Instanton Effects in QCD Sum Rules for the 0++0^{++} Hybrid

In this paper, we study instanton contributions to the correlator of the hybrid current $g\bar q \sigma_{\mu\nu}G^a_{\nu\mu}T^a q$. These contributions are then included in a QCD sum-rule analysis of the isoscalar $0^{++}$ hybrid mass. We find a mass at 1.83 GeV for the $(\bar uug+\bar ddg)/\sqrt{2}$ hybrid. However, for the $\bar ssg$ hybrid, we find the sum rules are unstable. We also study non-zero width effects, which affect the mass prediction. The mixing effects between these two states are studied and we find QCD sum rules support the existence of a flavor singlet hybrid with mass at around 1.9 GeV. Finally, we study the mixing effects between hybrid and glueball currents. The mixing between the $(\bar uug+\bar ddg)/\sqrt{2}$($\bar ssg$) and the glueball causes two states, one in the region 1.4-1.8 GeV(1.4-2.2 GeV), and the other in the range 1.8-2.2 GeV(2.2-2.6 GeV).Comment: 12 pages, revised versio

Aqua[N-(2,5-dihydroxybenzyl)imino­diacetato]copper(II)

The title complex, [Cu(C11H11NO6)(H2O)], contains a CuII atom in a distorted square-pyramidal geometry. The metal centre is coordinated in the basal sites by one water mol­ecule and two carboxyl­ate O atoms and one N atom of the tetra­dentate ligand [Cu—O range, 1.9376 (11)–1.9541 (12), Cu—N, 1.9929 (12) Å] while the apical site is occupied by a hydro­quinone O donor atom [Cu—O, 2.3746 (12) Å]. Inter­molecular hydrogen bonding inter­actions involving both hydro­quinone hydr­oxy groups and the coordinated water as donors give a three-dimensional framework structure

Effects of Linear-Polarized Near-Infrared Light Irradiation on Chronic Pain

In order to study the efficacy of linear-polarized near-infrared light irradiation (LPNIR) on relieving chronic pain in conjunction with nerve block (NB) or local block (LB), a 3-week prospective, randomized, double-blind, controlled study was conducted to evaluate the pre- and post-therapy pain intensity. Visual analogue scales (VASs) were measured in all patients before and 6 months after therapy visiting the pain clinic during the period of August 2007 to January 2008. A total of 52 patients with either shoulder periarthritis or myofascial pain syndrome or lateral epicondylitis were randomly assigned into two groups by drawing lots. Patients in Group I were treated with NB or LB plus LPNIR; Group II patients, for their part, were treated with the same procedures as in Group I, but not using LPNIR. In both groups, the pain intensity (VAS score) decreased significantly immediately after therapy as compared to therapy. There was a significant difference between the test and control groups immediately after therapy (P < 0.05), while no effect 6 months later. No side effects were observed. It is concluded that LPNIR is an effective and safe modality to treat various chronic pains, which has synergic effects with NB or LB

SR140333 counteracts NK-1 mediated cell proliferation in human breast cancer cell line T47D

<p>Abstract</p> <p>Background</p> <p>It has been demonstrated that certain NK-1 antagonists could reduce proliferation of several cancer cell lines, however, it is unknown whether SR140333 exerts proliferation inhibition in breast cancer cell line.</p> <p>Methods</p> <p>Immunohistochemical staining was carried out to investigate the immunolocation of NK-1 in breast cancer tissues and T47D cell line, thereafter, various concentrations of [Sar9, Met(O2)11]substance P and SR140333 were applied alone or combined. MTT assay was applied to detect cytoactivation and coulter counter was to detect growth curve. The Hoechst33258 staining was performed to detect apoptosis.</p> <p>Results</p> <p>We found that breast cancer and T47D cells bear positive expression of NK-1. SR140333 inhibited cell growth in a dose dependent manner. Furthermore, SR140333 could counteract [Sar9, Met(O2)11]substance P induced proliferation. Hoechst33258 staining revealed the presence of apoptosis after SR140333 treatment.</p> <p>Conclusions</p> <p>Our study demonstrated SR140333 exert proliferation inhibition in breast cancer cell line T47D and indicates NK-1 play a central role in the substance P related cell proliferation in breast cancer.</p

Bis{N,N-bis­[(diphenyl­phosphan­yl)meth­yl]aniline-κ2 P,P′}copper(I) tetra­fluoridoborate

In the cation of the title compound, [Cu(C32H29NP2)2]BF4, the CuI atom is four-coordinated in a distorted tetra­hedral geometry by four P atoms from two N,N-bis­[(diphenyl­phosphan­yl)meth­yl]aniline ligands. In the crystal, the cations are linked by C—H⋯π inter­actions, forming chains along the a axis. Intra­molecular C—H⋯N and inter­molecular C—H⋯F hydrogen bonds are also observed

SMART: A Situation Model for Algebra Story Problems via Attributed Grammar

Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability. Previous neural solvers of math word problems directly translate problem texts into equations, lacking an explicit interpretation of the situations, and often fail to handle more sophisticated situations. To address such limits of neural solvers, we introduce the concept of a \emph{situation model}, which originates from psychology studies to represent the mental states of humans in problem-solving, and propose \emph{SMART}, which adopts attributed grammar as the representation of situation models for algebra story problems. Specifically, we first train an information extraction module to extract nodes, attributes, and relations from problem texts and then generate a parse graph based on a pre-defined attributed grammar. An iterative learning strategy is also proposed to improve the performance of SMART further. To rigorously study this task, we carefully curate a new dataset named \emph{ASP6.6k}. Experimental results on ASP6.6k show that the proposed model outperforms all previous neural solvers by a large margin while preserving much better interpretability. To test these models' generalization capability, we also design an out-of-distribution (OOD) evaluation, in which problems are more complex than those in the training set. Our model exceeds state-of-the-art models by 17\% in the OOD evaluation, demonstrating its superior generalization ability