The function of GluR1 and GluR2 in cerebellar and hippocampal LTP and LTD is regulated by interplay of phosphorylation and O-GlcNAc modification.

Long-term potentiation (LTP) and long-term depression (LTD) are the current models of synaptic plasticity and widely believed to explain how different kinds of memory are stored in different brain regions. Induction of LTP and LTD in different regions of brain undoubtedly involve trafficking of AMPA receptor to and from synapses. Hippocampal LTP involves phosphorylation of GluR1 subunit of AMPA receptor and its delivery to synapse whereas; LTD is the result of dephosphorylation and endocytosis of GluR1 containing AMPA receptor. Conversely the cerebellar LTD is maintained by the phosphorylation of GluR2 which promotes receptor endocytosis while dephosphorylation of GluR2 triggers receptor expression at the cell surface and results in LTP. The interplay of phosphorylation and O-GlcNAc modification is known as functional switch in many neuronal proteins. In this study it is hypothesized that a same phenomenon underlies as LTD and LTP switching, by predicting the potential of different Ser/Thr residues for phosphorylation, O-GlcNAc modification and their possible interplay. We suggest the involvement of O-GlcNAc modification of dephosphorylated GluR1 in maintaining the hippocampal LTD and that of dephosphorylated GluR2 in cerebral LTP.

Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm.

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k-space. This paper introduces an improved iterative algorithm based on p-thresholding technique for CS-MRI image reconstruction. The use of p-thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p-thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p-thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.

Clustering based drug-drug interaction networks for possible repositioning of drugs against EGFR mutations: Clustering based DDI networks for EGFR mutations.

EGFRs are a vast group of receptor tyrosine kinases playing an important role in a number of tumors, including lungs, head and neck, breast, and esophageal cancers. A couple of techniques are being used in the process of drug design. Drug repositioning or repurposing is a rising idea that consists of distinguishing modern remedial indications for officially existing dynamic pharmaceutical compounds. Here, a novel approach of analyzing drug-drug interaction networks, based on clustering methodology is used to reposition effective compounds against mutant EGFR having G719X, exon 19 deletions/insertions, L858R, and L861Q mutations. Data about 2062 drugs are obtained, and mining is performed to filter only those drugs which fulfill Lipinski rule of five. Clustering is performed, and DDIs are built on the clusters to identify effective drug compounds. Only 1052 compounds fulfill Lipinski rule. 12 clusters are formed for 1052 drugs compounds. DDIs are developed for each cluster. Only 15 drugs are suggested to be more effective assuming strong interactions in a DDI.