=13.6 TeV. A full understanding of the detector and reconstruction efficiencies involved in the data acquisition is essential for many high precision measurements. A tag-and-probe method is developed to estimate the track reconstruction efficiency of the LHCb tracking subdetectors exploiting the decay of J/psi->mu mu. Discrepancies of the measured track reconstruction efficiencies between simulated and recorded data are studied and used to derive correction factors. An agreement between recorded and simulated data at the sub-percent level is achieved over almost the entire phase space and for all tracking subdetectors, illustrating an excellent understanding of the upgraded LHCb detector and its reconstruction sequences. A novel approach to derive the effect of interactions in the detector material is developed and used to provide first estimates, which contribute a systematic uncertainty on the order of a few percent to the track reconstruction efficiency. Further corrections are expected to reduce this systematic uncertainty to the order of one percent or less
Bridging Theory and Data Uncertainty-Aware Analyses for the LHC and Beyond
Full text linkUncertainties are crucial in particle physics, affecting experimental data and theoretical predictions. This thesis investigates the impact of uncertainties on global analyses and the estimation of uncertainties using machine learning architectures. In the first part of this thesis, we perform global analyses using effective field theory approaches. We start with the Standard Model effective field theory in the top, Higgs, and electroweak sectors, including public experimental likelihoods. In particular, we focus on the role of theory uncertainties and their interplay with correlations. Next, we perform the first global electric dipole moment analysis constraining model parameters from the hadronic- and weak-scale Lagrangians while exploring the impact of theory uncertainties on the parameter constraints. The second part discusses machine-learning methods that have become increasingly important with the growing data from future LHC runs. Thus, we study the calibration of systematic and statistical uncertainties and the precision and reliability of machine learning architectures for amplitude surrogate models. We compare Bayesian neural networks and repulsive ensembles as uncertainty estimators regarding their precision and use Kolmogorov-Arnold networks to explore the impact of activation functions.
Overall, this work emphasizes the importance of reducing theory uncertainties and paves new ways of uncertainty estimation using machine learning models in particle physics and global analyses
Aspects of Tensor Models and Tensor Field Theories
Full text linkThis thesis investigates random tensor models and their applications across quantum field theory. Originating in quantum gravity studies, tensor models provide a framework for generating discrete random geometries and have connections to several other fields, including topology, conformal field theory, and constructive field theory. Their extension to d-dimensional quantum field theories constitutes tensor field theories. The most important feature of tensor models is their melonic large N limit. The 1/N expansion allows for non-trivial and systematic resummations of correlation functions, making them interesting quantum field theory models. Other methods that are used in this thesis include combinatorics, asymptotic series analysis, and two-particle irreducible effective action techniques.
Three main themes are developed throughout this work. First, the research on tensor models with symplectic symmetry broadens our understanding of tensor models with various symmetry groups. We establish a formal relation between orthogonal and symplectic random tensor models, demonstrating that tensor models with O(N) symmetry are related to corresponding models with Sp(N) symmetry through the replacement N to -N. This duality extends to tensors transforming in arbitrary finite-dimensional representations of these groups and provides a framework for new fermionic models. Second, we analyze the zero-dimensional O(N ) vector model using constructive field theory techniques, particularly the Loop Vertex Expansion, establishing analyticity and Borel summability properties of the free energy. We derive transseries expansions that incorporate both perturbative and non-perturbative contributions. Third, we study a four-dimensional O(N)³ tensor field theory exhibiting asymptotic freedom in the ultraviolet while developing strong correlations in the infrared. Through numerical solution of the Schwinger–Dyson equations, we demonstrate how quantum fluctuations significantly modify the propagator and identify a threshold mass below which the running coupling diverges at a finite infrared scale
Probing Anomalous Gauge Couplings via Photon-Induced WW Production with Forward Protons at the ATLAS Experiment
Full text linkPhoton-induced WW production in proton-proton collisions at the Large Hadron Collider provides a clean environment to probe the electroweak structure of the Standard Model. Quasi-real photons, emitted by the electromagnetic fields of the protons, collide peripherally. The protons that remain intact scatter into the forward region of the experiment, where the ATLAS Forward Proton (AFP) detectors can measure their kinematic properties. This enables the full kinematic reconstruction of the WW system, independent of the central ATLAS detector. This thesis presents the measurement of the semileptonic photon-induced WW production in Run 2 of LHC at a centre-of-mass energy of
√s = 13 TeV, corresponding to an integrated luminosity of 14.6 fb−1. Two reconstruction methods for the hadronically decaying W boson are studied: the resolved and boosted topologies. Both reconstructions are used to test the sensitivity to potential anomalous quartic gauge couplings. Data-driven techniques are employed to estimate background contributions, with cross-checks between different methods confirming consistency. Good agreement is observed between the background predictions and the observed number of
events in data. The results are interpreted within the framework of the Standard Model Effective Field Theory, and are used to constrain the coefficients of dimension 8 operators. Confidence intervals for the anomalous quartic gauge couplings are derived, confirming existing limits