Theory for the negative longitudinal magnetoresistance in the quantum limit of Kramers Weyl semimetals

Negative magnetoresistance is rare in non-magnetic materials. Recently, negative magnetoresistance has been observed in the quantum limit of β-Ag2Se, where only one band of Landau levels is occupied in a strong magnetic field parallel to the applied current. β-Ag2Se is a material that hosts a Kramers Weyl cone with band degeneracy near the Fermi energy. Kramers Weyl cones exist at time-reversal invariant momenta in all symmorphic chiral crystals, and at a subset of these momenta, including the Γ point, in non-symmorphic chiral crystals. Here, we present a theory for the negative magnetoresistance in the quantum limit of Kramers Weyl semimetals. We show that, although there is a band touching similar to those in Weyl semimetals, negative magnetoresistance can exist without a chiral anomaly. We find that it requires screened Coulomb scattering potentials between electrons and impurities, which is naturally the case in β-Ag2Se

Weak signal extraction enabled by deep neural network denoising of diffraction data

The removal or cancellation of noise has wide-spread applications in imaging and acoustics. In applications in everyday life, such as image restoration, denoising may even include generative aspects, which are unfaithful to the ground truth. For scientific use, however, denoising must reproduce the ground truth accurately. Denoising scientific data is further challenged by unknown noise profiles. In fact, such data will often include noise from multiple distinct sources, which substantially reduces the applicability of simulation-based approaches. Here we show how scientific data can be denoised by using a deep convolutional neural network such that weak signals appear with quantitative accuracy. In particular, we study X-ray diffraction and resonant X-ray scattering data recorded on crystalline materials. We demonstrate that weak signals stemming from charge ordering, insignificant in the noisy data, become visible and accurate in the denoised data. This success is enabled by supervised training of a deep neural network with pairs of measured low- and high-noise data. We additionally show that using artificial noise does not yield such quantitatively accurate results. Our approach thus illustrates a practical strategy for noise filtering that can be applied to challenging acquisition problems.</p

Regulating cellular trace metal economy in algae.

As indispensable protein cofactors, Fe, Mn, Cu and Zn are at the center of multifaceted acclimation mechanisms that have evolved to ensure extracellular supply meets intracellular demand. Starting with selective transport at the plasma membrane and ending in protein metalation, metal homeostasis in algae involves regulated trafficking of metal ions across membranes, intracellular compartmentalization by proteins and organelles, and metal-sparing/recycling mechanisms to optimize metal-use efficiency. Overlaid on these processes are additional circuits that respond to the metabolic state as well as to the prior metal status of the cell. In this review, we focus on recent progress made toward understanding the pathways by which the single-celled, green alga Chlamydomonas reinhardtii controls its cellular trace metal economy. We also compare these mechanisms to characterized and putative processes in other algal lineages. Photosynthetic microbes continue to provide insight into cellular regulation and handling of Cu, Fe, Zn and Mn as a function of the nutritional supply and cellular demand for metal cofactors. New experimental tools such as RNA-Seq and subcellular metal imaging are bringing us closer to a molecular understanding of acclimation to supply dynamics in algae and beyond