Methotrexate Promotes Platelet Apoptosis via JNK-Mediated Mitochondrial Damage: Alleviation by N-Acetylcysteine and N-Acetylcysteine Amide

Thrombocytopenia in methotrexate (MTX)-treated cancer and rheumatoid arthritis (RA) patients connotes the interference of MTX with platelets. Hence, it seemed appealing to appraise the effect of MTX on platelets. Thereby, the mechanism of action of MTX on platelets was dissected. MTX (10 mu M) induced activation of pro-apoptotic proteins Bid, Bax and Bad through JNK phosphorylation leading Delta psi m dissipation, cytochrome c release and caspase activation, culminating in apoptosis. The use of specific inhibitor for JNK abrogates the MTX-induced activation of pro-apoptotic proteins and downstream events confirming JNK phosphorylation by MTX as a key event. We also demonstrate that platelet mitochondria as prime sources of ROS which plays a central role in MTX-induced apoptosis. Further, MTX induces oxidative stress by altering the levels of ROS and glutathione cycle. In parallel, the clinically approved thiol antioxidant N-acetylcysteine (NAC) and its derivative N-acetylcysteine amide (NACA) proficiently alleviate MTX-induced platelet apoptosis and oxidative damage. These findings underpin the dearth of research on interference of therapeutic drugs with platelets, despite their importance in human health and disease. Therefore, the use of antioxidants as supplementary therapy seems to be a safe bet in pathologies associated with altered platelet functions.

Computational modeling of reactive hot pressing of zirconium carbide

A model of reactive hot pressing of zirconium carbide (ZrCx, 0.5 < x < 1) has been constructed that incorporates four processes that occur in parallel: creep of zirconium (Zr), reaction of Zr and carbon (C), increase in volume fraction of hard phase with progressive reaction that reduces the creep of Zr and, finally, de-densification associated with volume reduction during reaction. The reasonable agreement of the model with experimental results verifies that plastic deformation of Zr is the main factor that is responsible for the low-temperature reactive densification of ZrC and that ZrC may be treated as a rigid inclusion that contributes little to densification. It predicts that densification is impaired by increasing carbon stoichiometry due to the increasing amount of starting hard phase and the greater contraction upon reaction. Additionally, the model predicts that mixtures of Zr and ZrC should show equal or better densification than Zr and C mixtures.

Hybrid Working Set Algorithm for SVM Learning With a Kernel Coprocessor on FPGA

Support vector machines (SVM) are a popular class of supervised models in machine learning. The associated compute intensive learning algorithm limits their use in real-time applications. This paper presents a fully scalable architecture of a coprocessor, which can compute multiple rows of the kernel matrix in parallel. Further, we propose an extended variant of the popular decomposition technique, sequential minimal optimization, which we call hybrid working set (HWS) algorithm, to effectively utilize the benefits of cached kernel columns and the parallel computational power of the coprocessor. The coprocessor is implemented on Xilinx Virtex 7 field-programmable gate array-based VC707 board and achieves a speedup of upto 25x for kernel computation over single threaded computation on Intel Core i5. An application speedup of upto 15x over software implementation of LIBSVM and speedup of upto 23x over SVMLight is achieved using the HWS algorithm in unison with the coprocessor. The reduction in the number of iterations and sensitivity of the optimization time to variation in cache size using the HWS algorithm are also shown.

FAST AND ACCURATE BILATERAL FILTERING USING GAUSS-POLYNOMIAL DECOMPOSITION

The bilateral filter is a versatile non-linear filter that has found diverse applications in image processing, computer vision, computer graphics, and computational photography. A common form of the filter is the Gaussian bilateral filter in which both the spatial and range kernels are Gaussian. A direct implementation of this filter requires O(sigma(2)) operations per pixel, where sigma is the standard deviation of the spatial Gaussian. In this paper, we propose an accurate approximation algorithm that can cut down the computational complexity to O(1) per pixel for any arbitrary sigma (constant-time implementation). This is based on the observation that the range kernel operates via the translations of a fixed Gaussian over the range space, and that these translated Gaussians can be accurately approximated using the so-called Gauss-polynomials. The overall algorithm emerging from this approximation involves a series of spatial Gaussian filtering, which can be efficiently implemented (in parallel) using separability and recursion. We present some preliminary results to demonstrate that the proposed algorithm compares favorably with some of the existing fast algorithms in terms of speed and accuracy.