Conductance fluctuations in disordered 2D topological insulator wires: From quantum spin-Hall to ordinary quantum phases

Impurities and defects are ubiquitous in topological insulators (TIs) and thus understanding the effects of disorder on electronic transport is important. We calculate the distribution of the random conductance fluctuations $P(G)$ of disordered 2D TI wires modeled by the Bernevig-Hughes-Zhang (BHZ) Hamiltonian with realistic parameters. As we show, the disorder drives the TIs into different regimes: metal (M), quantum spin-Hall insulator (QSHI), and ordinary insulator (OI). By varying the disorder strength and Fermi energy, we calculate analytically and numerically $P(G)$ across the entire phase diagram. The conductance fluctuations follow the statistics of the unitary universality class $\beta=2$. At strong disorder and high energy, however, the size of the fluctutations $\delta G$ reaches the universal value of the orthogonal symmetry class ($\beta=1$). At the QSHI-M and QSHI-OI crossovers, the interplay between edge and bulk states plays a key role in the statistical properties of the conductance.Comment: 17 pages, 5 figure

An improved model for sentiment analysis on luxury hotel review

This article proposes a heuristic model for sentiment analysis on luxury hotel reviews to analyse and explore marketing insights from attitudes and emotions expressed in reviews. We make several significant contributions to visual and multimedia analytics. This research will develop the practical application of visual and multimedia analytics as the research foundation is based on information analytics, geospatial analytics, statistical analytics and data management. Large amounts of data are generated by hotel customers on the Internet, which provides a good opportunity for managers and analysts to explore the hidden information. The analysis of luxury hotels involves different types of data, including real-world scale data, high-dimensional data and geospatial data. The diversity of data increases the difficulty of processing computational visual analytics. It leads to that some classical classification methods, which cost too much time and have high requirements for hardware, are excluded. The goal is to achieve a compromise between performance and cost. An experiment of this model is operated using data extracted from Booking.com. The entire framework of this experiment includes data collection, data preprocessing, feature engineering consisting of term frequency-inverse document frequency and Doc2Vec based feature generation and feature selection, Random Forest classification, data analysis and data visualization. The whole process combines statistical analysis, review sentiment analysis and visual analysis to make full use of this dataset and gain more decision-making information to improve luxury hotels' service quality. Compared with simple sentiment analysis, this integrated analytics in social media is expected to be used in practice to gain more insights. The result shows that luxury hotels should focus on staff training, cleanness of rooms and location choice to improve customer satisfaction. The sentiment distribution shows that scores are consistent with the emotion they show in reviews. Hotels in Spain have a much better average score than hotels in the other five countries. In the experiment, the sentiment analysis model is evaluated by receiver operating characteristic and precision-recall curve. It is proved that this model performs well. Twenty most essential features have been listed for future adjustments to the model

Empirical Research on the Fama-French Three-Factor Model and a Sentiment-Related Four-Factor Model in the Chinese Blockchain Industry

As one of the most significant components of financial technology (FinTech), blockchain technology arouses the interests of numerous investors in China, and the number of companies engaged in this field rises rapidly. The emotion of investors has an effect on stock returns, which is a hot topic in behavioral finance. Blockchain is an essential part of FinTech, and with the fast development of this technology, investors’ sentiment varies as well. The online information that directly reflects investors’ mood could be utilized for mining and quantifying to construct a sentiment index. For a better understanding of how well some factors adequately explain the return of stocks related to blockchain companies in the Chinese stock market, the Fama-French three-factor model (FFTFM) will be introduced in this paper. Furthermore, sentiment could be a new independent variable to enhance the explanatory power of the FFTFM. A comparison between those two models reveals that the sentiment factor could raise the explanatory power. The results also indicate that the Chinese blockchain industry does not own the size effect and book-to-market effect

Inference of hierarchical regulatory network of estrogen-dependent breast cancer through ChIP-based data

<p>Abstract</p> <p>Background</p> <p>Global profiling of in vivo protein-DNA interactions using ChIP-based technologies has evolved rapidly in recent years. Although many genome-wide studies have identified thousands of ERα binding sites and have revealed the associated transcription factor (TF) partners, such as AP1, FOXA1 and CEBP, little is known about ERα associated hierarchical transcriptional regulatory networks.</p> <p>Results</p> <p>In this study, we applied computational approaches to analyze three public available ChIP-based datasets: ChIP-seq, ChIP-PET and ChIP-chip, and to investigate the hierarchical regulatory network for ERα and ERα partner TFs regulation in estrogen-dependent breast cancer MCF7 cells. 16 common TFs and two common new TF partners (RORA and PITX2) were found among ChIP-seq, ChIP-chip and ChIP-PET datasets. The regulatory networks were constructed by scanning the ChIP-peak region with TF specific position weight matrix (PWM). A permutation test was performed to test the reliability of each connection of the network. We then used DREM software to perform gene ontology function analysis on the common genes. We found that FOS, PITX2, RORA and FOXA1 were involved in the up-regulated genes.</p> <p>We also conducted the ERα and Pol-II ChIP-seq experiments in tamoxifen resistance MCF7 cells (denoted as MCF7-T in this study) and compared the difference between MCF7 and MCF7-T cells. The result showed very little overlap between these two cells in terms of targeted genes (21.2% of common genes) and targeted TFs (25% of common TFs). The significant dissimilarity may indicate totally different transcriptional regulatory mechanisms between these two cancer cells.</p> <p>Conclusions</p> <p>Our study uncovers new estrogen-mediated regulatory networks by mining three ChIP-based data in MCF7 cells and ChIP-seq data in MCF7-T cells. We compared the different ChIP-based technologies as well as different breast cancer cells. Our computational analytical approach may guide biologists to further study the underlying mechanisms in breast cancer cells or other human diseases.</p

Origin of Immunoglobulin Isotype Switching

SummaryBackgroundFrom humans to frogs, immunoglobulin class switching introduces different effector functions to antibodies through an intrachromosomal DNA recombination process at the heavy-chain locus. Although there are two conventional antibody classes (IgM, IgW) in sharks, their heavy chains are encoded by 20 to >100 miniloci. These representatives of the earliest jawed vertebrates possess a primordial immunoglobulin gene organization where each gene cluster is autonomous and contains a few rearranging gene segments (VH-D1-D2-JH) with one constant region, μ or ω.ResultsV(D)J rearrangement always takes place within the μ cluster, but here we show that the VDJ can be expressed with constant regions from different clusters, although IgH genes are spatially distant, at >120 kb. Moreover, reciprocal exchanges take place between Igω and Igμ genes. Switching is augmented with deliberate immunization and is concomitant with somatic hypermutation activity. Because switching occurs independently of the partners' linkage position, some events involve transchromosomal recombination. The switch sites consist of direct joins between two genes in the 3′ intron flanking JH.ConclusionsOur data are consistent with a mechanism of cutting or joining of distal DNA lesions initiated by activation-induced cytidine deaminase (AID), in the absence of mammalian-type switch regions. We suggest that, in shark, with its many autonomous IgH targeted by programmed DNA breakage, factors predisposing broken DNA ends to translocate configured the earliest version of class switch recombination

ARF1 and GBF1 Generate a PI4P-Enriched Environment Supportive of Hepatitis C Virus Replication

Cellular levels of phosphatidylinositol 4-phosphate (PI4P) have been shown to be upregulated during RNA replication of several viruses, including the HCV replicon model. However, whether PI4P is required in an infectious HCV model remains unknown. Moreover, it is not established whether the host transport machinery is sequestered by the generation of PI4P during HCV infection. Here we found that PI4P was enriched in HCV replication complexes when Huh7.5.1 cells were infected with JFH1. HCV replication was inhibited upon overexpression of the PI4P phosphatase Sac1. The PI4P kinase PI4KIII$\beta$ was also found to be required for HCV replication. Moreover, the vesicular transport proteins ARF1 and GBF1 colocalized with PI4KIIIβ and were both required for HCV replication. During authentic HCV infection, PI4P plays an integral role in virus replication

Event Study and Principal Component Analysis Based on Sentiment Analysis – A Combined Methodology to Study the Stock Market with an Empirical Study

This paper provides an improved method by introducing Sentiment Analysis into the Event Study and Principal Component Analysis. The model is constructed by using the heuristic mean-end analysis. This method enables us to take into investors’ feelings towards related stocks when we study the stock market’s reaction to a given event. This paper investigates the Chinese A-shared market over 2013–2019 to study the influence of rumors and the offsetting impact of rumor clarifications on the stock price. The results indicate that no matter investor sentiment is bullish or bearish, stock price reacts significantly to rumors before as well as when the rumor goes public. Furthermore, clarification offsets the positive abnormal returns caused by rumors with bullish sentiment substantially at a limited level. Still, after five days, it creates a positive effect like the positive rumor does on the stock price. Under the bearish sentiment, clarification brings an insignificant impact on the stock price. The results indicate that the source of rumor may not come from the media and investment decisions established on rumors would be beneficial to investors before as well as after they are published. Moreover, official clarification causes an offset effect, but it is very limited