Accumulated clinical studies show that microbes living in humans interact closely with human hosts, and get involved in modulating drug efficacy and drug toxicity. Microbes have become novel targets for the development of antibacterial agents. Theref
ore, screening of microbe-drug associations can benefit greatly drug research and development. With the increase of microbial genomic and pharmacological datasets, we are greatly motivated to develop an effective computational method to identify new microbe-drug associations. In this paper, we proposed a novel method, Graph2MDA, to predict microbe-drug associations by using variational graph autoencoder (VGAE). We constructed multi-modal attributed graphs based on multiple features of microbes and drugs, such as molecular structures, microbe genetic sequences, and function annotations. Taking as input the multi-modal attribute graphs, VGAE was trained to learn the informative and interpretable latent representations of each node and the whole graph, and then a deep neural network classifier was used to predict microbe-drug associations. The hyperparameter analysis and model ablation studies showed the sensitivity and robustness of our model. We evaluated our method on three independent datasets and the experimental results showed that our proposed method outperformed six existing state-of-the-art methods. We also explored the meaningness of the learned latent representations of drugs and found that the drugs show obvious clustering patterns that are significantly consistent with drug ATC classification. Moreover, we conducted case studies on two microbes and two drugs and found 75%-95% predicted associations have been reported in PubMed literature. Our extensive performance evaluations validated the effectiveness of our proposed method.
We demonstrate that a three dimensional time-periodically driven lattice system can exhibit a second-order chiral skin effect and describe its interplay with Weyl physics. This Floquet skin-effect manifests itself, when considering open rather than p
eriodic boundary conditions for the system. Then an extensive number of bulk modes is transformed into chiral modes that are bound to the hinges (being second-order boundaries) of our system, while other bulk modes form Fermi arc surface states connecting a pair of Weyl points. At a fine tuned point, eventually all boundary states become hinge modes and the Weyl points disappear. The accumulation of an extensive number of modes at the hinges of the system resembles the non-Hermitian skin effect, with one noticeable difference being the localization of the Floquet hinge modes at increasing distances from the hinges in our system. We intuitively explain the emergence of hinge modes in terms of repeated backreflections between two hinge-sharing faces and relate their chiral transport properties to chiral Goos-Hanchen-like shifts associated with these reflections. Moreover, we formulate a topological theory of the second-order Floquet skin effect based on the quasi-energy winding around the Floquet-Brillouin zone for the family of hinge states. The implementation of a model featuring both the second-order Floquet skin effect and the Weyl physics is straightforward with ultracold atoms in optical superlattices.
Great enthusiasm in single atom catalysts (SACs) for the N2 reduction reaction (NRR) has been aroused by the discovery of Metal (M)-Nx as a promising catalytic center. However,the performance of available SACs,including poor activity and selectivity,
is far away from the industrial requirement because of the inappropriate adsorption behaviors of the catalytic centers. Through the first principles high throughput screening, we find that the rational construction of Fe-Fe dual atom centered site distributed on graphite carbon nitride (Fe2/gCN) compromises the ability to adsorb N2H and NH2, achieving the best NRR performance among 23 different transition metal (TM) centers. Our results show that Fe2/gCN can achieve a Faradic efficiency of 100% for NH3 production. Impressively, the limiting potential of Fe2/gCN is estimated as low as -0.13 V, which is hitherto the lowest value among the reported theoretical results. Multiple level descriptors (excess electrons on the adsorbed N2 and integrated crystal orbital Hamilton population) shed light on the origin of NRR activity from the view of energy, electronic structure, and basic characteristics. As a ubiquitous issue during electrocatalytic NRR, ammonia contamination originating from the substrate decomposition can be surmounted. Our predictions offer a new platform for electrocatalytic synthesis of NH3, contributing to further elucidate the structure-performance correlations.
We propose a versatile framework to dynamically generate Floquet higher-order topological insulators by multi-step driving of topologically trivial Hamiltonians. Two analytically solvable examples are used to illustrate this procedure to yield Floque
t quadrupole and octupole insulators with zero- and/or $pi$-corner modes protected by mirror symmetries. Furthermore, we introduce dynamical topological invariants from the full unitary return map and show its phase bands contain Weyl singularities whose topological charges form dynamical multipole moments in the Brillouin zone. Combining them with the topological index of Floquet Hamiltonian gives a pair of $mathbb{Z}_2$ invariant $ u_0$ and $ u_pi$ which fully characterize the higher-order topology and predict the appearance of zero- and $pi$-corner modes. Our work establishes a systematic route to construct and characterize Floquet higher-order topological phases.
In the lattice designs for the next generation storage ring light sources, longitudinal gradient bending magnets and anti-bending magnets have been adopted. A logical question raised by the trend of varying the longitudinal distribution of dipole str
ength is: what are the optimal distributions of the dipole and quadrupole fields in a lattice cell for the purpose of minimizing the natural emittance? We studied this problem by numerically optimizing the dipole and quadrupole distributions of the normalized cell optics using the particle swarm optimization algorithm. The results reveal the features of the longitudinal field variation of the optimized cell and show that when the quadrupole gradient is increased enough, the cell tends to split into two identical cells with similar features.
Higher order topological insulators (HOTI) have emerged as a new class of phases, whose robust in-gap corner modes arise from the bulk higher-order multipoles beyond the dipoles in conventional topological insulators. Here, we incorporate Floquet dri
ving into HOTIs, and report for the first time a dynamical polarization theory with anomalous non-equilibrium multipoles. Further, a proposal to detect not only corner states but also their dynamical origin in cold atoms is demonstrated, with the latter one never achieved before. Experimental determination of anomalous Floquet corner modes are also proposed.
Recent experimental evidence for a field-induced quantum spin liquid (QSL) in $alpha$-RuCl$_3$ calls for an understanding for the ground state of honeycomb Kitaev model under a magnetic field. In this work we address the nature of an enigmatic gaples
s paramagnetic phase in the antiferromagnetic Kitave model, under an intermediate magnetic field perpendicular to the plane. Combining theoretical and numerical efforts, we identify this gapless phase as a $U(1)$ QSL with spinon Fermi surfaces. We also reveal the nature of continuous quantum phase transitions involving this $U(1)$ QSL, and obtain a phase diagram of the Kitaev model as a function of bond anisotropy and perpendicular magnetic field.
For large-scale industrial processes under closed-loop control, process dynamics directly resulting from control action are typical characteristics and may show different behaviors between real faults and normal changes of operating conditions. Howev
er, conventional distributed monitoring approaches do not consider the closed-loop control mechanism and only explore static characteristics, which thus are incapable of distinguishing between real process faults and nominal changes of operating conditions, leading to unnecessary alarms. In this regard, this paper proposes a distributed monitoring method for closed-loop industrial processes by concurrently exploring static and dynamic characteristics. First, the large-scale closed-loop process is decomposed into several subsystems by developing a sparse slow feature analysis (SSFA) algorithm which capture changes of both static and dynamic information. Second, distributed models are developed to separately capture static and dynamic characteristics from the local and global aspects. Based on the distributed monitoring system, a two-level monitoring strategy is proposed to check different influences on process characteristics resulting from changes of the operating conditions and control action, and thus the two changes can be well distinguished from each other. Case studies are conducted based on both benchmark data and real industrial process data to illustrate the effectiveness of the proposed method.
Two-dimensional topological insulators and two-dimensional materials with ferroelastic characteristics are intriguing materials and many examples have been reported both experimentally and theoretically. Here, we present the combination of both featu
res - a two-dimensional ferroelastic topological insulator that simultaneously possesses ferroelastic and quantum spin Hall characteristics. Using first-principles calculations, we demonstrate Janus single-layer MSSe (M=Mo, W) stable two-dimensional crystals that show the long-sought ferroelastic topological insulator properties. The material features low switching barriers and strong ferroelastic signals, beneficial for applications in nonvolatile memory devices. Moreover, their topological phases harbor sizeable nontrivial band gaps, which supports the quantum spin Hall effect. The unique coexistence of excellent ferroelastic and quantum spin Hall phases in single-layer MSSe provides extraordinary platforms for realizing multi-purpose and controllable devices.
The family of Kitaev materials provides an ideal platform to study quantum spin liquids and their neighboring magnetic orders. Motivated by the possibility of a quantum spin liquid ground state in pressurized hyperhoneycomb iridate $beta$-Li$_2$IrO$_
3$, we systematically classify and study symmetric quantum spin liquids on the hyperhoneycomb lattice, using the Abrikosov-fermion representation. Among the 176 symmetric $U(1)$ spin liquids (and 160 $Z_2$ spin liquids), we identify 8 root $U(1)$ spin liquids in proximity to the ground state of the solvable Kitave model on hyperhonecyomb lattices. These 8 states are promising candidates for possible $U(1)$ spin liquid ground states in pressurized $beta$-Li$_2$IrO$_3$. We further discuss physical properties of these 8 $U(1)$ spin liquid candidates, and show that they all support nodal-line-shaped spinon Fermi surfaces.
