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Metabolically stable apelin analogs: development and functional role in water balance and cardiovascular function.
Clin Sci (Lond)
January 2025
Center for Interdisciplinary Research in Biology, College de France, Institut National de la Santé et de la Recherche Médicale, Paris, France.
Apelin, a (neuro) vasoactive peptide, plays a prominent role in controlling water balance and cardiovascular functions. Apelin and its receptor co-localize with vasopressin in magnocellular vasopressinergic neurons. Apelin receptors (Apelin-Rs) are also expressed in the collecting ducts of the kidney, where vasopressin type 2 receptors are also present.
View Article and Find Full Text PDFUnified Variational Approach Description of Ground-State Phases of the Two-Dimensional Electron Gas.
Phys Rev Lett
December 2024
Flatiron Institute, Center for Computational Quantum Physics, New York, New York 10010, USA.
The two-dimensional electron gas (2DEG) is a fundamental model, which is drawing increasing interest because of recent advances in experimental and theoretical studies of 2D materials. Current understanding of the ground state of the 2DEG relies on quantum Monte Carlo calculations, based on variational comparisons of different Ansätze for different phases. We use a single variational ansatz, a general backflow-type wave function using a message-passing neural quantum state architecture, for a unified description across the entire density range.
View Article and Find Full Text PDFQuasi-Two-Dimensional Antiferromagnetic Spin Fluctuations in the Spin-Triplet Superconductor Candidate CeRh_{2}As_{2}.
Phys Rev Lett
December 2024
Johns Hopkins University, Institute for Quantum Matter and Department of Physics and Astronomy, Baltimore, Maryland 21218, USA.
The tetragonal heavy-fermion superconductor CeRh_{2}As_{2} (T_{c}=0.3 K) exhibits an exceptionally high critical field of 14 T for B∥c. It undergoes a field-driven first-order phase transition between superconducting states, potentially transitioning from spin-singlet to spin-triplet superconductivity.
View Article and Find Full Text PDFTensor Network Computations That Capture Strict Variationality, Volume Law Behavior, and the Efficient Representation of Neural Network States.
Phys Rev Lett
December 2024
California Institute of Technology, Division of Chemistry and Chemical Engineering, Pasadena, California 91125, USA.
We introduce a change of perspective on tensor network states that is defined by the computational graph of the contraction of an amplitude. The resulting class of states, which we refer to as tensor network functions, inherit the conceptual advantages of tensor network states while removing computational restrictions arising from the need to converge approximate contractions. We use tensor network functions to compute strict variational estimates of the energy on loopy graphs, analyze their expressive power for ground states, show that we can capture aspects of volume law time evolution, and provide a mapping of general feed-forward neural nets onto efficient tensor network functions.
View Article and Find Full Text PDFDetecting Multipartite Entanglement Patterns Using Single-Particle Green's Functions.
Phys Rev Lett
December 2024
Brookhaven National Laboratory, Condensed Matter Physics and Materials Science Division, Upton, New York 11973, USA.
We present a protocol for detecting multipartite entanglement in itinerant many-body electronic systems using single-particle Green's functions. To achieve this, we first establish a connection between the quantum Fisher information and single-particle Green's functions by constructing a set of witness operators built out of single electron creation and destruction operators in a doubled system. This set of witness operators is indexed by a momentum k.
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