Effect of the Orbital Level Difference in Doped Spin-1 Chains

Doping of a two-orbital chain with mobile S=1/2 Fermions and strong Hund's rule couplings stabilizing the S=1 spins strongly depends on the presence of a level difference among these orbitals. By DMRG methods we find a finite spin gap upon doping and dominant pairing correlations without level-difference, whereas the presence of a level difference leads to dominant charge density wave (CDW) correlations with gapless spin-excitations. The string correlation function also shows qualitative differences between the two models.Comment: 4 pages, 4 figure

Doped two orbital chains with strong Hund's rule couplings - ferromagnetism, spin gap, singlet and triplet pairings

Different models for doping of two-orbital chains with mobile $S=1/2$ fermions and strong, ferromagnetic (FM) Hund's rule couplings stabilizing the S=1 spins are investigated by density matrix renormalization group (DMRG) methods. The competition between antiferromagnetic (AF) and FM order leads to a rich phase diagram with a narrow FM region for weak AF couplings and strongly enhanced triplet pairing correlations. Without a level difference between the orbitals, the spin gap persists upon doping, whereas gapless spin excitations are generated by interactions among itinerant polarons in the presence of a level difference. In the charge sector we find dominant singlet pairing correlations without a level difference, whereas upon the inclusion of a Coulomb repulsion between the orbitals or with a level difference, charge density wave (CDW) correlations decay slowest. The string correlation functions remain finite upon doping for all models.Comment: 9pages, 9figure

Structure of end states for a Haldane Spin Chain

Inelastic neutron scattering was used to probe edge states in a quantum spin liquid. The experiment was performed on finite length antiferromagnetic spin-1 chains in Y_2BaNi_{1-x}Mg_xO_5. At finite fields, there is a Zeeman resonance below the Haldane gap. The wave vector dependence of its intensity provides direct evidence for staggered magnetization at chain ends, which decays exponentially towards the bulk (xi = 8(1) at T=0.1K). Continuum contributions to the chain end spectrum indicate inter-chain-segment interactions. We also observe a finite size blue shift of the Haldane gap.Comment: 4 pages RevTex, 3 figure

Mid-Infrared Conductivity from Mid-Gap States Associated with Charge Stripes

The optical conductivity of La(2-x)Sr(x)NiO(4) has been interpreted in various ways, but so far the proposed interpretations have neglected the fact that the holes doped into the NiO(2) planes order in diagonal stripes, as established by neutron and X-ray scattering. Here we present a study of optical conductivity in La(2)NiO(4+d) with d=2/15, a material in which the charge stripes order three-dimensionally. We show that the conductivity can be decomposed into two components, a mid-infrared peak that we attribute to transitions from the filled valence band into empty mid-gap states associated with the stripes, and a Drude peak that appears at higher temperatures as carriers are thermally excited into the mid-gap states. The shift of the mid-IR peak to lower energy with increasing temperature is explained in terms of the Franck-Condon effect. The relevance of these results to understanding the optical conductivity in the cuprates is discussed.Comment: final version of paper (minor changes from previous version

Freezing of a Stripe Liquid

The existence of a stripe-liquid phase in a layered nickelate, La(1.725)Sr(0.275)NiO(4), is demonstrated through neutron scattering measurements. We show that incommensurate magnetic fluctuations evolve continuously through the charge-ordering temperature, although an abrupt decrease in the effective damping energy is observed on cooling through the transition. The energy and momentum dependence of the magnetic scattering are parametrized with a damped-harmonic-oscillator model describing overdamped spin-waves in the antiferromagnetic domains defined instantaneously by charge stripes.Comment: 4 2-col pages, including 5 figures; Final version, to be published in PR

Co-regulation map of the human proteome enables identification of protein functions

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this recordData availability: All mass spectrometry raw files generated in-house have been deposited in the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository36 with the dataset identifier PXD008888. The co-regulation map is hosted on our website at www.proteomeHD.net, and pair-wise co-regulation scores are available through STRING (https://string-db.org). A network of the top 0.5% co-regulated protein pairs can be explored interactively on NDEx (https://doi.org/10.18119/N9N30Q).Code availability: Data analysis was performed in R 3.5.1. R scripts and input files required to reproduce the results of this manuscript are available in the following GitHub repository: https://github.com/Rappsilber-Laboratory/ProteomeHD. R scripts related specifically to the benchmarking of the treeClust algorithm using synthetic data are available in the following GitHub repository: https://github.com/Rappsilber-Laboratory/treeClust-benchmarking. The R package data.table was used for fast data processing. Figures were prepared using ggplot2, gridExtra, cowplot and viridis.Note that the title of the AAM is different from the published versionThe annotation of protein function is a longstanding challenge of cell biology that suffers from the sheer magnitude of the task. Here we present ProteomeHD, which documents the response of 10,323 human proteins to 294 biological perturbations, measured by isotope-labelling mass spectrometry. We reveal functional associations between human proteins using the treeClust machine learning algorithm, which we show to improve protein co-regulation analysis due to robust selectivity for close linear relationships. Our co-regulation map identifies a functional context for many uncharacterized proteins, including microproteins that are difficult to study with traditional methods. Co-regulation also captures relationships between proteins which do not physically interact or co-localize. For example, co-regulation of the peroxisomal membrane protein PEX11β with mitochondrial respiration factors led us to discover a novel organelle interface between peroxisomes and mitochondria in mammalian cells. The co-regulation map can be explored at www.proteomeHD.net .Biotechnology & Biological Sciences Research Council (BBSRC)European Commissio