Quantitative analysis reveals increased histone modifications and a broad nucleosome-free region bound by histone acetylases in highly expressed genes in human CD4+ T cells

AbstractGenome-wide mapping of nucleosomes and histone modifications revealed meaningful patterns. Despite advances in resolving the associations between chromatin and transcription, quantitative chromatin dynamics have not been well defined. We quantitatively determined differences in histone modifications, nucleosome positions, DNA methylation, and transcription factor binding in highly expressed and repressed genes in human CD4+ T cells. We showed that the first (−1) nucleosome upstream of the transcription start site (TSS) is shifted to the 5′ direction, thus forming a broad nucleosome-free region (NFR) near the TSS in highly expressed genes in CD4+ T cells. Moreover, the transcription factor YY1 and histone acetyltransferases bind the NFR with high affinity. Most of histone acetylations drastically increase in transcription activation (>5 folds). We also suggested that single nucleotide polymorphisms (SNPs) occur at a much lower frequency in highly expressed genes than in repressed genes. Our analysis quantitatively revealed details of chromatin dynamics

Dynamical spontaneous scalarization in Einstein-Maxwell-scalar models in anti-de Sitter spacetime

The phenomenon of spontaneous scalarization of charged black holes has attracted a lot of attention. In this work, we study the dynamical process of the spontaneous scalarization of charged black hole in asymptotically anti-de Sitter spacetimes in Einstein-Maxwell-scalar models. Including various non-minimal couplings between the scalar field and Maxwell field, we observe that an initial scalar-free configuration suffers tachyonic instability and both the scalar field and the black hole irreducible mass grow exponentially at early times and saturate exponentially at late times. For fractional couplings, we find that though there is negative energy distribution near the black hole horizon, the black hole horizon area never decreases. But when the parameters are large, the evolution endpoints of linearly unstable bald black holes will be spacetimes with naked singularity and the cosmic censorship is violated. The effects of the black hole charge, cosmological constant and coupling strength on the dynamical scalarization process are studied in detail. We find that large enough cosmological constant can prevent the spontaneous scalarization.Comment: 20pages,8figure

Dynamical transitions in scalarization and descalarization through black hole accretion

We present the first fully nonlinear study on the accretion of scalar fields onto a seed black hole in anti-de Sitter spacetime in Einstein-Maxwell-scalar theory. Intrinsic critical phenomena in the dynamical transition between the bald and scalarized black holes are disclosed. In scalarizations, the transition is discontinuous and a metastable black hole acts as an attractor at the threshold. We construct a new physical mechanism to dynamically descalarize an isolated scalarized black hole. The first results on critical phenomena in descalarizations are revealed. The dynamical descalarizations can be either discontinuous or continuous at the threshold, distinguished by whether or not an attractor appears

GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion Recognition

In human-computer interaction, Speech Emotion Recognition (SER) plays an essential role in understanding the user's intent and improving the interactive experience. While similar sentimental speeches own diverse speaker characteristics but share common antecedents and consequences, an essential challenge for SER is how to produce robust and discriminative representations through causality between speech emotions. In this paper, we propose a Gated Multi-scale Temporal Convolutional Network (GM-TCNet) to construct a novel emotional causality representation learning component with a multi-scale receptive field. GM-TCNet deploys a novel emotional causality representation learning component to capture the dynamics of emotion across the time domain, constructed with dilated causal convolution layer and gating mechanism. Besides, it utilizes skip connection fusing high-level features from different gated convolution blocks to capture abundant and subtle emotion changes in human speech. GM-TCNet first uses a single type of feature, mel-frequency cepstral coefficients, as inputs and then passes them through the gated temporal convolutional module to generate the high-level features. Finally, the features are fed to the emotion classifier to accomplish the SER task. The experimental results show that our model maintains the highest performance in most cases compared to state-of-the-art techniques.Comment: The source code is available at: https://github.com/Jiaxin-Ye/GM-TCNe