Na2IrO3 as a spin-orbit-assisted antiferromagnetic insulator with a 340 meV gap

We study Na2IrO3 by ARPES, optics, and band structure calculations in the local-density approximation (LDA). The weak dispersion of the Ir 5d-t2g manifold highlights the importance of structural distortions and spin-orbit coupling (SO) in driving the system closer to a Mott transition. We detect an insulating gap {\Delta}_gap = 340 meV which, at variance with a Slater-type description, is already open at 300 K and does not show significant temperature dependence even across T_N ~ 15 K. An LDA analysis with the inclusion of SO and Coulomb repulsion U reveals that, while the prodromes of an underlying insulating state are already found in LDA+SO, the correct gap magnitude can only be reproduced by LDA+SO+U, with U = 3 eV. This establishes Na2IrO3 as a novel type of Mott-like correlated insulator in which Coulomb and relativistic effects have to be treated on an equal footing.Comment: Accepted in Physical Review Letters. Auxiliary and related material can be found at: http://www.phas.ubc.ca/~quantmat/ARPES/PUBLICATIONS/articles.htm

Determining the Surface-To-Bulk Progression in the Normal-State Electronic Structure of Sr2RuO4 by Angle-Resolved Photoemission and Density Functional Theory

In search of the potential realization of novel normal-state phases on the surface of Sr2RuO4 - those stemming from either topological bulk properties or the interplay between spin-orbit coupling (SO) and the broken symmetry of the surface - we revisit the electronic structure of the top-most layers by ARPES with improved data quality as well as ab-initio LDA slab calculations. We find that the current model of a single surface layer (\surd2x\surd2)R45{\deg} reconstruction does not explain all detected features. The observed depth-dependent signal degradation, together with the close quantitative agreement with LDA+SO slab calculations based on the LEED-determined surface crystal structure, reveal that (at a minimum) the sub-surface layer also undergoes a similar although weaker reconstruction. This points to a surface-to-bulk progression of the electronic states driven by structural instabilities, with no evidence for Dirac and Rashba-type states or surface magnetism.Comment: 4 pages, 4 figures, 1 table. Further information and PDF available at: http://www.phas.ubc.ca/~quantmat/ARPES/PUBLICATIONS/articles.htm

Rashba spin-splitting control at the surface of the topological insulator Bi2Se3

The electronic structure of Bi2Se3 is studied by angle-resolved photoemission and density functional theory. We show that the instability of the surface electronic properties, observed even in ultra-high-vacuum conditions, can be overcome via in-situ potassium deposition. In addition to accurately setting the carrier concentration, new Rashba-like spin-polarized states are induced, with a tunable, reversible, and highly stable spin splitting. Ab-initio slab calculations reveal that these Rashba state are derived from the 5QL quantum-well states. While the K-induced potential gradient enhances the spin splitting, this might be already present for pristine surfaces due to the symmetry breaking of the vacuum-solid interface.Comment: A high-resolution version can be found at http://www.physics.ubc.ca/~quantmat/ARPES/PUBLICATIONS/Articles/BiSe_K.pd

Spin-Orbital Entanglement and the Breakdown of Singlets and Triplets in Sr2RuO4 Revealed by Spin- and Angle-Resolved Photoemission Spectroscopy

Spin-orbit coupling has been conjectured to play a key role in the low-energy electronic structure of Sr2RuO4. By using circularly polarized light combined with spin-and angle-resolved photoemission spectroscopy, we directly measure the value of the effective spin-orbit coupling to be 130 +/- 30 meV. This is even larger than theoretically predicted and comparable to the energy splitting of the d(xy) and d(xz,yz) orbitals around the Fermi surface, resulting in a strongly momentum-dependent entanglement of spin and orbital character in the electronic wavefunction. As demonstrated by the spin expectation value calculated for a pair of electrons with zero total momentum, the classification of the Cooper pairs in terms of pure singlets or triplets fundamentally breaks down, necessitating a description of the unconventional superconducting state of Sr2RuO4 in terms of these newly found spin-orbital entangled eigenstates

Structure activity relationships of αv integrin antagonists for pulmonary fibrosis by variation in aryl substituents

Antagonism of alphav beta6 is emerging as a potential treatment of idiopathic pulmonary fibrosis based on strong target validation. Starting from an alphav beta3 antagonist lead and through simple variation in the nature and position of aryl substituent, the discovery of compounds with improved alphav beta6 activity is described. The compounds also have physicochemical properties commensurate with oral bioavailability and are high quality starting points for a drug discovery programme. Compounds 33S and 43E1 are pan alphav antagonists having ca 100 nM potency against alphav beta3, alphav beta5, alphav beta6 and alphav beta8 in cell adhesion assays. Detailed structure activity relationships with these integrins are described which also reveal substituents providing partial selectivity (defined as at least a 0.7 log difference in pIC50 values between the integrins in question) for alphav beta3 and alphav beta5

Pattern Recognition Analysis of Proton Nuclear Magnetic Resonance Spectra of Brain Tissue Extracts from Rats Anesthetized with Propofol or Isoflurane

BACKGROUND: General anesthesia is routinely used as a surgical procedure and its safety has been endorsed by clinical outcomes; however, its effects at the molecular level have not been elucidated. General anesthetics influence glucose metabolism in the brain. However, the effects of anesthetics on brain metabolites other than those related to glucose have not been well characterized. We used a pattern recognition analysis of proton nuclear magnetic resonance spectra to visualize the changes in holistic brain metabolic phenotypes in response to the widely used intravenous anesthetic propofol and the volatile anesthetic isoflurane. METHODOLOGY/PRINCIPAL FINDINGS: Rats were randomized into five groups (n = 7 each group). Propofol and isoflurane were administered to two groups each, for 2 or 6 h. The control group received no anesthesia. Brains were removed directly after anesthesia. Hydrophilic compounds were extracted from excised whole brains and measured by proton nuclear magnetic resonance spectroscopy. All spectral data were processed and analyzed by principal component analysis for comparison of the metabolite profiles. Data were visualized by plotting principal component (PC) scores. In the plots, each point represents an individual sample. The propofol and isoflurane groups were clustered separately on the plots, and this separation was especially pronounced when comparing the 6-h groups. The PC scores of the propofol group were clearly distinct from those of the control group, particularly in the 6-h group, whereas the difference in PC scores was more subtle in the isoflurane group and control groups. CONCLUSIONS/SIGNIFICANCE: The results of the present study showed that propofol and isoflurane exerted differential effects on holistic brain metabolism under anesthesia

A Systems Biology-Based Classifier for Hepatocellular Carcinoma Diagnosis

AIM: The diagnosis of hepatocellular carcinoma (HCC) in the early stage is crucial to the application of curative treatments which are the only hope for increasing the life expectancy of patients. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with HCC progression. However, those marker sets shared few genes in common and were poorly validated using independent data. Therefore, we developed a systems biology based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC diagnosis. METHODS AND RESULTS: In the Oncomine platform, genes differentially expressed in HCC tissues relative to their corresponding normal tissues were filtered by a corrected Q value cut-off and Concept filters. The identified genes that are common to different microarray datasets were chosen as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and the hub genes were chosen. After that, an HCC diagnostic classifier was constructed by Partial Least Squares modeling based on the microarray gene expression data of the hub genes. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.88∼92.71%) and area under ROC curve (approximating 1.0), and that the network topological features integrated into this classifier contribute greatly to improving the predictive performance. Furthermore, it has been demonstrated that this modeling strategy is not only applicable to HCC, but also to other cancers. CONCLUSION: Our analysis suggests that the systems biology-based classifier that combines the differential gene expression and topological features of human protein interaction network may enhance the diagnostic performance of HCC classifier

Understanding Novel Superconductors with Ab Initio Calculations

This chapter gives an overview of the progress in the field of computational superconductivity. Following the MgB2 discovery (2001), there has been an impressive acceleration in the development of methods based on Density Functional Theory to compute the critical temperature and other physical properties of actual superconductors from first-principles. State-of-the-art ab-initio methods have reached predictive accuracy for conventional (phonon-mediated) superconductors, and substantial progress is being made also for unconventional superconductors. The aim of this chapter is to give an overview of the existing computational methods for superconductivity, and present selected examples of material discoveries that exemplify the main advancements.Comment: 38 pages, 10 figures, Contribution to Springer Handbook of Materials Modellin