Chandra Observation of a Weak Shock in the Galaxy Cluster A2556

Based on a 21.5 ks \chandra\ observation of A2556, we identify an edge on the surface brightness profile (SBP) at about 160$h_{71}^{-1}$ kpc northeast of the cluster center, and it corresponds to a shock front whose Mach number $\mathcal{M}$ is calculated to be $1.25_{-0.03}^{+0.02}$. No prominent substructure, such as sub-cluster, is found in either optical or X-ray band that can be associated with the edge, suggesting that the conventional super-sonic motion mechanism may not work in this case. As an alternative solution, we propose that the nonlinear steepening of acoustic wave, which is induced by the turbulence of the ICM at the core of the cluster, can be used to explain the origin of the shock front. Although nonlinear steepening weak shock is expected to occur frequently in clusters, why it is rarely observed still remains a question that requires further investigation, including both deeper X-ray observation and extensive theoretical studies.Comment: 15 pages, 4 figures, accepted by Ap

Poly[diaqua-μ2-oxalato-di-μ4-succinato-diyttrium(III)]

In the title compound, [Y2(C4H4O4)2(C2O4)(H2O)2]n, the flexible succinate anion assumes a gauche conformation and bridges the eight-coordinated Y atoms, generating two-dimensional layers parallel to (010). The coordination polymer layers are linked into a three-dimensional framework by the rigid oxalate ligands. The oxalate ions are located on a center of inversion. Inter­molecular O—H⋯O hydrogen bonds help to stabilize the crystal structure

Targeting Acid-Sensing Ion Channels by Peptide Toxins

Acid-sensing ion channels (ASICs) are proton-gated ion channels that are highly expressed in the nervous system and play important roles in physiological and pathological conditions. They are also expressed in non-neuronal tissues with different functions. The ASICs rapidly respond to a reduction in extracellular pH with an inward current that is quickly inactivated despite the continuous presence of protons. Recently, protons have been identified as neurotransmitters in the brain. Until now, six different isoforms (ASIC1a, 1b, 2a, 2b, 3 and 4) in rodents have been discovered and they can be assembled into homotrimers or heterotrimers to form an ion channel. Peptide toxins targeting ASICs have been found from the venoms of spider Psalmotoxin-1 (PcTx1), sea anemones (APETx2 and PhcrTx1) and snakes (MitTx and mambalgins). They reveal different pharmacological properties and are selective blockers of ASICs, except for MitTx, which is a potent activator of ASICs. In this mini review, the structure, pharmacology and effects of peptide toxins on ASICs will be introduced and their therapeutic potentials for neurological and psychological diseases will be discussed

QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM

This paper focuses on online kernel learning over a decentralized network. Each agent in the network receives continuous streaming data locally and works collaboratively to learn a nonlinear prediction function that is globally optimal in the reproducing kernel Hilbert space with respect to the total instantaneous costs of all agents. In order to circumvent the curse of dimensionality issue in traditional online kernel learning, we utilize random feature (RF) mapping to convert the non-parametric kernel learning problem into a fixed-length parametric one in the RF space. We then propose a novel learning framework named Online Decentralized Kernel learning via Linearized ADMM (ODKLA) to efficiently solve the online decentralized kernel learning problem. To further improve the communication efficiency, we add the quantization and censoring strategies in the communication stage and develop the Quantized and Communication-censored ODKLA (QC-ODKLA) algorithm. We theoretically prove that both ODKLA and QC-ODKLA can achieve the optimal sublinear regret $\mathcal{O}(\sqrt{T})$ over $T$ time slots. Through numerical experiments, we evaluate the learning effectiveness, communication, and computation efficiencies of the proposed methods