A study on the turbulent transport of an advective nature in the fluid plasma

Advective nature of the electrostatic turbulent flux of plasma energy is studied numerically in a nearly adiabatic state. Such a state is represented by the Hasegawa-Mima equation that is driven by a noise that may model the destabilization due to the phase mismatch of the plasma density and the electric potential. The noise is assumed to be Gaussian and not to be invariant under reflection along a direction $\hat s$. It is found that the flux density induced by such noise is anisotropic: While it is random along $\hat s$, it is not along the perpendicular direction ${\hat s}_\perp$ and the flux is not diffusive. The renormalized response may be approximated as advective with the velocity being proportional to $(k\rho_s)^2$ in the Fourier space $\vec k$

Characteristics and treatments of large cystic brain metastasis: radiosurgery and stereotactic aspiration.

Brain metastasis represents one of the most common causes of intracranial tumors in adults, and the incidence of brain metastasis continues to rise due to the increasing survival of cancer patients. Yet, the development of cystic brain metastasis remains a relatively rare occurrence. In this review, we describe the characteristics of cystic brain metastasis and evaluate the combined use of stereotactic aspiration and radiosurgery in treating large cystic brain metastasis. The results of several studies show that stereotactic radiosurgery produces comparable local tumor control and survival rates as other surgery protocols. When the size of the tumor interferes with radiosurgery, stereotactic aspiration of the metastasis should be considered to reduce the target volume as well as decreasing the chance of radiation induced necrosis and providing symptomatic relief from mass effect. The combined use of stereotactic aspiration and radiosurgery has strong implications in improving patient outcomes

Sensitization of Human Pancreatic Cancer Cells Harboring Mutated K-ras to Apoptosis

Pancreatic cancer is a devastating human malignancy and gain of functional mutations in K-ras oncogene is observed in 75%–90% of the patients. Studies have shown that oncogenic ras is not only able to promote cell growth or survival, but also apoptosis, depending upon circumstances. Using pancreatic cancer cell lines with or without expressing mutated K-ras, we demonstrated that the inhibition of endogenous PKC activity sensitized human pancreatic cancer cells (MIA and PANC-1) expressing mutated K-ras to apoptosis, which had no apoptotic effect on BxPC-3 pancreatic cancer cells that contain a normal Ras as well as human lung epithelial BAES-2B cells. In this apoptotic process, the level of ROS was increased and PUMA was upregulated in a p73-dependent fashion in MIA and PANC-1 cells. Subsequently, caspase-3 was cleaved. A full induction of apoptosis required the activation of both ROS- and p73-mediated pathways. The data suggest that PKC is a crucial factor that copes with aberrant K-ras to maintain the homeostasis of the pancreatic cancer cells harboring mutated K-ras. However, the suppression or loss of PKC disrupts the balance and initiates an apoptotic crisis, in which ROS and p73 appear the potential, key targets

Semi-Automatic Annotation Tool to Build Large Dependency Tree-Tagged Corpus

PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200

Numerical Simulation of Water Quality Structure in Ise Bay at Tokai Heavy Rain Using Atmosphere-Ocean-Wave-Water Quality Coupled Model

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure