Density of states of a graphene in the presence of strong point defects

The density of states near zero energy in a graphene due to strong point defects with random positions are computed. Instead of focusing on density of states directly, we analyze eigenfunctions of inverse T-matrix in the unitary limit. Based on numerical simulations, we find that the squared magnitudes of eigenfunctions for the inverse T-matrix show random-walk behavior on defect positions. As a result, squared magnitudes of eigenfunctions have equal {\it a priori} probabilities, which further implies that the density of states is characterized by the well-known Thomas-Porter type distribution. The numerical findings of Thomas-Porter type distribution is further derived in the saddle-point limit of the corresponding replica field theory of inverse T-matrix. Furthermore, the influences of the Thomas-Porter distribution on magnetic and transport properties of a graphene, due to its divergence near zero energy, are also examined.Comment: 6 figure

Serum protein fingerprint of patients with gastric cancer by SELDI technology

To study the serum protein fingerprint of patients with gastric cancer and to screen for protein molecules closely related to gastric cancer during the onset and progression of the disease using surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Serum samples from 80 gastric cancers and 80 healthy volunteers. WCX2 protein chip and PBSII-C protein chips reader (Ciphergen Biosystems Ins.) were used. The protein fingerprint expression of all the serum samples and the resulting profiles between cancer and normal were analyzed with Biomarker Wizard system. A group of proteomic peaks were detected. Four differently expressed potential biomarkers were identified with the relative molecular weights of 5907, 8678, 11673 and 13725 Da. Among them, two proteins with m/z 8678 and 13725 Da down-regulated, and two proteins with m/z 5907 and 11673 Da were up-regulated in gastric cancers. This diagnostic model can distinguish gastric cancer from healthy controls with a sensitivity of 96% and a specificity of 93.3%. SELDI technology can be used to screen significant proteins of differential expression in the serum of gastric cancer patients.These different proteins could be specific biomarkers of the patients with gastric cancer in the serum and have the potential value of further investigation

Aspects of the stochastic Burgers equation and their connection with turbulence

We present results for the 1 dimensional stochastically forced Burgers equation when the spatial range of the forcing varies. As the range of forcing moves from small scales to large scales, the system goes from a chaotic, structureless state to a structured state dominated by shocks. This transition takes place through an intermediate region where the system exhibits rich multifractal behavior. This is mainly the region of interest to us. We only mention in passing the hydrodynamic limit of forcing confined to large scales, where much work has taken place since that of Polyakov. In order to make the general framework clear, we give an introduction to aspects of isotropic, homogeneous turbulence, a description of Kolmogorov scaling, and, with the help of a simple model, an introduction to the language of multifractality which is used to discuss intermittency corrections to scaling. We continue with a general discussion of the Burgers equation and forcing, and some aspects of three dimensional turbulence where - because of the mathematical analogy between equations derived from the Navier-Stokes and Burgers equations - one can gain insight from the study of the simpler stochastic Burgers equation. These aspects concern the connection of dissipation rate intermittency exponents with those characterizing the structure functions of the velocity field, and the dynamical behavior, characterized by different time constants, of velocity structure functions. We also show how the exponents characterizing the multifractal behavior of velocity structure functions in the above mentioned transition region can effectively be calculated in the case of the stochastic Burgers equation.Comment: 25 pages, 4 figure