Right-handed Majorana Neutrino Mass Matrices for Generating Bimaximal Mixings in Degenerate and Inverted Models of Neutrinos

An attempt is made to generate the bimaximal mixings of the three species of neutrinos from the textures of the right-handed Majorana neutrino mass matrices. We extend our earlier work in this paper for the generation of the nearly degenerate as well as the inverted hierarchical models of the left-handed Majorana neutrino mass matrices using the non-diagonal textures of the right-handed Majorana neutrino mass matrices and the diagonal form of the Dirac neutrino mass matrices, within the frame work of the seesaw mechanism in a model independent way. Such Majorana neutrino mass models are important to explain the recently reported result on the neutrinoless double beat decay (0/nu/beta/beta) experiment,together with the earlier established data on LMA MSW solar and atmospheric neutrino oscillations.Comment: 14 pages, To appear in IJMPA (2003

Bimaximal Mixings from the Texture of the Right-handed Majorana Neutrino Mass Matrix

We study the origin of neutrino masses and mixing angles which can accomodate the LMA MSW solutions of the solar neutrino anomaly as well as the solution of the atmospheric neutrino problem, within the framework of the see-saw mechanism. We employ the diagonal form of the Dirac neutrino mass matrices with the physical masses as diagonal elements in the hierarchical order. Such choice has been motivated from the fact that the known CKM angles for the quark sector, are relatively small. We consider both possibilities where the Dirac neutrino mass matrix is either the charged lepton or the up-quark mass matrix within the framework of SO(10) GUT with or without supersymmetry. The non-zero texture of the right-handed Majorana neutrino mass matrix $M_{R}$ is used for the generation of the desired bimaximal mixings in a model independent way. Both hierarchical and inverted hierarchical models of the left-handed Majorana neutrino mass matrices are generated and then discussed with examples

Numerical consistency check between two approaches to radiative corrections for neutrino masses and mixings

We briefly outline the two popular approaches on radiative corrections to neutrino masses and mixing angles, and then carry out a detailed numerical analysis for a consistency check between them in MSSM. We find that the two approaches are nearly consistent with a small discrepancy of a factor of 13 percent in mass eigenvalues at low energy scale, but the predictions on mixing angles are almost consistent. We check the stability of the three types of neutrino models, i.e., hierarchical, inverted hierarchical and degenerate models, under radiative corrections, using both approaches, and find consistent conclusions. The neutrino mass models which are found to be stable under radiative corrections in MSSM are the normal hierarchical model and the inverted hierarchical model with opposite CP parity. We also carry out numerical analysis on some important conjectures related to radiative corrections in MSSM, viz., radiative magnification of solar and atmospheric mixings in case of nearly degenerate model having same CP parity (MPR conjecture) and radiative generation of solar mass scale in exactly two-fold degenerate model with opposite CP parity and non-zero reactor angle (JM conjecture). We observe certain exceptions to these conjectures. Finally the effect of scale-dependent vacuum expectation value in neutrino mass renormalisation is discussed.Comment: 26 pages, 5 figures,references added, typos corrected and text modifie

Deviation from tri-bimaximal mixings in two types of inverted hierarchical neutrino mass models

An attempt is made to explore the possibility for deviations of solar mixing angle ($\theta_{12}$) from tri-bimaximal mixings, without sacrificing the predictions of maximal atmospheric mixing angle ($\theta_{23}=\pi/4$) and zero reactor angle ($\theta_{13}=0$). We find that the above conjecture can be automatically realised in the inverted hierarchical neutrino mass model having 2-3 symmetry, in the basis where charged lepton mass matrix is diagonal. For the observed ranges of $\bigtriangleup m^2_{21}$ and \bigtriangleup m^2_{23], we calculate the predictions on $\tan^2\theta_{12}=0.5, 0.45, 0.35$ for different input values of the parameters in the neutrino mass matrix. We also observe a possible crossing over from one type of inverted hierarchical model having same CP parity (Type-IHA) to other type having opposite CP parity (Type-IHB). Such neutrino mass matrices can be obtained from the canonical seesaw formula using diagonal form of Dirac neutrino mass matrix and non-diagonal texture of right-handed Majorana mass matrix, and may have important implications in model building using discrete as well as non-abelian symmetry groups.Comment: 13 pages, 7 figure

Discriminating neutrino mass models using Type II seesaw formula

In this paper we propose a kind of natural selection which can discriminate the three possible neutrino mass models, namely the degenerate, inverted hierarchical and normal hierarchical models, using the framework of Type II seesaw formula. We arrive at a conclusion that the inverted hierarchical model appears to be most favourable whereas the normal hierarchical model follows next to it. The degenerate model is found to be most unfavourable. We use the hypothesis that those neutrino mass models in which Type I seesaw term dominates over the Type II left-handed Higgs triplet term are favoured to survive in nature.Comment: No change in the results, a few references added, some changes in Type[IIB] calculation

Deciphering the Arginine-Binding Preferences at the Substrate-Binding Groove of Ser/Thr Kinases by Computational Surface Mapping

Protein kinases are key signaling enzymes that catalyze the transfer of γ-phosphate from an ATP molecule to a phospho-accepting residue in the substrate. Unraveling the molecular features that govern the preference of kinases for particular residues flanking the phosphoacceptor is important for understanding kinase specificities toward their substrates and for designing substrate-like peptidic inhibitors. We applied ANCHORSmap, a new fragment-based computational approach for mapping amino acid side chains on protein surfaces, to predict and characterize the preference of kinases toward Arginine binding. We focus on positions P−2 and P−5, commonly occupied by Arginine (Arg) in substrates of basophilic Ser/Thr kinases. The method accurately identified all the P−2/P−5 Arg binding sites previously determined by X-ray crystallography and produced Arg preferences that corresponded to those experimentally found by peptide arrays. The predicted Arg-binding positions and their associated pockets were analyzed in terms of shape, physicochemical properties, amino acid composition, and in-silico mutagenesis, providing structural rationalization for previously unexplained trends in kinase preferences toward Arg moieties. This methodology sheds light on several kinases that were described in the literature as having non-trivial preferences for Arg, and provides some surprising departures from the prevailing views regarding residues that determine kinase specificity toward Arg. In particular, we found that the preference for a P−5 Arg is not necessarily governed by the 170/230 acidic pair, as was previously assumed, but by several different pairs of acidic residues, selected from positions 133, 169, and 230 (PKA numbering). The acidic residue at position 230 serves as a pivotal element in recognizing Arg from both the P−2 and P−5 positions

A class of neuro-computational methods for assamese fricative classification

In this work, a class of neuro-computational classifiers are used for classification of fricative phonemes of Assamese language. Initially, a Recurrent Neural Network (RNN) based classifier is used for classification. Later, another neuro fuzzy classifier is used for classification. We have used two different feature sets for the work, one using the specific acoustic-phonetic characteristics and another temporal attributes using linear prediction cepstral coefficients (LPCC) and a Self Organizing Map (SOM). Here, we present the experimental details and performance difference obtained by replacing the RNN based classifier with an adaptive neuro fuzzy inference system (ANFIS) based block for both the feature sets to recognize Assamese fricative sounds