Creating nearly Heisenberg-limited matter-waves exploiting tunable interactions

The wave nature of matter implies wavepackets with minimal combined uncertainty in position and momentum, a limit which can hardly be reached for clouds of large atom numbers of interacting particles. Here, we report on a high-flux source of ultra-cold atoms realizing near-Heisenberg-limited expansion rates upon release from the trap. Depending on the value of the scattering length, we model our system either with a scaling approach based on the Thomas-Fermi approximation, or with a variational approach based on a Gaussian atomic density approximation, observing the transition between the weak and strong interaction regimes. Finally, we discuss applications of our methods to test foundational principles of quantum mechanics such as the superposition principle or their extension to other atomic species

Matter-wave collimation to picokelvin energies with scattering length and potential shape control

We study the impact of atomic interactions on an in-situ collimation method for matter-waves. Building upon an earlier study with $^{87}$Rb, we apply a lensing protocol to $^{39}$K where the atomic scattering length can be tailored by means of magnetic Feshbach resonances. Minimizing interactions, we show an enhancement of the collimation compared to the strong interaction regime, realizing ballistic 2D expansion energies of 438(77) pK in our experiment. Our results are supported by an accurate simulation, describing the ensemble dynamics, which we further use to study the behavior of various trap configurations for different interaction strengths. Based on our findings we propose an advanced scenario which allows for 3D expansion energies below 16 pK by implementing an additional pulsed delta-kick collimation directly after release from the trapping potential. Our results pave the way to achieve state-of-the-art quantum state in typical dipole trap setups required to perform ultra-precise measurements without the need of complex micro-gravity or long baselines environments

Counting Complex Disordered States by Efficient Pattern Matching: Chromatic Polynomials and Potts Partition Functions

Counting problems, determining the number of possible states of a large system under certain constraints, play an important role in many areas of science. They naturally arise for complex disordered systems in physics and chemistry, in mathematical graph theory, and in computer science. Counting problems, however, are among the hardest problems to access computationally. Here, we suggest a novel method to access a benchmark counting problem, finding chromatic polynomials of graphs. We develop a vertex-oriented symbolic pattern matching algorithm that exploits the equivalence between the chromatic polynomial and the zero-temperature partition function of the Potts antiferromagnet on the same graph. Implementing this bottom-up algorithm using appropriate computer algebra, the new method outperforms standard top-down methods by several orders of magnitude, already for moderately sized graphs. As a first application, we compute chromatic polynomials of samples of the simple cubic lattice, for the first time computationally accessing three-dimensional lattices of physical relevance. The method offers straightforward generalizations to several other counting problems.Comment: 7 pages, 4 figure

MHC-II dynamics are maintained in HLA-DR allotypes to ensure catalyzed peptide exchange

Presentation of antigenic peptides by major histocompatibility complex class II (MHC-II) proteins determines T helper cell reactivity. The MHC-II genetic locus displays a large degree of allelic polymorphism influencing the peptide repertoire presented by the resulting MHC-II protein allotypes. During antigen processing, the human leukocyte antigen (HLA) molecule HLA-DM (DM) encounters these distinct allotypes and catalyzes exchange of the placeholder peptide CLIP by exploiting dynamic features of MHC-II. Here, we investigate 12 highly abundant CLIP-bound HLA-DRB1 allotypes and correlate dynamics to catalysis by DM. Despite large differences in thermodynamic stability, peptide exchange rates fall into a target range that maintains DM responsiveness. A DM-susceptible conformation is conserved in MHC-II molecules, and allosteric coupling between polymorphic sites affects dynamic states that influence DM catalysis. As exemplified for rheumatoid arthritis, we postulate that intrinsic dynamic features of peptide–MHC-II complexes contribute to the association of individual MHC-II allotypes with autoimmune disease

Applied neurophysiology of the horse; implications for training, husbandry and welfare

Understanding the neural circuits underlying equine behaviour has the potential to help optimise strategies of husbandry and training. This review discusses two areas of neurophysiological research in a range of species and relates this information to the horse. The first discussion focuses on mechanisms of learning and motivation and assesses how this information can be applied to improve the training of the horse. The second concerns the identification of the equine neurophysiological phenotype, through behavioural and genetic probes, as a way of improving strategies for optimal equine husbandry and training success. The review finishes by identifying directions for future research with an emphasis on how neurophysiological systems (and thus behaviour) can be modified through strategic husbandry. This review highlights how a neurophysioloigical understanding of horse behaviour can play an important role in attaining the primary objectives of equitation science as well as improving the welfare of the hors

Multi-Scale Computational Studies Of Molecular Mechanisms In The Function Of Membrane-Proteins In The Family Of Neurotransmitter Transporters

In my thesis work, I investigate functional mechanisms of complex molecular machines in the cell membrane that carry out the transport (reuptake) of neurotransmitter molecules into the cell. I apply and develop methods of computational biophysics to reveal and quantify the molecular transport process that is essential for the ability of the cell to continue to signal. Such detailed understanding is highly significant because dysfunction of these transporter proteins is known to relate to depression, epilepsy and strokes, and to be involved in neurodegenerative diseases, such as Alzheimer's or Parkinson's disease. The mechanistic insights gained from the studies presented in this thesis pertain to (1) a conformational transition in the substrate translocation mechanism of glutamate transporters, and (2) allosteric changes in the substrate transport mechanism of the neurotransmitter-sodium symporter protein family: For the family of glutamate transporters, a major finding is that transient exposure of a protein-protein interface to solvent facilitates the conformational transition of the transporter and allows functionally relevant chloride ions to bind to the interface. In the study of neurotransmitter-sodium symporters, a major discovery is the identification of common allosteric pathways of pairwise interaction changes that connect the intra- with the extracellular side of the transporter. The computational approaches that enabled these mechanistic insights include Motion Planning, mixed Elastic Network Models, (targeted) Molecular Dynamics simulations, Free Energy Perturbations, and various statistical analysis methods