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Globally Integrable Quantum Systems and Their Perturbations
No full textIn this paper we present the notion of globally integrable quantum system that we introduced in [18]: we motivate it using the spectral theory of pseudodifferential operators and then we give some results on linear and nonlinear perturbations of a globally integrable quantum system. In particular, we give a spectral result ensuring stability of most of its eigenvalues under relatively bounded perturbations and two results controlling the growth of Sobolev norms when it is subject either to a linear unbounded time dependent perturbation or to a small nonlinear Hamiltonian nonlinear perturbation
INTEGRATIVE PHYSIOLOGY OF THE MOTOR OUTPUT
No full textThe present study focuses on the integrative properties of Central Nervous System (CNS), which coordinates the activity of multiple neural circuits in order to express the rhythmic motor output. The contribution of supraspinal structure to the integration of inputs arising from respiratory and locomotor neural networks, especially at birth, is still underestimated. To study the neurogenic drive responsible for tailoring rhythmic motor responses, we introduced the ex vivo central nervous system (CNS) from neonatal rodents with legs attached. The preparation allowed to extracellularly record a stable and spontaneous respiratory rhythm for more than 4h from spinal ventral roots (VRs). Serial surgical ablations unveiled the contribution of supraspinal structures to the respiratory motor output. Precollicular decerebration reduced respiratory burst duration and frequency, while pontobulbar transection increased them.
To explore whether sensory afferents from limbs modulate respiration during physical activity, we designed a novel experimental ex vivo platform. Hindlimbs of isolated CNS preparation from neonatal rats were kept attached to a robotic device (Bipedal Induced Kinetic Exercise, BIKE) driving passive pedaling at calibrated speeds. Brief sessions (5 min) of BIKE at maximum pedaling speed (3.5 Hz) augmented the respiratory rate in preparations with slow respiration in control, while a longer BIKE session (25 min) was required to slow down respiratory pace in preparations with intrinsically fast respiratory rhythm. However, regardless of the baseline respiratory frequency, BIKE always decreased duration of single bursts. Notably, Surgical ablation of suprapontine structures completely prevented modulation of breathing after intense training.
Like exercise, hypoxia is a multifaced stimulus. We wanted to assess if the respiratory modulation provided by Intermittent Hypoxia (IH) might rely on a neurogenic component, already effective at birth and involving the hypothalamus among suprapontine structures. To mimic IH ex vivo, CNS isolated from 0-3-day old rats was perfused with four to eight brief (5-min) bouts of mild-hypoxic/normocapnic modified Krebs solution, spaced by normoxic episodes, during continuous electrophysiological recordings from upper cervical ventral roots. IH protocol did not modify bath pH, but medullary and hypothalamic areas encountered lowered oxygen tension, more severe after the second postnatal day, with a partial recovery after each hypoxic bout. Single exposures to mild hypoxia were well tolerated and frequently elicited a spontaneous episode of irregular baseline activity in both, whole CNS preparations and spinal cords. IH transiently increased amplitude of respiratory bursts and stably sped up rhythm in neonates (P0-1) for up to 45 min after the end of the protocol. Contrarywise, IH ceased breathing activity after the second postnatal day. Respiratory facilitation mediated by IH faded in the absence of suprapontine structures. Identical
modulatory effects were observed with IH supplied through a HEPES buffer solution. Interestingly, IH increased c-Fos expression in hypothalamic areas, suggesting its selective activation during IH, which was confirmed by the absence of consistent c-Fos labeling in the hippocampus. Field recordings from hypothalamus revealed the appearance of a slow rhythmic pattern of discharges after IH.
The current work contributes to clarify modulatory suprapontine influences on the respiratory motor output at birth and how neural outputs elicited by physiological challenge, like IH and exercise, are integrated in the central nervous system to control respiratory tone during development
Elastic Plateau–Rayleigh instability in soft cylinders: Surface elasticity and periodic beading
No full textThe Plateau–Rayleigh instability shows that a cylindrical fluid flow can be destabilized by surface tension. Similarly, capillary forces can make an elastic cylinder unstable when the elastocapillary length is comparable to the cylinder's radius. While existing models predict a single isolated bulge as the result of an instability, experiments reveal a periodic sequence of bulges spaced out by thinned regions, a phenomenon known as beading instability. Most models assume that surface tension is independent of the deformation of the solid, neglecting variations due to surface stretch. In this work, we assume that surface tension arises from the deformation of material particles near the free surface, treating it as a pre-stretched elastic surface surrounding the body. Using the theoretical framework proposed by Gurtin and Murdoch, we show that a cylindrical solid can undergo a mechanical instability with a finite critical wavelength if the body is sufficiently soft or axially stretched. Post-buckling numerical simulations reveal a morphology in qualitative agreement with experimental observations. Period-halving secondary bifurcations are also observed. The results of this research have broad implications for soft materials, biomechanics, and microfabrication applications where surface tension plays a crucial role
Semi-empirical approach to [CII] line intensity mapping
No full textThe line intensity mapping technique involves measuring the cumulative emission from specific spectral lines emitted by galaxies and intergalactic gas. This method provides a way to study the matter distribution and the evolution of large-scale structures throughout the history of the Universe. However, modeling intensity mapping from ab-initio approaches can be challenging due to significant astrophysical uncertainties and noticeable degeneracies among astrophysical and cosmological parameters. To address these challenges, we develop a semi-empirical, data-driven framework for galaxy evolution, which features a minimal set of assumptions and parameters gauged on observations. By integrating this with stellar evolution and radiative transfer prescriptions for line emissions, we derive the cosmic [CII] intensity over an extended redshift range 0 ≲ z ≲ 10. Our approach is quite general and can be easily applied to other key lines used in intensity mapping studies, such as [OIII] and the CO ladder. We then evaluate the detectability of the [CII] power spectra using current and forthcoming observational facilities. Our findings offer critical insights into the feasibility and potential contributions of intensity mapping for probing the large-scale structure of the Universe and understanding galaxy evolution
High-pressure hydrogen phase diagram from quantum Monte Carlo and machine learning
No full textHigh-pressure hydrogen is of paramount importance in several fields, including planetary science, condensed matter physics, and energy production applications. Despite its significance, many properties of this system are still not fully understood, due to the difficulty of realizing the required extreme conditions in the laboratory and probing the compressed samples.
Numerical results are thus extremely valuable.
Quantum Monte Carlo (QMC) algorithms have been proven to be among the most effective methods for describing the physics and properties of high-pressure hydrogen, although their large computational cost limits their applicability to small systems.
In this thesis, we discuss techniques that aim at combining the accuracy of QMC methods with the efficiency of machine learning potentials (MLPs).
In particular, we employ the ∆-learning framework together with kernel ridge regression, and train models on the difference between QMC reference calculations and a computationally cheaper "baseline potential", which in our case was obtained with the density functional theory (DFT) method. This approach allows us to reach a higher accuracy with relatively small datasets, a crucial feature for resource-heavy algorithms like QMC. We also analyze the bias affecting both forces and pressures within the variational Monte Carlo (VMC) method when the wave function employed is not fully optimized, and propose a suitable correction.
We present two applications of our framework to high-pressure hydrogen. In the first one, we determined the deuterium Hugoniot with MLPs trained on both variational and diffusion Monte Carlo. We find a good agreement with experiments, even though our results suggest a slightly more compressible system for large pressures.
In the second application, we study the hydrogen liquid-liquid phase transition (LLPT).
We discuss results obtained with two MLPs trained on VMC and DFT data, respectively. For the latter, we employed MACE, a message passing neural network, to study the order of the transition in the thermodynamic limit. Our results predict a first-order transition between a defective molecular solid and an atomic liquid close to the melting line, and a liquid-liquid crossover at higher temperatures
Quantum black holes: from regularization to information paradoxes
No full textQuantum black holes, a broad class of objects that refine the solutions of general relativity by incorporating semiclassical and/or quantum gravitational effects, have recently attracted renewed attention within the scientific community. This resurgence of interest is largely driven by advances in gravitational wave astronomy, which have opened the possibility of testing some of these models in the near future. In this essay, we provide a concise overview of the key discussions that emerged during the “Black Hole Inside/Out" meeting, held in August 2024 in Copenhagen. We report these ideas, their connections to the information paradox, and the potential use of analogue gravity as a test bed for these concepts
Ensemble refinement of mismodeled cryo-EM RNA structures using all-atom simulations
No full textThe advent of single-particle cryogenic electron microscopy (cryo-EM) has enabled near-atomic resolution imaging of large macromolecules, enhancing functional insights. However, current cryo-EM refinement tools condense all single-particle images into a single structure, which can misrepresent highly flexible molecules like RNAs. Here, we combine molecular dynamics simulations with cryo-EM density maps to better account for the structural dynamics of a complex and biologically relevant RNA macromolecule. Namely, using metainference, a Bayesian method, we reconstruct an ensemble of structures of the group II intron ribozyme, which better matches experimental data, and we reveal inaccuracies of single-structure approaches in modeling flexible regions. An analysis of all RNA-containing structures deposited in the Protein Data Bank reveals that this issue affects most cryo-EM structures in the 2.5–4 Å range. Thus, RNA structures determined by cryo-EM require careful handling, and our method may be broadly applicable to other RNA systems