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In order to understand the origin of the elements in the universe, one must understand the nuclear reactions by which atomic nuclei are transformed. There are many different astrophysical environments that fulfill the conditions of different nucleosynthesis processes. Even though great progress has been made in recent decades in understanding the origin of the elements in the universe, some questions remain unanswered. In order to understand the processes, it is necessary to measure cross sections of the involved reactions and constrain theoretical model predictions. A variety of methods have been developed to measure nuclear reaction cross sections relevant for nuclear astrophysics. In this thesis, two different experiments and their results, both using the well-established activation method, are presented.
A measurement of the proton capture cross section on the p-nuclide 96Ru was performed at the Institute of Structure and Nuclear Astrophysics ISNAP - Notre Dame, USA. The main goal of this experiment was to compare the results with those obtained by Mei et al. in a pioneering experiment using the method of inverse kinematics at the GSI Helmholtzzentrum für Schwerionenforschung GmbH - Darmstadt, Germany. Therefore, the activations were taken out at the same center of mass energies of 9 MeV, 10 MeV and 11 MeV. Another activation was taken out at an energy of 3.2 MeV to compare the result to a measurement of Bork et al. who also used the activation method. While the results at 3.2 MeV agree quite well with those of Bork et al., the results at higher energies show significantly smaller cross sections than those measured by Mei et al.. Experimental details, the data analysis and sources of uncertainties are discussed.
The second part of this thesis describes a neutron capture cross section experiment. At the Institut für Kernphysik - Goethe Universtität Frankfurt an experimental setup allows to produce quasi maxwell-distributed neutron fields to measure maxwell-averaged cross sections (MACS) relevant for s-process nucleosynthesis. The setup was upgraded by a fast electric linear guide to transport samples from the activation to the detection site. The cyclic activation of the sample allows to increase the signal-to-noise ratio and to measure neutron captures that lead to nuclei with
half-lives on the order of seconds. In a first campaign, MACS of the reactions 51V(n,γ), 107,109Ag(n,γ) and 103Rh(n,γ) were measured. The new components of the setup aswell as the data analysis framework are described and the results of the measurements are discussed.
We study the polarization of relativistic fluids using the relativistic density operator at global and local equilibrium. In global equilibrium, a new technique to compute exact expectation values is introduced, which is used to obtain the exact polarization vector for fields of any spin. The same result has been extended to the case of massless fields. Furthermore, it is demonstrated that at local equilibrium not only the thermal vorticity but also the thermal shear contribute to the polarization vector. It is shown that assuming an isothermal local equilibrium, the new term can solve the polarization sign puzzle in heavy ion collisions.
Using combined data from the Relativistic Heavy Ion and Large Hadron Colliders, we constrain the shear and bulk viscosities of quark-gluon plasma (QGP) at temperatures of ∼150–350 MeV. We use Bayesian inference to translate experimental and theoretical uncertainties into probabilistic constraints for the viscosities. With Bayesian model averaging we propagate an estimate of the model uncertainty generated by the transition from hydrodynamics to hadron transport in the plasma’s final evolution stage, providing the most reliable phenomenological constraints to date on the QGP viscosities.
Tracking influenza a virus infection in the lung from hematological data with machine learning
(2022)
The tracking of pathogen burden and host responses with minimal-invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory Influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e. cytokines and leukocytes in the lung, from hematological data. We performed independently in vivo infection experiments to acquire extensive data for training and testing purposes of the models. We show here that lung viral load, neutrophil counts, cytokines like IFN-γ and IL-6, and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models shows that blood granulocytes and platelets play a crucial role in prediction and are highly involved in the immune response against IAV. The proposed in silico tools pave the path towards improved tracking and monitoring of influenza infections and possibly other respiratory infections based on minimal-invasively obtained hematological parameters.
Intrinsically disordered regions (IDRs) are essential for membrane receptor regulation but often remain unresolved in structural studies. TRPV4, a member of the TRP vanilloid channel family involved in thermo- and osmosensation, has a large N-terminal IDR of approximately 150 amino acids. With an integrated structural biology approach, we analyze the structural ensemble of the TRPV4 IDR and identify a network of regulatory elements that modulate channel activity in a hierarchical lipid-dependent manner through transient long-range interactions. A highly conserved autoinhibitory patch acts as a master regulator by competing with PIP2 binding to attenuate channel activity. Molecular dynamics simulations show that loss of the interaction between the PIP2-binding site and the membrane reduces the force exerted by the IDR on the structured core of TRPV4. This work demonstrates that IDR structural dynamics are coupled to TRPV4 activity and highlights the importance of IDRs for TRP channel function and regulation.
During infection the SARS-CoV-2 virus fuses its viral envelope with cellular membranes of its human host. Initial contact with the host cell and membrane fusion are both mediated by the viral spike (S) protein. Proteolytic cleavage of S at the S2′ site exposes its 40 amino acid long fusion peptide (FP). Binding of the FP to the host membrane anchors the S2 domain of S in both the viral and the host membrane. The reorganization of S2 then pulls the two membranes together. Here we use molecular dynamics (MD) simulations to study the two core functions of the SARS-CoV-2 FP: to attach quickly to cellular membranes and to form an anchor strong enough to withstand the mechanical force during membrane fusion. In eight 10 μs-long MD simulations of FP in proximity to endosomal and plasma membranes, we find that FP binds spontaneously to the membranes and that binding proceeds predominantly by insertion of two short amphipathic helices into the membrane interface. Connected via a flexible linker, the two helices can bind the membrane independently, yet binding of one promotes the binding of the other by tethering it close to the target membrane. By simulating mechanical pulling forces acting on the C-terminus of the FP we then show that the bound FP can bear forces up to 250 pN before detaching from the membrane. This detachment force is more than ten-fold higher than an estimate of the force required to pull host and viral membranes together for fusion. We identify a fully conserved disulfide bridge in the FP as a major factor for the high mechanical stability of the FP membrane anchor. We conclude, first, that the sequential binding of two short amphipathic helices allows the SARS-CoV-2 FP to insert quickly into the target membrane, before the virion is swept away after shedding the S1 domain connecting it to the host cell receptor. Second, we conclude that the double attachment and the conserved disulfide bridge establish the strong anchoring required for subsequent membrane fusion. Multiple distinct membrane-anchoring elements ensure high avidity and high mechanical strength of FP-membrane binding.
Focused ion beam induced deposition (FIBID) is a direct-write technique enabling the growth of individual nanostructures of any shape and dimension with high lateral resolution. Moreover, the fast and reliable writing of periodically arranged nanostructures can be used to fabricate devices for the investigation of collective phenomena and to design novel functional metamaterials. Here, FIBID is employed to prepare dc-Josephson junction arrays (dc-JJA) consisting of superconducting NbC dots coupled through the proximity effect via a granular metal layer. The fabrication is straightforward and allows the preparation of dc-JJA within a few seconds. Microstructure and composition of the arrays are investigated by transmission electron microscopy and energy dispersive X-ray spectroscopy. The superconductor-to-metal transition of the prepared dc-JJA is studied in a direct way, by tuning the Josephson junction resistance in 70 nm-spaced superconducting NbC dots. The observed magnetoresistance oscillations with a period determined by the flux quantum give evidence for the coherent charge transport by paired electrons. Moreover, the measured resistance minima correspond to two fundamental matching configurations of fluxons in the dc-JJA, caused by magnetic frustration. The robust properties of the prepared dc-JJA demonstrate the opportunities for a fast preparation of complex device configurations using direct-write approaches.
Changes in the efficacies of synapses are thought to be the neurobiological basis of learning and memory. The efficacy of a synapse depends on its current number of neurotransmitter receptors. Recent experiments have shown that these receptors are highly dynamic, moving back and forth between synapses on time scales of seconds and minutes. This suggests spontaneous fluctuations in synaptic efficacies and a competition of nearby synapses for available receptors. Here we propose a mathematical model of this competition of synapses for neurotransmitter receptors from a local dendritic pool. Using minimal assumptions, the model produces a fast multiplicative scaling behavior of synapses. Furthermore, the model explains a transient form of heterosynaptic plasticity and predicts that its amount is inversely related to the size of the local receptor pool. Overall, our model reveals logistical tradeoffs during the induction of synaptic plasticity due to the rapid exchange of neurotransmitter receptors between synapses.
Changes in the efficacies of synapses are thought to be the neurobiological basis of learning and memory. The efficacy of a synapse depends on its current number of neurotransmitter receptors. Recent experiments have shown that these receptors are highly dynamic, moving back and forth between synapses on time scales of seconds and minutes. This suggests spontaneous fluctuations in synaptic efficacies and a competition of nearby synapses for available receptors. Here we propose a mathematical model of this competition of synapses for neurotransmitter receptors from a local dendritic pool. Using minimal assumptions, the model produces a fast multiplicative scaling behavior of synapses. Furthermore, the model explains a transient form of heterosynaptic plasticity and predicts that its amount is inversely related to the size of the local receptor pool. Overall, our model reveals logistical tradeoffs during the induction of synaptic plasticity due to the rapid exchange of neurotransmitter receptors between synapses.
Transport of lipids across membranes is fundamental for diverse biological pathways in cells. Multiple ion-coupled transporters participate in lipid translocation, but their mechanisms remain largely unknown. Major facilitator superfamily (MFS) lipid transporters play central roles in cell wall synthesis, brain development and function, lipids recycling, and cell signaling. Recent structures of MFS lipid transporters revealed overlapping architectural features pointing towards a common mechanism. Here we used cysteine disulfide trapping, molecular dynamics simulations, mutagenesis analysis, and transport assays in vitro and in vivo, to investigate the mechanism of LtaA, a proton-dependent MFS lipid transporter essential for lipoteichoic acids synthesis in the pathogen Staphylococcus aureus. We reveal that LtaA displays asymmetric lateral openings with distinct functional relevance and that cycling through outward- and inward-facing conformations is essential for transport activity. We demonstrate that while the entire amphipathic central cavity of LtaA contributes to lipid binding, its hydrophilic pocket dictates substrate specificity. We propose that LtaA catalyzes lipid translocation by a ‘trap-and-flip’ mechanism that might be shared among MFS lipid transporters.