Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases
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Mozart-Rezeption in China vom frühen 20. Jahrhundert bis 1949
Full text linkDie vorliegende Dissertation untersucht die Rezeption Mozarts in China vom Beginn des 20. Jahrhunderts bis 1949 auf der Grundlage einer empirischen Analyse. Zunächst werden statistische Erhebungen aus vier Datenbanken ausgewertet, um die quantitativen Entwicklungen nachzuzeichnen. Auf dieser Basis wird der Forschungszeitraum in drei Phasen unterteilt: die Anfänge bis 1936, die Zeit des Zweiten Sino-Japanischen Krieges (1937–1946) und die Phase des Chinesischen Bürgerkriegs (1946–1949).
Die Rezeptionsgeschichte Mozarts in China setzte mit der Präsenz westlicher Einwanderer in der ausländischen Konzessionen Shanghais. Mit der Zeit verlagerte sich die Vermittlung von europäischen Musikern hin zu chinesischen Künstlern und Intellektuellen. Während des Zweiten Sino-Japanischen Krieges erfolgte eine geografische Expansion von den östlichen Metropolen in westliche Binnenstädte wie Chongqing. Die Rezeption fand schließlich mit dem Rückzug ausländischer Einwanderer während des Chinesischen Bürgerkriegs und der Gründung der Volksrepublik China ihr Ende.
Ein besonderer Fokus liegt auf den Institutionen und Persönlichkeiten, die in China zur Verbreitung von Mozarts Musik beitrugen, sowie auf der Rezeption innerhalb verschiedener sozialer Schichten. Wichtige Akteure waren das Shanghai Municipal Orchestra, die Nationale Musikhochschule Shanghai sowie christliche Schulen und Hochschulen. Zudem spielten jüdische und russische Emigranten sowie die japanische Besatzungszeit eine besondere Rolle.
Die politische Instabilität der Epoche spiegelte sich auch in der Wahrnehmung Mozarts wider. In einer Zeit, in der Nationalismus in China eine zentrale Rolle spielte, wurde Mozart nicht nur als musikalisches Genie, sondern auch als nationaler Patriot interpretiert. Während des Zweiten Weltkriegs führte der Zustrom jüdischer Flüchtlinge und die antifaschistische Haltung der Alliierten dazu, dass Mozarts Musik als Ausdruck von Hoffnung und Widerstand gegen das NS-Regime wahrgenommen wurde.
Diese Arbeit leistet einen Beitrag zur Erforschung globaler Rezeptionsprozesse westlicher Kunstmusik und bietet neue Perspektiven auf die Verflechtungen von Musik, Politik und kultureller Identität in China während der ersten Hälfte des 20. Jahrhunderts
Practices of personalized treatment selection among German psychotherapists: A preregistered mixed methods study
No full textA numerical model for aeolian sand transport and the concatenated dust emission
Full text linkDust is emitted, transported and deposited throughout the year, mainly from the vast sand seas on Earth affecting the weather, climate ecosystems and other cycles of the biosphere. It influences the various feedback mechanisms, and causes the largest uncertainties in the future climate projections. A particle-based 3D numerical model to study the dust aerosol emission is presented in this work. The model was validated using the well characterized sand saltation process which is one of the major mechanisms through which dust is entrained.
Using a scalable Discrete Element Method (DEM) model, which is coupled with the fluid dynamics of the turbulent wind, we confirm the existence of a quadratic scaling for the sand mass flux with the wind friction velocity. The impact threshold (minimal wind velocity for sustained transport) and fluid threshold (minimal wind velocity for grain entrainment) values for 200 micrometer sand grains which are key parameters in examining the grain initiation were found. Previous numerical studies never considered the sparsely-covered soils, and thus we developed a scheme to characterize this problem of low-sand availability. Thus we observe a transition from the quadratic scaling in the conventional erodible cases, to a cubic scaling over rigid surfaces. The universality of the model was also tested for
varying fluid conditions, due to the comprehensive depiction of the viscous layer close to the bed surface.
To simulate mixed sand-dust systems, the model was extended by including the crucial aspect of cohesion, which is modeled using the van der Waals interaction. The rolling resistance, lift force, as well as the stochastic turbulent fluctuations provided the means to verify the grain-size dependency in monodisperse systems on shear velocity which reaffirms the [Shao and Lu 2000] equation. The stochastic nature of cohesion [Shao and Klose 2016] further lowers the threshold values in the presence of turbulent fluctuations, thus stressing on the need to include it in future models. The direct numerical simulations for the first time allow to study the dust emission mechanisms - direct entrainment, saltation bombardment and aggregate disintegration at the micro-scale, as the grain clusters which form and break is captured. In bi-disperse sand-dust beds (10 micrometer dust grains dispersed over 200 micrometer sand grains), under limited supply of dust, we observe the lowering of fluid thresholds as a result of direct aerodynamic entrainment at nominal wind speeds below the saltation threshold. This is due to the fact that, the dust grains, unlike in a monodiperse system are exposed to higher winds because of the roughness elements (sand grains). We observe a quartic scaling for the vertical dust flux with shear velocity, with the scaling exponent having direct implications on the empirical relations in climate models. Finally, we conclude that in size regimes of 5 and 2.5 micrometer grains, they are not easily entrained due to cohesion, but dust is usually present as either mostly coated on sand, or as dust clusters, thus saltation bombardment and cluster disintegration to a certain extent are the dominating
mechanisms for emission as predicted before
Der Einfluss der lokalen Ausbreitung der Stimulation bei Parkinson-Patienten nach Tiefer Hirnstimulation auf die nicht-motorischen Symptome
Full text linkDie Parkinson-Krankheit ist die zweithäufigste neurodegenerative Erkrankung weltweit. Ein kurativer Therapieansatz existiert bis heute nicht. In erster Linie erhalten Patienten eine medikamentöse Therapie mit Levodopa, um den Dopaminspiegel im zentralen Nervensystem zu erhöhen und den durch Degeneration von dopaminergen Zellen bedingten Mangel auszugleichen. Auch andere Medikamente, die Symptome wie Rigor, Tremor und Akinese sowie Begleitsymptome lindern sollen, sind zugelassen. Im fortgeschrittenen Stadium kann die rein medikamentöse Therapie jedoch nicht mehr ausreichen. Es kommt in der Regel zu Wirkfluktuationen, zunehmenden Nebenwirkungen und durch den Fortschritt der Erkrankung zu nicht beherrschbaren Symptomen. In diesem Stadium ist für viele Patienten die Tiefe Hirnstimulation, insbesondere des Nucleus subthalamicus, eine gut etablierte Behandlungsoption. Über stereotaktisch implantierte Elektroden werden kontinuierlich elektrische Impulse abgegeben, welche die Aktivität der Neurone bei Parkinsonpatienten beeinflussen und insbesondere die motorischen Symptome regulieren sollen. Neben der positiven Auswirkung auf die motorischen Symptome beeinflusst die Tiefe Hirnstimulation jedoch auch die Lebensqualität, den Schlaf sowie die Stimmung der Patienten. Der Einfluss auf diese nicht-motorischen Symptome variiert jedoch stark zwischen den einzelnen Patienten und der Wirkmechanismus ist bis heute noch nicht eindeutig geklärt. In dieser Arbeit und der zugehörigen Publikation wurde der Einfluss der genauen lokalen Ausbreitung der elektrischen Stimulation auf die nicht-motorischen Symptome bei Parkinsonpatienten anhand einer prospektiven, offenen, multizentrischen und internationalen Studie untersucht. Präoperativ sowie sechs Monate nach aktiver Tiefer Hirnstimulation wurden die 91 eingeschlossenen Patienten zu ihren nicht-motorischen Symptomen befragt. Hierfür wurden etablierte Fragebögen, beispielsweise der NMSS sowie der PDQ-8, genutzt. Für jeden Patienten wurde die genaue Position seiner Stimulationselektroden in der Lead-DBS-Toolbox lokalisiert und das durch die Stimulation aktivierte Volumen berechnet. Die Ergebnisse zeigten, dass sich insbesondere der NMSS-Gesamtscore sowie die Domänen Stimmung/Apathie, Aufmerksamkeit und Gedächtnis nach der Stimulation signifikant verbesserten. Für die Domäne Stimmung/Apathie lagen die aktivierten Voxel vor allem im ventralen Randbereich des Nucleus subthalamicus, für Aufmerksamkeit/Gedächtnis in der assoziativen Unterregion. Der Ort der Neurostimulation beeinflusst also die verschiedenen nicht-motorischen Domänen unterschiedlich stark und scheint für die interindividuelle Variabilität der Ergebnisse nach Tiefer Hirnstimulation mit zu beeinflussen. Somit tragen die Ergebnisse einen Schritt dazu bei, dass das chirurgische Ziel patientenindividuell festgelegt werden muss, bzw. direktionale Stimulationstechniken effektiver genutzt werden können
Artificial Intelligence and Positron Emission Tomography for the Timely Diagnosis and Prognosis of Alzheimer’s Disease
Full text linkAlzheimer’s disease (AD) is the most common neurodegenerative disorder worldwide.
It is biologically characterized by the accumulation of protein pathology (β-amyloid plaques and tau tangles) and neurodegeneration. For the first time since its first description by Alois Alzheimer more than 100 years ago, effective anti-amyloid therapies are emerging that might slow down disease progression. To initiate treatment early-on, timely diagnosis and prognosis are crucial. Neuropathologic changes in AD begin up to two decades prior to clinical symptoms of dementia, making biomarkers of brain metabolism and protein accumulation promising indicators for timely identification. Positron emission tomography (PET) can depict these processes in vivo; however, the detection of initial and subtle pathology levels in PET scans is
challenging using established methods. Artificial intelligence (AI) offers a set of methods capable of identifying complex patterns within PET data, possibly allowing for more nuanced and personalized approaches. The aim of this dissertation was to investigate the potential of AI models applied to PET data to generate clinically
relevant markers of disease progression for the timely diagnosis or prognosis of AD.
Using AI, various types of AD progression markers were generated and analyzed, predominantly by applying AI to [18F]FDG PET scans, a marker of brain metabolism. In the first study, we observed that AI models can extract reliable information on β-amyloid from [18F]FDG PET and demographic data in individuals with low genetic AD risk (negative APOE-ϵ4 status). The potential of AI for such cross-modal
translation can facilitate the workflow for timely diagnoses by reducing the need for multiple PET assessments. The second study demonstrated that [18F]FDG PETderived brain age gaps (BAGs; difference between age and AI-derived brain age)
associated with early cognitive dysfunction, but not AD progression. Conversely, MRI-derived BAGs systematically reflected markers of cognitive performance and protein pathology, and predicted future clinical progression, thus representing a global summary score of deviation from healthy brain aging. Regional deviation from healthy brain aging was investigated in a multi-modal PET study, where we
showed that the spatial extent of all three hallmark pathologies uniquely reflects AD progression. Thus, spatial extent markers, which we demonstrated can be estimated
using deep learning, might be useful for monitoring AD. Finally, after the second study indicated potential for the prediction of clinical progression, we aimed to assess the potential of AI in predicting biological disease progression. Whole-brain [18F]FDG PET scans could be predicted for up to seven years after initial assessment,
allowing to preemptively analyze an individual’s future brain metabolic decline.
This dissertation highlighted that AI can yield different types of information from PET scans for timely diagnoses via cross-modal translation and estimation of deviation from healthy aging. In the context of prognoses, AI models can predict clinical and biological disease progression. AI-generated information from PET scans can thus enrich diagnostic and prognostic workflows with various patient data that may not otherwise be available to clinicians. Importantly, different markers could be generated based on broadly available [18F]FDG PET, thereby contributing to making diagnostic workflows more efficient and patient-friendly. Future studies should assess the impact of implementing such models in clinical practice. Collaborative efforts between clinicians and AI researchers could ultimately enable treatment initiation before patients experience debilitating cognitive decline from AD
Learning Spatiotemporal Representations of C. elegans From Bright-Field Microscopy Data Using Deep Learning Methods
Full text linkRecent biomedical research has led to a deep level of understanding of biological mecha-
nisms we never reached before, enabling us to develop novel tests and cures for disease and
improve our lives. However, there are still many disease and mechanisms to be researched
and fully understood. One important element of this research are experiments with model
organisms.
Model organisms play a crucial role in biomedical research and thanks to their wide spread
use in science, we understand these organisms in a level of detail that was not reached for
any other organism yet. Researchers use model organisms in their experiments to study
disease like Alzheimer’s and cancer, to understand aging and sleep and its underlying
biomedical mechanisms. One model organism is the small roundworm Caenorhabditis
elegans (C. elegans). Proposed as model organism by Sidney Brenner in the 1960s it
quickly became a highly researched organism. The transparent body allows effortless
observations of in vivo organs and inner processes, especially when applying stains that at-
tach to specific biomolecules, highlighting them for improved observations. Using modern
technologies, scientists can introduce all kinds of genetic mutations into an organism e.g.
to understand the influence and interplay of specific genes in their experiments. Their
findings do not only help to understand C. elegans but bring insights in human biology.
Additionally, compared to other organisms C. elegans are easy to breed and cultivate
under laboratory conditions making it a cheap and practical model organism. These and
other factors result in C. elegans popularity in research and wide use in experiments.
One of the main parts of the experiments conducted with C. elegans is quantifying its
behavior. As behavior is an output of the organism’s neural network, it gives scientists
valuable insights and helps them to understand the effects of their experiments. Together
with the aforementioned benefits, behavior quantification of C. elegans enables broad
possibilities for analysis and research. Unfortunately, traditional quantification of behavior
is done by hand during time consuming observations of the nematodes under a microscope.
Therefore, there is the need to automate this process with the promise to speed-up
experiments, allowing scientists to spend more time on other tasks like interpretation of
the results, obtained by the automated analysis, and conducting more experiments.
Recently, Machine Learning (ML) and Deep Learning (DL) methods specialized on
C. elegans have been proposed for tasks like detection, segmentation, tracking, pose
estimation and behavior quantization. At the same time, more and more high-resolution
recordings of C. elegans become available, thanks to the increased level of automatization
in science. Although recent methods are implementing automation in this domain with
increasing success, state-of-the-art approaches struggle when it comes to more challenging
poses of C. elegans like coiling and self-intersecting or complex behavior like mating and
aggregation. Additionally, many state-of-the-art approaches rely on hand-engineered
features, omitting one of DLs strongest abilities: to find robust, discriminating, and
possibly previously unknown features, suitable for the task. Based on this we see great
potential in additional research into behavior quantization using DL, to find solutions for
the aforementioned challenges and we aim to tackle them with this work.
Here, we present our work, focusing on closing the gap between high-resolution behavior
recording and time consuming and incomplete behavior quantification due to inaccessible
poses and behavior of C. elegans. We present our novel instance segmentation approach,
trained on synthetic data for segmentation of C. elegans in challenging scenes. We test
our method on video data including C. elegans with coiling and heavy bending poses,
as well as multiple individuals moving closely in parallel or overlapping each other. Ad-
ditionally, we designed a tracking algorithm to present the abilities of our contribution.
Our approach is capable of segmenting C. elegans in video frames depicting multiple
individuals in challenging scenarios where previous methods failed to retrieve correct
segmentation information. Our contribution allows for more detailed information required
in downstream tasks and therefore enables more precise quantification and studies of the
phenotype.
Next, we present our self-supervised representation learning method for behavior sequences.
By now, we focused on the spatial level of behavior by segmenting individual C. elegans
in image data to extract pose information on a pixel level. In this work, we include
the temporal component of behavior by learning how a pose changes in time using
video recordings of C. elegans. First, we train a contrastive learning network to embed
pose information without relying on curve or keypoint estimations. Second, we use the
pre-trained contrastive learning network to learn representations of behavior sequences.
We demonstrate the abilities of our approach by visualizing the embedding space and
coloring it using hand-engineered features computed by state-of-the-art methods. These
visualizations reveal that our network is able to capture hand-engineered features without
explicitly enforcing them during training. Thanks to the absence of explicit features, our
new approach is not limited to these but is rather able to capture properties previously
inaccessible. Additionally, as our method is self-supervised, it does not require pose or
behavior annotations and can directly be applied on videos of C. elegans, bridging the gap
between fast data acquisition and slow data labeling. Combining both approaches allows
to surpass the limitations of previous state-of-the-art methods and enables quantization
of challenging behavior that other methods left unsolved or only partially solved
Modeling techniques and statistical inference for multidimensional effects
Full text linkThe increasing complexity of biostatistical research questions requires statistical methods that can effectively address multidimensional problems.This thesis addresses issues arising from multidimensionality in statistical testing and modeling, with a focus on model-based equivalence tests and hazard regression models. Specifically, it examines three directions of multidimensionality: multivariate outcomes, model uncertainty, and multidimensional covariate effects. Four contributions discuss the necessity of adapting model-based equivalence tests and hazard regression models to account for these three aspects of multidimensionality.
The first contribution extends model-based equivalence tests to multivariate, potentially mixed-scale outcomes using generalized joint regression models. This approach overcomes the limitations of a previous approach that is only capable of bivariate binary outcomes and relies on the intersection-union principle leading to an overly conservative test, particularly for small sample sizes. In contrast, a new maximum of maxima approach is used to increase the power in finite samples while maintaining asymptotic validity.
The second contribution addresses model uncertainty, a common issue in applied research where often the true model is unknown. By incorporating model averaging to model-based equivalence tests and deploying a confidence interval-based testing approach, the proposed method offers a robust and numerically feasible alternative that retains the asymptotic properties.
The third and fourth contributions shift the focus to multidimensional covariate effects. In the third article, functional random coefficients are introduced to model heterogeneously time-varying covariate effects. Such coefficients are not only capable of time-varying and subgroup-specific covariate effects but also of covariate effects in which the time-variation itself is heterogeneous. The functional random coefficients are constructed as tensor product interactions of heterogeneity and time. While the third contribution introduces these effects to generalized additive models, the fourth article discusses their applicability in survival analysis by incorporating such effects into hazard regression models.
The methods are evaluated through simulations outlining their flexibility while either retaining the asymptotic properties of the model-based equivalence test or the prevention of overfitting of the regression models. The practical relevance of the proposed methods is demonstrated using case studies from pharmacology, toxicology, and oncology. This thesis thus contributes novel approaches that enhance the flexibility and applicability of statistical methods in multidimensional biostatistical research
Die funktionelle Rolle von Interleukin-6 in der akuten renalen Schädigung der neugeborenen Maus nach mechanischer Beatmung
Full text linkFrühgeborene sind aufgrund ihrer Lungenunreife häufig auf mechanische Beatmung angewiesen. Während diese Intervention lebensrettend sein kann, verursacht sie gleichzeitig jedoch nicht nur pulmonale Schäden, sondern geht u. a. auch mit einem erhöhten Risiko für eine akute Nierenschädigung einher. Die zugrundeliegenden Mechanismen einer beatmungsbedingten Nierenschädigung sind bisher allerdings weitgehend ungeklärt. Daher wurde in diesem Projekt der akute strukturelle und molekulare Effekt von postnataler mechanischer Beatmung am Mausmodell untersucht.
Da unsere Arbeitsgruppe zuvor bereits zeigen konnte, dass die Aktivierung des Interleukin (IL)-6/STAT3-Signalwegs maßgeblich an
der Entstehung einer postnatalen Hyperoxie-induzierten Nierenschädigung beteiligt ist, lag unser Untersuchungsfokus auf der Klärung der Rolle von IL-6 als möglichem Schlüsselregulator für ein akutes renales Schädigungsgeschehen.
Dazu wurden Wildtyp-Mäuse (Typ C57BL6N10–12) und homozygote Il6-Knockout-Mäuse (TypB6.129S2IL-6tm1Kopf/J10,13) am fünften Lebenstag vier bzw. acht Stunden lang mechanisch beatmet (unbeatmete Tiere aus beiden Gruppen dienten als Kontrollen). Anschließend wurden die Tiere zwecks Organentnahme euthanasiert und die Nieren für molekularbiologische Untersuchungen schock gefroren oder für histomorphometrische Analysen in Paraffin eingebettet.
Die Ergebnisse der Gen- und Proteinexpressionsmessungen zeigten post ventilationem eine signifikante renale Aktivierung der IL-6/STAT3-Signalkaskade, sowie eine deutliche Expressionssteigerung der ebenfalls proinflammatorischen Mediatoren Il1b (inklusive seiner downstream Effektoren) und Mcp1. Dieser ausgeprägte Entzündungsprozess war mit einem Expressionsanstieg von Ppargc1a und Sirt1 (mitochondriale Marker), Cdkn1a (Seneszenzmarker), sowie Serpine1 und Ctgf (Fibrosemarker) assoziiert. Zudem konnte eine auffällige Veränderung der mRNA-Expression podozytärer Marker festgestellt werden.
Histomorphologisch zeigte sich in den beatmeten Tieren außerdem eine Vergrößerung der nephrogenen Zone im Verhältnis zur Kortexgesamtfläche. Der Il6-Knockout konnte die beschriebenen Beatmungseffekte zum Teil verhindern oder zumindest attenuieren.
Somit konnte gezeigt werden, dass eine postnatale mechanische Beatmung im neonatalen Nierengewebe eine IL-6-vermittelte Entzündungsreaktion anstößt und damit störend in die zelluläre und mitochondriale Homöostase eingreift. Die frühe Hochregulation profibrotischer Mediatoren deutet dabei auf eine über die Akutphase hinausgehende, langfristige Schadensinduktion hin. Entsprechend ergibt sich das Potenzial für die Entwicklung präventivtherapeutischer Interventionen, wobei die weiterführende Erforschung eines anti-IL-6-basierten Ansatzes vielversprechend erscheint
The Importance of Being Unoriginal: John Picard of Lichtenberg and his Quaestiones
Full text linkThis doctoral research investigates the intellectual contributions of John Picard of Lichtenberg, a key figure in the Dominican reception of Thomas Aquinas’ ideas in Germany. The study focuses on his Quaestiones, a pioneering work that represents the earliest example of debates on Thomas’ controversial theories within the German intellectual landscape, particularly at the studium generale of Cologne. In contrast to German Dominican figures like Dietrich of Freiberg and Meister Eckhart, who often opposed Thomas, Picard is seen as a defender of his theses. The thesis provides a biographical account of Picard’s career, highlighting his progression from lector in Cologne to magister theologiae in Paris. The study also addresses textual issues related to the transmission of the Quaestiones, preserved in different form in manuscripts from the Vatican, Erfurt, and Kraków. Analyzing the manuscripts reveals that Picard’s work does not fit neatly into a single literary genre, reflecting evolving teaching practices in the German province. Picard’s Quaestiones demonstrates a nuanced understanding of Thomas’ evolving positions, offering a “historical” perspective rather than striving for coherence within a doctrinal system. Two case studies are examined in detail: one on the difference between natura and suppositum and another on the concept of dimensiones interminatae and the relationship between accidents and substantial forms. The fourth chapter re-evaluates the historiographical label “early Thomism,” proposing that it should refer to how Thomas’ texts were used, quoted, and defended rather than denoting a fixed doctrinal system. Ultimately, the study underscores the importance of textual transmission for understanding medieval scholastic dialogue. Picard’s textual connections with other authors of his time is framed as valuable for tracing intellectual networks between Paris, Oxford, and Cologne. This doctoral work culminates in a critical edition of Picard’s first 19 questions as part of the Corpus philosophorum Teutonicorum Medii Aevi project