Gate-tunable Topological Valley Transport in Bilayer Graphene

Valley pseudospin, the quantum degree of freedom characterizing the degenerate valleys in energy bands, is a distinct feature of two-dimensional Dirac materials. Similar to spin, the valley pseudospin is spanned by a time reversal pair of states, though the two valley pseudospin states transform to each other under spatial inversion. The breaking of inversion symmetry induces various valley-contrasted physical properties; for instance, valley-dependent topological transport is of both scientific and technological interests. Bilayer graphene (BLG) is a unique system whose intrinsic inversion symmetry can be controllably broken by a perpendicular electric field, offering a rare possibility for continuously tunable valley-topological transport. Here, we used a perpendicular gate electric field to break the inversion symmetry in BLG, and a giant nonlocal response was observed as a result of the topological transport of the valley pseudospin. We further showed that the valley transport is fully tunable by external gates, and that the nonlocal signal persists up to room temperature and over long distances. These observations challenge contemporary understanding of topological transport in a gapped system, and the robust topological transport may lead to future valleytronic applications

Topological Photonics

Topology is revolutionizing photonics, bringing with it new theoretical discoveries and a wealth of potential applications. This field was inspired by the discovery of topological insulators, in which interfacial electrons transport without dissipation even in the presence of impurities. Similarly, new optical mirrors of different wave-vector space topologies have been constructed to support new states of light propagating at their interfaces. These novel waveguides allow light to flow around large imperfections without back-reflection. The present review explains the underlying principles and highlights the major findings in photonic crystals, coupled resonators, metamaterials and quasicrystals.Comment: progress and review of an emerging field, 12 pages, 6 figures and 1 tabl

Accurate Prediction of Protein Structural Class

Because of the increasing gap between the data from sequencing and structural genomics, the accurate prediction of the structural class of a protein domain solely from the primary sequence has remained a challenging problem in structural biology. Traditional sequence-based predictors generally select several sequence features and then feed them directly into a classification program to identify the structural class. The current best sequence-based predictor achieved an overall accuracy of 74.1% when tested on a widely used, non-homologous benchmark dataset 25PDB. In the present work, we built a multiple linear regression (MLR) model to convert the 440-dimensional (440D) sequence feature vector extracted from the Position Specific Scoring Matrix (PSSM) of a protein domain to a 4-dimensinal (4D) structural feature vector, which could then be used to predict the four major structural classes. We performed 10-fold cross-validation and jackknife tests of the method on a large non-homologous dataset containing 8,244 domains distributed among the four major classes. The performance of our approach outperformed all of the existing sequence-based methods and had an overall accuracy of 83.1%, which is even higher than the results of those predicted secondary structure-based methods

Acute infarct of the corpus callosum presenting as alien hand syndrome: evidence of diffusion weighted imaging and magnetic resonance angiography

<p>Abstract</p> <p>Background</p> <p>Infarcts of the corpus callosum are rare and have not been well documented previously. As for a variety of signs and symptoms presented, alien hand syndrome (AHS) can be easily overlooked.</p> <p>Case presentation</p> <p>In this report, we present a patient with a mixed types of AHS coexistence secondary to the corpus callosum infarction, including a motor type of AHS by intermanual conflict (callosal type AHS) and a sensory type of AHS by alien hand and left hemianesthesia (posterior AHS).</p> <p>Conclusions</p> <p>Our case may contribute to the early recognition of AHS and to explore the abnormal neural mechanism of AHS. To our knowledge, rare reports have ever documented such mixed AHS coexisting secondary to the callosal lesion, based on advanced neuroimaging methods as in our case.</p

Viral integration drives multifocal HCC during the occult HBV infection

© 2019 The Author(s). Background & Aims: Although the prognosis of patients with occult hepatitis B virus (HBV) infection (OBI) is usually benign, a small portion may undergo cirrhosis and subsequently hepatocellular carcinoma (HCC). We studied the mechanism of life-long Integration of virus DNA into OBI host's genome, of which may induce hepatocyte transformation. Methods: We applied HBV capture sequencing on single cells from an OBI patient who, developed multiple HCC tumors and underwent liver resection in May 2013 at Tongji Hospital in China. Despite with the undetectable virus DNA in serum, we determined the pattern of viral integration in tumor cells and adjacent non-tumor cells and obtained the details of the viral arrangement in host genome, and furthermore the HBV integrated region in cancer genome. Results: HBV captured sequencing of tissues and individual cells revealed that samples from multiple tumors shared two viral integration sites that could affect three host genes, including CSMD2 on chr1 and MED30/EXT1 on chr8. Whole genome sequencing further indicated one hybrid chromosome formed by HBV integrations between chr1 and chr8 that was shared by multiple tumors. Additional 50 poorly differentiated liver tumors and the paired adjacent non-tumors were evaluated and functional studies suggested up-regulated EXT1 expression promoted HCC growth. We further observed that the most somatic mutations within the tumor cell genome were common among the multiple tumors, suggesting that HBV associated, multifocal HCC is monoclonal in origin. Conclusion: Through analyzing the HBV integration sites in multifocal HCC, our data suggested that the tumor cells were monoclonal in origin and formed in the absence of active viral replication, whereas the affected host genes may subsequently contribute to carcinogenesis

Antisense oligonucleotide inhibition of Heat Shock Protein (HSP) 47 improves bleomycin-induced pulmonary fibrosis in rats

<p>Abstract</p> <p>Background</p> <p>The most common pathologic form of pulmonary fibrosis arises from excessive deposition of extracellular matrix proteins such as collagen. The 47 kDa heat shock protein 47 (HSP47) is a collagen-specific molecular chaperone that has been shown to play a major role during the processing and/or secretion of procollagen.</p> <p>Objectives</p> <p>To determine whether inhibition of HSP47 could have beneficial effects in mitigating bleomycin-induced pulmonary fibrosis in rats.</p> <p>Methods</p> <p>All experiments were performed with 250–300 g male Wistar rats. Animals were randomly divided into five experimental groups that were administered: 1) saline alone, 2) bleomycin alone, 3) antisense HSP47 oligonucleotides alone, 4) bleomycin + antisense HSP47 oligonucleotides, and 5) bleomycin + sense control oligonucleotides. We investigated lung histopathology and performed immunoblot and immunohistochemistry analyses.</p> <p>Results</p> <p>In rats treated with HSP47 antisense oligonucleotides, pulmonary fibrosis was significantly reduced. In addition, treatment with HSP47 antisense oligonucleotides significantly improved bleomycin-induced morphological changes. Treatment with HSP47 antisense oligonucleotides alone did not produce any significant changes to lung morphology. Immunoblot analyses of lung homogenates confirmed the inhibition of HSP47 protein by antisense oligonucleotides. The bleo + sense group, however, did not exhibit any improvement in lung pathology compared to bleomycin alone groups, and also had no effect on HSP47 expression.</p> <p>Conclusion</p> <p>These findings suggest that HSP47 antisense oligonucleotide inhibition of HSP47 improves bleomycin-induced pulmonary fibrosis pathology in rats.</p

Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine

'Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools

<p>Abstract</p> <p>Background</p> <p>Knowing the subcellular location of proteins provides clues to their function as well as the interconnectivity of biological processes. Dozens of tools are available for predicting protein location in the eukaryotic cell. Each tool performs well on certain data sets, but their predictions often disagree for a given protein. Since the individual tools each have particular strengths, we set out to integrate them in a way that optimally exploits their potential. The method we present here is applicable to various subcellular locations, but tailored for predicting whether or not a protein is localized in mitochondria. Knowledge of the mitochondrial proteome is relevant to understanding the role of this organelle in global cellular processes.</p> <p>Results</p> <p>In order to develop a method for enhanced prediction of subcellular localization, we integrated the outputs of available localization prediction tools by several strategies, and tested the performance of each strategy with known mitochondrial proteins. The accuracy obtained (up to 92%) surpasses by far the individual tools. The method of integration proved crucial to the performance. For the prediction of mitochondrion-located proteins, integration via a two-layer decision tree clearly outperforms simpler methods, as it allows emphasis of biologically relevant features such as the mitochondrial targeting peptide and transmembrane domains.</p> <p>Conclusion</p> <p>We developed an approach that enhances the prediction accuracy of mitochondrial proteins by uniting the strength of specialized tools. The combination of machine-learning based integration with biological expert knowledge leads to improved performance. This approach also alleviates the conundrum of how to choose between conflicting predictions. Our approach is easy to implement, and applicable to predicting subcellular locations other than mitochondria, as well as other biological features. For a trial of our approach, we provide a webservice for mitochondrial protein prediction (named YimLOC), which can be accessed through the AnaBench suite at http://anabench.bcm.umontreal.ca/anabench/. The source code is provided in the Additional File <supplr sid="S2">2</supplr>.</p> <suppl id="S2"> <title> <p>Additional file 2</p> </title> <text> <p>This file contains scripts for the online server YimLOC. Please note that there scripts only codes for the ready-to-use STACK-mem-DT described in the main text. The scripts do not provide the training process.</p> </text> <file name="1471-2105-8-420-S2.pdf"> <p>Click here for file</p> </file> </suppl

Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels

iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model

DNA-binding proteins play crucial roles in various cellular processes. Developing high throughput tools for rapidly and effectively identifying DNA-binding proteins is one of the major challenges in the field of genome annotation. Although many efforts have been made in this regard, further effort is needed to enhance the prediction power