Refine
Year of publication
Document Type
- Doctoral Thesis (6459) (remove)
Language
Is part of the Bibliography
- yes (6459) (remove)
Keywords
- climate change (53)
- Klimawandel (52)
- Modellierung (35)
- Nanopartikel (26)
- machine learning (23)
- Fernerkundung (20)
- Deutschland (19)
- Spracherwerb (19)
- Synchronisation (19)
- Arabidopsis thaliana (18)
Institute
- Institut für Biochemie und Biologie (1036)
- Institut für Physik und Astronomie (776)
- Institut für Chemie (670)
- Institut für Geowissenschaften (503)
- Wirtschaftswissenschaften (403)
- Institut für Ernährungswissenschaft (277)
- Öffentliches Recht (255)
- Bürgerliches Recht (221)
- Historisches Institut (216)
- Institut für Informatik und Computational Science (203)
Prediction is often regarded as a central and domain-general aspect of cognition. This proposal extends to language, where predictive processing might enable the comprehension of rapidly unfolding input by anticipating upcoming words or their semantic features. To make these predictions, the brain needs to form a representation of the predictive patterns in the environment. Predictive processing theories suggest a continuous learning process that is driven by prediction errors, but much is still to be learned about this mechanism in language comprehension. This thesis therefore combined three electroencephalography (EEG) experiments to explore the relationship between prediction and implicit learning at the level of meaning.
Results from Study 1 support the assumption that the brain constantly infers und updates probabilistic representations of the semantic context, potentially across multiple levels of complexity. N400 and P600 brain potentials could be predicted by semantic surprise based on a probabilistic estimate of previous exposure and a more complex probability representation, respectively.
Subsequent work investigated the influence of prediction errors on the update of semantic predictions during sentence comprehension. In line with error-based learning, unexpected sentence continuations in Study 2 ¬– characterized by large N400 amplitudes ¬– were associated with increased implicit memory compared to expected continuations. Further, Study 3 indicates that prediction errors not only strengthen the representation of the unexpected word, but also update specific predictions made from the respective sentence context. The study additionally provides initial evidence that the amount of unpredicted information as reflected in N400 amplitudes drives this update of predictions, irrespective of the strength of the original incorrect prediction.
Together, these results support a central assumption of predictive processing theories: A probabilistic predictive representation at the level of meaning that is updated by prediction errors. They further propose the N400 ERP component as a possible learning signal. The results also emphasize the need for further research regarding the role of the late positive ERP components in error-based learning. The continuous error-based adaptation described in this thesis allows the brain to improve its predictive representation with the aim to make better predictions in the future.
Data preparation stands as a cornerstone in the landscape of data science workflows, commanding a significant portion—approximately 80%—of a data scientist's time. The extensive time consumption in data preparation is primarily attributed to the intricate challenge faced by data scientists in devising tailored solutions for downstream tasks. This complexity is further magnified by the inadequate availability of metadata, the often ad-hoc nature of preparation tasks, and the necessity for data scientists to grapple with a diverse range of sophisticated tools, each presenting its unique intricacies and demands for proficiency.
Previous research in data management has traditionally concentrated on preparing the content within columns and rows of a relational table, addressing tasks, such as string disambiguation, date standardization, or numeric value normalization, commonly referred to as data cleaning. This focus assumes a perfectly structured input table. Consequently, the mentioned data cleaning tasks can be effectively applied only after the table has been successfully loaded into the respective data cleaning environment, typically in the later stages of the data processing pipeline.
While current data cleaning tools are well-suited for relational tables, extensive data repositories frequently contain data stored in plain text files, such as CSV files, due to their adaptable standard. Consequently, these files often exhibit tables with a flexible layout of rows and columns, lacking a relational structure. This flexibility often results in data being distributed across cells in arbitrary positions, typically guided by user-specified formatting guidelines.
Effectively extracting and leveraging these tables in subsequent processing stages necessitates accurate parsing. This thesis emphasizes what we define as the “structure” of a data file—the fundamental characters within a file essential for parsing and comprehending its content. Concentrating on the initial stages of the data preprocessing pipeline, this thesis addresses two crucial aspects: comprehending the structural layout of a table within a raw data file and automatically identifying and rectifying any structural issues that might hinder its parsing. Although these issues may not directly impact the table's content, they pose significant challenges in parsing the table within the file.
Our initial contribution comprises an extensive survey of commercially available data preparation tools. This survey thoroughly examines their distinct features, the lacking features, and the necessity for preliminary data processing despite these tools. The primary goal is to elucidate the current state-of-the-art in data preparation systems while identifying areas for enhancement. Furthermore, the survey explores the encountered challenges in data preprocessing, emphasizing opportunities for future research and improvement.
Next, we propose a novel data preparation pipeline designed for detecting and correcting structural errors. The aim of this pipeline is to assist users at the initial preprocessing stage by ensuring the correct loading of their data into their preferred systems. Our approach begins by introducing SURAGH, an unsupervised system that utilizes a pattern-based method to identify dominant patterns within a file, independent of external information, such as data types, row structures, or schemata. By identifying deviations from the dominant pattern, it detects ill-formed rows. Subsequently, our structure correction system, TASHEEH, gathers the identified ill-formed rows along with dominant patterns and employs a novel pattern transformation algebra to automatically rectify errors. Our pipeline serves as an end-to-end solution, transforming a structurally broken CSV file into a well-formatted one, usually suitable for seamless loading.
Finally, we introduce MORPHER, a user-friendly GUI integrating the functionalities of both SURAGH and TASHEEH. This interface empowers users to access the pipeline's features through visual elements. Our extensive experiments demonstrate the effectiveness of our data preparation systems, requiring no user involvement. Both SURAGH and TASHEEH outperform existing state-of-the-art methods significantly in both precision and recall.
Quantified Self, die pro-aktive Selbstvermessung von Menschen, hat sich in den letzten Jahren von einer Nischenanwendung zu einem Massenphänomen entwickelt. Dabei stehen den Nutzern heute vielfältige technische Unterstützungsmöglichkeiten, beispielsweise in Form von Smartphones, Fitness-Trackern oder Gesundheitsapps zur Verfügung, welche eine annähernd lückenlose Überwachung unterschiedlicher Kontextfaktoren einer individuellen Lebenswirklichkeit erlauben.
In der Folge widmet sich diese Arbeit unter anderem der Fragestellung, inwieweit diese intensive und eigen-initiierte Beschäftigung, insbesondere mit gesundheitsbezogenen Daten, die weitgehend als objektiviert und damit belastbar gelten, die Gesundheitskompetenz derart aktiver Menschen erhöhen kann. Darüber hinaus werden Aspekte untersucht, inwieweit die neuen Technologien in der Lage sind, spezifische medizinische Erkenntnisse zu vertiefen und in der Konsequenz die daraus resultierenden Behandlungsprozesse zu verändern.
Während der Ursprung des Quantified Self im 2. Gesundheitsmarkt liegt, geht die vorliegende Arbeit der Frage nach, welche strukturellen, personellen und prozessualen Anknüpfungspunkte perspektivisch im 1. Gesundheitsmarkt existieren werden, wenn ein potentieller Patient in einer stärker emanzipierten Weise den Wunsch verspürt, oder eine entsprechende Forderung stellt, seine gesammelten Gesundheitsdaten in möglichst umfassender Form in eine medizinische Behandlung zu integrieren.
Dabei werden auf der einen Seite aktuelle Entwicklungen im 2. Gesundheitsmarkt untersucht, die gekennzeichnet sind von einer hohen Dynamik und einer großen Intransparenz. Auf der anderen Seite steht der als stark reguliert und wenig digitalisiert geltende 1. Gesundheitsmarkt mit seinen langen Entwicklungszyklen und ausgeprägten Partikularinteressen der verschiedenen Stakeholder.
In diesem Zuge werden aktuelle Entwicklungen des zugrunde liegenden Rechtsrahmens, speziell im Hinblick auf stärker patientenzentrierte und digitalisierte Normen untersucht, wobei insbesondere das Digitale Versorgung Gesetz eine wichtige Rolle einnimmt.
Ziel der Arbeit ist die stärkere Durchdringung von Wechselwirkungen an der Schnittstelle zwischen den beiden Gesundheitsmärkten in Bezug auf die Verwendung von Technologien der Selbstvermessung, um in der Folge zukünftige Geschäftspotentiale für existierende oder neu in den Markt drängende Dienstleister zu eruieren.
Als zentrale Methodik kommt hier eine Delphi-Studie zum Einsatz, die in einem interprofessionellen Ansatz versucht, ein Zukunftsbild dieser derzeit noch sehr jungen Entwicklungen für das Jahr 2030 aufzuzeigen. Eingebettet werden die Ergebnisse in die Untersuchung einer allgemeinen gesellschaftlichen Akzeptanz der skizzierten Veränderungen.
Due to their sessile lifestyle, plants are constantly exposed to pathogens and possess a multi-layered immune system that prevents infection. The first layer of immunity called pattern-triggered immunity (PTI), enables plants to recognise highly conserved molecules that are present in pathogens, resulting in immunity from non-adaptive pathogens. Adapted pathogens interfere with PTI, however the second layer of plant immunity can recognise these virulence factors resulting in a constant evolutionary battle between plant and pathogen. Xanthomonas campestris pv. vesicatoria (Xcv) is the causal agent of bacterial leaf spot disease in tomato and pepper plants. Like many Gram-negative bacteria, Xcv possesses a type-III secretion system, which it uses to translocate type-III effectors (T3E) into plant cells. Xcv has over 30 T3Es that interfere with the immune response of the host and are important for successful infection. One such effector is the Xanthomonas outer protein M (XopM) that shows no similarity to any other known protein. Characterisation of XopM and its role in virulence was the focus of this work.
While screening a tobacco cDNA library for potential host target proteins, the vesicle-associated membrane protein (VAMP)-associated protein 1-2 like (VAP12) was identified. The interaction between XopM and VAP12 was confirmed in the model species Nicotiana benthamiana and Arabidopsis as well as in tomato, a Xcv host. As plants possess multiple VAP proteins, it was determined that the interaction of XopM and VAP is isoform specific.
It could be confirmed that the major sperm protein (MSP) domain of NtVAP12 is sufficient for binding XopM and that binding can be disrupted by substituting one amino acid (T47) within this domain. Most VAP interactors have at least one FFAT (two phenylalanines [FF] in an acidic tract) related motif, screening the amino acid sequence of XopM showed that XopM has two FFAT-related motifs. Substitution of the second residue of each FFAT motif (Y61/F91) disrupts NtVAP12 binding, suggesting that these motifs cooperatively mediate this interaction. Structural modelling using AlphaFold further confirmed that the unstructured N-terminus of XopM binds NtVAP12 at its MSP domain, which was further confirmed by the generation of truncated XopM variants.
Infection of pepper leaves, with a XopM deficient Xcv strain did not result in a reduction of virulence in comparison to the Xcv wildtype, showing that the function of XopM during infection is redundant. Virus-induced gene silencing of NbVAP12 in N. benthamiana plants also did not affect Xcv virulence, which further indicated that interaction with VAP12 is also non-essential for Xcv virulence. Despite such findings, ectopic expression of wildtype XopM and XopMY61A/F91A in transgenic Arabidopsis seedlings enhanced the growth of a non-pathogenic Pseudomonas syringae pv. tomato (Pst) DC3000 strain. XopM was found to interfere with the PTI response allowing Pst growth independent of its binding to VAP. Furthermore, transiently expressed XopM could suppress reactive oxygen species (ROS; one of the earliest PTI responses) production in N. benthamiana leaves. The FFAT double mutant XopMY61A/F91A as well as the C-terminal truncation variant XopM106-519 could still suppress the ROS response while the N-terminal variant XopM1-105 did not. Suppression of ROS production is therefore independent of VAP binding. In addition, tagging the C-terminal variant of XopM with a nuclear localisation signal (NLS; NLS-XopM106-519) resulted in significantly higher ROS production than the membrane localising XopM106-519 variant, indicating that XopM-induced ROS suppression is localisation dependent.
To further characterise XopM, mass spectrometry techniques were used to identify post-translational modifications (PTM) and potential interaction partners. PTM analysis revealed that XopM contains up to 21 phosphorylation sites, which could influence VAP binding. Furthermore, proteins of the Rab family were identified as potential plant protein interaction partners. Rab proteins serve a multitude of functions including vesicle trafficking and have been previously identified as T3E host targets. Taking this into account, a model of virulence of XopM was proposed, with XopM anchoring itself to VAP proteins to potentially access plasma membrane associated proteins. XopM possibly interferes with vesicle trafficking, which in turn suppresses ROS production through an unknown mechanism.
In this work it was shown that XopM targets VAP proteins. The data collected suggests that this T3E uses VAP12 to anchor itself into the right place to carry out its function. While more work is needed to determine how XopM contributes to virulence of Xcv, this study sheds light onto how adapted pathogens overcome the immune response of their hosts. It is hoped that such knowledge will contribute to the development of crops resistant to Xcv in the future.
The order of destruction
(2024)
This book studies sugarcane monoculture, the dominant form of cultivation in the colonial Caribbean, in the later 1600s and 1700s up to the Haitian Revolution. Researching travel literature, plantation manuals, Georgic poetry, letters, and political proclamations, this book interprets texts by Richard Ligon, Henry Drax, James Grainger, Janet Schaw, and Toussaint Louverture. As the first extended investigation into its topic, this book reads colonial Caribbean monoculture as the conjunction of racial capitalism and agrarian capitalism in the tropics. Its eco-Marxist perspective highlights the dual exploitation of the soil and of enslaved agricultural producers under the plantation regime, thereby extending Marxist analysis to the early colonial Caribbean. By focusing on textual form (in literary and non-literary texts alike), this study discloses the bearing of monoculture on contemporary writers' thoughts. In the process, it emphasizes the significance of a literary tradition that, despite its ideological importance, is frequently neglected in (postcolonial) literary studies and the environmental humanities. Located at a crossroads of disciplines and perspectives, this study will be of interest to literary critics and historians working in the early Americas, to students and scholars of agriculture, colonialism, and (racial) capitalism, to those working in the environmental humanities, and to Marxist academics. It will be of great interest to scholars and researchers of language and literature, post-colonial studies, cultural studies, diaspora studies, and the Global South studies
Efraim Frisch (1873–1942) und Albrecht Mendelssohn Bartholdy (1874–1936) waren im klassischen Zeitalter der Intellektuellen (neben anderem) Zeitschriftenentrepeneure und Gründer der kleinen Zeitschriften Der Neue Merkur (1914–1916/1919–1925) und Europäische Gespräche (1923–1933). Sie stehen (nicht nur mit ihren Zeitschriften) für einen der wiederholt in der Moderne unternommenen Versuche, die in der Aufklärung erschlossenen Ressourcen – demokratischer Republikanismus und universelle und gleiche Rechte für alle Menschen – im Vertrauen auf ihre globale Umsetzbarkeit zu aktivieren. In der Zeit der Weimarer Republik gehörten sie zu den Republikanern, „die Weimar als Symbol ernst nahmen und zäh und mutig bemüht waren, dem Ideal konkreten Inhalt zu verleihen“ (Peter Gay). Ihr bislang unüberliefert gebliebenes Beispiel fügt sich ein in die Demokratiegeschichte der europäischen Moderne, in die Geschichte internationaler Gesellschaftsbeziehungen und die Geschichte der Selbstbehauptung intellektueller Autonomie.
Die zäsurenübergreifend den Zeitraum von 1900 bis ca. 1940 untersuchende Studie ermöglicht wesentliche Einblicke in die Biografien Frischs und Mendelssohn Bartholdys, in die deutsch-französische/europäisch-transatlantische Welt der kleinen (literarisch-politischen) Zeitschriften des frühen 20. Jahrhunderts sowie in das medien-intellektuelle Feld des späten Kaiserreiches und der Weimarer Republik in seiner humanistisch-demokratisch-republikanischen Tendenz. Darüber hinaus beinhaltet sie neue Erkenntnisse zur Geschichte der ‚Heidelberger Vereinigung‘ – der Arbeitsgemeinschaft für eine Politik des Rechts – um Prinz Max von Baden, zur deutschen Friedensdelegation in Versailles 1919 und ihrem Hamburger Nachleben, zum Handbuch der Politik sowie zur ersten amtlichen Aktenpublikation des Auswärtigen Amtes – der Großen Politik der Europäischen Kabinette 1871–1914. Schließlich zu den Bemühungen der ‚Internationalists‘ der 1920er Jahre, eine effektive Ächtung des Angriffskrieges herbeizuführen.
Carbohydrates play a vital role in all living organisms; serving as a cornerstone in primary metabolism through the release of energy from their hydrolysis and subsequent re-utilization (Apriyanto et al., 2022). Starch is the principal carbohydrate reserve in plants, providing essential energy for plant growth. Furthermore, starch serves as a significant carbohydrate source in the human diet. Beyond its nutritional value, starch has extensive industrial application associated with many aspects of human society, such as feed, pharmacy, textiles, and the production of biodegradable plastics. Understanding the mechanisms underlying starch metabolism in plants carries multifaceted benefits. Not only does it contribute to increasing crop yield and refining grain quality, but also can improve the efficiency of industrial applications.
Starch in plants is categorized into two classes based on their location and function: transitory starch and storage starch. Transitory starch is produced in chloroplasts of autotrophic tissues/organs, such as leaves. It is synthesized during the day and degraded during the night. Storage starch is synthesized in heterotrophic tissues/organs, such as endosperm, roots and tubers, which is utilized for plant reproduction and industrial application in human life. Most studies aiming to comprehend starch metabolism of Arabidopsis thaliana primarily focus on transitory starch.
Starch is stored as granular form in chloroplast and amyloplast. The parameters of starch granules, including size, morphology, and quantity per chloroplast serve as indicators of starch metabolism status. However, the understanding of their regulatory mechanism is still incomplete. In this research, I initially employed a simple and adapted method based on laser confocal scanning microscopy (LCSM) to observe size, morphology and quantity of starch granules within chloroplasts in Arabidopsis thaliana in vivo. This method facilitated a rapid and versatile analysis of starch granule parameters across numerous samples. Utilizing this approach, I compared starch granule number per chloroplast between mesophyll cells and guard cells in both wild type plants (Col-0) and several starch related mutants. The results revealed that the granule number is distinct between mesophyll cells and guard cells, even within the same genetic background, suggesting that guard cells operate a unique regulatory mechanism of starch granule number.
Subsequently, I redirected my attention toward examining starch morphology. Through microscopy analyses, I observed a gradual alteration in starch granule morphology in certain mutants during leaf aging. Specifically, in mutants such as sex1-8 and dpe2phs1ss4, there was a progressive alteration in starch granule morphology over time. Conversely, in Col-0 and ss4 mutant, these morphological alterations were not evident. This discovery suggests a new perspective to understand the development of starch morphology.
Further investigation revealed that mutants lacking either Disproportionating enzyme 2 (DPE2) or MALTOSE-EXCESS 1 (MEX1) exhibited gradual alterations in starch morphology with leaf aging. Notably, the most severe effects on starch morphology occurred in double mutants lacking either DPE2 or MEX1 in conjunction with a lack of starch synthase 4 (SS4). In these mutations, a transformation of the starch granule morphology from the typical discoid morphology to oval and eventually to a spherical shape.
To investigate the changes in the internal structure of starch during this alteration, I analyzed the chain length distribution (CLD) of the amylopectin of young, intermediate and old leaves of the mutants. Throughout starch granule development, I found an increased presence of short glucan chains within the granules, particularly evident in dpe2ss4 and mex1ss4 mutants, as well as their parental single mutants. Notably, the single mutant ss4 also showed an affected granule morphology, albeit not influenced by leaf aging..
The CLD pattern of the amylopectin reflects an integrative regulation involving several participants in starch synthesis, including starch synthases (SSs), starch branching/debranching enzymes (SBEs/DBEs). Therefore, I further detected the expression of related genes on transcription level and the enzymatic activity of their respective proteins. Results indicated altered gene expression of several regulators in these mutants, particularly demonstrating dramatic alterations in dpe2 and dpe2ss4 with leaf aging. These changes corresponded with the observed alterations in starch granule morphology.
Taken together, I have identified and characterized a progressive alteration in starch granule morphology primarily resulting from the deficiencies in DPE2 and MEX1. Furthermore, I have associated the CLD pattern with the granule morphogenesis, as well as the gene expression and enzymatic activity of proteins involved in starch synthesis. Unlike SS4, which is implicated in starch initiation, MEX1 and DPE2 are involved into starch degradation. MEX1 is located in chloroplast envelope and DPE2 is situated in the cytosol. Considering the locations and known functions of DPE2/MEX1 and SS4, I infer that there might be two pathways influencing starch morphology: an initiation-affected pathway via SS4 and a degradation-affected pathway via DPE2/MEX1.
Massive stars (Mini > 8 Msol) are the key feedback agents within galaxies, as they shape their surroundings via their powerful winds, ionizing radiation, and explosive supernovae. Most massive stars are born in binary systems, where interactions with their companions significantly alter their evolution and the feedback they deposit in their host galaxy. Understanding binary evolution, particularly in the low-metallicity environments as proxies for the Early Universe, is crucial for interpreting the rest-frame ultraviolet spectra observed in high-redshift galaxies by telescopes like Hubble and James Webb.
This thesis aims to tackle this challenge by investigating in detail massive binaries within the low-metallicity environment of the Small Magellanic Cloud galaxy. From ultraviolet and multi-epoch optical spectroscopic data, we uncovered post-interaction binaries. To comprehensively characterize these binary systems, their stellar winds, and orbital parameters, we use a multifaceted approach. The Potsdam Wolf-Rayet stellar atmosphere code is employed to obtain the stellar and wind parameters of the stars. Additionally, we perform consistent light and radial velocity fitting with the Physics of Eclipsing Binaries software, allowing for the independent determination of orbital parameters and component masses. Finally, we utilize these results to challenge the standard picture of stellar evolution and improve our understanding of low-metallicity stellar populations by calculating our binary evolution models with the Modules for Experiments in Stellar Astrophysics code.
We discovered the first four O-type post-interaction binaries in the SMC (Chapters 2, 5, and 6). Their primary stars have temperatures similar to other OB stars and reside far from the helium zero-age main sequence, challenging the traditional view of binary evolution. Our stellar evolution models suggest this may be due to enhanced mixing after core-hydrogen burning. Furthermore, we discovered the so-far most massive binary system undergoing mass transfer (Chapter 3), offering a unique opportunity to test mass-transfer efficiency in extreme conditions. Our binary evolution calculations revealed unexpected evolutionary pathways for accreting stars in binaries, potentially providing the missing link to understanding the observed Wolf-Rayet population within the SMC (Chapter 4). The results presented in this thesis unveiled the properties of massive binaries at low-metallicity which challenge the way the spectra of high-redshift galaxies are currently being analyzed as well as our understanding of massive-star feedback within galaxies.