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Perceptual simulation during linguistic and non-linguistic processing of motion events: a blank screen eye movement study
No full textUnderstanding whether conceptual representation during language comprehension and production relies on perceptual simulation remains a central debate in cognitive science. Grounded cognition theories propose that linguistic meaning is constructed through modality‑specific simulations, whereas symbolic accounts argue for amodal, language‑specific processing. This study examined whether perceptual simulations systematically drive conceptual representation by relating spontaneous non‑visual eye movements to the processing of motion events. Forty‑two participants completed two blank‑screen eye‑tracking experiments: a comprehension task using environmental sounds and spoken event descriptions, and a production task eliciting verbal descriptions of environmental sounds. Critical stimuli depicted horizontal or vertical motion events, allowing tests of whether gaze dynamics reflect the spatial properties of simulated motion. Across both experiments, participants showed greater overall gaze dispersion when processing motion events compared to non‑motion controls, suggesting that perceptual simulation of spatial event structure contributed to meaning construction in both comprehension and production. However, axis‑specific effects predicted by strong simulation accounts did not emerge. Linguistic processing demands modulated oculomotor behavior: verbal comprehension and speech‑planning phases elicited higher saccade rates and reduced travel distance, consistent with a shift of cognitive resources toward linguistic representations at the expense of simulation. Individual differences in visual imagery ability further shaped gaze behavior, indicating variability in reliance on perceptual simulation. These findings challenge claims that sensorimotor activation is automatic or necessary for conceptual processing, while demonstrating that perceptual simulation remains a flexible, context‑dependent resource. The study advances methodological approaches to non‑visual gaze analysis and refines theoretical accounts of how linguistic and non‑linguistic representations interact during meaning construction
Multimodal image analysis in biology
No full textLiving organisms are striking in their complexity at all levels of organization. No one single analytical method can capture all the information necessary to provide comprehensive knowledge about the cellular processes. The rapid development of new technologies for biological imaging and single cell analysis gives an opportunity to measure different aspects of the living systems at single-cell resolution. Combining these diverse data types can lead to an insight that would not have been possible if each modality were to be considered separately. However, there is no general recipe for a successful multimodal analysis project and it remains challenging to both keep high data quality and establish a reliable way to find correspondence between the two data types. In this thesis I show two different examples of combining different imaging modalities.
Firstly, I demonstrate how, making use of the stereotypical development of the marine worm Platynereis dumerilii, I developed fully automated deep-learning based registration pipeline that allowed to map multiple 3D smFISH datasets to the EM image stack of a whole body 6-days post fertilization larva of the animal with near single-cell accuracy. Automated registration enables systematic study of the connection between cell-type specific gene expression and cell phenotype.
In the second project I combined imaging mass spectrometry for spatially-resolved detection of 13C6-glucose-derived fatty acids in cellular lipids with microscopy and computational methods for data integration and analysis. I validated this method on a spatially-heterogeneous normoxia-hypoxia model of liver cancer cells. I demonstrated the single-cell heterogeneity of acetyl-CoA pool labelling degree upon ACLY knockdown that would be impossible to detect with bulk analysis.
Segmentation of matching objects in different modalities is a crucial step in multimodal image analysis. Transferable and easy to train segmentation of large biological images with neural networks remains challenging. In the final part of my thesis I show how feature normalization inside the neural network can lead to tiling artifacts or suboptimal performance, and propose a normalization strategy for successfully eliminating the artifacts while keeping high segmentation accuracy
Selective information transmission by particle distributions
No full textLiving cells respond to environmental cues under noisy conditions, making them ideal platforms for uncovering physical principles of computation in soft materials. Signal processing by spatial heterogeneities remains a yet under explored component of such cellular regulation. Motivated by the relaying of signals across membranes, I investigate information transmission by surface-bound particle distributions.
Extending maximum caliber methods, I address how microscopic constraints influence non-equilibrium particle dynamics, and identify a mechanism for information transmission arising from non-linear responses of equilibrium particle densities to spatial features in adjacent structures. This permits pattern recognition, with inter-particle interactions tuning the response function of noisy signal filters and resulting in the efficient encoding of information relevant to downstream tasks. I identify thresholding and edge-detecting regimes, and quantify how biophysical membrane properties affect information transmission by thresholding filters. I discover parameter regimes with optimal transmission where many biophysical systems fall – including nuclear pore complex distributions in Sphaeroforma arctica which show signatures of particle-mediated thresholding. Feedback from particle distributions to interaction energies is shown to improve selective information transmission.
These results indicate that spatial distributions of macromolecular complexes can selectively sense environmental cues, with fundamental implications for how physical interactions may encode computational logic in soft materials
EXCITED-STATE DYNAMICS AND ENERGY-TRANSFER PRO- CESSES IN ADVANCED ORGANIC SEMICONDUCTORS
No full textThis thesis investigates the excited-state dynamics and energy transfer mechanisms in organic
molecular systems, emphasizing how chemical structure, environment, and photonic confine-
ment govern their photophysical behavior. A combination of steady-state spectroscopy, time-
resolved fluorescence, and transient absorption or reflectance measurements was employed to
correlate molecular architecture with radiative and non-radiative decay pathways. Spiro-
bridged triphenylamine derivatives (FTN-H and FTN-(CN)6) were examined to understand how
electron-withdrawing cyano substituents and solvent polarity modulates charge-transfer (CT)
character and excited-state relaxation. Comprehensive studies across different solvents and ox-
ygen conditions indentified distinct photophysical pathways, including possible evidence for
thermally activated delayed fluorescence (TADF) FTN-(CN)6. The investigation then focused
on octaazadibenzo[cd,lm]perylene-2,9-dione (OAPPDO) derivatives in solution and thin-film
phases. While all compounds exhibited high fluorescence quantum yields in solution, solid-
state measurements uncovered additional channels including aggregate formation and triplet
generation via singlet fission (SF). Morphology-dependent dynamics revealed that crystalline
films proceed through excimer intermediates, whereas amorphous systems undergo more direct
SF pathways, with varying triplet formation efficiencies influenced by molecular packing. Fi-
nally, integrating OAPPDO VII into Fabry-Pérot microcavities demonstrated dynamic control
of emission properties thorough photonic confinement. Cavity thickness systematically modu-
lated fluorescence lifetimes via the Purcell effect acceleration, with the thinnest configuration
achieving weak coupling and suppressing triplet formation by accelerating singlet radiative de-
cay. Together, these results stablish that strategic manipulation of molecular design, environ-
mental conditions, and photonic architectures provides precise control over energy transfer pro-
cesses, critical for advancing organic optoelectronic and energy-conversion technologies
Generalized impairment of flexible, goal-directed control in alcohol dependence. Meta-analytical, behavioral, and circuit-level re-evaluation of the habit construct and the role of the pDMS
No full textLoss of control over drinking has long been recognized as a defining feature of alcohol use disorder (AUD), yet its mechanistic bases remain unclear. One influential account, the habit theory of addiction, proposes that chronic alcohol exposure shifts behavioral regulation from goal-directed to habitual control. However, this view is grounded largely in animal studies that rely on oversimplified dichotomous classifications of behavior and on overinterpreted null findings from classical outcome-devaluation paradigms. Human studies rarely replicate these results, instead pointing to impaired goal-directed regulation rather than strengthened habits.
Across three aims, this thesis critically re-examines the habit framework using both quantitative synthesis and experimental investigation. A meta-analysis of rodent studies (Aim 1) reveals that alcohol-dependent animals do not reliably exhibit a qualitative transition to habitual control, aligning with human evidence demonstrating preserved sensitivity to outcome value but weakened goal-directed processing.
Building on this foundation, I assessed behavioral control across instrumental learning, spatial navigation, and motor-skill paradigms (Aim 2). These experiments show that classically defined stimulus-response (S-R) habits are short-lived and readily overridden by goal-directed adjustments. Alcohol-dependent rats instead display a domain-general deficit in flexible adaptation to novel contingencies, accompanied by heightened automaticity. These findings support modern graded accounts of behavioral control rather than a categorical dominance of habit mechanisms.
Bidirectional chemogenetic manipulations further identify the posterior dorsomedial striatum (pDMS) as a domain-general hub for behavioral flexibility and a critical locus whose disengagement may underlie alcohol-induced impairments. In alcohol-naïve rats, pDMS inhibition reproduced the broad deficits in adaptive control observed in alcohol-dependent animals. Conversely, chemogenetic activation of the pDMS in alcohol-dependent rats restored flexible, goal-directed behavior across all domains tested, demonstrating that re-engaging this region is sufficient to rescue alcohol-induced deficits. Thus, pDMS activity is necessary for the expression of flexible, goal-directed control irrespective of behavioral domain. Complementary cell-type–specific manipulations show that D1-expressing medium spiny neurons (D1-MSNs) within the pDMS critically support flexible action selection without affecting the expression of instrumental habits.
Finally, experiments using a novel combination of CB1 receptor gain- and loss-of-function rat lines (Aim 3) reveal that sex moderates CB1 receptor signaling. Females showed greater habitual tendencies than males, with loss-of-function females displaying impaired operant acquisition and gain-of-function females exhibiting hyperlocomotion; behavioral effects that corresponded to elevated CB1R activity across the dorsal striatum and increased CB1R availability in the pDMS. These findings point to a sex-specific neurobiological contribution to differences in habit propensity and may help explain sex-dependent vulnerability to addiction.
Together, these findings challenge the classical habit theory of addiction and demonstrate that chronic alcohol exposure impairs goal-directed control in a broad, domain-general manner rather than generating a categorical shift toward S-R habits. This work reframes loss of control in alcohol dependence as an impairment in the flexible updating of action selection rooted in dorsomedial striatal dysfunction, offering experimental evidence for a more nuanced and mechanistically grounded account of addictive behavior
An hepatitis D virus infection model using human pluripotent stem cell-derived hepatocytes for virus-host interactions and antiviral evaluation
No full textAn estimated ~12 million people worldwide suffer from chronic hepatitis D virus (HDV) infection, which can cause the most aggressive form of viral hepatitis when co-infected with hepatitis B virus (HBV), leading to an accelerated process of liver dysfunction towards hepatic fibrosis, cirrhosis and hepatocellular carcinoma. HDV is a satellite virus and relies on its helper HBV to provide HBV surface antigen (HBsAg) for viral assembly and subsequent secretion. Currently, the sole antiviral drug approved by the European Medicines Agency specifically for HDV treatment is the entry inhibitor bulevirtide, however, it is only available in certain European countries. Despite this significant burden, our knowledge of HDV biology remains limited, thus restricting the development of targeted antiviral therapies. This is partly due to the lack of reliable HDV cell culture systems that mimic the physiological status of hepatocytes in vivo.
In this thesis, I have demonstrated the use of stem cell-derived hepatocyte-like cells (HLCs) as a novel cell culture model for the study of HDV. HLCs are fully susceptible to HDV infection across various tested genotypes. They endogenously express the cell entry receptor the sodium taurocholate co-transporting polypeptide at levels sufficient to mediate HBV/HDV entry. When co-infected with HBV or ectopically expressing HBsAg through adeno-associated virus transduction (HLCsHBsAg), HLCs are able to effectively produce and release infectious progeny virions, thus recapitulating the entire HDV life cycle.
Using the HLCsHBsAg system, I also demonstrated that the system supports the extracellular spread of HDV, which occurs in vivo but has been so far challenging to replicate in vitro. This allowed me to test available anti-HDV regimens that target HDV spread, underscoring the utility of HLCs as a platform for drug candidate evaluation.
By challenging the cells along the differentiation with HDV infection, I observed an increased susceptibility to HDV infection in fully matured HLCs. Using transcriptomic analysis and further confirmation studies, I identified CD63 as a novel HDV co-entry factor, which was found to be rate-limiting for HDV infection in immature hepatocytes. While this finding augments our understanding of HBV/HDV infection imminently, it also provides a guideline on how to use stem cell differentiation and stem cell-derived culture models to identify host factors of other viruses.
To conclude, my study provides a comprehensive and quantitative assessment of HDV infection, showing that HLCs can recapitulate the entire HDV life cycle and support extracellular spread, as well as revealing the identification of novel host co-factor(s), such as the entry co-factor CD63. As an alternative to hepatoma cells and primary human hepatocytes, HLCs represent a renewable, physiologically relevant and genetically tractable system for HDV-host interaction studies and anti-viral drug evaluation
Robust AI-Driven Spectral Imaging for Perioperative Care
No full textPhysicians face major challenges in perioperative decision-making, as they need to rely on clinical intuition and limited information for critical real-time judgments. Spectral imaging (SI) could support this process by rapidly and non-invasively revealing changes in tissue composition that alter spectral signatures. While such changes often remain invisible to the human eye or conventional RGB imaging, SI captures subtle variations in tissue reflectance spectra at each pixel. Combined with machine learning (ML), this high-dimensional data could efficiently yield clinically relevant insights.
Numerous proof-of-concept studies have demonstrated the potential of SI, particularly for estimating functional tissue parameters such as oxygenation, thereby enabling non-invasive distinction between perfused and ischemic tissue during surgery. However, several important clinical applications of SI remain underexplored:
Clinical Gap: Automated Surgical Scene Segmentation
Visual discrimination of tissue types remains an important challenge for surgeons, and automated surgical scene segmentation is a key component of surgical data science applications such as surgical phase recognition and robot-assisted surgery. However, SI-based segmentation, particularly in open surgeries, has received little attention. Consequently, it remains unclear whether SI offers advantages over other imaging modalities (e.g., RGB imaging) for surgical scene segmentation and how to optimally represent the input data in terms of spatial granularity (e.g., pixels, entire images). Leveraging the largest semantically annotated SI database to date, we close this gap and demonstrate that SI consistently outperforms RGB across all spatial granularities. Our image-based SI segmentation reaches performance comparable to a second human expert.
Clinical Gap: Sepsis Diagnosis and Mortality Prediction in Critically Ill Patients
Sepsis remains a leading cause of mortality and critical illness. Early detection is vital to reduce mortality risk, yet reliable biomarkers for timely diagnosis and outcome prediction are still lacking. Sepsis diagnosis in intensive care unit (ICU) patients is particularly challenging due to high baseline illness severity. SI could potentially close this gap by capturing early signs such as edema formation and microcirculatory dysfunction. However, prior studies compare sepsis patients to healthy volunteers or narrowly selected cohorts, introducing a substantial risk of shortcut learning from confounding factors such as age and treatment regimens. We address this critical gap through a prospective study in ICU patients, comprising the largest SI patient cohort to date, in which we diagnose sepsis and predict mortality on the day of admission. Our SI-based ML models achieve high accuracy, particularly when combined with minimal clinical data, and outperform widely used biomarkers and scores, while enabling rapid, non-invasive, cost-effective and mobile assessments.
Technical Gap: Investigation of Domain Shifts
A key challenge for SI analysis is its clinical translation. Numerous studies outside medical SI have shown that domain shifts between training and real-world application data can severely degrade algorithm performance, yet this issue has received little attention in medical SI. We are the first to investigate the impact of important real-world domain shifts: Illuminant and hardware-related shifts in functional tissue parameter estimation, geometric shifts (e.g., situs occlusions) in surgical scene segmentation, and population shifts in sepsis diagnosis and mortality prediction. Our results show that such shifts can substantially degrade downstream task performance.
Technical Gap: Mitigating performance degradation under domain shifts
We propose methods to mitigate the performance degradation under domain shifts and improve algorithm robustness. To address drops in functional tissue parameter estimation due to illuminant changes, we introduce the first intraoperative, live illuminant estimation approach. Our method outperforms state-of-the-art illuminant estimation techniques from nonmedical domains, achieving accuracy close to the ideal scenario of a perfectly known illuminant. Additionally, we provide recommendations to mitigate hardware-related bias in SI study design. To enable robust surgical scene segmentation under geometric domain shifts, we introduce a surgery-inspired data augmentation strategy which restores in-distribution performance across diverse out-of-distribution scenarios.
In conclusion, this thesis contributes substantial advancements towards the robust and reliable application of ML-based SI analysis in real-world clinical settings. Specifically, it enables, for the first time, (1) intraoperative functional tissue parameter estimation under illuminant and hardware-related shifts, (2) automated surgical scene segmentation under geometric domain shifts, and (3) automated sepsis diagnosis and mortality prediction among ICU patients. Our findings are supported by extensive validation studies which are among the largest in the field of medical SI to date. To support the research community and facilitate the clinical translation of SI, we have publicly released datasets, as well as our code and pretrained models
Census of Accreting SMBH at z > 4 across the Southern Hemisphere
No full textQuasars are the most luminous, non-transient sources in the Universe and provide unique insight into supermassive black hole (SMBH) growth, galaxy evolution, and intergalactic medium (IGM) properties. Their discovery within the first Gyr ((z > 6) is challenging because they are rare and difficult to distinguish photometrically from much more numerous contaminants. The current population, mainly bright and blue quasars, is concentrated in the northern hemisphere.
This thesis explores multiwavelength and time-domain approaches for quasar selection and characterization at z > 4, using data from radio to X-rays. We tested color cuts, machine learning (supervised and self-supervised), spectral energy distribution (SED) fitting, and variability analysis. A contrastive learning method applied to DESI Legacy Survey DR10 imaging achieved a 45% success rate, leading to 16 new spectroscopically confirmed quasars at z ≳ 6. Variability analysis of 285 z > 5.3 quasars with unWISE light curves identified 19 with significant (3σ) variability, marking one of the first detections of rest-frame optical variability beyond z ∼ 5.
We also developed AGNfitter-rx, an extended Bayesian SED-fitting tool from radio to X-rays. Its application to a z < 0.7 AGN sample demonstrates reliable physical characterization consistent with spectroscopic analyses. Together, these tools will enhance high-redshift quasar discovery and characterization in the upcoming era of Rubin/LSST, Euclid, Roman, JWST, 4MOST, and DESI
The Role of HIV-1 Nef in Antigen-Specific Interactions of CD4 T Cells and Dendritic Cells
No full textHIV-1 induced immunodeficiency is associated with severe impairment of both humoral and
cellular adaptive immune responses that persists even under therapy. CD4 T cells, the main
target cells for HIV-1 infection, execute important helper functions to both B and CD8 T cells
in the context of antigen-specific immune responses, suggesting a connection between the
functionality of CD4 T cells and adaptive immune dysfunction. A key step in generation of T
cell help is antigen-specific interaction with DCs which activates and polarizes T cells towards
a specialized effector phenotype, such as Tfh or Th1 cells, helping humoral or cellular
immunity, respectively. Importantly, previous research demonstrated that HIV-1 directly
interferes with CD4 T cell communication with B cells by action of the accessory Nef protein
which impairs immune synapse (IS) function and thus, help to B cells, leading to B cell
dysfunction in vivo. Concerning molecular architecture and dynamics, T cell IS formation with
B cells is fundamentally different from T cell IS formation with DCs, the latter representing a
crucial step in effector T helper cell generation and development of cellular immunity. Given
the importance of T cell-DC interaction in determining the course of an immune response, I
therefore aimed to investigate Nef’s role in CD4 T cell interaction with DCs in this study.
By first using an antigen-specific mouse system in which Nef is expressed in CD4 T cells, I
could demonstrate that Nef strongly interferes with early T cell-DC interaction dynamics and
impairs effective activation of both CD4 T cells and DCs on a transcriptional and functional
level ex vivo and in vivo. This dysfunction is in particular associated with inefficient Th1
polarization of CD4 T cells and establishment of a microenvironment which disfavors
development of Th1 cells and cellular immunity. By using different Nef mutants together with
a CRISPR/Cas9 KO approach, I could demonstrate that the inhibition exerted by Nef strongly
depends on its CD4 downmodulation function, thus revealing an unknown role for this effector
function in disruption of antigen-specific immune responses. In this context, I could observe
that Nef had the capacity to limit CD4 T cell help to an antiviral CD8 T cell memory response
in a preliminary LCMV infection experiment, suggesting Nef contributes to CD8 T cell
dysfunction in vivo. In line with this, HIV-1 infection impaired CD4 T cell help to an EBVspecific
memory CD8 T cell response in a complementary human ex vivo tonsil culture system
for several, although not all donors with high variability. To mechanistically dissect adaptive
immune cell interactions in the ex vivo tonsil culture system and to gain better understanding
of the underlying variability, I lastly established a workflow that enables highly efficient and
activation-neutral gene editing of tonsil CD4 T cells by CRISPR/Cas9 RNP nucleofection
without affecting immunocompetency of the system.
In summary, these findings provide evidence that HIV-1 Nef, by interfering with CD4 T cell
interaction with and licensing of DCs, limits efficient CD4 T cell effector cell generation and
in particular, Th1 responses in a murine cell context. As this was associated with suboptimal
help to memory CD8 T cell responses in ex vivo human tonsil culture and in an in vivo LCMV
infection model, Nef may in this way contribute to CD8 T cell dysfunction in the context of
HIV infection. This study therefore identifies Nef as a global antagonist of adaptive immune
responses and interesting viral therapeutic target