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Adaptive Geospatial Data Representation for Data Analytics and Sharing in the Mobility Domain
No full textIn recent years, the mobility domain has gained attention from urban planners and researchers due to its essential role in enhancing urban safety and development. This interest can be attributed to the increased availability of geospatial and mobility data from a wide variety of sources, such as OpenStreetMap and knowledge graphs. Geospatial and mobility data enable the development of predictive models such as accident and crime prediction, enhancing urban safety, and planning. However, there are specific challenges to utilizing geospatial and mobility data when building predictive models. First, mobility data is typically sparse. Data sparsity occurs when spatio-temporal events, such as traffic accidents, are scarce and scattered across geographic regions. Due to data sparsity, predicting future events at specific locations becomes challenging. Second, geospatial and mobility data from multiple sources are often utilized by machine learning pipelines to generate latent representations. The latent representations derived from multimodal data are richer in context and beneficial for several predictive tasks. However, the diversity in data sources makes it challenging for machine learning pipelines to integrate these sources effectively, resulting in ineffective latent representations. Third, personal mobility data can contain sensitive information, such as visited locations, traveled routes, and driver profiles. Applications relying on personal mobility data require effective and robust methods to confirm provenance and authenticity. However, existing methods in the mobility domain are neither effective nor robust, which makes tracing personal mobility data challenging. This lack of traceability of personal mobility data limits its use in predictive model development. This cumulative thesis summarizes several novel methods to address these challenges. First, we propose a novel adaptive clustering method for accident prediction (ACAP) to address the challenge of data sparsity. ACAP aggregates traffic accident events dynamically with a grid-growing algorithm while considering underlying data distribution. Furthermore, ACAP enhances the prediction results of traffic accident events by focusing on adaptive task-specific regions. Second, to address the challenge of ineffective latent representations of geospatial regions, we propose a multimodal and multitask approach for region representation learning (MAGRE). MAGRE leverages multitask learning combined with attention-based fusion to enhance the effectiveness of region latent representations. These effective latent representations maintain the semantics for several downstream predictive tasks. Furthermore, the adaptive representations generated by MAGRE can be aggregated for user regions of interest of any shape and size without retraining. Third, to address the challenge of the lack of traceability of personal mobility data, we propose a novel watermarking approach for GPS trajectories called W-Trace. W-Trace embeds watermarks within GPS trajectories and is robust to adversarial modifications, enhancing traceability. In addition, W-trace maintains the utility of watermarked GPS trajectories for several downstream tasks. In summary, this thesis presents three novel contributions: i) an adaptive aggregation method for accident event data, ii) an effective and adaptive representation learning approach for geospatial regions, and iii) an effective, robust, and utility-preserving watermarking method for GPS trajectories
Investigating Graphene-Based Systems: Interaction Effects, Localization, and Finite-Temperature Dynamics
Get PDFThis dissertation presents a comprehensive theoretical and numerical investigation into the electronic properties of graphene‐based systems, with a focus on interaction effects, localization phenomena, and finite‐temperature dynamics. Motivated by graphene's exceptional electronic characteristics and its potential relevance for quantum technologies, the work systematically explores various graphene geometries—including two‐dimensional sheets, one‐dimensional nanoribbons, and hybrid junction ribbons.
Central to the analysis is the application of the Hubbard model and tight-binding approximations to capture the essential physics of electron–electron interactions in low-dimensional carbon structures. The study delves into the topological and symmetry aspects of carbon nanoribbons, revealing how edge configurations and variations in ribbon width critically influence the material's electronic behavior. Advanced numerical techniques, such as Hamiltonian Monte Carlo, are employed to examine the localization of electronic states in the nonperturbative regime, offering quantitative insights into the energy distributions and wavefunction profiles of localized edge states.
Furthermore, the dissertation develops an effective one-dimensional tight-binding framework for graphene nanoribbons with junctions, enabling a precise determination of low-energy constants and an assessment of interaction effects under finite-temperature conditions. The work also leverages thermal field theory to investigate the impact of thermal fluctuations on Hubbard interactions on a hexagonal lattice.
Overall, the findings deepen our understanding of quantum phenomena in graphene-based materials and suggest several avenues for future research
Missing images : autobiographical memory in Aphantasia and blindness
Get PDFMental visual imagery, especially the ability to construct naturalistic scenes seems central to vivid episodic autobiographical memory (AM). This mini review will first highlight the neural anatomy of different aspects of mental imagery, focusing on the roles of the hippocampus, ventromedial prefrontal cortex and posterior neocortex and the consequences of damage to these regions to AM. We will then contrast the consequences of missing images for AM in two special populations with no apparent brain damage: Congenital Aphantasia (i.e., lack of visual imagery) and congenital blindness (i.e., lack of visual perception). We propose that Aphantasia leads to impaired scene construction and reduced AM reliving. Despite limited evidence on AM in congenitally blind individuals, they seem to rely on auditory and tactile information to construct (scene) imagery, which in turn may support vivid AM reliving. The main findings here suggest that mental scene imagery, rather than visual encoding, is crucial for AM. This conclusion has far-reaching implications for understanding memory disorders, mental health, and a call to protect our imagination
Longitudinale Charakterisierung von strukturellen Biomarkern bei intermediärer altersabhängiger Makuladegeneration (AMD)
Get PDFIn dieser longitudinalen Studie wurde der Einfluss struktureller Hochrisiko-Biomarker auf die Krankheitsprogression bei nicht-exsudativer altersabhängiger Makuladegeneration (AMD) mittels räumlich aufgelöster mesopischer und skotopischer Fundus-kontrollierter Perimetrie untersucht. Über einen Zeitraum von bis zu vier Jahren wurden Bildgebungsdaten mit funktionellen Messungen korreliert
Case Report: Practical approach to unmask unspecific adverse effects under lipid-lowering medication
Get PDFThe nocebo effect, driven by negative expectations rather than pharmacological mechanisms, contributes significantly to medication non-adherence, particularly in lipid-lowering therapy. Up to 50% of reported statin-related adverse effects may result from nocebo responses, leading to unnecessary discontinuation and increased cardiovascular risk. Blinded provocation tests may offer a solution for the differentiation of true drug intolerance from nocebo-driven symptoms. Although this methodology is well-established in experimental studies, it has not been transferred to routine clinical practice so far. We present a 65-year-old female with hypercholesterolemia and cardiovascular risk factors who experienced recurrent, dose-dependent leftsided lower abdominal pain with different lipid-lowering drugs. These symptoms prompted repeated and ultimately continuous treatment discontinuations, each followed by resolution of complaints. Despite extensive evaluations, no organic cause was found. To assess the role of nocebo effects, a six-week single-blinded, placebo-controlled crossover provocation test with a commercially available placebo preparation and atorvastatin placed in neutral pill containers was conducted. Upon initiation of the provocation phase, the patient experienced similar intermittent symptoms under both treatments. The pain ratings on a numeric rating scale did not significantly differ during placebo (mean: 2.75) and atorvastatin administration (mean: 3.26), suggesting that these symptoms were not pharmacologically induced. Following information of the patient, atorvastatin therapy could be continued. During continued intake over several weeks, symptoms further diminished, reinforcing the therapeutic value of addressing nocebo effects. This case report provides for the first time the structured and detailed step-by-step description of a pragmatic approach for a prospective blinded, placebo-controlled provocation testing that can directly be implemented in routine clinical practice. This method enables the distinction of true drug intolerance from nocebo effects, thereby enabling necessary therapies and highlighting its diagnostic and therapeutic potential
Synthetic Studies toward Bacillaene and Analogues
No full textThirty years after its first isolation, bacillaene, a polyketide and polyene antibiotic initially discovered in Bacillus subtilis, remains a subject of ongoing research. While significant contributions to the study of this natural product have come from the fields of biochemistry and molecular biology, no contributions were reported from the field of synthetic organic chemistry.
Despite its intriguing structure, the synthesis of bacillaene is severely hampered due to its highly labile molecular architecture.
In this work, the great challenge of developing a total synthesis of the natural product bacillaene and a simplified analogue was pursued. It features improved syntheses of bimetallic linchpin reagents, iterative cross-coupling strategies and investigations into isomerization processes. Furthermore, the purification and isolation of unstable polyenes is described in detail. Additionally, a valuable method for monitoring polyene iteration via UV-vis shift analyses was developed.
The synthesis of the hexaene core of bacillaene was achieved with excellent geometrical purity and subsequently compared to the authentic natural product. A first bacillaene analogue was successfully synthesized, however its final isolation and characterization was not achieved.
Additionally, methods for the synthesis of the enamide sidechain were explored. The Peterson olefination method for enamide formation, as described by Fürstner, was successfully implemented on a test system. Finally, based on the insights gained from this work, a synthetic strategy is proposed that might enable a future total synthesis of the natural product bacillaene
Amtliche Bekanntmachungen, 55. Jahrgang, Nr. 16
No full textÄnderung und zugleich Neubekanntmachung der Promotionsordnung der Agrar-, Ernährungs- und Ingenieurwissenschaftlichen Fakultät der Rheinischen Friedrich-Wilhelms-Universität Bonn vom 18. März 202
Development of Quantum Chemistry based Workflows for the Theoretical Description of Organic Electronics
Get PDFOrganic electronics (OE) have become increasingly significant in daily life. Groundbreaking inventions such as organic solar cells (OSCs), organic light-emitting diodes (OLEDs), and organic field-effect transistors (OFETs) have revolutionized the field of sustainable materials for energy conversion. OE enable the fabrication of molecular circuits with integrated functionalities like sensors and detectors, useful in fields such as molecular computing, nanomedicine, and the Internet of Things (IoT). Compared to conventional inorganic electronics, OE offer advantages regarding their sustainability and reduced production cost due to the use of organic or even plant-based materials, as well as versatility and tunable properties for customized solutions like printable screens, curved photovoltaic windows, intelligent textiles, and pathogen-filtering blood nanorobots.
The chemical space of potential candidates for OE is vast, making the discovery and application of new materials a promising yet tedious process. Supramolecular chemistry is not only a versatile playground for new topologies but also a workhorse in the design of OE materials. Computational chemistry can accelerate the discovery timeline and enhance the understanding of underlying design principles and functionality. However, the complexity and size of OE places severe constraints on this approach. A promising solution is to combine the strengths of experimental and computational approaches in property targeted, yet broadly applicable workflows, requiring both accurate methods like density functional theory (DFT) and fast methods like semiempirical quantum-mechanical (SQM) methods and force fields (FF). This work presents several such workflows tailored to different chemical spaces and target properties in OE that are shortly introduced in the following.
Artificial molecular muscles (AMMs) are a versatile subgroup within the field of molecular machines. In Chapter 1, a workflow to determine a standardized and reproducible structure model of AMMs is presented, which is then verified on a benchmark of experimentally studied AMMs.
Supra- and macromolecular topological molecules are typically synthesized from smaller building blocks via C-C coupling reactions, enabling the customization of size, connectivity, and chirality. In this spirit, in Chapter 2, a workflow to examine the formation and stability of different strained anti-aromatic hoops is described and compared to experimental data. In Chapter 3, a workflow for evaluating the flexibility of molecular spoked wheels (MSWs) is presented and the feasibility of specific MSW designs is predicted.
Apart from single molecules or in solution, OE materials can exist as covalent or molecular crystals with varying degrees of order, ranging from amorphous powder to aligned thin films. Covalent organic frameworks (COFs) are part of the highly structured thin film group, yet the inherent polymorphism and high porosity challenge experimental structure determination. In Chapter 4, a workflow for solid state ensemble generation of COFs is presented, aiding in the interpretation of experimentally measured structures and opto-electronic properties.
Semiconducting organic molecular crystals typically exhibit intermediate charge transport rates in the hopping regime. However, accurate modeling with molecular dynamics requires the reliable evaluation of a substantial number of coupling integrals in a reasonably short time. In Chapter 5, this demand is addressed by implementing the Dimer PROjection method (DIPRO) with a density matrix tight-binding method (PTB) and extended tight-binding methods (GFN-xTB).
For practical applications, molecular OE materials are often fabricated as thin films with crystal structures highly dependent on experimental conditions resulting in various polymorphs. As single-structure theoretical descriptions are insufficient and crystal structure prediction (CSP) is computationally demanding and theoretically challenging, a cluster approach is in the focus of Chapter 6. Therein, a workflow is presented based on the automated interaction site screening (aISS) using GFN-xTB methods to generate ensembles of flat and stacked aggregates. The workflow is then applied to build a novel merocyanine benchmark set and compared to experimental data
Ultracold fermions in periodically-driven superlattices
Get PDFThis thesis presents quantum simulation of strongly-correlated systems beyond standard Hubbard models, using ultracold fermionic potassium atoms in both static and periodically-driven optical superlattices. For this study, we utilize a three-dimensional optical lattice setup, controlling particle interactions via magnetic Feshbach resonances and tunneling between lattice sites through optical lattice intensity. High-resolution absorption imaging combined with radio-frequency spectroscopy distinguishes between singly and doubly occupied sites.
To enhance our systems capabilities beyond the standard Hubbard model, we extend the apparatus with an in-plane optical superlattice, creating a bi-chromatic structure by superposition of two optical lattices with commensurate lattice spacings. Using a phase locked loop with an environmental feed forward, we create an excellent phase stability of the superlattice exceeding 3 mrad. This precision allows us to explore both static and periodically-driven one-dimensional tight-binding models with strong interactions.
We characterize the static superlattice through radio-frequency spectroscopy and Rabi oscillations, and validate the experimental data against theoretical calculations. In a tilted superlattice configuration, we successfully prepare and detect repulsively bound atom pairs, representing a highly excited eigenstate of the system.
Furthermore, we demonstrate control over pair tunneling dynamics in a double-well potential using Floquet engineering, employing a low-noise periodic modulation of the optical superlattice tilt. Using an adiabatic band mapping technique, we directly observe the tunneling dynamics in the driven superlattice. We realize dynamic localization in quarter-filled wells and density-assisted tunneling up to the third harmonic order in half-filled wells. We observe a crossover from density-assited tunneling to dominant pair tunneling by tuning the effective interactions. Remarkably, the pair tunneling is not only enhanced relative to the suppressed single-particle tunneling but also exceeds the superexchange rate of a static double-well by more than a factor of two.
This opens the possibility to study many-body systems with dominant pair tunneling, that extend beyond the standard Hubbard model
Functional role of RIM3 in the regulation of neuronal network excitability : Insights from transcriptomics and network activity analysis
Get PDFNeuronal networks are highly intricate systems, where synapses play a crucial role in intercellular communication and neuronal plasticity. While the large isoforms of the Rab3 interacting molecule (RIM) family, particularly RIM1 and RIM2, are well characterized in presynaptic function, the smaller isoform, RIM3, remains poorly understood. This study provides the first functional characterization of a newly generated RIM3 knockout (KO) mouse line, investigating its role in synaptic function, plasticity, spontaneous network activity, and the regulation of excitability in neuronal networks. Using transcriptomics and proteomics approaches, we show that RIM3 deletion induces significant alterations at the mRNA level without affecting the proteome. These changes suggest that RIM3 participates in synapse-related processes, including neurotransmitter release and synaptic plasticity, and may influence activity-dependent gene transcription via transcriptional modulation rather than direct protein changes.
Network-level activity was assessed using multielectrode arrays (MEAs), revealing that culture density significantly impacts neuronal synchronization and maturation. Notably, RIM3 deletion led to contrasting effects on network activity across different culture densities: reduced activity in high-density networks and increased synchronicity in low-density networks. These results suggest that RIM3 plays a dual role in regulating excitability, promoting activity in highly synchronized networks and constraining it in less synchronized ones. Furthermore, loss of RIM3 in glutamatergic neurons increased firing rates, while its deletion in GABAergic neurons disrupted network synchronization. These findings indicate that RIM3 may act as a negative regulator of excitability in both neuronal subtypes at low densities. Homeostatic plasticity, on the other hand, was not affected by the deletion of RIM3.
In conclusion, this study provides novel insights into the neuronal functions of RIM3, highlighting its role in modulating network excitability. These findings lay the groundwork for future research on RIM3’s involvement in neurological disorders such as epilepsy and schizophrenia