A study on expression levels of matrix metalloproteinases and their inhibitors in patients with ulcerative colitis

Purpose: To investigate the expression levels of matrix metalloproteinases and their inhibitors in ulcerative colitis patientsMethods: In this investigation, the expression of matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) were evaluated in 50 patients with ulcerative colitis, which comprised of 36 males and 14 females. The tissue samples were obtained from the core needle of sample and evaluated using immunochemistry techniques.Results: The prevailing MMP-9 and TIMP-1 expressions in the glandular epithelium and inflammatory cells were observed. In addition, the expressions were coupled with architectural changes in the tissues and inflammation in the lamina propria. It was also observed that the expression of MMPs and inhibitors with weak activation established the progress of ulcerative colitis. Statistical correlation between the MMP expressions and histopathological parameters was also very significant. MMP expression correlated with weaker expression of TIMPs.Conclusion: MMPs, especially MMP-2, MMP-7 and MMP-9, may provide a potential target for controlling ulcerative colitis.Keywords: Ulcerative colitis, Matrix metalloproteinases, Tissue inhibitors of metalloproteinases, Lamina propria, Inflammatio

A study on the expression levels of matrix metalloproteinases and their inhibitors in patients with ulcerative colitis

Purpose: To investigate the expression matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) in ulcerative colitis (UC) patients admitted to The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.Methods: The expression of MMPs and TIMPs) was evaluated in 50 patients (36 males, 14 females) with UC who were admitted to The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China. Tissue samples obtained from core needle biopsies were evaluated using immunochemistry techniques.Results: MMP-9 and TIMP-1 were strongly expressed in the glandular epithelium and inflammatory cells. In addition, the expression was coupled with changes in the tissue architecture and inflammation in the lamina propria. The expression of MMPs and weak activation of their inhibitors was related to UC progression. There were significant correlations between MMP expression and histopathological parameters (p = 0.051, R = 0.392). MMP expression correlated with weaker expression of TIMPs (p = 0.032, R = 0.0581)Conclusion: MMP-2, MMP-7, and MMP-9 are potential targets for therapeutic control of UC.Keywords: Glandular epithelium, Inflammatory cells, Inhibitors, Matrix  metalloproteinases (MMPs), Tissue inhibitors of metalloproteinases, Ulcerative coliti

Dark Energy and Fate of the Universe

We explore the ultimate fate of the Universe by using a divergence-free parametrization for dark energy $w(z)=w_0+w_a({\ln (2+z)\over 1+z}-\ln2)$. Unlike the CPL parametrization, this parametrization has well behaved, bounded behavior for both high redshifts and negative redshifts, and thus can genuinely cover many theoretical dark energy models. After constraining the parameter space of this parametrization by using the current cosmological observations, we find that, at the 95.4% confidence level, our Universe can still exist at least 16.7 Gyr before it ends in a big rip. Moreover, for the phantom energy dominated Universe, we find that a gravitationally bound system will be destroyed at a time $t \simeq P\sqrt{2|1+3w(-1)|}/[6\pi |1+w(-1)|]$, where $P$ is the period of a circular orbit around this system, before the big rip.Comment: 5 pages, 3 figures; typos corrected, publication version, Sci China-Phys Mech Astron, doi: 10.1007/s11433-012-4748-

Joint Multi-view Unsupervised Feature Selection and Graph Learning

Despite the recent progress, the existing multi-view unsupervised feature selection methods mostly suffer from two limitations. First, they generally utilize either cluster structure or similarity structure to guide the feature selection, neglecting the possibility of a joint formulation with mutual benefits. Second, they often learn the similarity structure by either global structure learning or local structure learning, lacking the capability of graph learning with both global and local structural awareness. In light of this, this paper presents a joint multi-view unsupervised feature selection and graph learning (JMVFG) approach. Particularly, we formulate the multi-view feature selection with orthogonal decomposition, where each target matrix is decomposed into a view-specific basis matrix and a view-consistent cluster indicator. Cross-space locality preservation is incorporated to bridge the cluster structure learning in the projected space and the similarity learning (i.e., graph learning) in the original space. Further, a unified objective function is presented to enable the simultaneous learning of the cluster structure, the global and local similarity structures, and the multi-view consistency and inconsistency, upon which an alternating optimization algorithm is developed with theoretically proved convergence. Extensive experiments demonstrate the superiority of our approach for both multi-view feature selection and graph learning tasks

On the influence of China’s real estate industry on national economic growth

Ob die Immobilienentwicklung in China überhitzt ist und ob es Blasen in der Branche gibt, steht im Mittelpunkt der wissenschaftlichen Auseinandersetzung. Allerdings ist die Meinung verbreitet, dass das Immobilieninvestment zu einem gewissen Ausmaß das Wirtschaftswachstum fördern kann, aber die empirischen Forschungsergebnisse über die Intensität des Einflusses variieren. Diese Arbeit benutzt das Input-Output-Model, um die treibende Kraft der Immobilieninvestitionen auf nationale Wirtschaft zu untersuchen. Basierend auf Zeitreihendaten des Chinesischen Bruttoinlandsprodukts und der Immobilieninvestitionen im Zeitraum von 1986 bis 2008, verwenden wir auch quantitative Methoden, wie z.B. Kointegrationsanalyse und Impulsantwortanalyse, um die Beziehung zwischen den Immobilieninvestitionen und dem Wirtschaftswachstum zu untersuchen. Diese Arbeit listet auch einige bestehende Probleme in der jüngsten Entwicklung der Immobilienwirtschaft Chinas auf und gibt einige Ratschläge.Whether the real estate development in China is overheated and whether there are bubbles in the industry is the focus of some dispute among researchers. However, they agree that real estate investment to some extent can promote China’s economic growth but empirical results on the intensity of the influence differ widely. This thesis uses the Input-Output Model to examine the driving effect of the real estate investment on the national economy. Based on time-series data of China’s GDP and real estate investment in the period of 1986 to 2008, we also use quantitative methods, such as co-integration analysis and impulse response analysis, to examine the relationship between the real estate and economy growth. We also list some problems existing in the recent development of China’s real estate industry and provide some proper advice based on previous literatures and theories

On compression rate of quantum autoencoders: Control design, numerical and experimental realization

Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information. In this paper, we establish an upper bound of the compression rate for a given quantum autoencoder and present a learning control approach for training the autoencoder to achieve the maximal compression rate. The upper bound of the compression rate is theoretically proven using eigen-decomposition and matrix differentiation, which is determined by the eigenvalues of the density matrix representation of the input states. Numerical results on 2-qubit and 3-qubit systems are presented to demonstrate how to train the quantum autoencoder to achieve the theoretically maximal compression, and the training performance using different machine learning algorithms is compared. Experimental results of a quantum autoencoder using quantum optical systems are illustrated for compressing two 2-qubit states into two 1-qubit states