Block-block entanglement and quantum phase transitions in one-dimensional extended Hubbard model

In this paper, we study block-block entanglement in the ground state of one-dimensional extended Hubbard model. Our results show that the phase diagram derived from the block-block entanglement manifests richer structure than that of the local (single site) entanglement because it comprises nonlocal correlation. Besides phases characterized by the charge-density-wave, the spin-density-wave, and phase-separation, which can be sketched out by the local entanglement, singlet superconductivity phase could be identified on the contour map of the block-block entanglement. Scaling analysis shows that ${\rm log}_2(l)$ behavior of the block-block entanglement may exist in both non-critical and the critical regions, while some local extremum are induced by the finite-size effect. We also study the block-block entanglement defined in the momentum space and discuss its relation to the phase transition from singlet superconducting state to the charge-density-wave state.Comment: 8 pages, 9 figure

Entanglement and quantum phase transition in the extended Hubbard model

We study quantum entanglement in one-dimensional correlated fermionic system. Our results show, for the first time, that entanglement can be used to identify quantum phase transitions in fermionic systems.Comment: 5 pages, 4 figure

Bayesian nonparametric mixtures of Exponential Random Graph Models for ensembles of networks

Ensembles of networks arise in various fields where multiple independent networks are observed, for example, a collection of student networks from different classes. However, there are few models that describe both the variations and characteristics of networks in an ensemble at the same time. In this manuscript, we propose to model ensembles of networks using a Dirichlet Process Mixture of Exponential Random Graph Models (DPM-ERGMs), which divides an ensemble into different clusters and models each cluster of networks using a separate Exponential Random Graph Model (ERGM). By employing a Dirichlet process mixture, the number of clusters can be determined automatically and changed adaptively with the data provided. Moreover, in order to perform full Bayesian inference for DPM-ERGMs, we develop a Metropolis-within-slice sampling algorithm to address the problem of sampling from the intractable ERGMs on an infinite sample space. We also demonstrate the performance of DPM-ERGMs with both simulated and real datasets

Fermionic concurrence in the extended Hubbard dimer

In this paper, we introduce and study the fermionic concurrence in a two-site extended Hubbard model. Its behaviors both at the ground state and finite temperatures as function of Coulomb interaction $U$ (on-site) and $V$ (nearest-neighbor) are obtained analytically and numerically. We also investigate the change of the concurrence under a nonuniform field, including local potential and magnetic field, and find that the concurrence can be modulated by these fields.Comment: 5 pages, 7 figure

MMP28 (epilysin) as a novel promoter of invasion and metastasis in gastric cancer

Background\ud The purpose of this study was to investigate invasion and metastasis related genes in gastric cancer.\ud \ud Methods\ud The transwell migration assay was used to select a highly invasive sub-line from minimally invasive parent gastric cancer cells, and gene expression was compared using a microarray. MMP28 upregulation was confirmed using qRT-PCR. MMP28 immunohistochemistry was performed in normal and gastric cancer specimens. Invasiveness and tumor formation of stable cells overexpressing MMP28 were tested in vitro and in vivo.\ud \ud Results\ud MMP28 was overexpressed in the highly invasive sub-cell line. Immunohistochemistry revealed MMP28 expression was markedly increased in gastric carcinoma relative to normal epithelia, and was significantly associated with depth of tumor invasion, lymph node metastasis and poorer overall survival. Ectopic expression of MMP28 indicated MMP28 promoted tumor cell invasion in vitro and increased gastric carcinoma metastasis in vivo.\ud \ud Conclusions\ud This study indicates MMP28 is frequently overexpressed during progression of gastric carcinoma, and contributes to tumor cell invasion and metastasis. MMP28 may be a novel therapeutic target for prevention and treatment of metastases in gastric cancer

A new assessment model for tumor heterogeneity analysis with [18]F-FDG PET images

It has been shown that the intratumor heterogeneity can be characterized with quantitative analysis of the [18]F-FDG PET image data. The existing models employ multiple parameters for feature extraction which makes it difficult to implement in clinical settings for the quantitative characterization. This article reports an easy-to-use and differential SUV based model for quantitative assessment of the intratumor heterogeneity from 3D [18]F-FDG PET image data. An H index is defined to assess tumor heterogeneity by summing voxel-wise distribution of differential SUV from the [18]F-FDG PET image data. The summation is weighted by the distance of SUV difference among neighboring voxels from the center of the tumor and can thus yield increased values for tumors with peripheral sub-regions of high SUV that often serves as an indicator of augmented malignancy. Furthermore, the sign of H index is used to differentiate the rate of change for volume averaged SUV from its center to periphery. The new model with the H index has been compared with a widely-used model of gray level co-occurrence matrix (GLCM) for image texture characterization with phantoms of different configurations and the [18]F-FDG PET image data of 6 lung cancer patients to evaluate its effectiveness and feasibility for clinical uses. The comparison of the H index and GLCM parameters with the phantoms demonstrate that the H index can characterize the SUV heterogeneity in all of 6 2D phantoms while only 1 GLCM parameter can do for 1 and fail to differentiate for other 2D phantoms. For the 8 3D phantoms, the H index can clearly differentiate all of them while the 4 GLCM parameters provide complicated patterns in the characterization. Feasibility study with the PET image data from 6 lung cancer patients show that the H index provides an effective single-parameter metric to characterize tumor heterogeneity in terms of the local SUV variation, and it has higher correlation with tumor volume change after radiotherapy (R2 = 0.83) than the 4 GLCM parameters (R2 = 0.63, 0.73, 0.59 and 0.75 for Energy, Contrast, Local Homogeneity and Entropy respectively). The new model of the H index has the capacity to characterize the intratumor heterogeneity feature from 3D [18]F-FDG PET image data. As a single parameter with an intuitive definition, the H index offers potential for clinical applications