Grunberger Diabetes Institute

In this work we present a systematic in-depth study of how we can alter the magnetotransport properties of magnetic topological insulator thin films by tuning the parameters of the molecular-beam epitaxy. First, we show how a varying substrate temperature changes the surface morphology and, when chosen properly, leads to a high crystal quality. Next, the effect of the chromium concentration on the film roughness and crystal quality is investigated. Finally, both the substrate temperature and the chromium concentration are investigated with respect to their effect on the magnetotransport properties of the magnetic topological insulator thin films. It becomes apparent that the substrate temperature and the chromium concentration can be used to tune the Fermi level of the film which allows to make the material intrinsically charge neutral. A very low chromium concentration furthermore allows to tune the magnetic topological insulator into a regime where strong superconducting correlations can be expected when combining the material with a superconductor.

The spectrum of cardiorenal and metabolic diseases comprises many disorders, including obesity, type 2 diabetes (T2D), chronic kidney disease (CKD), atherosclerotic cardiovascular disease (ASCVD), heart failure (HF), dyslipidemias, hypertension, and associated comorbidities such as pulmonary diseases and metabolism dysfunction–associated steatotic liver disease and metabolism dysfunction–associated steatohepatitis (MASLD and MASH, respectively, formerly known as nonalcoholic fatty liver disease and nonalcoholic steatohepatitis [NAFLD and NASH]). Because cardiorenal and metabolic diseases share pathophysiologic pathways, two or more are often present in the same individual. Findings from recent outcome trials have demonstrated benefits of various treatments across a range of conditions, suggesting a need for practice recommendations that will guide clinicians to better manage complex conditions involving diabetes, cardiorenal, and/or metabolic (DCRM) diseases. To meet this need, we formed an international volunteer task force comprising leading cardiologists, nephrologists, endocrinologists, and primary care physicians to develop the DCRM 2.0 Practice Recommendations, an updated and expanded revision of a previously published multispecialty consensus on the comprehensive management of persons living with DCRM. The recommendations are presented as 22 separate graphics covering the essentials of management to improve general health, control cardiorenal risk factors, and manage cardiorenal and metabolic comorbidities, leading to improved patient outcomes.

Reconstructing Hamiltonians from local measurements is key to enabling reliable quantum simulations: both validating the implemented model and identifying any leftover terms with sufficient precision is a problem of increasing importance. Here we propose a deep-learning-assisted variational algorithm for Hamiltonian reconstruction by preprocessing a dataset that is diagnosed to contain thermal measurements of local operators. We demonstrate the efficient and precise reconstruction of local Hamiltonians, while long-range interacting Hamiltonians are reconstructed approximately. Away from equilibrium, for periodically and random multipolar driven systems, we reconstruct the effective Hamiltonian widely used for Floquet engineering of metastable steady states. Moreover, our approach allows us to reconstruct an effective quasilocal Hamiltonian even in the heating regime beyond the validity of the prethermal plateau, where perturbative expansions fail. Published by the American Physical Society 2024

Abstract Superconducting circuits are an extremely versatile platform to realize quantum information hardware, and, as was recently realized, to emulate topological materials, such as three-dimensional Weyl semimetals or two-dimensional Chern insulators. We here show how a simple arrangement of capacitors and conventional superconductor-insulator-superconductor (SIS) junctions can realize a nonlinear capacitive element with a surprising property: it can be quasiperiodic with respect to the quantized Cooper-pair charge. Integrating this element into a larger circuit opens the door towards the engineering of an even broader class of systems. First, we use it to simulate a protected Dirac material defined in the transport degrees of freedom. The presence of the Dirac points leads to a suppression of the classical part of the finite-frequency current noise. Second, we exploit the quasiperiodicity to implement the Aubry-André model, and thereby emulate Anderson localization in charge space. Importantly, the realization by means of transport degrees of freedom allows for a straightforward generalization to arbitrary dimensions. Moreover, our setup implements a truly non-interacting version of the model, in which the macroscopic quantum mechanics of the circuit already incorporates microscopic interaction effects. We propose that measurements of the quantum fluctuations of the charge can be used to directly probe the localization effect. Finally, we present an outlook in which the nonlinear capacitance is employed in a quantum circuit emulating a magic-angle effect akin to twisted bilayer graphene.

Abstract Quantum circuit theory is a powerful and ever-evolving tool to predict the dynamics of superconducting circuits. In its language, quantum phase slips (QPSs) are famously considered to be the exact dual to the Josephson effect. However, this duality renders the integration of QPS junctions into a unified theoretical framework very difficult, and as we show, gives rise to serious inconsistencies for different formalisms, and in some cases difficulties to include time-dependent flux driving. We propose to resolve these issues by reducing and compactifying the Hilbert space describing the QPS processes. Our treatment provides for the first time a unified description of the Aharonov-Bohm and Aharonov-Casher effects, properly defines the valid form of inductive interactions to an environment, and allows to account for recent insights on how to include electromotive forces. Finally, we show that the compactification is likewise important for correctly predicting the available computational space for qubit architectures involving QPS junctions.

Painful diabetic neuropathy (PDN) is a progressive condition that deprives many patients of quality of life. With limited treatment options available, successful pain management can be difficult to achieve. We reviewed results of recent data evaluating high frequency spinal cord stimulation (SCS). from the SENZA-PDN randomized clinical trial (NCT03228420), the largest such trial to date, demonstrated 10-kHz spinal cord stimulation substantially reduced PDN refractory to conventional medical management along with improvements in health-related quality-of-life measures that were sustained over 12 months. These data supported the recent U.S. Food & Drug Administration (FDA) approval for 10-kHz SCS in PDN patients and contributed to the body of evidence on SCS available to health care professionals managing the effects of PDN. High frequency spinal cord simulation appears to hold promise in treatment of painful diabetic neuropathy. We look forward to future works in the literature that will further elucidate these promising findings.

In this work, we studied the bulk band structure of a topological insulator (TI) ${\mathrm{Bi}}_{2}{\mathrm{Se}}_{3}$ and determined the contributions of the Bi and Se orbital states to the valence bands using standing-wave excited hard x-ray photoemission spectroscopy (SW-HAXPES). This SW technique can provide the element-resolved information and extract individual Bi and Se contributions to the ${\mathrm{Bi}}_{2}{\mathrm{Se}}_{3}$ valence band. Comparisons with density-functional theory calculations (local density approximation and $\mathit{GW}$) reveal that the Bi $6s$, Bi $6p$, and Se $4p$ states are dominant in the ${\mathrm{Bi}}_{2}{\mathrm{Se}}_{3}$ HAXPES valence band. These findings pave a way for studying the element-resolved band structure and orbital contributions of this class of TIs.

International audience

Acting as artificial synapses, two-terminal memristive devices are considered fundamental building blocks for the realization of artificial neural networks. Organized into large arrays with a top-down approach, memristive devices in conventional crossbar architecture demonstrated the implementation of brain-inspired computing for supervised and unsupervised learning. Alternative way using unconventional systems consisting of many interacting nano-parts have been proposed for the realization of biologically plausible architectures where the emergent behavior arises from a complexity similar to that of biological neural circuits. However, these systems were unable to demonstrate bio-realistic implementation of synaptic functionalities with spatio-temporal processing of input signals similarly to our brain. Here we report on emergent synaptic behavior of biologically inspired nanoarchitecture based on self-assembled and highly interconnected nanowire (NW) networks realized with a bottom up approach. The operation principle of this system is based on the mutual electrochemical interaction among memristive NWs and NW junctions composing the network and regulating its connectivity depending on the input stimuli. The functional connectivity of the system was shown to be responsible for heterosynaptic plasticity that was experimentally demonstrated and modelled in a multiterminal configuration, where the formation of a synaptic pathway between two neuron terminals is responsible for a variation in synaptic strength also at non-stimulated terminals. These results highlight the ability of nanowire memristive architectures for building brain-inspired intelligent systems based on complex networks able to physically compute the information arising from multi-terminal inputs.