Improvement of dielectric loss of doped Ba0.5Sr0.5TiO3 thin films for tunable microwave devices

Al2O3-Ba0.5Sr0.5TiO3 (Al2O3-BST) thin films, with different Al2O3 contents, were deposited on (100) LaAlO3 substrate by pulsed laser deposition (PLD) technique. The Al2O3-BST films was demosnstrated to be a suitable systems to fabricate ferroelectric thin films with low dielectric loss and higher figure of merit for tunable microwave devices. Pure BST thin films were also fabricated for comparison purpose. The films' structure and morphology were analyzed by X-ray diffractiopn and scanning electron microscopy, respectively; nad showed that the surface roughness for the Al2O3-BST films increased with the Al2O3 content. Apart from that, the broadening in the intensity peak in XRD result indicating the grain size of the Al2O3-BST films reduced with the increasing of Al2O3 dopant. We measured the dielctric properties of Al2O3-BST films with a home-made non-destructive dual resonator method at frequency ~ 7.7 GHZ. The effect of doped Al2O3 into BST thin films significantly reduced the dielectric constant, dielectric loss and tunability compare to pure BST thin film. Our result shows the figure of merit (K), used to compare the films with varied dielectric properties, increased with the Al2O3 content. Therefore Al2O3-BST films show the potential to be exploited in tunable microwave devices.Comment: 8 pages, 4 figures, 1 table. Accepted & tentatively for Feb 15 2004 issue, Journal of Applied Physic

Investigating neovascularization in rat decellularized intestine - an in vitro platform for studying angiogenesis

One of the main challenges currently faced by tissue engineers is the loss of tissues post implantation due to delayed neovascularization. Several strategies are under investigation to create vascularized tissue but none have yet overcome this problem. In this study we produced a decellularized natural vascular scaffold from rat intestine to use as an in vitro platform for neovascularization studies for tissue engineered constructs. Decellularization resulted in almost complete (97%) removal of nuclei and DNA, while collagen, glycosaminoglycans and laminin content was preserved. Decellularization did, however, result in the loss of elastin and fibronectin. Some proangiogenic factors were retained, as fragments of decellularized intestine were able to stimulate angiogenesis in the chick chorioallantoic membrane assay. We demonstrated that decellularization left perfusable vascular channels intact, and these could be repopulated with human dermal microvascular endothelial cells. Optimization of reendothelialisation of the vascular channels showed this was improved by continuous perfusion of the vasculature and further improved by infusion of human dermal fibroblasts into the intestinal lumen, from where they invaded into the decellularized tissue. Finally we explored the ability of the perfused cells to form new vessels. In the absence of exogenous angiogenic stimuli, Dll4, a marker of endothelial capillary-tip cell activation during sprouting angiogenesis was absent, indicating the reformed vasculature was largely quiescent. However, after addition of VEGFA, Dll4 positive endothelial cells could be detected, demonstrating this engineered vascular construct maintained its capacity for neovascularization. In summary we have demonstrated how a natural xenobiotic vasculature can be used as an in vitro model platform to study 3 neovascularization and provide information on factors that are critical for efficient reendothelialisation of decellularized tissue

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

π0→γ∗γ\pi^0\to\gamma^*\gamma transition form factor within Light Front Quark Model

We study the transition form factor of $\pi^0\to\gamma^* \gamma$ as a function of the momentum transfer $Q^2$ within the light-front quark model (LFQM). We compare our result with the experimental data by BaBar as well as other calculations based on the LFQM in the literature. We show that our predicted form factor fits well with the experimental data, particularly those at the large $Q^2$ region.Comment: 11 pages, 4 figures, accepted for publication in PR

Comment on ``A new efficient method for calculating perturbative energies using functions which are not square integrable'': regularization and justification

The method recently proposed by Skala and Cizek for calculating perturbation energies in a strict sense is ambiguous because it is expressed as a ratio of two quantities which are separately divergent. Even though this ratio comes out finite and gives the correct perturbation energies, the calculational process must be regularized to be justified. We examine one possible method of regularization and show that the proposed method gives traditional quantum mechanics results.Comment: 6 pages in REVTeX, no figure

Origins of ferromagnetism in transition-metal doped Si

We present results of the magnetic, structural and chemical characterizations of Mn<sup>+</sup>-implanted Si displaying <i>n</i>-type semiconducting behavior and ferromagnetic ordering with Curie temperature,T<sub>C</sub> well above room temperature. The temperature-dependent magnetization measured by superconducting quantum device interference (SQUID) from 5 K to 800 K was characterized by three different critical temperatures (T*<sub>C</sub>~45 K, T<sub>C1</sub>~630-650 K and T<sub>C2</sub>~805-825 K). Their origins were investigated using dynamic secondary mass ion spectroscopy (SIMS) and transmission electron microscopy (TEM) techniques, including electron energy loss spectroscopy (EELS), Z-contrast STEM (scanning TEM) imaging and electron diffraction. We provided direct evidences of the presence of a small amount of Fe and Cr impurities which were unintentionally doped into the samples together with the Mn<sup>+</sup> ions, as well as the formation of Mn-rich precipitates embedded in a Mn-poor matrix. The observed T*<sub>C</sub> is attributed to the Mn<sub>4</sub>Si<sub>7</sub> precipitates identified by electron diffraction. Possible origins of and are also discussed. Our findings raise questions regarding the origin of the high ferromagnetism reported in many material systems without a careful chemical analysis