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To treat diseases associated with vagal nerve control of peripheral organs, it is necessary to selectively activate efferent and afferent fibers in the vagus. As a result of the nerve’s complex anatomy, fiber-specific activation proves challenging. Spatially selective neuromodulation using micromagnetic stimulation(μMS) is showing incredible promise. This neuromodulation technique uses microcoils(μcoils) to generate magnetic fields by powering them with a time-varying current. Following the principles of Faraday’s law of induction, a highly directional electric field is induced in the nerve from the magnetic field. In this study on rodent cervical vagus, a solenoidalμcoil was oriented at an angle to left and right branches of the nerve. The aim of this study was to measure changes in the mean arterial pressure (MAP) and heart rate (HR) followingμMS of the vagus. Theμcoils were powered by a single-cycle sinusoidal current varying in pulse widths(PW = 100, 500, and 1000μsec) at a frequency of 20 Hz. Under the influence of isoflurane,μMS of the left vagus at 1000μsec PW led to an average drop in MAP of 16.75 mmHg(n = 7). In contrast,μMS of the right vagus under isoflurane resulted in an average drop of 11.93 mmHg in the MAP(n = 7). Surprisingly, there were no changes in HR to either right or left vagalμMS suggesting the drop in MAP associated with vagusμMS was the result of stimulation of afferent, but not efferent fibers. In urethane anesthetized rats, no changes in either MAP or HR were observed uponμMS of the right or left vagus(n = 3). These findings suggest the choice of anesthesia plays a key role in determining the efficacy ofμMS on the vagal nerve. Absence of HR modulation uponμMS could offer alternative treatment options using VNS with fewer heart-related side-effects.

 

The overall level of negative affect (NeA) has been linked to impaired health. However, whether the diurnal timing of NeA matters and whether the NeA-health relationship is mediated by sleep quality remain unclear.

Using a longitudinal dataset (2006, 2009 and 2014 waves) consisting of 1959 participants, we examined the within-person impact of both bedtime NeA and non-bedtime NeA measured by Day Reconstruction Method (DRM) on subjective health measured by Visual Analogue Scale (VAS), and the mediating effect of sleep quality on the NeA-health relationships by fixed effect models.

Bedtime NeA predicted poorer health, while non-bedtime NeA was unrelated to health. The deleterious impact of bedtime NeA reduced and became non-significant after sleep quality was controlled for. Bedtime NeA also significantly predicted impaired sleep quality.

Bedtime NeA is a stronger predictor of poorer health than non-bedtime NeA, and the deleterious influence of bedtime NeA on health seems to operate through poor sleep quality. Therefore, interventions to reduce bedtime NeA could potentially improve subsequent sleep quality, thereby protecting people to some extent from impaired health status.

 

Accurate prediction of dynamical systems in unstructured meshes has recently shown successes in scientific simulations. Many dynamical systems have a nonnegligible level of stochasticity introduced by various factors (e.g. chaoticity), so there is a need for a unified framework that captures both deterministic and stochastic components in the rollouts of these systems. Inspired by regeneration learning, we propose a new model that combines generative and sequential networks to model dynamical systems. Specifically, we use an autoencoder to learn compact representations of full-space physical variables in a low-dimensional space. We then integrate a transformer with a conditional normalizing flow model to model the temporal sequence of latent representations. We evaluate the new model in both deterministic and stochastic systems. The model outperforms several competitive baseline models and makes more accurate predictions of deterministic systems. Its own prediction error is also reflected in its uncertainty estimations. When predicting stochastic systems, the proposed model generates high-quality rollout samples. The mean and variance of these samples well match the statistics of samples computed from expensive numerical simulations. 

Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical principles into the model. Most PiDL approaches regularize training by embedding governing equations into the loss function, yet this depends heavily on extensive hyperparameter tuning to weigh each loss term. To this end, we propose to leverage physics prior knowledge by “baking” the discretized governing equations into the neural network architecture via the connection between the partial differential equations (PDE) operators and network structures, resulting in a PDE-preserved neural network (PPNN). This method, embedding discretized PDEs through convolutional residual networks in a multi-resolution setting, largely improves the generalizability and long-term prediction accuracy, outperforming conventional black-box models. The effectiveness and merit of the proposed methods have been demonstrated across various spatiotemporal dynamical systems governed by spatiotemporal PDEs, including reaction-diffusion, Burgers’, and Navier-Stokes equations.

 

Molecular dynamics (MD) is the primary computational method by which modern structural biology explores macromolecule structure and function. Boltzmann generators have been proposed as an alternative to MD, by replacing the integration of molecular systems over time with the training of generative neural networks. This neural network approach to MD enables convergence to thermodynamic equilibrium faster than traditional MD; however, critical gaps in the theory and computational feasibility of Boltzmann generators significantly reduce their usability. Here, we develop a mathematical foundation to overcome these barriers; we demonstrate that the Boltzmann generator approach is sufficiently rapid to replace traditional MD for complex macromolecules, such as proteins in specific applications, and we provide a comprehensive toolkit for the exploration of molecular energy landscapes with neural networks.

 

Using in situ atomic-resolution scanning transmission electron microscopy, atomic movements and rearrangements associated with diffusive solid to solid phase transformations in the Pt−Sn system are captured to reveal details of the underlying atomistic mechanisms that drive these transformations. In the PtSn4 to PtSn2 phase transformation, a periodic superlattice substructure and a unique intermediate structure precede the nucleation and growth of the PtSn2 phase. At the atomic level, all stages of the transformation are templated by the anisotropic crystal structure of the parent PtSn4 phase. In the case of the PtSn2 to Pt2Sn3 transformation, the anisotropy in the structure of product Pt2Sn3 dictates the path of transformation. Analysis of atomic configurations at the transformation front elucidates the diffusion pathways and lattice distortions required for these phase transformations. Comparison of multiple Pt−Sn phase transformations reveals the structural parameters governing solid to solid phase transformations in this technologically interesting intermetallic system. 

We conduct a systematic search for quasars with periodic variations from the archival photometric data of the Zwicky Transient Facility by cross-matching with the quasar catalogues of the Sloan Digital Sky Survey and Véron-Cetty and Véron. We first select out 184 primitive periodic candidates using the generalized Lomb–Scargle periodogram and autocorrelation function and then estimate their statistical significance of periodicity based on two red-noise models, i.e. damped random walk (DRW) and single power-law (SPL) models. As such, we finally identify 106 (DRW) and 86 (SPL) candidates with the most significant periodic variations out of 143 700 quasars. We further compare DRW and SPL models using Bayes factors, which indicate a relative preference of the SPL model for our primitive sample. We thus adopt the candidates identified with SPL as the final sample and summarize its basic properties. We extend the light curves of the selected candidates by supplying other archival survey data to verify their periodicity. However, only three candidates (with 6–8 cycles of periods) meet the selection criteria. This result clearly implies that, instead of being strictly periodic, the variability must be quasi-periodic or caused by stochastic red-noise. This exerts a challenge to the existing search approaches and calls for developing new effective methods.

 

Anisotropic planar polaritons - hybrid electromagnetic modes mediated by phonons, plasmons, or excitons - in biaxial two-dimensional (2D) van der Waals crystals have attracted significant attention due to their fundamental physics and potential nanophotonic applications. In this Perspective, we review the properties of planar hyperbolic polaritons and the variety of methods that can be used to experimentally tune them. We argue that such natural, planar hyperbolic media should be fairly common in biaxial and uniaxial 2D and 1D van der Waals crystals, and identify the untapped opportunities they could enable for functional (i.e. ferromagnetic, ferroelectric, and piezoelectric) polaritons. Lastly, we provide our perspectives on the technological applications of such planar hyperbolic polaritons.