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Investigation of the kinetics and intermediates of pyridyl and pyrimidyl substrates during the Soai reaction and Asymmetric alkylation of photoswitchable systems or by β-amino alcohol mediated catalysis
Get PDFReacting to paradoxes in organizations
Get PDFMy thesis presents three empirical studies, which address one basic research question: How can followers and leaders react to paradoxes in organizations, so that they promote positive outcomes and mitigate negative ones? Paradoxes (i.e., competing, yet interrelated elements) are inherent in organizations and can lead to positive outcomes such as innovation and sustainable work performance, but also to negative outcomes such as experiences of tension and organizational decline. Theory on paradoxes emphasizes that how individuals experience and react to paradoxes determines whether outcomes will be positive or negative. Nonetheless, individual experiences of and reactions to paradoxes were rarely studied empirically. I argue in my dissertation, that followers and leaders can react constructively to paradoxes by thinking complexly and behaving complexly, which should promote positive outcomes (e.g., followers’ performance) and mitigate negative ones (e.g., followers’ experienced tension).
In three empirical studies I investigated constructive individual reactions in two regards: a) complex thinking to make sense of paradoxes, as characterized by differentiation and integration and as influenced by cultural factors, and b) complex behavior leaders exhibit to meet paradoxical demands (i.e., paradoxical leader behavior). Study 1 is an online experiment investigating whether individuals experience paradoxical task demands negatively. Results showed that individuals working on paradoxical task demands experience tension. They were able to reduce their experienced tension by thinking complexly about the paradoxical demands and, if they did so, they also reported less negative affect and less stress. Study 2 is a field study investigating whether paradoxical leader behavior is associated with lower negative experiences of followers. Results showed that this was the case, and the negative association was even stronger when followers made sense of paradoxical leader behavior by thinking complexly about it. Study 3 is a meta-analysis investigating how paradoxical leader behavior is related to a variety of follower outcomes. Results showed that paradoxical leader behavior is positively related to followers’ performance (i.e., task performance, organizational citizenship behavior, creative and innovative performance) and well-being (i.e., hedonic and eudaimonic well-being). A non-significant relationship to followers’ negative well-being was moderated by cultural influences on how complexly individuals think about paradoxes.
Taken together, the findings support the central assumptions of paradox research that the positive and negative outcomes of organizational paradoxes depend on both the way followers think and the way leaders behave in response to paradoxes. Finally, I discuss theoretical implications, limitations, as well as practical implications, for example, how followers and leaders can treat paradoxes constructively in order to promote positive outcomes and mitigate negative ones
Integrating deep learning and genetic approaches to uncover molecular mechanisms of cellular organization
Get PDFOrganisation is a fundamental principle of life. Matter needs to be arranged in space in such a way that it enables reproduction. In biology, this organisation occurs at different scales: from whole ecosystems to multicellular organisms and their tissues, to individual cells and subcellular compartments with defined functions.
The blueprint for the spatial organisation of individual organisms is outlined in their genomes. Understanding these blueprints to define what differentiates individual organisms from one another is a fundamental task in biology. Since an organism's identity is defined by its genome, relating its structural composition directly to its genome provides deeper biological insights into how life is organised. In cell biology, we focus on understanding these relationships from the level of individual cells to tissues.
One biochemical method to analyse the spatial composition of individual cells builds on subcellular fractionation. In this approach cells are split into their distinct compartments, for example by sequential centrifugation steps. The composition of each compartment can then be investigated separately. By coupling cellular fractionation to mass spectrometry (MS), this in principle allows for the unbiased identification of the subcellular localisation of all components in a cell. This technique can further be combined with perturbing a cell’s genome to directly link specific genes to their effect on subcellular composition. I demonstrated the strengths of this approach in my characterisation of the molecular mechanisms underlying activation of the immune sensor NLRP3. Using subcellular fractionation coupled to mass spectrometry, we identified the recruitment of NLRP3 to the trans-Golgi network as a key mechanism governing inflammasome activation.
While this approach can generate deep biological insights, it is restricted to a comparatively low number of genes that can be investigated and provides limited spatial resolution. Light microscopy delivers much higher spatial resolution while also allowing for high-throughput analysis of composition and architecture of millions of cells. However, gaining biological insights from microscopy images is not trivial. In recent years, a new method has emerged from computer vision research that uses machine learning powered by deep neural networks to identify and compress complex patterns into a representative feature space. This approach, called deep learning, shows promise for extracting meaningful biological information from microscopy data.
Another technology that allows for the investigation of spatial composition at the level of tissues is deep visual proteomics (DVP). In DVP, we use microscopy images to identify cells within the larger spatial context of tissues and analyse them further using mass spectrometry. This allows us to collect unbiased information on the molecular composition of these cells while preserving spatial information. By increasing MS sensitivity, we can even break this down to investigate the molecular composition of single cells. Using this approach, I was able to delineate key markers defining hepatocyte zonation in the liver.
Taking it a step further, I used deep learning models to unbiasedly phenotype hepatocytes on the basis of their subcellular distribution of alpha-1 antitrypsin (AAT) in the fibrogenic liver disease AAT deficiency (AATD), which is characterised by the misfolding and accumulation of AAT. Combining this deep learning-driven phenotyping of cellular morphology with DVP, resulted in the identification of a terminal hepatocyte state marked by globular protein aggregates with a distinct proteomic signature, that holds promise for understanding and ultimately counteracting the molecular mechanisms underlying AATD disease progression.
The above-described approaches are observational, linking distinct cellular compositions assayed using microscopy and MS to their functional implications. However, the high throughput facilitated by modern microscopes allows for the assessment of various aspects of cellular composition over millions of cells, which is compatible with a perturbational approach that looks at the effect of all coding genes on specific subcellular phenotypes.
To enable this type of analysis, I developed spatially resolved CRISPR screening (SPARCS). SPARCS uses automated high-speed laser microdissection to physically isolate phenotypic variants in situ for subsequent genotyping. This enables robust, genome-wide, high-throughput screening for spatial cellular phenotypes. Using SPARCS, I was able to identify most known regulators of the cellular process of macroautophagy in a single experiment, and even identified a gene with a previously undescribed cellular phenotype. SPARCS opens up a new paradigm for investigating the genetic basis of subcellular phenotypes that can be applied to a variety of biological contexts.
Finally, to facilitate the types of spatial analysis performed throughout this thesis, I developed a software platform called scPortrait that generates single-cell images from raw microscopy data. These single-cell images can be used for deep learning-based cell phenotyping, as demonstrated throughout this thesis, but also for the development of new deep learning models that generate even deeper biological insights. Completely open source and building on available open data formats, scPortrait is maximally compatible and provides a framework for the routine implementation of deep learning-based investigation of cellular composition across various areas of biology
Das Reale des Berührens
Get PDFDie Dissertation untersucht das Berühren als Figur des Sexuellen im Spannungsfeld von Psychoanalyse, Dekonstruktion und feministischer Theorie. Ausgehend von Lacans Axiom, dass es keinen Geschlechtsverkehr gibt (il n’y a pas de rapport sexuel), wird das Sexuelle als ontologische Negativität und als das Reale gefasst, das sich jeder symbolischen Repräsentation entzieht. In der Auseinandersetzung mit Luce Irigarays Kritik am phallogozentrischen Diskurs und ihrer Relektüre des Berührens als Ort einer anderen Differenz wird die Frage nach einer Ästhetik des Sexuellen als Ethik des Begehrens formuliert. Anhand textnaher Lektüren bei Proust, Balzac und Barbey d’Aurevilly wird gezeigt, wie Literatur die Unmöglichkeit eines Schreibens des Sexuellen ausstellt und zugleich die Bewegung eines écrire à partir d(eux) entfaltet. Es ist ein Schreiben aus der Differenz. Literatur erscheint so als Ort der „Ent-Schreibung“ des Sexuellen, an dem das Reale der Berührung, das Unsagbare und die Leere, um die sich Sprache organisiert, sichtbar werden
Phänotypische Charakterisierung von Patienten mit schwerer chronisch obstruktiver Lungenerkrankung (COPD) und Identifizierung von behandelbaren Eigenschaften
Get PDFImproved media formulations for primary cell cultures of Botryllus schlosseri and a morphological characterization case study of thraustochytrids associated with Botryllus primary cell cultures
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Interaction of sulfur mustard and other alkylating compounds with human transient receptor potential (TRPA1) channels
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Stadiengerechte Diagnostik und Behandlung von Makulopathien aus dem pachychoroidalen Erkrankungsspektrum
Get PDFDas pachychoroidale Erkrankungsspektrum umfasst eine Reihe von Erkrankungen des Auges, bei der es durch eine Überlastung des venösen Schenkels der choroidalen Durchblutung zu Stauungsphänomenen in der Aderhaut kommt. Die Erkrankungsstadien reichen von einer Schädigung des retinalen Pigmentepithels über subretinale Flüssigkeitsansammlungen bis hin zu Neovaskularisationen und führen zu einer Verschlechterung des retinalen Funktion und damit des Sehens. Im Rahmen dieser Habilitation wurden Diagnostik und Behandlung des pachychoroidalen Erkrankungsspektrums untersucht