Evolutionary history of histone demethylase families: distinct evolutionary patterns suggest functional divergence

<p>Abstract</p> <p>Background</p> <p>Histone methylation can dramatically affect chromatin structure and gene expression and was considered irreversible until recent discoveries of two families of histone demethylases, the KDM1 (previously LSD1) and JmjC domain-containing proteins. These two types of proteins have different functional domains and distinct substrate specificities. Although more and more KDM1 and JmjC proteins have been shown to have histone demethylase activity, our knowledge about their evolution history is limited.</p> <p>Results</p> <p>We performed systematic phylogenetic analysis of these histone demethylase families and uncovered different evolutionary patterns. The <it>KDM1 </it>genes have been maintained with a stable low copy number in most organisms except for a few duplication events in flowering plants. In contrast, multiple genes for JmjC proteins with distinct domain architectures were present before the split of major eukaryotic groups, and experienced subsequent birth-and-death evolution. In addition, distinct evolutionary patterns can also be observed between animal and plant histone demethylases in both families. Furthermore, our results showed that some <it>JmjC </it>subfamilies contain only animal genes with specific demethylase activities, but do not have plant members.</p> <p>Conclusion</p> <p>Our study improves the understanding about the evolutionary history of <it>KDM1 </it>and <it>JmjC </it>genes and provides valuable insights into their functions. Based on the phylogenetic relationship, we discussed possible histone demethylase activities for several plant JmjC proteins. Finally, we proposed that the observed differences in evolutionary pattern imply functional divergence between animal and plant histone demethylases.</p

Hydrodynamic Coefficients of Yawed Square Cylinder in Oscillating Flow

Experimental and Computational Hydraulic

Spin-tensor Meissner currents of ultracold bosonic gas in an optical lattice

We investigate the Meissner currents of interacting bosons subjected to a staggered artificial gauge field in a three-leg ribbon geometry, realized by spin-tensor--momentum coupled spin-1 atoms in a 1D optical lattice. By calculating the current distributions using the state-of-the-art density-matrix renormalization-group method, we find a rich phase diagram containing interesting Meissner and vortex phases, where the currents are mirror symmetric with respect to the {\color{red}middle leg} (i.e., they flow in the same direction on the two boundary legs opposite to that on the middle leg), leading to the spin-tensor type Meissner currents, which is very different from previously observed chiral edge currents under uniform gauge field. The currents are uniform along each leg in the Meissner phase and form vortex-antivortex pairs in the vortex phase. Besides, the system also support a polarized phase that spontaneously breaks the mirror symmetry, whose ground states are degenerate with currents either uniform or forming vortex-antivortex pairs. We also discuss the experimental schemes for probing these phases. Our work provides useful guidance to ongoing experimental research on synthetic flux ribbons and paves the way for exploring novel many-body phenomena therein.Comment: 10 pages, 9 figure

Exploring interacting topological insulator of extended Su-Schrieffer-Heeger model

Exploring topological phases in interacting systems is a challenging task. We investigate many-body topological physics of interacting fermions in an extended Su-Schrieffer-Heeger (SSH) model, which extends the two sublattices of SSH model into four sublattices and thus is dubbed SSH4 model, based on the density-matrix renormalization-group numerical method. The interaction-driven phase transition from topological insulator to charge density wave (CDW) phase can be identified by analyzing the variations of entanglement spectrum, entanglement entropies, energy gaps, CDW order parameter, and fidelity. We map the global phase diagram of the many-body ground state, which contains nontrivial topological insulator, trivial insulator and CDW phases, respectively. In contrast to interacting SSH model, in which the phase transitions to the CDW phase are argued to be first-order phase transitions, the phase transitions between the CDW phase and topologically trivial/nontrivial phases are shown to be continuous phase transitions. Finally, we {also} show the phase diagram of interacting spinful SSH4 model, where the attractive (repulsive) on-site spin interaction amplifies (suppresses) the CDW phase. The models analyzed here can be implemented with ultracold atoms on optical superlattices.Comment: 9 pages, 5 figure

Discharge Forecasting By Applying Artificial Neural Networks At The Jinsha River Basin, China

Flood prediction methods play an important role in providing early warnings to government offices. The ability to predict future river flows helps people anticipate and plan for upcoming flooding, preventing deaths and decreasing property destruction. Different hydrological models supporting these predictions have different characteristics, driven by available data and the research area. This study applied three different types of Artificial Neural Networks (ANN) and an autoregressive model to study the Jinsha river basin (JRB), in the upper part of the Yangtze River in China. The three ANN techniques include feedforward back propagation neural networks (FFBPNN), generalized regression neural networks (GRNN), and the radial basis function neural networks (RBFNN). Artificial Neural Networks (ANN) has shown Great deal of accuracy as compared to statistical autoregressive (AR) model because statistical model cannot able to simulate the non-linear pattern. The results varied across the cases used in the study; based on available data and the study area, FFBPNN showed the best applicability, compared to other techniques

Quantum Algorithm for Maximum Biclique Problem

Identifying a biclique with the maximum number of edges bears considerable implications for numerous fields of application, such as detecting anomalies in E-commerce transactions, discerning protein-protein interactions in biology, and refining the efficacy of social network recommendation algorithms. However, the inherent NP-hardness of this problem significantly complicates the matter. The prohibitive time complexity of existing algorithms is the primary bottleneck constraining the application scenarios. Aiming to address this challenge, we present an unprecedented exploration of a quantum computing approach. Efficient quantum algorithms, as a crucial future direction for handling NP-hard problems, are presently under intensive investigation, of which the potential has already been proven in practical arenas such as cybersecurity. However, in the field of quantum algorithms for graph databases, little work has been done due to the challenges presented by the quantum representation of complex graph topologies. In this study, we delve into the intricacies of encoding a bipartite graph on a quantum computer. Given a bipartite graph with n vertices, we propose a ground-breaking algorithm qMBS with time complexity O^*(2^(n/2)), illustrating a quadratic speed-up in terms of complexity compared to the state-of-the-art. Furthermore, we detail two variants tailored for the maximum vertex biclique problem and the maximum balanced biclique problem. To corroborate the practical performance and efficacy of our proposed algorithms, we have conducted proof-of-principle experiments utilizing IBM quantum simulators, of which the results provide a substantial validation of our approach to the extent possible to date