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1. Unraveling dynamic protein structures by two-dimensional infrared spectra with a pretrained machine learning model

   https://doi.org/10.1073/pnas.2409257121  

   Wu, Fan; Huang, Yan; Yang, Guokun; Ye, Sheng; Mukamel, Shaul; Jiang, Jun (July 2024, Proceedings of the National Academy of Sciences)

   Dynamic protein structures are crucial for deciphering their diverse biological functions. Two-dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral characteristics to dynamic structures poses a formidable challenge. Here, we present a pretrained machine learning model based on 2DIR spectra analysis. This model has learned signal features from approximately 204,300 spectra to establish a “spectrum-structure” correlation, thereby tracing the dynamic conformations of proteins. It excels in accurately predicting the dynamic content changes of various secondary structures and demonstrates universal transferability on real folding trajectories spanning timescales from microseconds to milliseconds. Beyond exceptional predictive performance, the model offers attention-based spectral explanations of dynamic conformational changes. Our 2DIR-based pretrained model is anticipated to provide unique insights into the dynamic structural information of proteins in their native environments. 

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2. $2+1$ dimensional Floquet systems and lattice fermions: Exact bulk spectral equivalence

   https://doi.org/10.21468/SciPostPhysCore.8.2.035  

   Iadecola, Thomas; Sen, Srimoyee; Sivertsen, Lars (January 2025, SciPost Physics Core)

   A connection has recently been proposed between periodically driven systems known as Floquet insulators in continuous time and static fermion theories in discrete time. This connection has been established in a(1+1) $(1+1)$-dimensional free theory, where an explicit mapping between the spectra of a Floquet insulator and a discrete-time Dirac fermion theory has been formulated. Here we investigate the potential of static discrete-time theories to capture Floquet physics in higher dimensions, where so-called anomalous Floquet topological insulators can emerge that feature chiral edge states despite having bulk bands with zero Chern number. Starting from a particular model of an anomalous Floquet system, we provide an example of a static discrete-time theory whose bulk spectrum is an exact analytic match for the Floquet spectrum. The spectra with open boundary conditions in a particular strip geometry also match up to finite-size corrections. However, the models differ in several important respects. The discrete-time theory is spatially anisotropic, so that the spectra do not agree for all lattice terminations, e.g. other strip geometries or on half spaces. This difference can be attributed to the fact that the static discrete-time model is quasi-one-dimensional in nature and therefore has a different bulk-boundary correspondence than the Floquet model. 

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3. Secondary Proton Beam Instability in a Turbulent Solar Wind at 50 R _⊙

   https://doi.org/10.3847/1538-4357/acf656  

   Markovskii, S A; Vasquez, Bernard J (September 2023, The Astrophysical Journal)

   Abstract We investigate a secondary proton beam instability coexisting with the ambient solar wind turbulence at 50R_☉. Three-dimensional hybrid numerical simulations (particle ions and a quasi-neutralizing electron fluid) are carried out with the plasma parameters in the observed range. In the turbulent background, the particle distribution function, in particular the slope of the “bump-on-tail” responsible for the instability, is time-dependent and inhomogeneous. The presence of the turbulence substantially reduces the growth rate and saturation level of the instability. We derive magnetic power spectra from the observational data and perform a statistical analysis to evaluate the average turbulence intensity at 50R_☉. This information is used to link the observed frequency spectrum to the wavenumber spectrum in the simulations. We verify that Taylor’s frozen-in hypothesis is valid for this purpose to a sufficient extent. To reproduce the typical magnetic power spectrum of the instability observed concurrently with the background turbulence, an artificial spacecraft probe is run through the simulation box. The thermal-ion instabilities are often seen as power elevations in the kinetic range of scales above an extrapolation of the turbulence spectrum from larger scales. We show that the elevated power in the simulations is much higher than the background level. Therefore, the turbulence at the average intensity does not obscure the secondary proton beam instability, as opposed to the solar wind at 1 au, in which the ambient turbulence typically obscures thermal-ion instabilities. 

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4. Convective and Orographic Origins of the Mesoscale Kinetic Energy Spectrum

   https://doi.org/10.1029/2024GL110804  

   Kouhen, Salah; Storer, Benjamin A; Aluie, Hussein; Marshall, David P; Christensen, Hannah M (November 2024, Geophysical Research Letters)

   Abstract The mesoscale spectrum describes the distribution of kinetic energy in the Earth's atmosphere between length scales of 10 and 400 km. Since the first observations, the origins of this spectrum have been controversial. At synoptic scales, the spectrum follows a −3 spectral slope, consistent with two‐dimensional turbulence theory, but a shallower −5/3 slope was observed at the shorter mesoscales. The cause of the shallower slope remains obscure, illustrating our lack of understanding. Through a novel coarse‐graining methodology, we are able to present a spatio‐temporal climatology of the spectral slope. We find convection and orography have a shallowing effect and can quantify this using “conditioned spectra.” These are typical spectra for a meteorological condition, obtained by aggregating spectra where the condition holds. This allows the investigation of new relationships, such as that between energy flux and spectral slope. Potential future applications of our methodology include predictability research and model validation. 

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5. Dynamic factor models for binary data in circular spaces: an application to the US Supreme Court

   https://doi.org/10.1093/jrsssc/qlae025  

   Lei, Rayleigh; Rodriguez, Abel (May 2024, Journal of the Royal Statistical Society Series C: Applied Statistics)

   Abstract Latent factor models are widely used in the social and behavioural sciences as scaling tools to map discrete multivariate outcomes into low-dimensional, continuous scales. In political science, dynamic versions of classical factor models have been widely used to study the evolution of justices’ preferences in multi-judge courts. In this paper, we discuss a new dynamic factor model that relies on a latent circular space that can accommodate voting behaviours in which justices commonly understood to be on opposite ends of the ideological spectrum vote together on a substantial number of otherwise closely divided opinions. We apply this model to data on nonunanimous decisions made by the US Supreme Court between 1937 and 2021, and show that for most of this period, voting patterns can be better described by a circular latent space. 

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