Model based on COVID-19 evidence to predict and improve pandemic control.

Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus.

Metabolic switch in the aging astrocyte supported via integrative approach comprising network and transcriptome analyses.

Dysregulated central-energy metabolism is a hallmark of brain aging. Supplying enough energy for neurotransmission relies on the neuron-astrocyte metabolic network. To identify genes contributing to age-associated brain functional decline, we formulated an approach to analyze the metabolic network by integrating flux, network structure and transcriptomic databases of neurotransmission and aging. Our findings support that during brain aging: (1) The astrocyte undergoes a metabolic switch from aerobic glycolysis to oxidative phosphorylation, decreasing lactate supply to the neuron, while the neuron suffers intrinsic energetic deficit by downregulation of Krebs cycle genes, including mdh1 and mdh2 (Malate-Aspartate Shuttle); (2) Branched-chain amino acid degradation genes were downregulated, identifying dld as a central regulator; (3) Ketone body synthesis increases in the neuron, while the astrocyte increases their utilization, in line with neuronal energy deficit in favor of astrocytes. We identified candidates for preclinical studies targeting energy metabolism to prevent age-associated cognitive decline.

The impact of the suppression of highly connected protein interactions on the corona virus infection.

Several highly effective Covid-19 vaccines are in emergency use, although more-infectious coronavirus strains, could delay the end of the pandemic even further. Because of this, it is highly desirable to develop fast antiviral drug treatments to accelerate the lasting immunity against the virus. From a theoretical perspective, computational approaches are useful tools for antiviral drug development based on the data analysis of gene expression, chemical structure, molecular pathway, and protein interaction mapping. This work studies the structural stability of virus-host interactome networks based on the graphical representation of virus-host protein interactions as vertices or nodes connected by commonly shared proteins. These graphical network visualization methods are analogous to those use in the design of artificial neural networks in neuromorphic computing. In standard protein-node-based network representation, virus-host interaction merges with virus-protein and host-protein networks, introducing redundant links associated with the internal virus and host networks. On the contrary, our approach provides a direct geometrical representation of viral infection structure and allows the effective and fast detection of the structural robustness of the virus-host network through proteins removal. This method was validated by applying it to H1N1 and HIV viruses, in which we were able to pinpoint the changes in the Interactome Network produced by known vaccines. The application of this method to the SARS-CoV-2 virus-host protein interactome implies that nonstructural proteins nsp4, nsp12, nsp16, the nuclear pore membrane glycoprotein NUP210, and ubiquitin specific peptidase USP54 play a crucial role in the viral infection, and their removal may provide an efficient therapy. This method may be extended to any new mutations or other viruses for which the Interactome Network is experimentally determined. Since time is of the essence, because of the impact of more-infectious strains on controlling the spread of the virus, this method may be a useful tool for novel antiviral therapies.

Thermal Stability of Hollow Porous Gold Nanoparticles: A Molecular Dynamics Study.

Hollow nanoparticle structures play a major role in nanotechnology and nanoscience since their surface to volume ratio is significantly larger than that of filled ones. While porous hollow nanoparticles offer a significant improvement of the available surface area, there is a lack of theoretical understanding, and scarce experimental information, on how the porosity controls or dominates the stability. Here we use classical molecular dynamics simulations to shed light on the particular characteristics and properties of gold porous hollow nanoparticles and how they differ from the nonporous ones. Adopting gold as a prototype, we show how, as the temperature increases, the porosity introduces surface stress and minor transitions that lead to various scenarios, from partial shrinkage for small filling factors to abrupt compression and the loss of spherical shape for large filling. Our work provides new insights into the stability limits of porous hollow nanoparticles, with important implications for the design and practical use of these enhanced geometries.

Author Correction: Nucleation of superfluid-light domains in a quenched dynamics.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

The Stochastic Transport Dynamics of a Conserved Quantity on a Complex Network.

The stochastic dynamics of conserved quantities is an emergent phenomena observed in many complex systems, ranging from social and to biological networks. Using an extension of the Ehrenfest urn model on a complex network, over which a conserved quantity is transported in a random fashion, we study the dynamics of many elementary packets transported through the network by means of a master equation approach and compare with the mean field approximation and stochastic simulations. By use of the mean field theory, it is possible to compute an approximation to the ensemble average evolution of the number of packets in each node which, in the thermodynamic limit, agrees quite well with the results of the master equation. However, the master equation gives a more complete description of the stochastic system and provides a probabilistic view of the occupation number at each node. Of particular relevance is the standard deviation of the occupation number at each node, which is not uniform for a complex network. We analyze and compare different network topologies (small world, scale free, Erdos-Renyi, among others). Given the computational complexity of directly evaluating the asymptotic, or equilibrium, occupation number probability distribution, we propose a scaling relation with the number of packets in the network, that allows to construct the asymptotic probability distributions from the network with one packet. The approximation, which relies on the same matrix found in the mean field approach, becomes increasingly more accurate for a large number of packets.

Nucleation of superfluid-light domains in a quenched dynamics.

Strong correlation effects emerge from light-matter interactions in coupled resonator arrays, such as the Mott-insulator to superfluid phase transition of atom-photon excitations. We demonstrate that the quenched dynamics of a finite-sized complex array of coupled resonators induces a first-order like phase transition. The latter is accompanied by domain nucleation that can be used to manipulate the photonic transport properties of the simulated superfluid phase; this in turn leads to an empirical scaling law. This universal behavior emerges from the light-matter interaction and the topology of the array. The validity of our results over a wide range of complex architectures might lead to a promising device for use in scaled quantum simulations.

Ion implantation in nanodiamonds: size effect and energy dependence.

Nanoparticles are ubiquitous in nature and are increasingly important for technology. They are subject to bombardment by ionizing radiation in a diverse range of environments. In particular, nanodiamonds represent a variety of nanoparticles of significant fundamental and applied interest. Here we present a combined experimental and computational study of the behaviour of nanodiamonds under irradiation by xenon ions. Unexpectedly, we observed a pronounced size effect on the radiation resistance of the nanodiamonds: particles larger than 8 nm behave similarly to macroscopic diamond (i.e. characterized by high radiation resistance) whereas smaller particles can be completely destroyed by a single impact from an ion in a defined energy range. This latter observation is explained by extreme heating of the nanodiamonds by the penetrating ion. The obtained results are not limited to nanodiamonds, making them of interest for several fields, putting constraints on processes for the controlled modification of nanodiamonds, on the survival of dust in astrophysical environments, and on the behaviour of actinides released from nuclear waste into the environment.

Bending energy of 2D materials: graphene, MoS2 and imogolite.

The bending process of 2D materials, subject to an external force, is investigated, and applied to graphene, molybdenum disulphide (MoS2), and imogolite. For graphene we obtained 3.43 eV Å2 per atom for the bending modulus, which is in good agreement with the literature. We found that MoS2 is ∼11 times harder to bend than graphene, and has a bandgap variation of ∼1 eV as a function of curvature. Finally, we also used this strategy to study aluminosilicate nanotubes (imogolite) which, in contrast to graphene and MoS2, present an energy minimum for a finite curvature radius. Roof tile shaped imogolite precursors turn out to be stable, and thus are expected to be created during imogolite synthesis, as predicted to occur by self-assembly theory.

Dipole-induced exchange bias.

The discovery of dipole-induced exchange bias (EB), switching from negative to positive sign, is reported in systems where the antiferromagnet and the ferromagnet are separated by a paramagnetic spacer (AFM-PM-FM). The magnitude and sign of the EB is determined by the cooling field strength and the PM thickness. The same cooling field yields negative EB for thin spacers, and positive EB for thicker ones. The EB decay profile as a function of the spacer thickness, and the change of sign, are attributed to long-ranged dipole coupling. Our model, which accounts quantitatively for the experimental results, ignores the short range interfacial exchange interactions of the usual EB theories. Instead, it retains solely the long range dipole field that allows for the coupling of the FM and AFM across the PM spacer. The experiments allow for novel switching capabilities of long range EB systems, while the theory allows description of the structures where the FM and AFM are not in atomic contact. The results provide a new approach to design novel interacting heterostructures.

Au13-nAgn clusters: a remarkably simple trend.

The planar to three dimensional transition of Au13-nAgn clusters is investigated. To do so the low lying energy configurations for all possible concentrations (n values) are evaluated. Many thousands of possible conformations are examined. They are generated using the procedure developed by Rogan et al. in combination with the semi-empirical Gupta potential. A large fraction of these (the low lying energy ones) are minimized by means of Density Functional Theory (DFT) calculations. We employ the Tao, Perdew, Staroverov, and Scuseria (TPSS) meta-GGA functional and the Perdew, Burke and Ernzerhof (PBE) GGA functional, and compare their results. The effect of spin-orbit coupling is studied as well as the s-d hybridization. As usual in this context the results are functional-dependent. However, both functionals lead to agreement as far as trends are concerned, yielding just two relevant motifs, but their results differ quantitatively.

Generalization of the Ehrenfest urn model to a complex network.

The Ehrenfest urn model is extended to a complex directed network, over which a conserved quantity is transported in a random fashion. The evolution of the conserved number of packets in each urn, or node of the network, is illustrated by means of a stochastic simulation. Using mean-field theory we were able to compute an approximation to the ensemble-average evolution of the number of packets in each node which, in the thermodynamic limit, agrees quite well with the results of the stochastic simulation. Using this analytic approximation we are able to find the asymptotic dynamical state of the system and the time scale to approach the equilibrium state, for different networks. The study is extended to large scale-free and small-world networks, in which the relevance of the connectivity distribution and the topology of the network for the distribution of time scales of the system is apparent. This analysis may contribute to the understanding of the transport properties in real networks subject to a perturbation, e.g., the asymptotic state and the time scale required to approach it.

A strategy to find minimal energy nanocluster structures.

An unbiased strategy to search for the global and local minimal energy structures of free standing nanoclusters is presented. Our objectives are twofold: to find a diverse set of low lying local minima, as well as the global minimum. To do so, we use massively the fast inertial relaxation engine algorithm as an efficient local minimizer. This procedure turns out to be quite efficient to reach the global minimum, and also most of the local minima. We test the method with the Lennard-Jones (LJ) potential, for which an abundant literature does exist, and obtain novel results, which include a new local minimum for LJ13 , 10 new local minima for LJ14 , and thousands of new local minima for 15≤N≤65. Insights on how to choose the initial configurations, analyzing the effectiveness of the method in reaching low-energy structures, including the global minimum, are developed as a function of the number of atoms of the cluster. Also, a novel characterization of the potential energy surface, analyzing properties of the local minima basins, is provided. The procedure constitutes a promising tool to generate a diverse set of cluster conformations, both two- and three-dimensional, that can be used as an input for refinement by means of ab initio methods.

Diversity driven unbiased search of minimum energy cluster configurations.

The determination of the spatial distributions that atoms adopt to form condensed matter is a problem of crucial importance, since most physical properties depend on the atomic arrangement. This is especially relevant for clusters, where periodicity is nonexistent. Several optimization procedures have been implemented to tackle this problem, with ever increasing success. Here we put forward a search scheme which preserves as large a diversity as allowed by the use of phenomenological potentials, generating in an unbiased fashion a bank of configurations to be explored; a procedure we denominate diversity driven unbiased search (DDUS). It consists in the generation, using phenomenological potentials, of a data bank of putative minima rather than a single, or just a few, configurations which are based on the conformational space annealing method (CSA). All of the configurations in the bank are thereafter refined by means of DFT computations. Certainly, in spite of our efforts to generate a bank as diverse as possible, not all relevant structures might be included in it, since quantum effects are ignored. The procedure is applied to several examples of rhodium, palladium, silver, platinum and gold clusters, between 5 and 23 atoms in size. The main conclusion we reach is that unbiased search, among a significant number of candidates, quite often leads to rather unexpectedly low symmetry configurations, which turn out to be the lowest energy ones within our scheme.

The structure and properties of small Pd clusters.

The zero-temperature minimal energy structure of small free-standing Pd clusters (14≤N≤21, where N is the number of atoms in the cluster), their characteristics and their magnetic configurations are investigated. Results obtained using five different phenomenological many-body potentials (implemented in combination with a genetic algorithm search) are refined by means of various density functional theory (DFT) techniques. The agreement and differences between the results obtained with our procedure, using these five potentials, are displayed in detail. While phenomenological potentials yield values that approach the minimal energies of larger clusters, as compared with DFT results, they fail to predict the right symmetry group for some of the clusters with N>14. We find that the minimal energy configurations are not necessarily associated with high symmetry of the atomic arrangement. Actually, several cases of previously overlooked low symmetry structures turn out to have lower energies than more symmetric ones.

Alternative search strategy for minimal energy nanocluster structures: the case of rhodium, palladium, and silver.

An alternative strategy to find the minimal energy structure of nanoclusters is presented and implemented. We use it to determine the structure of metallic clusters. It consists in an unbiased search, with a global minimum algorithm: conformational space annealing. First, we find the minima of a many-body phenomenological potential to create a data bank of putative minima. This procedure assures us the generation of a set of cluster configurations of large diversity. Next, the clusters in this data bank are relaxed by ab initio techniques to obtain their energies and geometrical structures. The scheme is successfully applied to magic number 13 atom clusters of rhodium, palladium, and silver. We obtained minimal energy cluster structures not previously reported, which are different from the phenomenological minima. Moreover, they are not always highly symmetric, thus casting some doubt on the customary biased search scheme, which consists in relaxing with density functional theory global minima chosen among high symmetry structures obtained by means of phenomenological potentials.

Ab initio study of cubyl chains and networks.

The spatial arrangements and physical properties of one- and two-dimensional structures, based on the amazing cubane (C(8)H(8)) molecule, are investigated in detail. In particular, we compute the electronic structure, both by first principle calculations and by semiempirical methods. The elastic and vibrational properties are evaluated as well. All these results are compared with those of the single cubane molecule, in order to elucidate the influence of dimensionality.