Thermodynamics of f(R)f(R) Gravity with Disformal Transformation

We study thermodynamics in $f(R)$ gravity with the disformal transformation. The transformation applied to the matter Lagrangian has the form of \g_{\m\n} = A(\phi,X)g_{\m\n} + B(\phi,X)\pa_\m\f\pa_\n\f with the assumption of the Minkowski matter metric \g_{\m\n} = \e_{\m\n}, where $\phi$ is the disformal scalar and $X$ is the corresponding kinetic term of $\phi$. We verify the generalized first and second laws of thermodynamics in this disformal type of $f(R)$ gravity in the Friedmann-Lema\^{i}tre-Robertson-Walker (FLRW) universe. In addition, we show that the Hubble parameter contains the disformally induced terms, which define the effectively varying equations of state for matter.Comment: 23 pages, no figure, published version in Entropy 21, 172 (2019

Non-Hermitian skin effects on many-body localized and thermal phases

Localization in one-dimensional interacting systems can be caused by disorder potentials or non-Hermiticity. The former phenomenon is the many-body localization (MBL), and the latter is the many-body non-Hermitian skin effect (NHSE). In this work, we numerically investigate the interplay between these two kinds of localization, where the energy-resolved MBL arises from a deterministic quasiperiodic potential in a fermionic chain. We propose a set of eigenstate properties and long-time dynamics that can collectively distinguish the two localization mechanisms in the presence of non-Hermiticity. By computing the proposed diagnostics, we show that the thermal states are vulnerable to the many-body NHSE while the MBL states remain resilient up to a strong non-Hermiticity. Finally, we discuss experimental observables that can probe the difference between the two localizations in a non-Hermitian quasiperiodic fermionic chain. Our results pave the way toward experimental observations on the interplay of interaction, quasiperiodic potential, and non-Hermiticity.Comment: 7 pages, 4 figure

Surface Electron-Hole Rich Species Active in the Electrocatalytic Water Oxidation.

Iridium and ruthenium and their oxides/hydroxides are the best candidates for the oxygen evolution reaction under harsh acidic conditions owing to the low overpotentials observed for Ru- and Ir-based anodes and the high corrosion resistance of Ir-oxides. Herein, by means of cutting edge operando surface and bulk sensitive X-ray spectroscopy techniques, specifically designed electrode nanofabrication and ab initio DFT calculations, we were able to reveal the electronic structure of the active IrOx centers (i.e., oxidation state) during electrocatalytic oxidation of water in the surface and bulk of high-performance Ir-based catalysts. We found the oxygen evolution reaction is controlled by the formation of empty Ir 5d states in the surface ascribed to the formation of formally IrV species leading to the appearance of electron-deficient oxygen species bound to single iridium atoms (μ1-O and μ1-OH) that are responsible for water activation and oxidation. Oxygen bound to three iridium centers (μ3-O) remains the dominant species in the bulk but do not participate directly in the electrocatalytic reaction, suggesting bulk oxidation is limited. In addition a high coverage of a μ1-OO (peroxo) species during the OER is excluded. Moreover, we provide the first photoelectron spectroscopic evidence in bulk electrolyte that the higher surface-to-bulk ratio in thinner electrodes enhances the material usage involving the precipitation of a significant part of the electrode surface and near-surface active species

Computational Comparative Study of Tuberculosis Proteomes Using a Model Learned from Signal Peptide Structures

Secretome analysis is important in pathogen studies. A fundamental and convenient way to identify secreted proteins is to first predict signal peptides, which are essential for protein secretion. However, signal peptides are highly complex functional sequences that are easily confused with transmembrane domains. Such confusion would obviously affect the discovery of secreted proteins. Transmembrane proteins are important drug targets, but very few transmembrane protein structures have been determined experimentally; hence, prediction of the structures is essential. In the field of structure prediction, researchers do not make assumptions about organisms, so there is a need for a general signal peptide predictor

Lipid exposure prediction enhances the inference of rotational angles of transmembrane helices

Background: Since membrane protein structures are challenging to crystallize, computational approaches are essential for elucidating the sequence-to-structure relationships. Structural modeling of membrane proteins requires a multidimensional approach, and one critical geometric parameter is the rotational angle of transmembrane helices. Rotational angles of transmembrane helices are characterized by their folded structures and could be inferred by the hydrophobic moment; however, the folding mechanism of membrane proteins is not yet fully understood. The rotational angle of a transmembrane helix is related to the exposed surface of a transmembrane helix, since lipid exposure gives the degree of accessibility of each residue in lipid environment. To the best of our knowledge, there have been few advances in investigating whether an environment descriptor of lipid exposure could infer a geometric parameter of rotational angle.Results: Here, we present an analysis of the relationship between rotational angles and lipid exposure and a support-vector-machine method, called TMexpo, for predicting both structural features from sequences. First, we observed from the development set of 89 protein chains that the lipid exposure, i.e., the relative accessible surface area (rASA) of residues in the lipid environment, generated from high-resolution protein structures could infer the rotational angles with a mean absolute angular error (MAAE) of 46.32° More importantly, the predicted rASA from TMexpo achieved an MAAE of 51.05°, which is better than 71.47° obtained by the best of the compared hydrophobicity scales. Lastly, TMexpo outperformed the compared methods in rASA prediction on the independent test set of 21 protein chains and achieved an overall Matthew's correlation coefficient, accuracy, sensitivity, specificity, and precision of 0.51, 75.26%, 81.30%, 69.15%, and 72.73%, respectively. TMexpo is publicly available at http://bio-cluster.iis.sinica.edu.tw/TMexpo.Conclusions: TMexpo can better predict rASA and rotational angles than the compared methods. When rotational angles can be accurately predicted, free modeling of transmembrane protein structures in turn may benefit from a reduced complexity in ensembles with a significantly less number of packing arrangements. Furthermore, sequence-based prediction of both rotational angle and lipid exposure can provide essential information when high-resolution structures are unavailable and contribute to experimental design to elucidate transmembrane protein functions

Specificities (%) of various predictors on the non-signal peptide protein benchmark datasets.

<p>Specificities (%) of various predictors on the non-signal peptide protein benchmark datasets.</p

Sensitivities (%) of various predictors on the signal peptide protein benchmark datasets.

<p>Sensitivities (%) of various predictors on the signal peptide protein benchmark datasets.</p