The influence of μ-opioid and noradrenaline reuptake inhibition in the modulation of pain responsive neurones in the central amygdala by tapentadol in rats with neuropathy

Treatments for neuropathic pain are either not fully effective or have problematic side effects. Combinations of drugs are often used. Tapentadol is a newer molecule that produces analgesia in various pain models through two inhibitory mechanisms, namely central μ-opioid receptor (MOR) agonism and noradrenaline reuptake inhibition. These two components interact synergistically, resulting in levels of analgesia similar to opioid analgesics such as oxycodone and morphine, but with more tolerable side effects. The right central nucleus of the amygdala (CeA) is critical for the lateral spinal ascending pain pathway, regulates descending pain pathways and is key in the emotional-affective components of pain. Few studies have investigated the pharmacology of limbic brain areas in pain models. Here we determined the actions of systemic tapentadol on right CeA neurones of animals with neuropathy and which component of tapentadol contributes to its effect. Neuronal responses to multimodal peripheral stimulation of animals with spinal nerve ligation or sham surgery were recorded before and after two doses of tapentadol. After the higher dose of tapentadol either naloxone or yohimbine were administered. Systemic tapentadol resulted in dose-dependent decrease in right CeA neuronal activity only in neuropathy. Both naloxone and yohimbine reversed this effect to an extent that was modality selective. The interactions of the components of tapentadol are not limited to the synergy between the MOR and α2-adrenoceptors seen at spinal levels, but are seen at this supraspinal site where suppression of responses may relate to the ability of the drug to alter affective components of pain

Hole-Doped Cuprate High Temperature Superconductors

Hole-doped cuprate high temperature superconductors have ushered in the modern era of high temperature superconductivity (HTS) and have continued to be at center stage in the field. Extensive studies have been made, many compounds discovered, voluminous data compiled, numerous models proposed, many review articles written, and various prototype devices made and tested with better performance than their nonsuperconducting counterparts. The field is indeed vast. We have therefore decided to focus on the major cuprate materials systems that have laid the foundation of HTS science and technology and present several simple scaling laws that show the systematic and universal simplicity amid the complexity of these material systems, while referring readers interested in the HTS physics and devices to the review articles. Developments in the field are mostly presented in chronological order, sometimes with anecdotes, in an attempt to share some of the moments of excitement and despair in the history of HTS with readers, especially the younger ones.Comment: Accepted for publication in Physica C, Special Issue on Superconducting Materials; 27 pages, 2 tables, 30 figure

Phase transition in site-diluted Josephson junction arrays: A numerical study

We numerically investigate the intriguing effects produced by random percolative disorder in two-dimensional Josephson-junction arrays. By dynamic scaling analysis, we evaluate critical temperatures and critical exponents with high accuracy. It is observed that, with the introduction of site-diluted disorder, the Kosterlitz-Thouless phase transition is eliminated and evolves into a continuous transition with power-law divergent correlation length. Moreover, genuine depinning transition and creep motion are studied, evidence for distinct creep motion types is provided. Our results not only are in good agreement with the recent experimental findings, but also shed some light on the relevant phase transitions.Comment: 7 pages, 8 figures, Phys. Rev. B (in press

Transport through a quantum dot coupled to two Majorana bound states

We investigate electron transport inside a ring system composed of a quantum dot (QD) coupled to two Majorana bound states confined at the ends of a one-dimensional topological superconductor nanowire. By tuning the magnetic flux threading through the ring, the model system we consider can be switched into states with or without zero-energy modes when the nanowire is in its topological phase. We find that the Fano profile in the conductance spectrum due to the interference between bound and continuum states exhibits markedly different features for these two different situations, which consequently can be used to detect the Majorana zero-energy mode. Most interestingly, as a periodic function of magnetic flux, the conductance shows $2\pi$ periodicity when the two Majorana bound states are nonoverlapping (as in an infinitely long nanowire) but displays $4\pi$ periodicity when the overlapping becomes nonzero (as in a finite length nanowire). We map the model system into a QD--Kitaev ring in the Majorana fermion representation and affirm these different characteristics by checking the energy spectrum.Comment: 8 pages, 8 figure

Averaging approximation to singularly perturbed nonlinear stochastic wave equations

An averaging method is applied to derive effective approximation to the following singularly perturbed nonlinear stochastic damped wave equation \nu u_{tt}+u_t=\D u+f(u)+\nu^\alpha\dot{W} on an open bounded domain $D\subset\R^n$\,, $1\leq n\leq 3$\,. Here $\nu>0$ is a small parameter characterising the singular perturbation, and $\nu^\alpha$\,, $0\leq \alpha\leq 1/2$\,, parametrises the strength of the noise. Some scaling transformations and the martingale representation theorem yield the following effective approximation for small $\nu$, u_t=\D u+f(u)+\nu^\alpha\dot{W} to an error of \ord{\nu^\alpha}\,.Comment: 16 pages. Submitte

Pulsar Glitches in a Strangeon Star Model

Pulsar-like compact stars provide us a unique laboratory to explore properties of dense matter at supra-nuclear densities. One of the models for pulsar-like stars is that they are totally composed of "strangeons", and in this paper we studied the pulsar glitches in a strangeon star model. Strangeon stars would be solidified during cooling, and the solid stars would be natural to have glitches as the result of starquakes. Based on the starquake model established before, we proposed that when the starquake occurs, the inner motion of the star which changes the moment of inertia and has impact on the glitch sizes, is divided into plastic flow and elastic motion. The plastic flow which is induced in the fractured part of the outer layer, would move tangentially to redistribute the matter of the star and would be hard to recover. The elastic motion, on the other hand, changes its shape and would recover significantly. Under this scenario, we could understand the behaviors of glitches without significant energy releasing, including the Crab and the Vela pulsars, in an uniform model. We derive the recovery coefficient as a function of glitch size, as well as the time interval between two successive glitches as the function of the released stress. Our results show consistency with observational data under reasonable ranges of parameters. The implications on the oblateness of the Crab and the Vela pulsars are discussed.Comment: MNRAS, accepte

Efficient Processing Node Proximity via Random Walk with Restart

Graph is a useful tool to model complicated data structures. One important task in graph analysis is assessing node proximity based on graph topology. Recently, Random Walk with Restart (RWR) tends to pop up as a promising measure of node proximity, due to its proliferative applications in e.g. recommender systems, and image segmentation. However, the best-known algorithm for computing RWR resorts to a large LU matrix factorization on an entire graph, which is cost-inhibitive. In this paper, we propose hybrid techniques to efficiently compute RWR. First, a novel divide-and-conquer paradigm is designed, aiming to convert the large LU decomposition into small triangular matrix operations recursively on several partitioned subgraphs. Then, on every subgraph, a “sparse accelerator” is devised to further reduce the time of RWR without any sacrifice in accuracy. Our experimental results on real and synthetic datasets show that our approach outperforms the baseline algorithms by at least one constant factor without loss of exactness