Application of topological soliton in modeling protein folding: Recent progress and perspective

Proteins are important biological molecules whose structures are closely related to their specific functions. Understanding how the protein folds under physical principles, known as the protein folding problem, is one of the main tasks in modern biophysics. Coarse-grained methods play an increasingly important role in the simulation of protein folding, especially for large proteins. In recent years, we proposed a novel coarse-grained method derived from the topological soliton model, in terms of the backbone Cα chain. In this review, we will first systematically address the theoretical method of topological soliton. Then some successful applications will be displayed, including the thermodynamics simulation of protein folding, the property analysis of dynamic conformations, and the multi-scale simulation scheme. Finally, we will give a perspective on the development and application of topological soliton.

Inverse Ising techniques to infer underlying mechanisms from data

As a problem in data science the inverse Ising(or Potts)problem is to infer the parameters of a Gibbs-Boltzmann distributions of an Ising(or Potts)model from samples drawn from that distribution.The algorithmic and computational interest stems from the fact that this inference task cannot be carried out efficiently by the maximum likelihood criterion,since the normalizing constant of the distribution(the partition function)cannot be calculated exactly and efficiently.The practical interest on the other hand flows from several outstanding applications,of which the most well known has been predicting spatial contacts in protein structures from tables of homologous protein sequences.Most applications to date have been to data that has been produced by a dynamical process which,as far as it is known,cannot be expected to satisfy detailed balance.There is therefore no a priori reason to expect the distribution to be of the Gibbs-Boltzmann type,and no a priori reason to expect that inverse Ising(or Potts)techniques should yield useful information.In this review we discuss two types of problems where progress nevertheless can be made.We find that depending on model parameters there are phases where,in fact,the distribution is close to Gibbs-Boltzmann distribution,a non-equilibrium nature of the under-lying dynamics notwithstanding.We also discuss the relation between inferred Ising model parameters and parameters of the underlying dynamics.

具有半子模式欧拉拓扑绝缘体的声学观测

Topological band theory has conventionally been concerned with the topology of bands around a single gap. Only recently non-Abelian topologies that thrive on involving multiple gaps were studied, unveiling a new horizon in topological physics beyond the conventional paradigm. Here, we report on the first experimental realization of a topological Euler insulator phase with unique meronic characterization in an acoustic metamaterial. We demonstrate that this topological phase has several nontrivial features:First, the system cannot be described by conventional topological band theory, but has a nontrivial Euler class that captures the unconventional geometry of the Bloch bands in the Brillouin zone.Second, we uncover in theory and probe in experiments a meronic configuration of the bulk Bloch states for the first time. Third, using a detailed symmetry analysis, we show that the topological Euler insulator evolves from a non-Abelian topological semimetal phase via. the annihilation of Dirac points in pairs in one of the band gaps. With these nontrivial properties, we establish concretely an unconventional bulk-edge correspondence which is confirmed by directly measuring the edge states via. pump-probe techniques. Our work thus unveils a nontrivial topological Euler insulator phase with a unique meronic pattern and paves the way as a platform for non-Abelian topological phenomena.

Online search tool for graphical patterns in electronic band structures

Many functional materials can be characterized by a specific pattern in their electronic band structure,for example,Dirac materials,characterized by a linear crossing of bands;topological insulators,characterized by a“Mexican hat”pattern or an effectively free electron gas,characterized by a parabolic dispersion.To find material realizations of these features,manual inspection of electronic band structures represents a relatively easy task for a small number of materials.However,the growing amount of data contained within modern electronic band structure databases makes this approach impracticable.To address this problem,we present an automatic graphical pattern search tool implemented for the electronic band structures contained within the Organic Materials Database.The tool is capable of finding user-specified graphical patterns in the collection of thousands of band structures from high-throughput calculations in the online regime.Using this tool,it only takes a few seconds to find an arbitrary graphical pattern within the ten electronic bands near the Fermi level for 26,739 organic crystals.The source code of the developed tool is freely available and can be adapted to any other electronic band structure database.

A high-throughput data analysis and materials discovery tool for strongly correlated materials

Modeling of f-electron systems is challenging due to the complex interplay of the effects of spin–orbit coupling,electron–electron interactions,and the hybridization of the localized f-electrons with itinerant conduction electrons.This complexity drives not only the richness of electronic properties but also makes these materials suitable for diverse technological applications.In this context,we propose and implement a data-driven approach to aid the materials discovery process.By deploying state-of-the-art algorithms and query tools,we train our learning models using a large,simulated dataset based on existing actinide and lanthanide compounds.The machine-learned models so obtained can then be used to search for new classes of stable materials with desired electronic and physical properties.We discuss the basic structure of our f-electron database,and our approach towards cleaning and correcting the structure data files.Illustrative examples of the applications of our database include successful prediction of stable superstructures of double perovskites and identification of a number of physically-relevant trends in strongly correlated features of f-electron based materials.

Piecewise quadratic growth during the 2019 novel coronavirus epidemic

The temporal growth in the number of deaths in the COVID-19 epidemic is subexponential.Here we show that a piecewise quadratic law provides an excellent fit during the thirty days after the first three fatalities on January 20 and later since the end of March 2020.There is also a brief intermediate period of exponential growth.During the second quadratic growth phase,the characteristic time of the growth is about eight times shorter than in the beginning,which can be understood as the occurrence of separate hotspots.Quadratic behavior can be motivated by peripheral growth when further spreading occurs only on the outskirts of an infected region.We also study numerical solutions of a simple epidemic model,where the spatial extend of the system is taken into account.To model the delayed onset outside China together with the early one in China within a single model with minimal assumptions,we adopt an initial condition of several hotspots,of which one reaches saturation much earlier than the others.At each site,quadratic growth commences when the local number of infections has reached a certain saturation level.The total number of deaths does then indeed follow a piecewise quadratic behavior.

Dense matter with eXTP

In this White Paper we present the potential of the Enhanced X-ray Timing and Polarimetry(eXTP) mission for determining the nature of dense matter; neutron star cores host an extreme density regime which cannot be replicated in a terrestrial laboratory. The tightest statistical constraints on the dense matter equation of state will come from pulse profile modelling of accretion-powered pulsars, burst oscillation sources, and rotation-powered pulsars. Additional constraints will derive from spin measurements, burst spectra, and properties of the accretion flows in the vicinity of the neutron star. Under development by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of Sciences, the eXTP mission is expected to be launched in the mid 2020 s.