Scuola Internazionale Superiore di Studi Avanzati di Trieste

The coexistence between ferromagnetic ordering and superconducting transport in tunnel ferromagnetic Josephson junctions (SFS JJs) accounts for a wide range of unconventional physical properties. The integration of both insulating ferromagnets or multi-layered insulator-ferromagnet barriers allows to combine ferromagnetic switching properties with peculiar low quasiparticle dissipation, which could enhance the capabilities of SFS JJs as active elements in quantum circuits. Here we show that split-transmon qubits based on tunnel ferromagnetic JJs realize an ideal playground to study noise fluctuations in ferromagnetic Josephson devices. By considering the transport properties of measured Al-based tunnel SFS JJs, we report on a theoretical study of the competition between intrinsic magnetization fluctuations in the barrier and quasiparticles dissipation, thus providing specific operation regimes to identify and disentangle the two noise sources, depending on the peculiar properties of the F layer and F/S interface.

We consider Higgs bundles satisfying a notion of numerical flatness (H-nflatness) that was introduced in [5; 4], and show that they have Jordan-Hölder filtrations whose quotients are stable, locally free and H-nflat. This is applied to show that curve semistable Higgs bundles on simply connected Calabi–Yau varieties have vanishing discriminant.

The spliceosome machinery catalyzes precursor messenger (pre-m)RNA splicing. In each cycle, the spliceosome experiences massive compositional and conformational remodeling fueled by the concerted action of specific RNA-dependent ATPases/helicases. Intriguingly, these enzymes are allosterically activated to perform ATP hydrolysis and trigger helicase activity only upon pre-mRNA binding. Yet, the molecular mechanism underlying the RNA-driven regulation of their ATPase function remains elusive. Here, we focus on the Prp2 ATPase/helicase which contributes to reshaping the spliceosome into its catalytic competent state. By performing classical and quantum-classical molecular dynamics simulations, we unprecedentedly unlock the molecular terms governing the Prp2 ATPase/helicase function. Namely, we dissect the molecular mechanism of ATP hydrolysis, and we disclose that RNA binding allosterically triggers the formation of a set of interactions linking the RNA binding tunnel to the catalytic site. This activates the Prp2's ATPase function by optimally placing the nucleophilic water and the general base of the enzymatic process to perform ATP hydrolysis. The key structural motifs, mechanically coupling RNA gripping and the ATPase/helicase functions, are conserved across all DExH-box helicases. This mechanism could thus be broadly applicable to all DExH-box helicase family.

Background: The role of domain-general cognitive abilities in the etiology of Developmental Dyscalculia (DD) is a hotly debated issue. Aims: In the present study, we tested whether WISC-IV cognitive profiles can be useful to single out DD. Methods and procedures: Using a stringent 2-SD cutoff in a standardized numeracy battery, we identified children with DD (N = 43) within a clinical sample referred for assessment of learning disability and compared them in terms of WISC cognitive indexes to the remaining children without DD (N = 100) employing cross-validated logistic regression. Outcomes and results: Both groups showed higher Verbal Comprehension and Perceptual Reasoning than Working Memory and Processing Speed, and DD scores were generally lower. Predictive accuracy of WISC indexes in identifying DD individuals was low (AUC = 0.67) and it dropped to chance level in discriminating DD from selected controls (N = 43) with average math performance but matched on global IQ. The inclusion of a visuospatial memory score as an additional predictor did not improve classification accuracy. Conclusions and implications: These results demonstrate that cognitive profiles do not reliably discriminate DD from non-DD children, thereby weakening the appeal of domain-general accounts.

Machine-learning (ML) has become a key workhorse in molecular simulations. Building an ML model in this context involves encoding the information on chemical environments using local atomic descriptors. In this work, we focus on the Smooth Overlap of Atomic Positions (SOAP) and their application in studying the properties of liquid water both in the bulk and at the hydrophobic air-water interface. By using a statistical test aimed at assessing the relative information content of different distance measures defined on the same data space, we investigate if these descriptors provide the same information as some of the common order parameters that are used to characterize local water structure such as hydrogen bonding, density, or tetrahedrality to name a few. Our analysis suggests that the ML description and the standard order parameters of the local water structure are not equivalent. In particular, a combination of these order parameters probing local water environments can predict SOAP similarity only approximately, and vice versa, the environments that are similar according to SOAP are not necessarily similar according to the standard order parameters. We also elucidate the role of some of the metaparameters in the SOAP definition in encoding chemical information.

Hydrogen is the most abundant element in the Universe. However, understanding the properties of dense hydrogen is still an open challenge because—under megabar pressures—the quantum nature of both electrons and protons emerges, producing deviations from the common behaviour of condensed-matter systems. Experiments are challenging and can access only limited observables, and the interplay between electron correlation and nuclear quantum motion makes standard simulations unreliable. Here we present the computed phase diagram of hydrogen and deuterium at low temperatures and high pressures using state-of-the-art methods to describe both many-body electronic correlation and quantum anharmonic motion of protons. Our results show that the long-sought atomic metallic hydrogen phase—predicted to host room-temperature superconductivity—forms at 577(4) GPa. The anharmonic vibrations of nuclei pushes the stability of this phase towards pressures much larger than previous estimates or attained experimental values. Before atomization, molecular hydrogen transforms from a metallic phase (phase III) to another metallic structure that is still molecular (phase VI) at 410(20) GPa. Isotope effects increase the pressures of both transitions by 63 and 32 GPa, respectively. We predict signatures in optical spectroscopy and d.c. conductivity that can be experimentally used to distinguish between the two structural transitions.

Understanding the microscopic origins of collective reorientational motions in aqueous systems requires techniques that allow us to reach beyond our chemical imagination. Herein, we elucidate a mechanism using a protocol that automatically detects abrupt motions in reorientational dynamics, showing that large angular jumps in liquid water involve highly cooperative orchestrated motions. Our automatized detection of angular fluctuations, unravels a heterogeneity in the type of angular jumps occurring concertedly in the system. We show that large orientational motions require a highly collective dynamical process involving correlated motion of many water molecules in the hydrogen-bond network that form spatially connected clusters going beyond the local angular jump mechanism. This phenomenon is rooted in the collective fluctuations of the network topology which results in the creation of defects in waves on the THz timescale. The mechanism we propose involves a cascade of hydrogen-bond fluctuations underlying angular jumps and provides new insights into the current localized picture of angular jumps, and its wide use in the interpretations of numerous spectroscopies as well in reorientational dynamics of water near biological and inorganic systems. The role of finite size effects, as well as of the chosen water model, on the collective reorientation is also elucidated.

We prove that the isomonodromic tau function on a torus with Fuchsian singularities and generic monodromies in GL(N,C)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$GL(N,{\mathbb {C}})$$\end{document} can be written in terms of a Fredholm determinant of Plemelj operators. We further show that the minor expansion of this Fredholm determinant is described by a series labeled by charged partitions. As an example, we show that in the case of SL(2,C)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$SL(2,{\mathbb {C}})$$\end{document} this combinatorial expression takes the form of a dual Nekrasov–Okounkov partition function, or equivalently of a free fermion conformal block on the torus. Based on these results we also propose a definition of the tau function of the Riemann–Hilbert problem on a torus with generic jump on the A-cycle.

Time-reversal symmetric topological insulators are generically robust with respect to weak local interaction unless symmetry-breaking transitions take place. Using dynamical mean-field theory, we solve an interacting model of quantum spin Hall insulators and show the existence at intermediate coupling of a symmetry-breaking transition to a nontopological insulator characterized by exciton condensation. This transition is of first order. For a larger interaction strength, the insulator evolves into a Mott one. The transition is continuous if magnetic order is prevented, and notably, for any finite Hund's exchange, it progresses through a Mott localization before the condensate coherence is lost. We show that the correlated excitonic state corresponds to a magneto-electric insulator, which allows for direct experimental probing. Finally, we discuss the fate of the helical edge modes across the excitonic transition.

Plain Language Summary Solute mixing is the process that homogenizes chemical species' concentrations over space in time. Solute mixing rates are crucial to chemical reactions associated with environmental flow phenomena. Classical flow and transport models use solute concentration values averaged over length scales of a few millimeters and higher. But concentrations can show substantial variations at single pores' sub‐millimetric length scales, which impact large‐scale reaction rates. Besides, the heterogeneous porous structure of subsurface natural materials causes flow velocity variations between solid grains, which impact spatial variations of concentration within solute plumes. We present solute transport experiments in a flow cell mimicking the geometry of natural porous media. The transparent flow cell allows direct measuring of the concentration of a fluorescent dye injected at various flow rates to infer their impact on concentration variations. The recently introduced lamellar mixing theory describes the overall time dynamics of concentration variations well. However, the maximum mixing rates differ from theoretical predictions due to an additional mechanism currently not considered in the theory: the aggregation of solute lamellae. This work establishes the full relevance of the lamellar mixing theory to porous media flow when accounting for lamellae aggregation.

The aim of this study was to psychophysically evaluate the prevalence of smell and taste dysfunction two years after mildly symptomatic SARS‐CoV‐2 infection compared to that observed at one‐year follow‐up and while considering the background of chemosensory dysfunction in the no‐COVID‐19 population. This is a prospective case‐control study 93 patients with PCR‐positive SARS‐CoV‐2 infection and 93 matched controls. Self‐reported olfactory and gustatory dysfunction was assessed by Sino‐nasal‐Outcome‐Test‐22, item “Sense of smell or taste”. Psychophysical ortho‐ and retronasal olfactory function and gustatory performance were estimated using the extended Sniffin’ Sticks test battery, 20 powdered tasteless aromas, and taste strips test, respectively. Nasal trigeminal sensitivity was assessed by sniffing a 70% solution of acetic acid. The two psychophysical assessments of chemosensory function took place after a median of 409 days (range: 366‐461) and 765 days (range: 739‐800) from the first SARS‐CoV‐2 positive swab, respectively. At two‐year follow‐up, cases exhibited a decrease in the prevalence of olfactory (27.9%% vs 42.0%; absolute difference, ‐14.0%; 95% CI, ‐21.8% to ‐2.6%; p = 0.016) and gustatory dysfunction (14.0% vs 25.8%; absolute difference, ‐11.8%; 95% CI, ‐24.2% to 0.6%; p = 0.098). Subjects with prior COVID‐19 were more likely than controls to have an olfactory (27.9% vs 10.8 %; absolute difference, 17.2%; 95% CI, 5.2% to 28.8%) but not gustatory dysfunction (14.0% vs 9.7%; absolute difference, 4.3%; 95% CI, ‐5.8% to 14.4% p = 0.496) still two years after the infection. Overall, 3.2% of cases were still anosmic two‐year after the infection. While a proportion of subjects recovered from long‐lasting smell/taste dysfunction more than one year after COVID‐19, cases still exhibited a significant excess of olfactory dysfunction two years after SARS‐CoV‐2 infection when compared to matched controls. This article is protected by copyright. All rights reserved

Performing alchemical transformations, in which one molecular system is nonphysically changed to another system, is a popular approach adopted in performing free energy calculations associated with various biophysical processes, such as protein-ligand binding or the transfer of a molecule between environments. While the sampling of alchemical intermediate states in either parallel (e.g., Hamiltonian replica exchange) or serial manner (e.g., expanded ensemble) can bridge the high-probability regions in the configurational space between two end states of interest, alchemical methods can fail in scenarios where the most important slow degrees of freedom in the configurational space are, in large part, orthogonal to the alchemical variable, or if the system gets trapped in a deep basin extending in both the configurational and alchemical space. To alleviate these issues, we propose to use alchemical variables as an additional dimension in metadynamics, making it possible to both sample collective variables and to enhance sampling in free energy calculations simultaneously. In this study, we validate our implementation of "alchemical metadynamics" in PLUMED with test systems and alchemical processes with varying complexities and dimensionalities of collective variable space, including the interconversion between the torsional metastable states of a toy system and the methylation of a nucleoside both in the isolated form and in a duplex. We show that multidimensional alchemical metadynamics can address the challenges mentioned above and further accelerate sampling by introducing configurational collective variables. The method can trivially be combined with other metadynamics-based algorithms implemented in PLUMED. The necessary PLUMED code changes have already been released for general use in PLUMED 2.8.

[This corrects the article DOI: 10.3389/fnagi.2022.848991.].

The complexity of mathematical models in biology has rendered model reduction an essential tool in the quantitative biologist's toolkit. For stochastic reaction networks described using the Chemical Master Equation, commonly used methods include time-scale separation, the Linear Mapping Approximation and state-space lumping. Despite the success of these techniques, they appear to be rather disparate and at present no general-purpose approach to model reduction for stochastic reaction networks is known. In this paper we show that most common model reduction approaches for the Chemical Master Equation can be seen as minimising a well-known information-theoretic quantity between the full model and its reduction, the Kullback-Leibler divergence defined on the space of trajectories. This allows us to recast the task of model reduction as a variational problem that can be tackled using standard numerical optimisation approaches. In addition we derive general expressions for the propensities of a reduced system that generalise those found using classical methods. We show that the Kullback-Leibler divergence is a useful metric to assess model discrepancy and to compare different model reduction techniques using three examples from the literature: an autoregulatory feedback loop, the Michaelis-Menten enzyme system and a genetic oscillator.

The operator entanglement (OE) is a key quantifier of the complexity of a reduced density matrix. In out-of-equilibrium situations, e.g., after a quantum quench of a product state, it is expected to exhibit an entanglement barrier. The OE of a reduced density matrix initially grows linearly as entanglement builds up between the local degrees of freedom; it then reaches a maximum and ultimately decays to a small finite value as the reduced density matrix converges to a simple stationary state through standard thermalization mechanisms. Here, by performing a new data analysis of the published experimental results of Brydges et al. [Science 364, 260 (2019)], we obtain the first experimental estimation of the OE of a subsystem reduced density matrix in a quantum many-body system. We employ the randomized-measurements toolbox and we introduce and develop a new efficient method to postprocess experimental data in order to extract higher-order density-matrix functionals and access the OE. The OE thus obtained displays the expected barrier as long as the experimental system is large enough. For smaller systems, we observe a barrier with a double-peak structure, the origin of which can be interpreted in terms of pairs of quasiparticles being reflected at the boundary of the qubit chain. As U(1) symmetry plays a key role in our analysis, we introduce the notion of symmetry-resolved operator entanglement (SROE), in addition to the total OE. To gain further insights into the SROE, we provide a thorough theoretical analysis of this new quantity in chains of noninteracting fermions, which, in spite of their simplicity, capture most of the main features of OE and SROE. In particular, we uncover three main physical effects: the presence of a barrier in any charge sector, a time delay for the onset of the growth of SROE, and an effective equipartition between charge sectors.

A bstract A local flavour symmetry acting on the quarks of the Standard Model can automatically give rise to an accidental global U(1) which remains preserved from sources of explicit breaking up to a large operator dimension, while it gets spontaneously broken together with the flavour symmetry. Such non-fundamental symmetries are often endowed with a mixed QCD anomaly, so that the strong CP problem is automatically solved via the axion mechanism. We illustrate the general features required to realise this scenario, and we discuss a simple construction based on the flavour group SU(3) × SU(2) × U(1) F to illustrate how mass hierarchies can arise while ensuring at the same time a high quality Peccei-Quinn symmetry.

A bstract The muon anomalous magnetic moment continues to attract attention due to the possible tension between the experimentally measured value and the theoretical Standard Model prediction. With the aim to reduce the uncertainty on the hadronic light-by-light contribution to the magnetic moment, we derive short-distance constraints in the Melnikov-Vainshtein regime which are useful for data-driven determinations. In this kinematical region, two of the four electromagnetic currents are close in the four-point function defining the hadronic light-by-light tensor. To obtain the constraints, we develop a systematic operator product expansion of the tensor in question to next-to-leading order in the expansion in operators. We evaluate the leading in α s contributions and derive constraints for the next-to-leading operators that are also valid nonperturbatively.

Real-world datasets characterized by discrete features are ubiquitous: from categorical surveys to clinical questionnaires, from unweighted networks to DNA sequences. Nevertheless, the most common unsupervised dimensional reduction methods are designed for continuous spaces, and their use for discrete spaces can lead to errors and biases. In this Letter we introduce an algorithm to infer the intrinsic dimension (ID) of datasets embedded in discrete spaces. We demonstrate its accuracy on benchmark datasets, and we apply it to analyze a metagenomic dataset for species fingerprinting, finding a surprisingly small ID, of order 2. This suggests that evolutive pressure acts on a low-dimensional manifold despite the high dimensionality of sequences’ space.

Classical gravity is understood as the geometry of spacetime, and it seems very different from the other known interactions. In this review, I will instead stress the analogies: Like strong interactions, the low energy effective field theory of gravity is related to a nonlinearly realized symmetry, and like electroweak interactions, it is a gauge theory in Higgs phase, with a massive connection. I will also discuss the possibility of finding a UV complete quantum field theoretic description of all interactions.